phd machine learning usa

Machine Learning (Ph.D.)

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Doctor of Philosophy with a major in Machine Learning

The Doctor of Philosophy with a major in Machine Learning program has the following principal objectives, each of which supports an aspect of the Institute’s mission:

• Create students that are able to advance the state of knowledge and practice in machine learning through innovative research contributions.
• Create students who are able to integrate and apply principles from computing, statistics, optimization, engineering, mathematics and science to innovate, and create machine learning models and apply them to solve important real-world data intensive problems.
• Create students who are able to participate in multidisciplinary teams that include individuals whose primary background is in statistics, optimization, engineering, mathematics and science.
• Provide a high quality education that prepares individuals for careers in industry, government (e.g., national laboratories), and academia, both in terms of knowledge, computational (e.g., software development) skills, and mathematical modeling skills.
• Foster multidisciplinary collaboration among researchers and educators in areas such as computer science, statistics, optimization, engineering, social science, and computational biology.
• Foster economic development in the state of Georgia.
• Advance Georgia Tech’s position of academic leadership by attracting high quality students who would not otherwise apply to Tech for graduate study.

All PhD programs must incorporate a standard set of Requirements for the Doctoral Degree .

The central goal of the PhD program is to train students to perform original, independent research.  The most important part of the curriculum is the successful defense of a PhD Dissertation, which demonstrates this research ability.  The academic requirements are designed in service of this goal.

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in nine schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Aerospace Engineering, Chemical and Biomolecular Engineering, Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Summary of General Requirements for a PhD in Machine Learning

• Core curriculum (4 courses, 12 hours). Machine Learning PhD students will be required to complete courses in four different areas: Mathematical Foundations, Probabilistic and Statistical Methods in Machine Learning, ML Theory and Methods, and Optimization.   
• Area electives (5 courses, 15 hours).
• Responsible Conduct of Research (RCR) (1 course, 1 hour, pass/fail).  Georgia Tech requires that all PhD students complete an RCR requirement that consists of an online component and in-person training. The online component is completed during the student’s first semester enrolled at Georgia Tech.  The in-person training is satisfied by taking PHIL 6000 or their associated academic program’s in-house RCR course.
• Qualifying examination (1 course, 3 hours). This consists of a one-semester independent literature review followed by an oral examination.
• Doctoral minor (2 courses, 6 hours).
• Research Proposal.  The purpose of the proposal is to give the faculty an opportunity to give feedback on the student’s research direction, and to make sure they are developing into able communicators.
• PhD Dissertation.

Almost all of the courses in both the core and elective categories are already taught regularly at Georgia Tech.  However, two core courses (designated in the next section) are being developed specifically for this program.  The proposed outlines for these courses can be found in the Appendix. Students who complete these required courses as part of a master’s program will not need to repeat the courses if they are admitted to the ML PhD program.

Core Courses

Machine Learning PhD students will be required to complete courses in four different areas. With the exception of the Foundations course, each of these area requirements can be satisfied using existing courses from the College of Computing or Schools of ECE, ISyE, and Mathematics.

Machine Learning core:

Mathematical Foundations of Machine Learning. This required course is the gateway into the program, and covers the key subjects from applied mathematics needed for a rigorous graduate program in ML. Particular emphasis will be put on advanced concepts in linear algebra and probabilistic modeling. This course is cross-listed between CS, CSE, ECE, and ISyE.

ECE 7750 / ISYE 7750 / CS 7750 / CSE 7750 Mathematical Foundations of Machine Learning

Probabilistic and Statistical Methods in Machine Learning

• ISYE 6412 , Theoretical Statistics
• ECE 7751 / ISYE 7751 / CS 7751 / CSE 7751 Probabilistic Graphical Models
• MATH 7251 High Dimension Probability
• MATH 7252 High Dimension Statistics

Machine Learning: Theory and Methods.   This course serves as an introduction to the foundational problems, algorithms, and modeling techniques in machine learning.  Each of the courses listed below treats roughly the same material using a mix of applied mathematics and computer science, and each has a different balance between the two. 

• CS 7545 Machine Learning Theory and Methods
• CS 7616 , Pattern Recognition
• CSE 6740 / ISYE 6740 , Computational Data Analysis
• ECE 6254 , Statistical Machine Learning
• ECE 6273 , Methods of Pattern Recognition with Applications to Voice

Optimization.   Optimization plays a crucial role in both developing new machine learning algorithms and analyzing their performance.  The three courses below all provide a rigorous introduction to this topic; each emphasizes different material and provides a unique balance of mathematics and algorithms.

• ECE 8823 , Convex Optimization: Theory, Algorithms, and Applications
• ISYE 6661 , Linear Optimization
• ISYE 6663 , Nonlinear Optimization
• ISYE 7683 , Advanced Nonlinear Programming

After core requirements are satisfied, all courses listed in the core not already taken can be used as (appropriately classified) electives.

