Market design, game theory, optimization (integer programming, search, stochastic optimization
Data structures, algorithms, parsing.
Theory of computation, symbolic computation.
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Markov chains, Isoperimetry and Functional Analysis, Combinatorics, Computational Number Theory, and Algorithms.
Discrete Probability, Combinatorics, Convex Geometry, and Applications to Data Analysis.
Computational integer and combinatorial optimization, applications in sports and the social sciences.
Combinatorial optimization; constraint programming; mathematical programming; integration of constraint programming and mathematical programming.
Discrete Mathematics, primarily Graph Theory and Combinatorics.
Key:
Daniel De Roux
Jakob Hofstad
Su Jia
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Thomas Lavastida
Aditya Raut
Lingqing Shen
Olha Silina
Ziye Tang
Alexey Vasilevskii
Weizhong Zhang
Rudy Zhou
Mik Zlatin
Key:
Related Web Sites
ACO Seminars Home Page
OR Seminars
CS Theory Lunch Home Page
Operations Research Group
Neil Simonetti's Travelling Salesman Problem Page
Michael Trick's Operation Research Home Page
Michael Trick's Operation Research Blog
Bennet Yee's Computer Security Home Page
Advice on Research and Writing
Pittsburgh Supercomputing Center
Application System
Application management.
to continue an application.
to start a new application.
Undergraduate Catalog
Department of mathematical sciences courses, about course numbers:.
Each Carnegie Mellon course number begins with a two-digit prefix that designates the department offering the course (i.e., 76-xxx courses are offered by the Department of English). Although each department maintains its own course numbering practices, typically, the first digit after the prefix indicates the class level: xx-1xx courses are freshmen-level, xx-2xx courses are sophomore level, etc. Depending on the department, xx-6xx courses may be either undergraduate senior-level or graduate-level, and xx-7xx courses and higher are graduate-level. Consult the Schedule of Classes each semester for course offerings and for any necessary pre-requisites or co-requisites.
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Doctoral Admissions
Students applying to the Ph.D. program in Computer Science or interdisciplinary degrees the Computer Science Department co-manages must apply online.
Apply to the Ph.D. in CS & Interdisciplinary Degrees
To apply to the Ph.D. in CS program you need to use the School of Computer Science Online Application .
To apply to an Interdisciplinary degree program you need to apply via the online application for the Ph.D. in CS and select the interdisciplinary program ( ACO , CNBC, PAL) in the appropriate section of the online application .
Students are admitted for the fall semester. The doctoral program does not admit students in the spring semester.
Applications for all programs and all supporting documents, including letters of recommendation, must be received by the final deadline.
You may continue to edit your application until the final deadline.
Deadlines & Fees
Important Deadlines
Application Fees
Application Opens: September 4, 2024
Deadlines and fees are the same for all CSD Ph.D. programs.
We expect all applicants to submit payment with their online application. However, we do waive the required fee under certain circumstances.
Fees for application to a limited number of our programs can be waived for participants in certain programs.
In addition, if you are unable to pay the application fee, SCS will consider a fee waiver request. Visit the . There is waiver application that is separate from the graduate program application available on that page.
Early Deadline - November 20, 2024 (3:00 PM EST)
Early application deadline fee $80 per program. PLEASE NOTE: This is for a fee reduction only, not early admission consideration.
Fee Waiver Deadline - December 4, 2024 (3:00 PM EST)
NO fee waiver requests will be considered after the deadline. You must apply via the .
Final Deadline - December 11, 2024 (3:00 PM EST)
Application fee, after November 20, $100 per program.
You are able to edit or update some parts of your application up to the final deadline.
Regardless of which fee deadline you choose to pay by, applications must be completed, including all supporting documents and letters of recommendation, by the final deadline to be considered for review for admission.
Required Documents & Support Materials - All Programs
You must submit the following with your application:
: GRE scores are optional. An application without GRE scores is not at a disadvantage; however, applicants who have taken the GREs are encouraged to submit their scores. The GRE at Home Test is not accepted.
:
If you will be studying on an F-1 or J-1 visa, and English is not a native language for you (native language is defined as spoken at home from birth), we are required to formally evaluate your English proficiency via one of these standardized tests:
We do not accept the "TOEFL ITP Plus for China,” since speaking is not scored. Applicants currently in mainland China are encouraged to take the IELTS test.
Please refer to details on the page and in the .
: A PDF of your most recent transcript from each college and/or university you attended, even if no degree was granted.
: Your resumé or curriculum vitae in PDF format.
: Include a concise one- or two-page essay describing your primary research interests, related experiences and objective in pursuing a Ph.D. in computer science.
: We request 3 letters, at least two of which are from faculty or recent employers.
Contacts and Additional Information
For more on the application process, please refer to the SCS Online Graduate Application Instructions .
For questions specific to admissions for the Ph.D. in Computer Science email: [email protected]
For general questions about applying to doctoral programs in the School of Computer Science: https://www.cs.cmu.edu/about-scs/contact ; select "Graduate Programs and Admissions".
Prospective Students
Bachelor's Admissions
Master's Admissions
Doctoral Programs
Admitted Doctoral Student Open House
We encourage you to explore all the doctoral degree programs offered in the School of Computer Science to see if a specific program might be the best fit for the research you want to pursue.
You can get information about doctoral programs in each of the seven departments by visiting the School of Computer Science Doctoral Programs overview page .
Undergraduate Admission
Mathematical sciences, help us push the boundaries of our collective mathematical knowledge even further..
"One of my favorite things about being a math major is all of the amazing faculty I have gotten to know! All of my professors have been so friendly, accommodating and incredibly knowledgeable."
Mellon College of Science
Mathematics provides much of the language and quantitative underpinnings of the natural and social sciences — mathematical scientists built the foundation for modern computational and computer science and developed many of the most-used tools in business management. Whether you are drawn to the abstract beauty of theoretical math or the problem-solving elegance of applied math, our Mathematical Sciences program has a place for you. Here, thinkers and challengers are exploring and defining mathematics. Our faculty are advancing the leading edge of science, conducting research in an array of fields. The collaborative nature of CMU means we work across disciplines to support real-world applications.
Mathematical Sciences Majors and Minors
Choose the path that fits you best. Browse all current Mathematical Sciences curriculums and courses. (opens in new window)
Bachelor of Science Minor
Mathematical Sciences offers wide latitude to tailor your courses to your interests. Within the major, you can choose a concentration that aligns with your goals:
The Mathematical Sciences concentration is the least structured of our programs, in recognition of the variety of interests that can be productively coupled with the study of mathematical sciences. It can be an appropriate choice for students planning for graduate studies or those seeking to design their curriculum to take a second major from another department in the University.
The Operations Research and Statistics concentration prepares students to enter operations research. Mathematicians with a background in operations research are especially valuable in such diverse activities as project planning, production scheduling, market forecasting and finance.
The Statistics concentration prepares students for areas ranging from experimental design and data analysis in the sciences, medicine, and engineering, to modeling and forecasting in business and government, to actuarial applications in the financial and insurance industries.
The Discrete Mathematics and Logic Concentration provides a background in discrete mathematics, mathematical logic, and theoretical computer science. This concentration prepares the student to do research in these and related fields, or to apply their ideas elsewhere.
The Computational and Applied Mathematics Concentration provides the background needed to support the computational and mathematical analysis needs of a variety of businesses and industries and is well suited to students with an interest in the physical sciences and engineering.
The minor will allow you to take courses across mathematical disciplines and includes space for electives.
Computational Finance
Bachelor of Science
The Mellon College of Science, the Heinz College of Public Policy and Management and the Tepper School of Business jointly offer this degree uniquely designed to prepare you for the quantitative needs of the finance industry. You will develop a deep knowledge of mathematics, probability, statistics, and the applications of these disciplines to finance. Graduates work in finance or other industries where applied mathematics training is appropriate, or pursue advanced degrees in economics, finance or the mathematical sciences.
Mathematical Sciences and Economics
This flexible program allows you to develop depth in both fields of study, providing courses that complement and develop depth of understanding of economic theory, applied economics, and applied mathematics. Students pursuing this degree will be well prepared to begin their research careers in academia, government, and industry.
Discrete Mathematics and Logic
This minor develops the fundamentals of discrete mathematics and logic necessary to understand the mathematical foundations of many computer-related disciplines.
Department of Mathematical Sciences
Mathematical Sciences Website
Class of 2023, Six Months After Graduation
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High School Course Requirements
*Four years of mathematics should include at least algebra, geometry, trigonometry, analytic geometry, elementary functions (pre-calculus) and preferably calculus. Advanced mathematics courses are encouraged, especially a course in calculus.
Submit your application for the Mellon College of Science.
Get Started
You’re not just one thing. You’re a scientist. An artist. A technologist. A maker. A writer. Carnegie Mellon has been mixing it up for decades, and whatever you want to pursue, we’ve got the right mix for you.
The CMU Experience
How to Major in Mixing-It-Up
Machine Learning - CMU
Phd program in machine learning.
Carnegie Mellon University's doctoral program in Machine Learning is designed to train students to become tomorrow's leaders through a combination of interdisciplinary coursework, hands-on applications, and cutting-edge research. Graduates of the Ph.D. program in Machine Learning will be uniquely positioned to pioneer new developments in the field, and to be leaders in both industry and academia.
Understanding the most effective ways of using the vast amounts of data that are now being stored is a significant challenge to society, and therefore to science and technology, as it seeks to obtain a return on the huge investment that is being made in computerization and data collection. Advances in the development of automated techniques for data analysis and decision making requires interdisciplinary work in areas such as machine learning algorithms and foundations, statistics, complexity theory, optimization, data mining, etc.
The Ph.D. Program in Machine Learning is for students who are interested in research in Machine Learning. For questions and concerns, please contact us .
The PhD program is a full-time in-person committment and is not offered on-line or part-time.
Mastery of proficiencies in Teaching and Presentation skills.
Successful defense of a Ph.D. thesis.
Teaching Ph.D. students are required to serve as Teaching Assistants for two semesters in Machine Learning courses (10-xxx), beginning in their second year. This fulfills their Teaching Skills requirement.
Conference Presentation Skills During their second or third year, Ph.D. students must give a talk at least 30 minutes long, and invite members of the Speaking Skills committee to attend and evaluate it.
Research It is expected that all Ph.D. students engage in active research from their first semester. Moreover, advisor selection occurs in the first month of entering the Ph.D. program, with the option to change at a later time. Roughly half of a student's time should be allocated to research and lab work, and half to courses until these are completed.
Master of Science in Machine Learning Research - along the way to your PhD Degree.
Other Requirements In addition, students must follow all university policies and procedures .
Rules for the MLD PhD Thesis Committee (applicable to all ML PhDs): The committee should be assembled by the student and their advisor, and approved by the PhD Program Director(s). It must include:
At least one MLD Core Faculty member
At least one additional MLD Core or Affiliated Faculty member
At least one External Member, usually meaning external to CMU
A total of at least four members, including the advisor who is the committee chair
Financial Support
Application Information
For applicants applying in Fall 2024 for a start date of August 2025 in the Machine Learning PhD program, GRE Scores are OPTIONAL. The committee uses GRE scores to gauge quantitative skills, and to a lesser extent, also verbal skills.
Proof of English Language Proficiency If you will be studying on an F-1 or J-1 visa, and English is not a native language for you (native language…meaning spoken at home and from birth), we are required to formally evaluate your English proficiency. We require applicants who will be studying on an F-1 or J-1 visa, and for whom English is not a native language, to demonstrate English proficiency via one of these standardized tests: TOEFL (preferred), IELTS, or Duolingo. We discourage the use of the "TOEFL ITP Plus for China," since speaking is not scored. We do not issue waivers for non-native speakers of English. In particular, we do not issue waivers based on previous study at a U.S. high school, college, or university. We also do not issue waivers based on previous study at an English-language high school, college, or university outside of the United States. No amount of educational experience in English, regardless of which country it occurred in, will result in a test waiver.
Submit valid, recent scores: If as described above you are required to submit proof of English proficiency, your TOEFL, IELTS or Duolingo test scores will be considered valid as follows: If you have not received a bachelor’s degree in the U.S., you will need to submit an English proficiency score no older than two years. (scores from exams taken before Sept. 1, 2023, will not be accepted.) If you are currently working on or have received a bachelor's and/or a master's degree in the U.S., you may submit an expired test score up to five years old. (scores from exams taken before Sept. 1, 2019, will not be accepted.)
Graduate Online Application
Admissions application opens September 4, 2024
Early Application Deadline – November 20, 2024 (3:00 p.m. EST)
Final Application Deadline - December 11, 2024 (3:00 p.m. EST)
Graduate programs
Doctor of Philosophy program
Requirements and reporting
Thesis and dissertation information
Support for doctoral students
Research performance evaluation
Ph.D. student handbook
The doctoral degree emphasizes the creation of new knowledge through extensive independent research, including the formulation of hypotheses, the interpretation of phenomena revealed by research, and the extraction of general principles upon which predictions can be made. An important part of this process is presenting and defending the results. Ph.D. candidates are expected to present their results at research review meetings, at national and international conferences, and, in particular, in peer-reviewed publications. In addition to disseminating the new results, these activities offer ways for Ph.D. candidates to establish themselves as members of the international technical community. In the MSE department, doctoral research can be conducted in a range of areas, including nanomaterials, biomaterials, materials for energy applications, metals, ceramics, electronic materials, and magnetic materials. Each doctoral student’s research is guided by a faculty advisor and a dissertation committee with milestones that allow graduation in four years or less. The milestones and expectations for doctoral students are described below.
» » Graduate PhD Program
The Department of Mathematics offers a program leading to the degree of Doctor of Philosophy.
The PhD program is an intensive course of study designed for the full-time student planning a career in research and teaching at the university level or in quantitative research and development in industry or government. Admission is limited and highly selective. Successful applicants have typically pursued an undergraduate major in mathematics.
In the first year of PhD studies, students must pass written examinations in the areas of the basic . In the second year an oral examination on two selected topics must be passed. Subsequent years are devoted to seminars, research, and the preparation of a dissertation. Students are required to serve as a teaching assistant or instructor for four years beginning with the second year of study. All students must serve as a primary instructor for at least one semester; all others semesters students will serve as a teaching assistant. En route to the Ph.D., students will earn three degrees: a Master of Arts (after year one), a Master of Philosophy (after year four), and the Doctorate of Philosophy (after a successful thesis defense).
There are also allied doctoral programs in , , and .
The Mathematics Department is housed in a comfortable building containing an excellent , computing and printing facilities, faculty and graduate student offices, a lounge for tea and conversation, and numerous seminar and lecture rooms.
The department has a broad fellowship program designed to help qualified students achieve the PhD degree in the shortest practicable time. Each student admitted to the PhD program is appointed a fellow in the Department of Mathematics for a period of five years, contingent on good progress. A fellow receives a stipend for the nine-month academic year and is exempt from payment of tuition.
A fellow in the Department of Mathematics may hold a fellowship from a source outside Columbia University. When not prohibited by the terms of the outside fellowship, the University supplements the outside stipend to bring it up to the level of the University fellowship. Candidates for admission are urged to apply for fellowships for which they are eligible (e.g., National Science Foundation, Ford and Hertz Foundations).
All students admitted to the PhD program become fellows in the Department and are exempt from tuition. Students may be responsible for certain : a student activity fee and transcript fee.
Students in the PhD program are entitled to affordable University housing near the Department in Morningside Heights. This makes it possible to live comfortably in the University neighborhood on the fellowship stipend.
The PhD program in mathematics has an enrollment of approximately 60 students. Typically, 10-12 students enter each year. While students come from all over the world, they form an intellectually cohesive and socially supportive group.
New York City is America’s major center of culture. Columbia University’s remarkably pleasant and sheltered , near the Hudson River and Riverside Park, is situated within 20-30 minutes of Lincoln Center, Broadway theaters, Greenwich Village, and major museums. Most department members live within a short walk of the University.
Since receiving its charter from King George II in 1754, Columbia University has played an eminent role in American education. In addition to its various faculties and professional schools (such as Engineering, Law, and Medicine), the University has close ties with nearby museums, schools of music and theology, the United Nations, and the city government.
The application deadline is typically early December for admission the following September. Precise details on requirements and deadlines can be found . Applicants must submit all required documents by the posted deadline. Students whose undergraduate degree was not from an English-speaking country must also submit scores from the TOEFL or IELTS. Applications must be filed .
:
Graduate School of Arts and Sciences Columbia University Office of Student Affairs 107 Low Library, MC 4304 New York, NY 10027 212-854-6729
Michael Harris Director of Graduate Studies Department of Mathematics Columbia University 2990 Broadway 509 Mathematics, MC 4406 New York, NY 10027
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Carnegie Mellon University Africa
CMU-Africa is the only U.S. research university offering its master’s degrees with a full-time faculty, staff, and operations in Africa. CMU-Africa is addressing the critical shortage of high-quality engineering talent required to accelerate development in Africa—home to the fastest-growing workforce in the world. Our full-time graduate programs are educating future leaders who will use their hands-on, experiential learning to advance technology innovation and grow the businesses that will transform Africa.
Concentration areas
Course catalog
Academic calendar
Learn more about the application process and relevant dates, as well as tuition information and frequently asked questions.
Graduate degree programs
Master of science in electrical and computer engineering (ms ece).
MS ECE is a 10-16 month program that covers a broad and diverse set of areas and permeates nearly all areas of application of importance in society today. ECE ranges from nanotechnology to large-scale systems and impacts areas such as communications, computing and networking, energy and cyber-physical systems, biotechnology, robotics, computer vision, information storage and security, data analytics, distributed systems, and privacy. Faculty and students in ECE seek to advance education and technology in all areas of this field and are engaged in teaching and research that advances both the fundamentals of the field through advances in materials, devices, circuits, signal processing, control, computer architecture, and software systems as well as through the design, building, and demonstration of systems at all scales. The MS ECE program is for students who are interested in creating technology solutions not only for today but for the future. Students are prepared to become engineering leaders through fundamental and hands-on courses in communication networks, machine learning, data analytics, robotics, energy systems, internet of things, and software engineering. There is also an Advanced Study Program for the MS ECE: MS-AD in ECE .
Master of Science in Information Technology (MSIT)
MSIT is a 16-20 month program that includes technology, business, and innovation, preparing the next generation of ICT leaders in Africa. This program is for students interested in an interdisciplinary curriculum that covers key topics in data science, cyber security, software engineering, and networks, among others. On average, a student spends over 900 hours interfacing with industry and gaining practical experience during the completion of their degree.