In addition to meeting the core area requirements, each student is required to complete five elective courses. These courses are required for getting a complete breadth in ML. These courses must be chosen from at least two of the five subject areas listed below. In addition, students can use up to six special problems research hours to satisfy this requirement. 

i. Statistics and Applied Probability : To build breadth and depth in the areas of statistics and probability as applied to ML.

• AE 6505 , Kalman Filtering
• AE 8803 Gaussian Processes
• BMED 6700 , Biostatistics
• ECE 6558 , Stochastic Systems
• ECE 6601 , Random Processes
• ECE 6605 , Information Theory
• ISYE 6402 , Time Series Analysis
• ISYE 6404 , Nonparametric Data Analysis
• ISYE 6413 , Design and Analysis of Experiments
• ISYE 6414 , Regression Analysis
• ISYE 6416 , Computational Statistics
• ISYE 6420 , Bayesian Statistics
• ISYE 6761 , Stochastic Processes I
• ISYE 6762 , Stochastic Processes II
• ISYE 7400 , Adv Design-Experiments
• ISYE 7401 , Adv Statistical Modeling
• ISYE 7405 , Multivariate Data Analysis
• ISYE 8803 , Statistical and Probabilistic Methods for Data Science
• ISYE 8813 , Special Topics in Data Science
• MATH 6221 , Probability Theory for Scientists and Engineers
• MATH 6266 , Statistical Linear Modeling
• MATH 6267 , Multivariate Statistical Analysis
• MATH 7244 , Stochastic Processes and Stochastic Calculus I
• MATH 7245 , Stochastic Processes and Stochastic Calculus II

ii. Advanced Theory: To build a deeper understanding of foundations of ML.

• AE 8803 , Optimal Transport Theory and Applications
• CS 7280 , Network Science
• CS 7510 , Graph Algorithms
• CS 7520 , Approximation Algorithms
• CS 7530 , Randomized Algorithms
• CS 7535 , Markov Chain Monte Carlo Algorithms
• CS 7540 , Spectral Algorithms
• CS 8803 , Continuous Algorithms
• ECE 6283 , Harmonic Analysis and Signal Processing
• ECE 6555 , Linear Estimation
• ISYE 7682 , Convexity
• MATH 6112 , Advanced Linear Algebra
• MATH 6241 , Probability I
• MATH 6262 , Advanced Statistical Inference
• MATH 6263 , Testing Statistical Hypotheses
• MATH 6580 , Introduction to Hilbert Space
• MATH 7338 , Functional Analysis
• MATH 7586 , Tensor Analysis
• MATH 88XX, Special Topics: High Dimensional Probability and Statistics

iii. Applications: To develop a breadth and depth in variety of applications domains impacted by/with ML.

• AE 6373 , Advanced Design Methods
• AE 8803 , Machine Learning for Control Systems
• AE 8803 , Nonlinear Stochastic Optimal Control
• BMED 6780 , Medical Image Processing
• BMED 6790 / ECE 6790 , Information Processing Models in Neural Systems
• BMED 7610 , Quantitative Neuroscience
• BMED 8813 BHI, Biomedical and Health Informatics
• BMED 8813 MHI, mHealth Informatics
• BMED 8813 MLB, Machine Learning in Biomedicine
• BMED 8823 ALG, OMICS Data and Bioinformatics Algorithms
• CHBE 6745 , Data Analytics for Chemical Engineers
• CHBE 6746 , Data-Driven Process Engineering
• CS 6440 , Introduction to Health Informatics
• CS 6465 , Computational Journalism
• CS 6471 , Computational Social Science
• CS 6474 , Social Computing
• CS 6475 , Computational Photography
• CS 6476 , Computer Vision
• CS 6601 , Artificial Intelligence
• CS 7450 , Information Visualization
• CS 7476 , Advanced Computer Vision
• CS 7630 , Autonomous Robots
• CS 7632 , Game AI
• CS 7636 , Computational Perception
• CS 7643 , Deep Learning
• CS 7646 , Machine Learning for Trading
• CS 7647 , Machine Learning with Limited Supervision
• CS 7650 , Natural Language Processing
• CSE 6141 , Massive Graph Analysis
• CSE 6240 , Web Search and Text Mining
• CSE 6242 , Data and Visual Analytics
• CSE 6301 , Algorithms in Bioinformatics and Computational Biology
• ECE 4580 , Computational Computer Vision
• ECE 6255 , Digital Processing of Speech Signals
• ECE 6258 , Digital Image Processing
• ECE 6260 , Data Compression and Modeling
• ECE 6273 , Methods of Pattern Recognition with Application to Voice
• ECE 6550 , Linear Systems and Controls
• ECE 8813 , Network Security
• ISYE 6421 , Biostatistics
• ISYE 6810 , Systems Monitoring and Prognosis
• ISYE 7201 , Production Systems
• ISYE 7204 , Info Prod & Ser Sys
• ISYE 7203 , Logistics Systems
• ISYE 8813 , Supply Chain Inventory Theory
• HS 6000 , Healthcare Delivery
• MATH 6759 , Stochastic Processes in Finance
• MATH 6783 , Financial Data Analysis

iv. Computing and Optimization: To provide more breadth and foundation in areas of math, optimization and computation for ML.