Master of Science in Engineering Artificial Intelligence (MS EAI)
MS EAI is a 16-20 month program that opens the door to advanced skills that enable engineers to design powerful solutions to today's challenges. The MS EAI degree intersects with specific engineering disciplines but more importantly cuts across important problems in areas such as transportation, building systems, manufacturing, energy, agriculture, security, health, and climate. Students learn to combine a foundation in artificial intelligence, machine learning, and data science with their engineering, information technology, and software skills through theoretical and practical hands-on study of real-world applications.
Choosing a degree program
The MS ECE degree is for students who want to engineer underlying technologies to create new tools, algorithms, and technologies.
The MSIT degree is for students who want to apply existing tools and technologies to solve customer problems.
The MS EAI degree is for students with solid math and programming skills who want to engineer new solutions to engineering challenges in their discipline and where artificial intelligence and machine learning are integral to the design or operation of the engineered system.
CMU-Africa concentrations
A concentration allows a student to select their coursework in order to focus their learning in a specific area of expertise. Learn more about the exciting options available.
Carnegie Mellon University School of Computer Science
Scs graduate application fee waiver.
The School of Computer Science offers graduate application fee waivers for reasons related to financial hardship and to participants of certain programs.
Please note that applicants who are granted a fee waiver will be reviewed for admission in the same manner as all other applicants. Reviewers of your academic credentials will not have access to any information about fee waiver requests.
Waivers for Participants of Programs
We provide fee waivers for participants of certain programs and organizations (see the list below). If you qualify, you are able to submit your fee waiver right from the SCS Graduate Application's application/payment screen. After selecting your program, include evidence of participation by uploading the appropriate documents (receipt for registration, membership card, certificates of participation, etc.). Application fees will be waived for up to two of our programs.
Qualifying Programs and Organizations
AccessComputing
ACM Richard Tapia Celebration of Diversity in Computing
American Indian Science and Engineering Society
AISES
Annual Biomedical Research Conference for Minoritized Scientists
ABRCMS
Asian American and Pacific Islander Serving Institutions
AAPISIs
Association for Computing Machinery's Council on Women in Computing
ACM-W
Black in AI
BAI
CMU alums who graduated with one or more degrees at any level (undergrad, master's or Ph.D.) in any department/college at CMU.
CMU Alum
CMU Rales Fellows
CMU students currently enrolled in a degree-granting program at any level (undergrad, master's or Ph.D.) in any department/college at CMU.
CMU Student
Engineers Without Borders
EWB
Fulbright
Grace Hopper Celebration
HCII Summer Undergraduate Research Program at CMU
Hispanic-Serving Institutions
HSIs
Historically Black Colleges and Universities
HBCUs
International Society for Computational Biology
ISCB
Jack Kent Cooke Foundation
Jackie Robinson Foundation Scholars Program
LatinX in AI
LXAI
Leadership Alliance Summer Research Early Identification Program
Lesbians Who Tech
Louis Stokes Alliances for Minority Participation
LSAMP
Masakhane NLP
McNair Scholars
Mellon Mays Undergraduate Fellowship Program
MMUF
National Society of Black Engineers
NSBE
Out for Undergrad O4U Tech
Out in Science, Technology, Engineering and Mathematics
oSTEM
Queer in AI
Research Experiences for Undergraduates in Software Engineering Program at CMU
REUSE
Research on Equity and Sustained Participation in Engineering, Computing & Technology Conference
RESPECT
Robotics Institute Summer Scholars at CMU
RISS
Society for the Advancement of Chicanos and Native Americans in Science
SACNAS
Society of Asian Scientists and Engineers
SASE
Society of Hispanic Professional Engineers
SHPE
Society of Women Engineers
SWE
The Sadie Collective
Tribal Colleges and Universities
TCUs
United States Military
Widening NLP
Women in Cybersecurity Conference
WiCyS
Women in Data Science Worldwide
WiDs
Women in Machine Learning
WiML
Women Who Code
Top of Page
If you would like to suggest an organization/event for us to add to our list above, please complete our suggestion form with the name of the organization/event and a link to a website or provide another source of background information about the organization/event. We will update our organization/event list on November 1, 2024.
Waivers Related to Financial Hardship
If you are currently attending a U.S. based institution and feel that you are financially unable to pay, obtain a letter from your Financial Aid Office explaining your inability to pay and their support of a waiver. Upon receipt of this letter, your fee will be waived.
Please do not include information about exchange rates, we are aware of those situations.
After submission we will confirm your need and waive the application fees for up to two programs.
Financial Fee Waiver Deadline
We will accept financial fee waiver requests starting on September 4, 2024 .
We will not consider any late submissions by email.
The financial fee waiver request is closed for the 2023-2024 Application Season.
Deadline for Requesting Fee Waivers: December 6, 2023 (3 p.m. EST)
Helpful Links
SCS Graduate Application
Graduate Admissions Overview
Application Instructions
Frequently Asked Questions
Master's Programs
Doctoral Programs
Program Leadership
Biomedical Engineering
College of engineering, ph.d. program.
Formal coursework for a Ph.D. must cover at least three out of five core areas: physiology and cellular/molecular biology, biomaterials and tissue engineering, biomechanics, biomedical imaging and bioinformatics, and neuroengineering; Each of these core courses must be of 9 units or more. Graduate level introductory courses in each core area are available for students who are unfamiliar with the subject area. Aside from the core area requirement, considerable flexibility is allowed in the selection of courses to adapt to diverse interests, educational backgrounds, and career plans. Students are also allowed to take a certain number of upper-level undergraduate courses to broaden their background.
Students start thesis research within a few weeks of matriculation. Research during the first year defines the theme for the Ph.D. Qualifying Examination at the beginning of the second year. The purpose of the Qualifying Examination is to ensure that the student is sufficiently prepared and motivated to complete Ph.D. thesis research. Students submit a research document and take an oral examination with questions centered around the subject of the document. The questions may range from fundamental knowledge, prior research, to future prospect. By passing the Qualifying Examination, the student is formally accepted as a Ph.D. candidate.
The ensuing Ph.D. research must demonstrate the student’s ability to conduct an original, coherent, and independent investigation, to abstract principles, and to interpret the results in a logical manner. The student must pass a Ph.D. Proposal Examination, designed to assess the plan for completing the Ph.D. research, within the first three years of residence. Ph.D. dissertation and oral defense must be completed within six years of passing the Ph.D. Qualifying Examination.
Other Requirements
All students are required to take Biomedical Engineering Seminar (42-701) or (42-801) during each semester of residence. All Ph.D. students must also complete three semesters of Teaching Assistantship. Detailed requirements are described in the Graduate Student Handbook.
Program Handbook
Frequently Asked Questions
M.S. Program
Direct Entry
Students entering the Ph.D. program without an M.S. degree are classified as Direct Entry. Direct Entry students must satisfactorily complete at least 84 units of coursework, among which at most 21 units may be advanced undergraduate courses. Most Direct Entry students graduate within 4-5 years of full-time study.
Advanced Entry
Qualified candidates with an approved M.S. degree may be accepted into the Advanced Entry Ph.D. program. Advanced Entry students are required to complete 42 units of coursework, among which at most 9 units may be advanced undergraduate courses. Advanced Entry students are expected to devote most of the effort to research starting the first year. Many of them are able to graduate in no more than 4 years.
The Department of Biomedical Engineering participates in a combined M.D.-Ph.D. Program with the University of Pittsburgh School of Medicine, to offer M.D. degree from the University of Pittsburgh and Ph.D. from Carnegie Mellon University. The aim is to allow physician-engineers to blend research and clinical perspectives in treating patients.
Prospective students should apply directly to the University of Pittsburgh School of Medicine , indicating an interest in the Ph.D. Program in Biomedical Engineering at Carnegie Mellon University. During the first semester of the second year of medical school, the student should submit an application to the Ph.D. program, which may include supporting documents previously submitted to the University of Pittsburgh School of Medicine.
Students formally enter the Ph.D. program after completing their second year of medical school, although research may start as soon as the summer before the first semester of medical school and during the subsequent two summer semesters. This allows the student to gain a total of six months of research before officially entering the Ph.D. program.
Ph.D. requirements are similar to those for the Advanced Entry Ph.D. program except that there are no specific core course requirements, such that students may tailor biomedical engineering -relevant courses in consultation with the advisor. Completion of the Ph.D. program is targeted at 3-4 years. The student then returns to the University of Pittsburgh School of Medicine to completes the last two years of M.D. training.
BME PhD Admission and Completion
As a top ranked graduate school, CMU is selective in its PhD admissions. Once admitted, CMU BME has a regular PhD review process that tracks student progress and ensures supportive mentorship. As a result, the large majority of our students complete their PhD.
BME PhD Financial Support
All full time Ph.D. students accepted through the normal application process are provided continued support for the duration specified in the admission offer letter, subject to successful progress evaluated each semester, including tuition, fees and a competitive stipend.
Much of the efficiency of the Ph.D. Program, where most students graduate within 5 years, may be attributed to the early start of research and the rigorous system of performance assessment held...
Students start thesis research within a few weeks of matriculation. Research during the first year also defines the theme for the Ph.D. Qualifying Examination at the beginning of the second year.
CMU Student Associations
Graduate Student Assembly
Society of Hispanic Engineers
ADMINISTRATION
B.S. in HCI Major Curriculum
Important: design course changes for fall 2023.
05-651: Interaction Design Studio 1 and 05-392/692: Interaction Design Overview are being retired and combined into one new course: 05-360/05-660: Interaction Design Fundamentals (12 units).
Any undergraduate student who would normally take 05-651 OR 05-392 will replace that course with 05-360. It will be offered both semesters beginning fall 2023.
(Note: students who have already successfully taken 05-651 OR 05-392 have already completed this requirement and will not need to take the new replacement course, 05-360.)
Bachelor of Science in HCI Degree Requirements
The primary major in HCI supports students by preparing them with very strong technical knowledge, skills, and understanding. HCI majors must take a minimum of 360 units (35 courses) distributed as follows:
After each course name, the number of units for the course is shown [in square brackets].
Please note that many courses have prerequisites or corequisites, documented in CMU’s course catalog.
15-112: Fundamentals of Programming and Computer Science [12]
07-131: Great Practical Ideas in Computer Science [2]
07-128: Freshman Immigration Course [3]
15-122: Principles of Imperative Computation [12]
15-150: Principles of Functional Programming [12]
15-151: Mathematical Foundations of Computer Science [12]
15-210: Parallel and Sequential Data Structures and Algorithms [12]
15-213: Introduction to Computer Systems [12]
Mathematics and Statistics Core (4 courses)
Prerequisite: 21-120: Differential and Integral Calculus [10]
21-122: Integration and Approximation [10]
21-259: Calculus in Three Dimensions [10]
15-259: Probability and Computing [12]
21-325: Probability [9]
36-218: Probability Theory for Computer Scientists [9]
36-225: Introduction to Probability Theory [9]
15-251: Great Ideas in Theoretical Computer Science [12]
21-241: Matrices and Linear Transformations [11]
21-242: Matrix Theory [11]
36-226: Introduction to Statistical Inference [9]
36-401: Modern Regression [9]
HCI Core (6 courses)
Research and evaluation (2 courses).
Required: 05-410: User-Centered Research and Evaluation [12]
36-202: Statistics & Data Science Methods [9]
36-315: Statistical Graphics and Visualization [9]
70-208: Regression Analysis [9]
Ideation and Design (2 courses)
05-360: Interaction Design Fundamentals [12]
05-361: Advanced Interaction Design [12]
05-291: Learning Media Design [12]
05-315: Persuasive Design [12]
05-317: Design of Artificial Intelligence (AI) Products and Services [12]
05-418: Design Educational Games [12]
05-452: Service Design [12]
05-470: Digital Service Innovation [12] Some (but not all) special topics classes ( 05-499 ) might also count towards the advanced design class requirement. Please consult with an HCI undergraduate advisor.
Technical Core (2 courses)
05-380: Prototyping Algorithmic Experiences [15]
05-431: Software Structures for User Interfaces [12]
Psychology (1 course)
85-211: Cognitive Psychology [9]
85-213: Human Information Processing and Artificial Intelligence [9]
85-241: Social Psychology [9]
85-251: Personality [9]
85-370: Perception [9]
85-408: Visual Cognition [9]
85-421: Language and Thought [9]
88-120: Reason, Passion and Cognition [9] Note: The Psychology course fulfills the Category 1: Cognition, Choice and Behavior requirement for HCI majors.
HCI Electives (4 courses)
05-470: Digital Service Innovation [12]
Some (but not all) special topics classes ( 05-499 ) might also count towards the advanced design class requirement. Please consult with an HCI undergraduate advisor.
05-318: Human AI Interaction [12]
05-333: Gadgets, Sensors and Activity Recognition in HCI [12]
05-434: Machine Learning in Practice [12]
05-839: Interactive Data Science [12]
10-315: Introduction to Machine Learning (SCS Majors) [12]
11-411: Natural Language Processing [12]
15-281: Artificial Intelligence: Representation and Problem Solving [12]
15-365: Experimental Animation [12]
15-388: Practical Data Science [9]
15-462: Computer Graphics [12]
15-464: Technical Animation [12]
15-466: Computer Game Programming [12]
15-494: Cognitive Robotics: The Future of Robot Toys [12]
16-467: Human Robot Interaction [12]
17-428: Machine Learning and Sensing [12]
17-437: Web Application Development [12]
17-537: Artificial Intelligence Methods for Social Good [9]
Select 2 more electives: The remaining 2 electives can be chosen from the above lists or from this pre-approved list of HCI electives . Other options will require approval from the program director.
HCI Capstone Project (1 course)
Required: 05-571: Undergraduate Project in HCI [12]
Science and Engineering (4 courses)
Four courses in the domain of science and engineering are required, of which at least one must have a laboratory component and at least two must be from the same department. These courses typically come from the Mellon College of Science and the College of Engineering (CIT). Courses with a primary focus on programming, computation or mathematics are not acceptable for science or engineering courses. Requirements for this component of the degree are listed under the SCS General Education Requirements .
Humanities and Arts (7 courses)
These requirements follow the SCS General Education requirements for Humanities & Arts. Requirements for this component of the degree are listed under the SCS General Education Requirements .
NOTE: The Psychology requirement of the HCI core will satisfy the General Education requirement for Category 1: Cognition, Choice & Behavior.
Free Electives (4 courses)
A free elective is any Carnegie Mellon course. However, a maximum of 9 units of Physical Education and/or Military Science (ROTC) and/or Student-Led (StuCo) courses may be used toward fulfilling graduation requirements. These could be used for optional Research Track or an optional minor or concentration.
Human-Computer Interaction Institute Undergraduate Programs Email us
Haiyi Zhu Director of HCII Undergraduate Programs
Amelia Baisley Academic Program Coordinator
Ashley Kosko Academic Coordinator
Graduate programs
Graduate study in MSE systematically develops the fundamental scientific and engineering principles that govern the behavior and application of materials.
Requirements and reporting
Thesis and dissertation information
Support for doctoral students
Research performance evaluation
MS in Additive Manufacturing
MS in AI Engineering
MS in in Computational Materials Science & Engineering
MS in Materials Science (Research)
MS in Materials Science and Engineering (Coursework)
Dual degree programs
Request more information
Graduate Application Support Program (GrASP)
Graduate Student Life
Graduate Student Advisory Committee
The Master of Science in Materials Science & Engineering degree is course-based and can be completed in one year.
The Master of Science in Materials Science degree requires equal numbers of research and coursework units, and can be completed in two years (three or four semesters).
The Doctoral degree is research-based and can be completed in four years.
Students apply to our graduate programs, not to specific research projects. After the first semester the matching process begins.
Research group sizes are typically around five students per advisor.
Our faculty’s research falls into five areas: advanced materials processing and manufacturing, computation and informatics, materials for information technology, soft and bioactive materials, and sustainable energy production, conversion and storage.
The median salary of our M.S. students after graduation is $75K. The median salary of our Ph.D. students after graduation is $95K.
Distinguished Service Professor
Anand Rao is a Distinguished Services Professor of Applied Data Science and AI in the Heinz College of Information Systems and Public Policy at Carnegie Mellon University
Anand Rao has focused on research, innovation, applications, business and societal adoption of data, analytics, and artificial intelligence over his 35-year consulting, industry, and academic career. Anand was the Global Artificial Intelligence Leader for PwC, a Partner in their Data, Analytics, and AI practice, and the Innovation lead for AI in PwC’s Products and Technology segment. Anand led a team of practitioners who advised C-level executives, develop, and implement advanced analytics and AI-based solutions across several industry sectors including financial services, insurance, healthcare, technology-media-telecommunications, retail, aerospace, and defense sectors. With his PhD and research career in Artificial Intelligence and his subsequent experience in management consulting he brings business domain knowledge, software engineering expertise, statistical expertise, and modeling expertise to generate unique insights into the practice of ‘data science’ and artificial intelligence.
Prior to joining management consulting, Anand was the Chief Research Scientist at the Australian Artificial Intelligence Institute building agent-based models and simulation systems and conducting research in the theory and practice of multi-agent systems.
Anand’s current research interests include operationalizing AI, responsible AI, systems thinking, ROI of AI, theory and practice of building agent-based models and digital twins, behavioral economics, and human decision-making.
He received his PhD from University of Sydney (with a University Postgraduate Research Award-UPRA) in 1988 and an MBA (with Award of Distinction) from Melbourne Business School in 1997. Anand has also co-edited four books on Intelligent Agents and has published over fifty papers in Computer Science and Artificial Intelligence in major journals, conferences, and workshops. In addition, he has authored over hundred articles in the business and trade press.
He has received widespread recognition for his extraordinary contributions in the field of consulting and Artificial Intelligence Research. He has received the Most Influential Paper Award for the Decade in 2007 from the Autonomous Agents & Multi-Agent Systems organization for his contribution on the Belief-Desire-Intention Architecture; MBA Award of Distinction from Melbourne Business School, 1997 and University Postgraduate Research Award (UPRA) from University of Sydney, 1985; Distinguished Alumnus Award from Birla Institute of Technology and Science, Pilani, India; He was recognized as one of Top 50 Data & Analytics professionals in USA and Canada by Corinium; one of Top 50 professionals in InsureTech; one of Top 25 Technology Leaders in Consulting; and has won a number of awards for his academic and business papers.
Anand is an Adjunct Professor in BITS Pilani’s APPCAIR AI Center. He also serves on the Advisory Board of Oxford University’s Institute for Ethics in AI, World Economic Forum’s Global AI Council, OECD’s Network of Experts on AI (ONE), OECD’s AI Compute initiative, Advisory Board of Northwestern’s MBAi program, Responsible AI Institute, Nordic AI Institute, and International Congress for the Governance of AI.