• AE 6513 , Mathematical Planning and Decision-Making for Autonomy
• AE 8803 , Optimization-Based Learning Control and Games
• CS 6515 , Introduction to Graduate Algorithms
• CS 6550 , Design and Analysis of Algorithms
• CSE 6140 , Computational Science and Engineering Algorithms
• CSE 6643 , Numerical Linear Algebra
• CSE 6644 , Iterative Methods for Systems of Equations
• CSE 6710 , Numerical Methods I
• CSE 6711 , Numerical Methods II
• ECE 6553 , Optimal Control and Optimization
• ISYE 6644 , Simulation
• ISYE 6645 , Monte Carlo Methods
• ISYE 6662 , Discrete Optimization
• ISYE 6664 , Stochastic Optimization
• ISYE 6679 , Computational methods for optimization
• ISYE 7686 , Advanced Combinatorial Optimization
• ISYE 7687 , Advanced Integer Programming

v. Platforms : To provide breadth and depth in computing platforms that support ML and Computation.

• CS 6421 , Temporal, Spatial, and Active Databases
• CS 6430 , Parallel and Distributed Databases
• CS 6290 , High-Performance Computer Architecture
• CSE 6220 , High Performance Computing
• CSE 6230 , High Performance Parallel Computing

Qualifying Examination

The purpose of the Qualifying Examination is to judge the candidate’s potential as an independent researcher.

The Ph.D. qualifying exam consists of a focused literature review that will take place over the course of one semester.  At the beginning of the second semester of their second year, a qualifying committee consisting of three members of the ML faculty will assign, in consultation with the student and the student’s advisor, a course of study consisting of influential papers, books, or other intellectual artifacts relevant to the student’s research interests.  The student’s focus area and current research efforts (and related portfolio) will be considered in defining the course of study.

At the end of the semester, the student will submit a written summary of each artifact which highlights their understanding of the importance (and weaknesses) of the work in question and the relationship of this work to their current research.  Subsequently, the student will have a closed oral exam with the three members of the committee.  The exam will be interactive, with the student and the committee discussing and criticizing each work and posing questions related the students current research to determine the breadth of student’s knowledge in that specific area.  

The success of the examination will be determined by the committee’s qualitative assessment of the student’s understanding of the theory, methods, and ultimate impact of the assigned syllabus.

The student will be given a passing grade for meeting the requirements of the committee in both the written and the oral part. Unsatisfactory performance on either part will require the student to redo the entire qualifying exam in the following semester year. Each student will be allowed only two attempts at the exam.

Students are expected to perform the review by the end of their second year in the program.

Doctoral Dissertation

The primary requirement of the PhD student is to do original and substantial research.  This research is reported for review in the PhD dissertation, and presented at the final defense.  As the first step towards completing a dissertation, the student must prepare and defend a Research Proposal.  The proposal is a document of no more than 20 pages in length that carefully describes the topic of the dissertation, including references to prior work, and any preliminary results to date.  The written proposal is submitted to a committee of three faculty members from the ML PhD program, and is presented in a public seminar shortly thereafter.  The committee members provide feedback on the proposed research directions, comments on the strength of writing and oral presentation skills, and might suggest further courses to solidify the student’s background.  Approval of the Research Proposal by the committee is required at least six months prior to the scheduling of the PhD defense. It is expected that the student complete this proposal requirement no later than their fourth year in the program. The PhD thesis committee consists of five faculty members: the student’s advisor, three additional members from the ML PhD program, and one faculty member external to the ML program.  The committee is charged with approving the written dissertation and administering the final defense.  The defense consists of a public seminar followed by oral examination from the thesis committee.

Doctoral minor (2 courses, 6 hours): 

The minor follows the standard Georgia Tech requirement: 6 hours, preferably outside the student’s home unit, with a GPA in those graduate-level courses of at least 3.0.  The courses for the minor should form a cohesive program of study outside the area of Machine Learning; no ML core or elective courses may be used to fulfill this requirement and must be approved by your thesis advisor and ML Academic Advisor.  Typical programs will consist of three courses two courses from the same school (any school at the Institute) or two courses from the same area of study. 

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Graduate Education

Office of graduate and postdoctoral education, machine learning (ml), program contact.

Stephanie Niebuhr Georgia Institute of Technology 801 Atlantic Drive Atlanta, GA 30332-0405

Application Deadlines

Application deadline varies by home school.

• Aerospace Engineering: March 3
• Biomedical Engineering: December 1
• Electrical and Computer Engineering: December 16
• Industrial & Systems Engineering: December 15
• Mathematics: December 15
• School of Chemical & Biomolecular Engineering: December 15
• School of Computational Science & Engineering: December 15
• School of Computer Science: December 15
• School of Interactive Computing: December 15

Admittance Terms

Degree programs.