Publications
Recognized as one of Top 50 Data & Analytics professionals in USA and Canada by Corinium;
Recognized as one of Top 50 Innovators of Data & Analytics professionals in USA and Canada by Corinium
Recognized as one of Top 50 professionals in InsureTech
Recognized as the Top 25 Technology Leaders in Consulting by Consulting Report
Recognized as The Enterprise CXO Leader of the year by CogX
Recognized as 50 Outstanding AI in Business Influencers by Engatica
Won the ASBPE Azbe Bronze Regional Award in the Technical Paper category for the paper on “It’s Time to get excited about boring AI”
Won the Marcomm Platinum award in 2022 for the paper “It’s Time to get excited about boring AI”
Won the National Silver Award by ASBPE for the Best Technical article in 2019 for the paper “How to build disruptive strategic flywheels”.
Won the National Gold Award by ASBPE for the Best Technical article in 2018 and the FOLIO editorial award for the paper “A Strategist’s Guide to Artificial Intelligence”
“Sizing the Prize” won the most quoted Consulting Thought Leadership Award
Distinguished Alumnus Award from BITS Pilani, India in 2018
Most Influential Paper Award for the Decade in 2007 from the Autonomous Agents & Multi-Agent Systems organization for his contribution on the Belief-Desire-Intention Architecture.
MBA Award of Distinction from Melbourne Business School, 1997
University Postgraduate Research Award (UPRA) from University of Sydney, 1985;
Intelligent Agent Systems: Theoretical and Practical Issues. Based on a Workshop Held at PRICAI '96, Cairns, Australia, August 26-30, 1996
Intelligent Agents IV: Agent Theories, Architectures, and Languages, 4th International Workshop, ATAL'97, Providence, Rhode Island, USA, July 24-26, 1997, Proceedings
Intelligent Agents V: Agents Theories, Architectures, and Languages: 5th International Workshop, ATAL'98, Paris, France, July 4-7, 1998, Proceedings (Lecture Notes in Computer Science, 1555) 1999th Edition
Foundations of Rational Agency , 1999
Gupta, P., Young, A.W., & Rao, A. (2022). Investigating Cargo Loss in Logistics Systems using Low-Cost Impact Sensors. ArXiv, abs/2201.00301 .
Gupta, P., Hoda, S., & Rao, A. (2022). Intelligent Systematic Investment Agent: an ensemble of deep learning and evolutionary strategies. ArXiv, abs/2203.13125 .
Singh, A., Chen, J., Zhang, L., Rasekh, A., Golbin, I., & Rao, A.S. (2021). Independent Ethical Assessment of Text Classification Models: A Hate Speech Detection Case Study. ArXiv, abs/2108.07627 .
Mishra, S., Koopman, R., Prato, G.D., Rao, A.S., Osorio-Rodarte, I., Kim, J., Spatafora, N., Strier, K., & Zaccaria, A. (2021). AI Specialization for Pathways of Economic Diversification. ArXiv, abs/2103.11042 .
Golbin, I., Rao, A.S., Hadjarian, A., & Krittman, D. (2020). Responsible AI: A Primer for the Legal Community. 2020 IEEE International Conference on Big Data (Big Data) , 2121-2126.
Hoda, S., Singh, A., Rao, A.S., Ural, R., Hodson, N. (2020). Consumer Demand Modeling During COVID-19 Pandemic. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
Gigioli, P., Sagar, N., Rao, A.S., & Voyles, J. (2018). Domain-Aware Abstractive Text Summarization for Medical Documents. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) , 1155-1162.
Zhang, S., Dev, S., Voyles, J., and Rao, A.S. (2018). Attention-based Multi-Task Learning in Pharmacovigilance. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
Dev, S., Zhang, S., Voyles, J., & Rao, A.S. (2017). Automated classification of adverse events in pharmacovigilance. BIBM .
Ramchandani, P., Paich, M., & Rao, A.S. (2017). Incorporating Learning into Decision Making in Agent Based Models. EPIA .
Rao, A.S., & Verweij, G. (2017). Sizing the prize: what’s the real value of AI for your business and how can you capitalise?
Campbell, J.P., Chan, M., Li, K., Lombardi, L.J., Lombardi, L., Purushotham, M., & Rao, A.S. (2014). Modeling of Policyholder Behavior for Life Insurance and Annuity Products.
Rao, A.S., & Georgeff, M. (1995). BDI Agents: From Theory to Practice. ICMAS .
Rao, A.S., & Georgeff, M. (1991). Modeling Rational Agents within a BDI-Architecture. KR .
Rao, A.S., & Georgeff, M. (1992). An Abstract Architecture for Rational Agents. KR .
Rao, A.S., & Georgeff, M. (1998). Decision Procedures for BDI Logics. J. Log. Comput., 8 , 293-342.
Kinny, D., Georgeff, M., & Rao, A.S. (1996). A Methodology and Modelling Technique for Systems of BDI Agents. MAAMAW .
Rao, A.S. (1996). AgentSpeak(L): BDI Agents Speak Out in a Logical Computable Language. MAAMAW .
Ingrand, F., Georgeff, M., & Rao, A.S. (1992). An architecture for real-time reasoning and system control. IEEE Expert, 7 , 34-44.
Rao, A.S., & Georgeff, M. (1991). Asymmetry Thesis and Side-Effect Problems in Linear-Time and Branching-Time Intention Logics. IJCAI .
Rao, A.S., & Georgeff, M. (1995). Formal Models and Decision Procedures for Multi-Agent Systems.
Kinny, D., Ljungberg, M., Rao, A.S., Sonenberg, L., Tidhar, G., & Werner, E. (1992). Planned Team Activity. MAAMAW .
Rao, A.S., Georgeff, M., & Sonenberg, L. (1992). Social Plans: A Preliminary Report.
Rao, A.S., & Georgeff, M. (1993). A Model-Theoretic Approach to the Verification of Situated Reasoning Systems. IJCAI .
Georgeff, M., & Rao, A.S. (1995). The Semantics of Intention Maintenance for Rational Agents. IJCAI .
Rao, A.S. (1994). Means-End Plan Recognition - Towards a Theory of Reactive Recognition. KR .
Weerasooriya, D., Rao, A.S., & Ramamohanarao, K. (1994). Design of a Concurrent Agent-Oriented Language. ECAI Workshop on Agent Theories, Architectures, and Languages .
Rao, A.S., & Georgeff, M. (1991). Deliberation and its Role in the Formation of Intentions. UAI .
Rao, A.S. (1995). Decision Procedures for Propositional Linear-Time Belief-Desire-Intention Logics. ATAL .
Cavedon, L., Rao, A.S., & Tidhar, G. (1996). Social and Individual Commitment. PRICAI Workshop on Intelligent Agent Systems .
Rao, A.S. (1997). A Unified View of Plans as Recipes.
Gigioli, P., Sagar, N., Voyles, J., & Rao, A.S. (2018). Domain-Aware Abstractive Text Summarization for Medical Documents. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) , 2338-2343.
Rao, A.S., & Foo, N. (1989). Formal Theories of Belief Revision. KR .
Zhang, S., Dev, S., Voyles, J., & Rao, A.S. (2018). Attention-Based Multi-Task Learning in Pharmacovigilance. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) , 2324-22328.
Cavedon, L., & Rao, A.S. (1996). Bringing About Rationality: Incorporating Plans Into a BDI Agent Architecture. PRICAI .
Rao, A.S., & Georgeff, M. (1991). Intelligent Real-Time Network Management.
Cavedon, L., Padgam, L., Rao, A.S., & Sonenbergy, E. (1995). Revisiting rationality for agents with intentions 1.
Rao, A.S., & Georgeff, M. (1993). A Model-Theoretic Approach to the Verification of Agent-Oriented Systems.
Foo, N., Garner, B., Rao, A.S., & Tsui, E. (1992). Semantic distance in conceptual graphs.
Rao, A.S., & Foo, N. (1989). Minimal Change and Maximal Coherence: A Basis for Belief Revision and Reasoning about Actions. IJCAI .
Tidhar, G., Sonenberg, L., & Rao, A.S. (1998). On team knowledge and common knowledge. Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160) , 301-308.
Foo, N., Rao, A.S., Taylor, A., & Walker, A. (1988). Deduced Relevant Types and Constructive Negation. ICLP/SLP .
Georgeff, M., & Rao, A.S. (1996). A profile of the Australian Artificial Intelligence Institute [World Impact]. IEEE Expert, 11 , 89-.
Rao, A.S., & Georgeff, M. (1993). Intentions and Rational Commitment.
Crnogorac, L., Rao, A.S., & Ramamohanarao, K. (1998). Classifying Inheritance Mechanisms in Concurrent Object Oriented Programming. ECOOP .
Crnogorac, L., Rao, A.S., & Ramamohanarao, K. (1997). Analysis of Inheritance Mechanisms in Agent-Oriented Programming. IJCAI .
Bansal, A., Ramamohanarao, K., & Rao, A.S. (1997). Distributed Storage of Replicated Beliefs to Facilitate Recovery of Distributed Intelligent Agents. ATAL .
Rao, A.S., Georgeff, M., & Sonenberg, E. (1992). Social plans: a preliminary report (abstract). ACM Sigois Bulletin, 13 , 10.