• PhD, Machine Learning

Areas of Research

Our world-class faculty and students specialize in areas including, but not limited to:

• Computer Vision
• Natural Language Processing
• Deep Learning
• Game Theory
• Neuro Computing
• Ethics and Fairness
• Artificial Intelligence
• Internet of Things
• Machine Learning Theory
• Systems for Machine Learning
• Bioinformatics
• Computational Finance
• Health Systems
• Information Security
• Logistics and Manufacturing

Interdisciplinary Programs

The Machine Learning Ph.D. is an interdisciplinary doctoral program spanning three colleges (Computing, Engineering, Sciences).  Students are admitted through one of eight participating home schools:

• Computer Science (Computing)
• Computational Science and Engineering (Computing)
• Interactive Computing (Computing)– see  Computer Science
• Aerospace Engineering (Engineering)
• Biomedical Engineering (Engineering)
• Electrical and Computer Engineering (Engineering)
• Mathematics (Sciences)
• Industrial Systems Engineering (Engineering)

Admission to the ML PhD program is contingent on meeting the requirement for admission into one of these schools. It is possible that, due to space or other constraints, that you are admitted to the general PhD program in your home school but not the ML PhD program.

The ML PhD program is a cohesive, interdisciplinary course of study subject to a unique set of curriculum requirements; see the program webpage for more information.

Standardized Tests

IELTS Academic Requirements

• Varies among home units.

TOEFL Requirements

GRE Requirements

Application Requirements

Please note that application requirements may vary by home unit, including the application deadlines and test score requirements, as well as support for incoming students (including guarantees of teaching assistantships and/or fellowships). Please review the home unit links above or contact them directly for details.

Program Costs

• Go to " View Tuition Costs by Semester ," and select the semester you plan to start. Graduate-level programs are divided into sections: Graduate Rates–Atlanta Campus, Study Abroad, Specialty Graduate Programs, Executive Education Programs
• Find the degree and program you are interested in and click to access the program's tuition and fees by credit hour PDF.
• In the first column, determine the number of hours (or credits) you intend to take for your first semester.
• Determine if you will pay in-state or out-of-state tuition. Learn more about the difference between in-state and out-of-state . For example, if you are an in-state resident and planning to take six credits for the Master of Architecture degree, the tuition cost will be $4,518.
• The middle section of the document lists all mandatory Institute fees. To see your total tuition plus mandatory fees, refer to the last two columns of the PDF.

Program Links

The Office of Graduate Education has prepared an admissions checklist to help you navigate through the admissions process.

College of Computing

Ph.d. in machine learning, about the curriculum.

The central goal of the Ph.D. program is to train students to perform original, independent research. The most important part of the curriculum is the successful defense of a Ph.D. dissertation, which demonstrates this research ability.

The curriculum is designed with the following principal educational goals:

•    Students will develop a solid understanding of fundamental principles across a range of core areas in the machine learning discipline. •    Students will develop a deep understanding and set of skills and expertise in a specific theoretical aspect or application area of the machine learning discipline. •    The students will be able to apply and integrate the knowledge and skills they have developed and demonstrate their expertise and proficiency in an application area of practical importance. •    Students will be able to engage in multidisciplinary activities by being able to communicate complex ideas in their area of expertise to individuals in other fields, be able to understand complex ideas and concepts from other disciplines, and be able to incorporate these concepts into their own work. The curriculum for the Ph.D. in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech:  •    Computer Science (Computing) •    Computational Science and Engineering (Computing) •    Interactive Computing (Computing) – see Computer Science •     Aerospace Engineering (Engineering) •     Biomedical Engineering (Engineering) •     Electrical and Computer Engineering (Engineering) •     Industrial Systems Engineering (Engineering) •     Mathematics (Sciences) Students must complete four core courses, five electives, a qualifying exam, and a doctoral dissertation defense. All doctorate students are advised by ML Ph.D. Program Faculty . All coursework must be completed before the Ph.D. proposal. An overall GPA of 3.0 is required for the Ph.D. coursework.

Research Opportunities

Our faculty comes from all six colleges across Georgia Tech’s campus, creating many interdisciplinary research opportunities for our students. Our labs focus on research areas such as artificial intelligence, data science, computer vision, natural language processing, optimization, machine learning theory, forecasting, robotics, computational biology, fintech, and more.

External applications are only accepted for the Fall semester each year. The application deadline varies by home school. 

The Machine Learning Ph.D. admissions process works bottom-up through the home schools. Admissions decisions are made by the home school, and then submitted to the Machine Learning Faculty Advisory Committee (FAC) for final approval. Support for incoming students (including guarantees of teaching assistantships and/or fellowships) is determined by the home schools. 

After the admissions have been approved by the FAC, the home school will communicate the acceptance to the prospective student. The home school will also communicate all rejections.

Get to Know Current ML@GT Students

Learn more about our current students, their interests inside and outside of the lab, favorite study spots, and more.