Rao, A.S. (1995). Integrated Agent Architecture: Execution and Recognition of Mental-States. DAI .
Rao, A.S. (1998). A Report on Expert Assistants at the Autonomous Agents Conference. Knowl. Eng. Rev., 13 , 175-178.
Cavedon, L., Rao, A.S., Sonenberg, L., & Tidhar, G. (1997). Teamwork via Team Plans in Intelligent Autonomous Agent Systems. WWCA .
Rao, A.S., Kinny, D., Tidhar, G., & Ljungberg, M. (1992). Skills and Capabilities in RealTime Team Formation.
Morley, D.N., Georgeff, M., & Rao, A.S. (1994). A Monotonic Formalism for Events and Systems of Events. J. Log. Comput., 4 , 701-720.
Rao, A.S., & Georgeff, M. (1993). Verification of Agent-Oriented Situated Systems: A Model-Theoretic Approach.
Rao, A.S. (1993). Decision Procedures for Propositional Belief-Desire-Intention Logics.
Foo, N., & Rao, A.S. (2005). Belief revision in a microworld. Annals of Mathematics and Artificial Intelligence, 4 , 135-155.
Foo, N., Garner, B., Rao, A.S., & Tsui, E. (1992). SEMANTIC DISTANCE IN CONCEPTUAL GRAPHS.
What is the future of content in the generative AI age? , Tech Effects, May 16, 2023
Companies that change the game can change the world , (with Sundar Subramanian and Harshavardan Kasturirangan), Strategy+Business, January 26, 2023.
The three powers that game changing companies share , (With Sundar Subramanian) The Leadership Agenda, PwC, 2023.
What are digital twins and How are they used to predict the future , (With Bret Greenstein) Techopedia, July 25, 2022.
3 Ways Digital Twins can transform your business , Techopedia, April 1, 2022.
Six predictions for AI in 2022 , Digital Pulse, February 1, 2022.
MVP vs EVP: Is it time to introduce ethics into the agile startup model ?, TechCrunch, January 5, 2022. (also in TechBoyz and Newsbreak )
How organizations can mitigate the risks of AI , Harvard Business Review, December 20,2021
Six AI business predictions for 2022, Tech Effect , December 2, 2021.
3 components CIO’s need to create an Ethical AI framework , Information Week, November 15, 2021
Building and Implementing Responsible AI: A Practical Guide , Enterprise AI, November 24, 2021
Operationalizing Artificial Intelligence: Making the Promise a Reality , October 27, 2021
Ethical underpinnings of AI – Interview , Medium, September 12, 2021
How a portfolio approach to AI helps your ROI , Tech Effect, September 9, 2021
AI TechTrend interview with Anand Rao , TechTrend, September 7, 2021
Solving AI’s ROI problem. It’s not that easy . PwC Tech Effect, July 20, 2021.
How AI can help create more caring company cultures , Quartz, July 13, 2021.
It's time to get excited about boring AI , Strategy + Business, June 24, 2021
Three systems thinking tools for exploring the distrust doom loop , Towards Data Science, June 6, 2021
Five types of thinking for a high-performing data scientist , Towards Data Science, April 25, 2021 (also appears in KDNuggets , June 2021)
Six stages to a successful AI Governance , Towards Data Science, February 21, 2021
How to avoid the extremes of anthropocentrism and AI centrism , Start it up, February 14, 2021
Gain trust by addressing the Responsible AI gaps , Towards Data Science, February 4, 2021
A new way to think about modeling for uncertain times , Towards Data Science, January 28, 2021
3 Essential steps to exploit the power of AI , InformationWeek, January 22, 2021
AI leaders make the most of the COVID-19 crisis to increase the role of AI , Towards Data Science, January 18, 2021.
Top-down and end-to-end governance for the responsible use of AI , Towards Data Science, January 11, 2021.
Ten principles of Responsible AI for Corporates , Towards Data Science, December 17,2020
Responsible AI: A primer for the legal community in Legal AI 2020 : The Fourth Annual Workshop on Applications of Artificial Intelligence in the Legal Industry , as part of the IEEE International Conference on Big Data , December 2020.
Consumer demand modeling during COVID19 Pandemic . In the IEEE International Conference on Bioinformatics and Biomedicine (BIBM) , December 2020.
Five views of AI Risk: Understanding the darker side of AI , Towards Data Science, November 28, 2020.
Time to combine agile programming and agile data science , Towards Data Science, November 6, 2020.
Five critical questions to explain Explainable AI , Towards Data Science, Oct 30, 2020
Balancing Innovation, Economics, and Regulation to increase Responsible AI adoption , The Innovation, Oct 25, 2020.
Ten human abilities and four intelligences to exploit human-centered AI , The Innovation, Oct 10, 2020.
Model Lifecycle: From ideas to value, Towards Data Science, Sep 26, 2020.
Data, Automation, Analytics, AI — The Unbeatable Quartet , The Innovation, Sep 17, 2020.
Model Evolution: From Standalone Models to Model Factory , Towards Data Science, Sep 13, 2020
Consequences of mistaking models for software , Towards Data Science, Sep 6, 2020.
Data Scientists are from Mars and Software Developers are from Venus (Part 1) , Towards Data Science, August 29, 2020.
Democratization of AI: A Double-Edged Sword , Towards Data Science, August 16, 2020.
Making predictive analytics work in an uncertain world, Information Week , August 10, 2020.
3 ways COVID-19 is transforming advanced analytics and AI , WEF, Agenda, July 23, 2020
Democratizing Artificial Intelligence is a Double-Edged Sword , Strategy+Business, June 15, 2020
Comprehensive AI Governance needed , PwC White Paper, May 2020.
Lessons from COVID-19 modeling: The interplay of data, models and behavior, WEF , Agenda, May 12, 2020
Using Behavioral Economics to Improve P/C Insurance Outcomes, Carrier Management , May 20, 2020
Managing the risks of machine learning and artificial intelligence models in the financial services industry , PwC White Paper, 2020
The New COO: Powering Operations with Artificial Intelligence , PwC White Paper, June 2020.
Comprehensive AI Governance Needed Now , PwC White Paper, March 2020.
Moving AI forward: Why you need to slow down now to scale later , Information Week, February 28, 2020.
This is what the world’s CEOs really think of AI , WEF Agenda, June 25, 2019.
Practical Guide to Responsible AI , PwC White Paper, 2019.
Strategy + Business: How to build powerful strategic flywheels - Gaming, artificial intelligence, and deep learning are paving the way for dynamic and resilient 21st-century business models
PwC White Paper: Gaining competitive advantage through Artificial Intelligence – Policy Making and National AI Strategies. April, 2019.
ITU News: How ‘responsible AI’ can boost sustainable development . April 17, 2019
Strategy + Business: What is fair when it comes to AI Bias? April 12, 2019
China Watch: Future in the Balance . April 3, 2019
Strategy + Business: Is AI the Next Frontier for National Competitive Advantage? . January 22, 2019.
Strategy + Business: Digital-Native Retailers Are Giving Physical Stores a Radical Makeover . January 18, 2019.
PwC: Six AI Priorities you cannot afford to ignore . January 5, 2019
BIBM18-IEEE Conference (Madrid): Attention-based multi-task learning in pharmacovigilance (with Shinan Zhang, Shantanu Dev, Joseph Voyles) (December 2018)
BIBM18-IEEE Conference (Madrid): Domain-aware abstractive text summarization in medical documents (with Paul Gigioli, Nikhita Sagar, Joseph Voyles) (December 2018)
CIO Review: Enterprise AI: How to Meet the Challenges (August 2018)
Digital Pulse: Opening the Blacbox with Explainable AI (August 2018)
PwC White Paper: Explainable AI: Driving Business Value Through Greater Understanding (July 2018)
Strategy + Business – The Future of AI depends on Trust (July 2018)
Integration and Trade Journal: Vol 22: No 44: July 2018: Planet Algorithm: Artificial Intelligence for a Predictive and Inclusive form of Integration in Latin America: Artificial Intelligence: Opportunities and Risks (July 2018)
Strategy + Business – How smarter phones will transform Tech, Media, and Telecom (June 2018)
PwC White Paper: The Smarter Phone (Mobile World Congress) (May 2018)
Next in Tech – PwC Blog: What it means to open AI’s Black Box (May 2018)
PwC White Paper: How do actuarial and data science skills converge at P&C (re-)insurers (Mar 2018)
PwC White Paper: How do actuarial and data science skills converge at Life (re-)insurers (Mar 2018)
PwC White Paper: The Macroeconomic Impact of AI (Feb 2018)
PwC White Paper: Fourth Industrial Revolution for the Earth (World Economic Forum) (Jan 2018)
PwC White Paper: AI Predictions 2018 (Jan 2018)
Next in Tech – PwC Blog: Briefing: Artificial Intelligence (Jan 2018)
Next in Tech – PwC Blog: Top 10 Ten AI Technology Trends for 2018 (Jan 2018/Dec 2017)
Demystifying Machine Learning Part 1: Data, Processing Power, and open source , Big Data Made Simple, Feb 9, 2016.
Demystifying Machine Learning Part 2: Supervised, Unsupervised, and Reinforcement Learning , Big Data Made Simple, February 15, 2016.
Do you need a Chief Data Scientist , Big Data Made Simple, July 22, 2016.
The deep learning robot that recognizes your face , Big Data Made Simple, Nov 15, 2016.