Career Outlook

The machine learning doctorate degree prepares students for a variety of positions in industry, government, and academia. These positions include research, development, product managers, and entrepreneurs. 

Graduates are well prepared for position in industry in areas such as internet companies, robotic and manufacturing companies and financial engineering, to mention a few. Positions in government and with government contractors in software and systems are also possible career paths for program graduates. Graduates are also well-suited for positions in academia involving research and education in departments concerned with the development and application of data-driven models in engineering, the sciences, and computing. 

Frequently Asked Questions

For additional questions regarding the ML Ph.D. program, please take a look at our frequently asked questions.

You can also view the ML Handbook which has detailed information on the program and requirements.

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• PhD Program

The PhD in Machine Learning is an interdisciplinary doctoral program spanning three colleges (Computing, Engineering, Sciences).  Students are admitted through one of nine participating home schools:

• Contact SCS
• Contact CSE
• Contact ChBE
• Contact BME
• Contact ECE
• Contact ISYE
• ​​​​​​​ Contact MATH

Application requirements and deadlines follow the same as that of the home unit an applicant is applying through. For example, ML PhD applicants to the ECE home unit follow the same rules as the PhD ECE application requirements and deadlines. 

External applications are only accepted for the Fall semester each year.  The application deadline varies by home school with the earliest deadline of December 1. Most home schools have a final deadline of December 15. Check with home schools above for more specific details. 

Click here for application information and to apply  

Applicants must meet all admissions standards (including requirements on the minimum GPA, minimum GRE/TOEFL scores) of the home unit, which may vary. After an initial review, the unit’s representative of the ML Ph.D. Faculty Advisory Committee (FAC) will submit their candidates for review and the final admission decision will be made by the ML FAC.

Note most home units have made the GRE optional for fall 2023 applications. Contact the home unit at the above links for any specific info. 

The committee’s decision to admit will be based on (1) prior academic performance of the applicant in a B.S. or M.S. program at a recognized institution, including coursework and independent research projects, (2) prior work experience relevant to ML, (3) the applicant’s statement of purpose, and (4) the letters of support.

Please note that application requirements may vary by home unit, including the application deadlines and test score requirements, as well as support for incoming students (including guarantees of teaching assistantships and/or fellowships) are determined by the home units. Please review the home unit links above or contact them directly for details.

Have Questions?

Please contact the above  home units directly for questions related to:.

• Application deadlines
• Application fee waivers
• Assistantship/fellowship opportunities
• Program fit
• Advising Matching
• GRE requirements - Many units have made this test optional. 
• TOEFL minimum requirements and TOEFL waivers are determined by the GT Graduate Education Office:  https://grad.gatech.edu/english-proficiency . Note home units may required higher scores. 
• Desired content in Statement of Purpose and Recommendation Letters

For technical application questions, please contact  [email protected]: 

• Creating or using an account login
• Forgotten password
• Uploading documents 
• Difficulty with recommender emails
• How to access application status information (including application checklist)
• Difficulty with the touchnet payment system

For general inquiries about curriculum or program requirements, please see FAQs or contact [email protected] .

Georgia Tech Transfer Students

If you are already enrolled in a Ph.D. program in one of the nine participating schools noted above, you may apply to the ML Ph.D. program as a transfer student.  You will be subject to the standard ML curriculum and qualifying requirements, so this is recommended only for graduate students in their first or second year.  

Potential transfer students must have a ML PhD Program thesis adviso r  who is willing to support them on a research assistantship. For more information, please email [email protected] .

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Carnegie Mellon University School of Computer Science

Machine learning department.

Ph.D. in Machine Learning

Machine learning is dedicated to furthering scientific understanding of automated learning and to producing the next generation of tools for data analysis and decision-making based on that understanding. The doctoral program in machine learning trains students to become tomorrow's leaders in this rapidly growing area.

Joint Ph.D. in Machine Learning and Public Policy

The Joint Ph.D. Program in Machine Learning and Public Policy is a new program for students to gain the skills necessary to develop state-of-the-art machine learning technologies and apply these technologies to real-world policy issues.

Joint Ph.D. in Neural Computation and Machine Learning

This Ph.D. program trains students in the application of machine learning to neuroscience by combining core elements of the machine learning Ph.D. program and the Ph.D. in neural computation offered by the Center for the Neural Basis of Cognition.

Joint Ph.D. in Statistics and Machine Learning

This joint program prepares students for academic careers in both computer science and statistics departments at top universities. Students in this track will be involved in courses and research from both the Department of Statistics and the Machine Learning Department.

Visit the Website

• Back to Doctoral Programs

More Information

• Graduate Studies

Machine Learning and Big Data PhD Track

Optional PhD Tracks:   Statistical Genetics ,  Statistics in the Social Sciences ,  Machine Learning and Big Data

About The UW Department of Statistics now offers a PhD track in the area of Machine Learning and Big Data. All incoming and current students are eligible to apply. The goal of the PhD track is to prepare students to tackle large data analysis tasks with the most advanced tools in existence today, while building a strong methodological foundation. Students in this track will have a multidisciplinary experience, taking courses across departments and interacting with faculty and graduate students from these departments. A similar PhD track is being offered in  Computer Science and Engineering  (CSE), and students from both of these tracks will interact significantly in the core courses.

More details about ML @ UW can be found  here  and  here .

For application details, click  here .

Program Requirements

• Statistics Core:  STAT 570 ,  STAT 581 ,  STAT 582
• ML/BD Core:
• (i) Foundational ML:  STAT 535 (ii) One advanced ML course:  STAT 538  or  STAT 548 (iii) One CSE course:  CSE 544  (Databases) or CSE 512 (Visualization) (iv) One MLBD related elective such as a course from the list below and Two electives from the general electives list:        * Advanced Statistical Learning ( STAT 538 )       * Machine Learning for Big Data ( STAT 548 )       * Graphical Models ( CSE 515 )       * Visualization (CSE 512)       * Databases ( CSE 544 )       * Convex Optimization ( EE 578 )
• All other statistics PhD requirements hold, except  STAT 571  may be used to satisfy the Applied Data Analysis Project.
• STAT 583 is not required.

Advanced Data Science Transcriptable Option A student in the MLBD track can, in addition, choose to enroll/satisfy the Advanced Data Science Option. To further expand students' education and create a campus-wide community, students will register for at least 4 quarters in the weekly  eScience Community Seminar . Satisfying this option means that the student will have "ADS" listed on their transcript.

• eScience ADSO

ML Lunch Series A lunchtime seminar on a topic related to machine learning is held nearly weekly on Tuesdays during term. Lunch is provided. Updates are posted  here .

ML Mailing List General announcements related to machine learning are made on the  ML mailing list .

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PhD Program

Program overview.

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Rigorous, discipline-based research is the hallmark of the MIT Sloan PhD Program. The program is committed to educating scholars who will lead in their fields of research—those with outstanding intellectual skills who will carry forward productive research on the complex organizational, financial, and technological issues that characterize an increasingly competitive and challenging business world.

Start here.

Learn more about the program, how to apply, and find answers to common questions.

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Start Your Application

Visit this section to find important admissions deadlines, along with a link to our application.

Click here for answers to many of the most frequently asked questions.

PhD studies at MIT Sloan are intense and individual in nature, demanding a great deal of time, initiative, and discipline from every candidate. But the rewards of such rigor are tremendous:  MIT Sloan PhD graduates go on to teach and conduct research at the world's most prestigious universities.

PhD Program curriculum at MIT Sloan is organized under the following three academic areas: Behavior & Policy Sciences; Economics, Finance & Accounting; and Management Science. Our nine research groups correspond with one of the academic areas, as noted below.

MIT Sloan PhD Research Groups

Behavioral & policy sciences.

Economic Sociology

Institute for Work & Employment Research

Organization Studies

Technological Innovation, Entrepreneurship & Strategic Management

Economics, Finance & Accounting

Accounting  

Management Science

Information Technology

System Dynamics  

Those interested in a PhD in Operations Research should visit the Operations Research Center .  

PhD Program Structure

Additional information including coursework and thesis requirements.

MIT Sloan Predoctoral Opportunities

MIT Sloan is eager to provide a diverse group of talented students with early-career exposure to research techniques as well as support in considering research career paths.

Rising Scholars Conference

The fourth annual Rising Scholars Conference on October 25 and 26 gathers diverse PhD students from across the country to present their research.

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The goal of the MIT Sloan PhD Program's admissions process is to select a small number of people who are most likely to successfully complete our rigorous and demanding program and then thrive in academic research careers. The admission selection process is highly competitive; we aim for a class size of nineteen students, admitted from a pool of hundreds of applicants.

What We Seek

• Outstanding intellectual ability
• Excellent academic records
• Previous work in disciplines related to the intended area of concentration
• Strong commitment to a career in research

MIT Sloan PhD Program Admissions Requirements Common Questions

Dates and Deadlines

Admissions for 2024 is closed. The next opportunity to apply will be for 2025 admission. The 2025 application will open in September 2024. 

More information on program requirements and application components

Students in good academic standing in our program receive a funding package that includes tuition, medical insurance, and a fellowship stipend and/or TA/RA salary. We also provide a new laptop computer and a conference travel/research budget.

Funding Information

Throughout the year, we organize events that give you a chance to learn more about the program and determine if a PhD in Management is right for you.

PhD Program Events

Docnet recruiting forum at university of minnesota.

We will be joining the DocNet consortium for an overview of business academia and a recruitment fair at University of Minnesota, Carlson School of Management.

September 25 PhD Program Overview

During this webinar, you will hear from the PhD Program team and have the chance to ask questions about the application and admissions process.

DocNet Recruiting Forum - David Eccles School of Business

MIT Sloan PhD Program will be joining the DocNet consortium for an overview of business academia and a recruitment fair at Utah, David Eccles School of Business.