Time to dive in: Leveraging public data with a data lake , Big Data Made Simple, Nov 18, 2016
The Science (and Art) of Data, Part 1 , Insurance Thought Leadership, April 16,2014
The Science (and Art) of Data, Part 2 , Insurance Thought Leadership, April 24,2014
Two ways to innovate in Life Insurance , Insurance Thought Leadership, January 5, 2015
What comes after predictive analytics , Insurance Thought Leadership, February 10, 2015
Reinventing life insurance , Insurance Thought Leadership, April 15, 2015
The rise of robo-advisors , Insurance Thought Leadership, June 5, 2015
Insurance at a tipping point – Part 1 , Insurance Thought Leadership, July 9, 2015
Insurance at a tipping point – Part 2 , Insurance Thought Leadership, July 20, 2015
Insurance at a tipping point – Part 3 , Insurance Thought Leadership, July 31, 2015
InsurTech: Golden Opportunity to Innovate , Insurance Thought Leadership, March 29, 2016
How to think about the rise of the machines , Insurance Thought Leadership, April 11, 2016
AI: Everywhere and Nowhere – Part 1 , Insurance Thought Leadership, June 2, 2016
AI: Everywhere and Nowhere – Part 2 , Insurance Thought Leadership, June 6, 2016
AI: Everywhere and Nowhere – Part 3 , Insurance Thought Leadership, June 8, 2016
Innovation solutions from elsewhere , Insurance Thought Leadership, March 27, 2017
Leveraging AI in Commercial Insurance , Insurance Thought Leadership, April 7, 2017
Strategist’s guide to AI , Insurance Thought Leadership, June 5, 2017
10 Trends on Big Data, Advanced Analytics , Insurance Thought Leadership, October 4, 2017
How do actuarial, data skills converge , Insurance Thought Leadership, April 4, 2018
Insurance Innovators Interview Series , Carrier Management, June 8, 2015
Four types of Artificial Intelligence: How to decide which one to apply to commercial insurance , Carrier Management, March 27, 2018
Man and Machine: The future of insurance work and workforce , Carrier Management, March 31, 2017.
Using behavioral economics to improve P/C Insurance outcomes , Carrier Management, March 20, 2020.
AI in commercial insurance , Carrier Management, August 21, 2016
AI in personal insurance: ChatBot agents, RPA 'no shoring' admin bots and more , Carrier Management, August 3, 2016
PwC’s Anand Rao: We are only in 1984 in terms of the evolution of AI , Siliconrepublic, April 13, 2018.
Modeling of Policyholder Behavior for Life Insurance and Annuity Products: A Survey and Literature Review , Society of Actuaries, 2014
Behavioral Simulations Using agent-based modeling to understand policyholder behaviors , Society of Actuaries, October 1, 2012
What’s a data scientist? Where can I find one ?. Digital Insurance, April 8, 2013
Adapting to a customer-centric world. Digital Insurance , February 28, 2013.
Claims on the move , CLM, August 26, 2010.
Ten tactics for improving operating margins in Tough Times , Diamond Management & Technology Consultants, 2009.
Additional Information
How different industries benefit from edge AI , George Lawton, TechTarget, June 22, 2023.
How to assess your AI projects’ ROI as recession hits , VentureBeat, Ben Dickson, December 23, 2022
Why the US risks falling behind in AI Leadership , John Edwards, InformationWeek, December 14, 2022
Artificial Intelligence – a strategy playbook for leaders , Scott Birch, Business Chief, September 8, 2022.
Artificial Intelligence to boost sustainability and profit , Becci Knowles, Sustainability, August 3, 2022
How to scale the power of AI simulation , Sharon Goldman, VentureBeat, May 4, 2022.
AiThority Interview with Dr. Anand Rao , Global AI Lead, Sudipto Ghosh, May 17, 2022
Artificial Intelligence – a strategy playbook for leaders , Scott Birch, September 8, 2022.
What you need to know about AI ethics , by John Edwards, February 18, 2022.
10 top AI and Machine Learning trends for 2022 , George Lawton, Feb 3, 2022.
Anand Rao appointed to the Virginia Thurston Healing Garden Support Center, Board of Trustees , The Sun, January 28, 2022.
Explainable AI: Global Analytics Summit examines efforts to understand how AI systems work and why that’s essential , by Texas McCombs News, Medium, Jan 6, 2022.
Data Science and AI Predictions for 2022 , by Alex Woodie, Datanami, Jan 3, 2022.
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The impact of Artificial Intelligence is already here, it’s just not very evenly distributed , by Joe McKendrick, September 29, 2018.
PwC Chile launch Center of Artificial Intelligence and Applied Analytics for Latin America , Consultancy.lat, August 28, 2018.
Reality Check: How Smart is AI, Anyway? A look at AI applications , by Andrew Williams, Robotics Business Review, May 17, 2018.
PwC’s Anand Rao: ‘We are only in 1984 in terms of the evolution of AI’ , by John Kennedy Silicon Republic, April 13, 2018.
VC firm invest heavily in AI, pushing the hype bubble bigger , Alex Woodie, Datanami, January 19, 2018.
PwC: AI will contribute $15.7 trillion to global economy by 2030 , by Paul Hill, Neowin, June 29, 2017.
AI will add $15.7 trillion to the global economy , by Blomberg News, June 28, 2017.
China is betting big on AI – and here’s why it’s going to pay off , by Meng Jing, June 27, 2017.
AI drives better business decisions , by MIT Technology Review Insights, June 20, 2016.
The 4 percent rule no longer applies for most retirees , by Kelley Holland, April 22, 2015.
Technology Forecast - India
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PhD Application Process and How to Write a PhD Application
COMMENTS
Admissions
The Department of Mathematical Sciences at Carnegie Mellon University invites applications to our graduate program in Mathematics. To be admitted to this program applicants must document competence equivalent to graduation from a recognized U.S. four-year college, university or institute of technology.
Ph.D. Programs
Doctor of Philosophy in Algorithms, Combinatorics, and Optimization (ACO) This program is administered jointly by the Department of Mathematical Sciences, the Department of Computer Science, and the Tepper School of Business. It focuses on discrete mathematics and algorithmic issues arising in computer science and operations research ...
Graduate
The primary intent of our graduate program is to train mathematical scientists for a variety of career opportunities. Our graduates pursue research and teaching careers in traditional university settings, conduct research in industrial and government laboratories, and work in the information technology and financial industries.
Requirements
Usually, the thesis advisor is a member of the Department of Mathematical Sciences at Carnegie Mellon. On occasion students are permitted to choose an advisor from outside the Department or even outside the university.
ACO Program
The Ph.D program in Algorithms, Combinatorics, and Optimization at Carnegie Mellon is intended to fill this gap. The program brings together the study of the mathematical structure of discrete objects and the design and analysis of algorithms in areas such as graph theory, combinatorial optimization, integer programming, polyhedral theory, computational algebra, geometry, and number theory.
Mellon College of Science
Department of Mathematical Sciences Carnegie Mellon University Wean Hall 6113 Pittsburgh, PA 15213 412-268-2545 ...
Outcomes and Destinations
2016 Post-doctoral Associate, Carnegie Mellon University, Pittsburgh, PA Lecturer, Chulalongkorn University, Bangkok, Thailand Software Developer (2), Epic Systems (2), Madison, WI Software Engineer, Heap, San Francisco, CA IB Quantitative Research/Risk Management, J.P. Morgan Chase, New York, NY Associate, Morgan Stanley, Unknown, NY
ACO Program Home Page
Carnegie Mellon University has taken the initiative of offering an interdisciplinary Ph.D program in Algorithms, Combinatorics, and Optimization. It is administered jointly by the Tepper School of Business (Operations Research group) , the Computer Science Department ( Algorithms and Complexity group ), and the Department of Mathematical ...
Application Management
First-time users: Create an account to start a new application.
Department of Mathematical Sciences Courses
Fall and Spring: 1 unit The purpose of this course is to introduce math majors to the different degree programs in Mathematical Sciences, and to inform math majors about relevant topics such as advising, math courses, graduate schools, and typical career paths in the mathematical sciences.
Doctoral Admissions
Contacts and Additional Information For more on the application process, please refer to the SCS Online Graduate Application Instructions. For questions specific to admissions for the Ph.D. in Computer Science email: [email protected]
Mathematical Sciences
Carnegie Mellon's Mathematical Science program provides individual attention to students seeking to explore and define mathematics.
Mathematical Sciences
As a doctoral candidate in our mathematical sciences program, you will: Build your pedagogical skills along with your research. Every student takes a full year of classes to learn how to teach college-level math. Participate in two semester-long teaching internships, where you'll teach math under the supervision of an experienced faculty member.
PhD Program in Machine Learning
PhD Program in Machine Learning Carnegie Mellon University's doctoral program in Machine Learning is designed to train students to become tomorrow's leaders through a combination of interdisciplinary coursework, hands-on applications, and cutting-edge research. Graduates of the Ph.D. program in Machine Learning will be uniquely positioned to pioneer new developments in the field, and to be ...
Mathematics Graduate Programs
Get information about graduate mathematics degrees and programs offered at CMU.
CMU Math Placement
The Math Placement assessment will help decide what mathematics courses to enroll in at Central Michigan University and help you prepare for your course.
Doctor of Philosophy program
Doctor of Philosophy program. The doctoral degree emphasizes the creation of new knowledge through extensive independent research, including the formulation of hypotheses, the interpretation of phenomena revealed by research, and the extraction of general principles upon which predictions can be made. An important part of this process is ...