October PhD Program Overview

Complete PhD Admissions Event Calendar

Unlike formulaic approaches to training scholars, the PhD Program at MIT Sloan allows students to choose their own adventure and develop a unique scholarly identity. This can be daunting, but students are given a wide range of support along the way - most notably having access to world class faculty and coursework both at MIT and in the broader academic community around Boston.

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Profiles of our current students

MIT Sloan produces top-notch PhDs in management. Immersed in MIT Sloan's distinctive culture, upcoming graduates are poised to innovate in management research and education.

Academic Job Market

Doctoral candidates on the current academic market

Academic Placements

Graduates of the MIT Sloan PhD Program are researching and teaching at top schools around the world.

view recent placements 

MIT Sloan Experience

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The PhD Program is integral to the research of MIT Sloan's world-class faculty. With a reputation as risk-takers who are unafraid to embrace the unconventional, they are engaged in exciting disciplinary and interdisciplinary research that often includes PhD students as key team members.

Research centers across MIT Sloan and MIT provide a rich setting for collaboration and exploration. In addition to exposure to the faculty, PhD students also learn from one another in a creative, supportive research community.

Throughout MIT Sloan's history, our professors have devised theories and fields of study that have had a profound impact on management theory and practice.

From Douglas McGregor's Theory X/Theory Y distinction to Nobel-recognized breakthroughs in finance by Franco Modigliani and in option pricing by Robert Merton and Myron Scholes, MIT Sloan's faculty have been unmatched innovators.

This legacy of innovative thinking and dedication to research impacts every faculty member and filters down to the students who work beside them.

Faculty Links

• Accounting Faculty
• Economic Sociology Faculty
• Finance Faculty
• Information Technology Faculty
• Institute for Work and Employment Research (IWER) Faculty
• Marketing Faculty
• Organization Studies Faculty
• System Dynamics Faculty
• Technological Innovation, Entrepreneurship, and Strategic Management (TIES) Faculty

Student Research

“MIT Sloan PhD training is a transformative experience. The heart of the process is the student’s transition from being a consumer of knowledge to being a producer of knowledge. This involves learning to ask precise, tractable questions and addressing them with creativity and rigor. Hard work is required, but the reward is the incomparable exhilaration one feels from having solved a puzzle that had bedeviled the sharpest minds in the world!” -Ezra Zuckerman Sivan Alvin J. Siteman (1948) Professor of Entrepreneurship

Sample Dissertation Abstracts - These sample Dissertation Abstracts provide examples of the work that our students have chosen to study while in the MIT Sloan PhD Program.

We believe that our doctoral program is the heart of MIT Sloan's research community and that it develops some of the best management researchers in the world. At our annual Doctoral Research Forum, we celebrate the great research that our doctoral students do, and the research community that supports that development process.

The videos of their presentations below showcase the work of our students and will give you insight into the topics they choose to research in the program.

Attention To Retention: The Informativeness of Insiders’ Decision to Retain Shares

2024 PhD Doctoral Research Forum Winner - Gabriel Voelcker

Watch more MIT Sloan PhD Program  Doctoral Forum Videos

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Faculty Directory

Meet our faculty.

Computer Science, PhD

Computer science phd degree.

In the Computer Science program, you will learn both the fundamentals of computation and computation’s interaction with the world. Your work will involve a wide range of areas including theoretical computer science, artificial intelligence and machine learning, economics and computer science, privacy and security, data-management systems, intelligent interfaces, operating systems, computer graphics, computational linguistics, robotics, networks, architectures, program languages, and visualization.

You will be involved with researchers in several interdisciplinary initiatives across the University, such as the Center for Research on Computation and Society , the Data Science Initiative , and the Berkman Klein Center for Internet and Society .

Examples of projects current and past students have worked on include leveraging machine learning to solve real-world sequential decision-making problems and using artificial intelligence to help conservation and anti-poaching efforts around the world.

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Computer Science Degree

Harvard School of Engineering offers a  Doctor of Philosophy (Ph.D) degree in Computer Science , conferred through the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences. Prospective students apply through Harvard Griffin GSAS; in the online application, select “Engineering and Applied Sciences” as your program choice and select "PhD Computer Science" in the Area of Study menu.

In addition to the Ph.D. in Computer Science, the Harvard School of Engineering also offers master’s degrees in  Computational Science and Engineering as well as in Data Science which may be of interest to applicants who wish to apply directly to a master’s program.

Computer Science Career Paths

Graduates of the program have gone on to a range of careers in industry in companies like Riot Games as game director and Lead Scientist at Raytheon. Others have positions in academia at University of Pittsburgh, Columbia, and Stony Brook. More generally, common career paths for individuals with a PhD in computer science include: academic researcher/professor, industry leadership roles, industry research scientist, data scientist, entrepreneur/startup founder, product developer, and more.