Graduate PhD Program
The PhD program is an intensive course of study designed for the full-time student planning a career in research and teaching at the university level or in quantitative research and development in industry or government. Admission is limited and highly selective. Successful applicants have typically pursued an undergraduate major in mathematics.
Best Applied Math Programs in America
The applied math discipline is geared toward students who hope to use their mathematical prowess in business organizations, government agencies and other job sites. These are the best graduate ...
Carnegie Mellon mathematics? : r/math
If you want the best chance at getting into a good PhD program, I would go with Brown. Department rankings aren't as important as they would be if you were choosing between PhD programs, and anyway, CMU, Berkeley, and Brown all have great math departments, whereas Brown is pretty clearly the most selective undergrad institution out of your choices.
Evaluate my profile for top 4 CS PhD program (Stanford, CMU, MIT
Evaluate my profile for top 4 CS PhD program (Stanford, CMU, MIT, Berkeley) Where does my application profile stand for PhD programs in CS/AI/ML at Stanford/Berkeley/CMU/MIT? CS Major with Data Science Certificate at top 100 CS program (US) 3.2 GPA Overall (3.75 CS Major GPA) A's in systems programming, discrete math, data mining, data science ...
Apply to our graduate programs
Apply to the graduate programs in the College of Engineering using the online application. You do not have to complete the online application in one sitting. You may access your application and change your answers as many times as you like.
Academics
Graduate degree programs Master of Science in Electrical and Computer Engineering (MS ECE) MS ECE is a 10-16 month program that covers a broad and diverse set of areas and permeates nearly all areas of application of importance in society today.
CMU PhD Admissions : r/cmu
The Computer Science PhD program does not require a GRE, although other programs at CMU might. I barely look at GRE scores when evaluating a PhD applicant. To get a sense of whether your application to any of the SCS PhD programs, you might read this document from my colleague More Harchol Balter, which lays out the process.
I want to apply to CMU math phd as an international applicant. I
I want to apply to CMU math phd as an international applicant. I remember that the deadline was december 10 (today) but when I visited the website I found that the deadline for submitting material is december 20.
SCS Graduate Application Fee Waiver
The School of Computer Science offers graduate application fee waivers for reasons related to financial hardship and to participants of certain programs.
Ph.D. Programs
The Department of Biomedical Engineering participates in a combined M.D.-Ph.D. Program with the University of Pittsburgh School of Medicine, to offer M.D. degree from the University of Pittsburgh and Ph.D. from Carnegie Mellon University. The aim is to allow physician-engineers to blend research and clinical perspectives in treating patients.
B.S. in HCI Major Curriculum
Carnegie Mellon University offers an undergraduate major in Human-Computer Interaction within the School of Computer Science.
Graduate programs
<p>Graduate study in MSE systematically develops the fundamental scientific and engineering principles that govern the behavior and application of materials.</p>
Anand Srinivasa Rao
Anand Rao is a Distinguished Services Professor of Applied Data Science and AI in the Heinz College of Information Systems and Public Policy at Carnegie Mellon University Anand Rao has focused on research, innovation, applications, business and societal adoption of data, analytics, and artificial intelligence over his 35-year consulting ...
IMAGES
COMMENTS
The Department of Mathematical Sciences at Carnegie Mellon University invites applications to our graduate program in Mathematics. To be admitted to this program applicants must document competence equivalent to graduation from a recognized U.S. four-year college, university or institute of technology.
Doctor of Philosophy in Algorithms, Combinatorics, and Optimization (ACO) This program is administered jointly by the Department of Mathematical Sciences, the Department of Computer Science, and the Tepper School of Business. It focuses on discrete mathematics and algorithmic issues arising in computer science and operations research ...
The primary intent of our graduate program is to train mathematical scientists for a variety of career opportunities. Our graduates pursue research and teaching careers in traditional university settings, conduct research in industrial and government laboratories, and work in the information technology and financial industries.
Usually, the thesis advisor is a member of the Department of Mathematical Sciences at Carnegie Mellon. On occasion students are permitted to choose an advisor from outside the Department or even outside the university.
The Ph.D program in Algorithms, Combinatorics, and Optimization at Carnegie Mellon is intended to fill this gap. The program brings together the study of the mathematical structure of discrete objects and the design and analysis of algorithms in areas such as graph theory, combinatorial optimization, integer programming, polyhedral theory, computational algebra, geometry, and number theory.
Department of Mathematical Sciences Carnegie Mellon University Wean Hall 6113 Pittsburgh, PA 15213 412-268-2545 ...
2016 Post-doctoral Associate, Carnegie Mellon University, Pittsburgh, PA Lecturer, Chulalongkorn University, Bangkok, Thailand Software Developer (2), Epic Systems (2), Madison, WI Software Engineer, Heap, San Francisco, CA IB Quantitative Research/Risk Management, J.P. Morgan Chase, New York, NY Associate, Morgan Stanley, Unknown, NY
Carnegie Mellon University has taken the initiative of offering an interdisciplinary Ph.D program in Algorithms, Combinatorics, and Optimization. It is administered jointly by the Tepper School of Business (Operations Research group) , the Computer Science Department ( Algorithms and Complexity group ), and the Department of Mathematical ...
First-time users: Create an account to start a new application.
Fall and Spring: 1 unit The purpose of this course is to introduce math majors to the different degree programs in Mathematical Sciences, and to inform math majors about relevant topics such as advising, math courses, graduate schools, and typical career paths in the mathematical sciences.
Contacts and Additional Information For more on the application process, please refer to the SCS Online Graduate Application Instructions. For questions specific to admissions for the Ph.D. in Computer Science email: [email protected]
Carnegie Mellon's Mathematical Science program provides individual attention to students seeking to explore and define mathematics.
As a doctoral candidate in our mathematical sciences program, you will: Build your pedagogical skills along with your research. Every student takes a full year of classes to learn how to teach college-level math. Participate in two semester-long teaching internships, where you'll teach math under the supervision of an experienced faculty member.
PhD Program in Machine Learning Carnegie Mellon University's doctoral program in Machine Learning is designed to train students to become tomorrow's leaders through a combination of interdisciplinary coursework, hands-on applications, and cutting-edge research. Graduates of the Ph.D. program in Machine Learning will be uniquely positioned to pioneer new developments in the field, and to be ...
Get information about graduate mathematics degrees and programs offered at CMU.
The Math Placement assessment will help decide what mathematics courses to enroll in at Central Michigan University and help you prepare for your course.
Doctor of Philosophy program. The doctoral degree emphasizes the creation of new knowledge through extensive independent research, including the formulation of hypotheses, the interpretation of phenomena revealed by research, and the extraction of general principles upon which predictions can be made. An important part of this process is ...
The PhD program is an intensive course of study designed for the full-time student planning a career in research and teaching at the university level or in quantitative research and development in industry or government. Admission is limited and highly selective. Successful applicants have typically pursued an undergraduate major in mathematics.
The applied math discipline is geared toward students who hope to use their mathematical prowess in business organizations, government agencies and other job sites. These are the best graduate ...
If you want the best chance at getting into a good PhD program, I would go with Brown. Department rankings aren't as important as they would be if you were choosing between PhD programs, and anyway, CMU, Berkeley, and Brown all have great math departments, whereas Brown is pretty clearly the most selective undergrad institution out of your choices.
Evaluate my profile for top 4 CS PhD program (Stanford, CMU, MIT, Berkeley) Where does my application profile stand for PhD programs in CS/AI/ML at Stanford/Berkeley/CMU/MIT? CS Major with Data Science Certificate at top 100 CS program (US) 3.2 GPA Overall (3.75 CS Major GPA) A's in systems programming, discrete math, data mining, data science ...
Apply to the graduate programs in the College of Engineering using the online application. You do not have to complete the online application in one sitting. You may access your application and change your answers as many times as you like.
Graduate degree programs Master of Science in Electrical and Computer Engineering (MS ECE) MS ECE is a 10-16 month program that covers a broad and diverse set of areas and permeates nearly all areas of application of importance in society today.
The Computer Science PhD program does not require a GRE, although other programs at CMU might. I barely look at GRE scores when evaluating a PhD applicant. To get a sense of whether your application to any of the SCS PhD programs, you might read this document from my colleague More Harchol Balter, which lays out the process.
I want to apply to CMU math phd as an international applicant. I remember that the deadline was december 10 (today) but when I visited the website I found that the deadline for submitting material is december 20.
The School of Computer Science offers graduate application fee waivers for reasons related to financial hardship and to participants of certain programs.
The Department of Biomedical Engineering participates in a combined M.D.-Ph.D. Program with the University of Pittsburgh School of Medicine, to offer M.D. degree from the University of Pittsburgh and Ph.D. from Carnegie Mellon University. The aim is to allow physician-engineers to blend research and clinical perspectives in treating patients.
Carnegie Mellon University offers an undergraduate major in Human-Computer Interaction within the School of Computer Science.
<p>Graduate study in MSE systematically develops the fundamental scientific and engineering principles that govern the behavior and application of materials.</p>
Anand Rao is a Distinguished Services Professor of Applied Data Science and AI in the Heinz College of Information Systems and Public Policy at Carnegie Mellon University Anand Rao has focused on research, innovation, applications, business and societal adoption of data, analytics, and artificial intelligence over his 35-year consulting ...