Admissions & Academic Requirements

Prospective students apply through the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences (Harvard Griffin GSAS). In the online application, select  “Engineering and Applied Sciences” as your program choice and select "PhD Engineering Sciences: Electrical Engineering​." Please review the  admissions requirements and other information  before applying. Our website also provides  admissions guidance ,  program-specific requirements , and a  PhD program academic timeline . In the application for admission, select “Engineering and Applied Sciences” as your degree program choice and your degree and area of interest from the “Area of Study“ drop-down. PhD applicants must complete the Supplemental SEAS Application Form as part of the online application process.

Academic Background

Applicants typically have bachelor’s degrees in the natural sciences, mathematics, computer science, or engineering.

Standardized Tests

GRE General: Not Accepted

Computer Science Faculty & Research Areas

View a list of our computer science faculty  and  computer science affiliated research areas . Please note that faculty members listed as “Affiliates" or "Lecturers" cannot serve as the primary research advisor.

Computer Science Centers & Initiatives

View a list of the research centers & initiatives  at SEAS and the computer science faculty engagement with these entities .

Graduate Student Clubs

Graduate student clubs and organizations bring students together to share topics of mutual interest. These clubs often serve as an important adjunct to course work by sponsoring social events and lectures. Graduate student clubs are supported by the Harvard Kenneth C. Griffin School of Arts and Sciences. Explore the list of active clubs and organizations .

Funding and Scholarship

Learn more about financial support for PhD students.

• How to Apply

Learn more about how to apply  or review frequently asked questions for prospective graduate students.

In Computer Science

• First-Year Exploration
• Concentration Information
• Secondary Field
• Senior Thesis
• AB/SM Information
• Student Organizations
• PhD Timeline
• PhD Course Requirements
• Qualifying Exam
• Committee Meetings (Review Days)
• Committee on Higher Degrees
• Research Interest Comparison
• Collaborations
• Cross-Harvard Engagement
• Lecture Series
• Clubs & Organizations
• Centers & Initiatives
• Alumni Stories
• Graduate Student Stories
• Undergraduate Student Stories

Neuroscience Institute

Ph.d in neural computation.

Computational neuroscience is an area of brain science that uses technology to develop and analyze large data sets that are used to understand the complexities of neurobiological systems. In recent years, these methods have become more and more vital to the field of neuroscience as a whole. The use of quantitative methods in neurophysiology has led to important advances, and there has been a continuing stream of related work within mathematics and applied physics. More recently, engineers, computer scientists, and statisticians have contributed to the field, further expanding the definition of computational neuroscience.  At the same time, the number of investigators with requisite skills who are ac­tively engaged in this domain of research is relatively small. There is a widely recognized need for increased training in the application of computational, mathematical, and sta­tistical methods to biology and medicine, and to problems in neuroscience in particular.

The Ph.D. Program in Neural Computation seeks to train new scientists in the field. The environment at Carnegie Mellon University and the University of Pittsburgh has much to offer to students interested in computational approaches and it is a perfect home for the Ph.D. Program in Neural Computation. The neuroscience community in Pittsburgh is known for being particularly strong in computation.  The program also offers joint Ph.D. degrees with  Machine Learning  and  Statistics .

This program is designed to attract students with strong quantitative backgrounds and to train them in quantitative disciplines relevant to neuroscience and also to provide them the essential background in experimental neuroscience.  

In doing so, we leverage the special strengths of our institution and the unique neuroscience community here in Pittsburgh. Training faculty and courses will be drawn both from CMU and Pitt as described. The PNC PhD program is designed for stu­dents with backgrounds in computer science, physics, statistics, mathematics, and engineering who are interested in computational neuroscience, particularly with an emphasis on quantitative methods from computer science, machine learning, statistics and nonlinear dynamics.

The program consists of the following core activities:

• Coursework in computational neuroscience, quantitative methodologies and experimental neuroscience
• Research milestone presentations
• Exposure to experimental approaches through rotations or thesis research
• Training in teaching, scientific presentations and responsible conduct of research
• Successful defense of a PhD Thesis

Additional satellite activities through the CNBC will also foster students’ professional and scientific development.   Read more about the curriculum .

The PNC program is overseen by the PNC training faculty, the Academic Program Manager, and the Program Directors.  Questions about any aspect of the program should be directed either to the Academic Program Manager,   Melissa Stupka , or one of the Program Directors:   Steve Chase at CMU and   Gelsy  Torres-Oviedo  at University of Pittsburgh.

Joint Programs

• PNC/ Machine Learning
• PNC/ Statistics
• M.D.-Ph.D. Program

The CMU Rales Fellow Program is dedicated to developing a diverse community of STEM leaders from underrepresented and under-resourced backgrounds by eliminating cost as a barrier to education. Learn more about this program for master's and Ph.D. students. Learn more

Diversity in Neuroscience

• CMU Diversity, Equity, and Inclusion
• Dietrich College Diversity and Inclusion
• Mellon College of Science Diversity
• CMU Rales Fellows Program

Neural Computation Contacts

Academic program manager.

Melissa Stupka Mellon Institute 116C [email protected]

Program Director (CMU)

Steve Chase Professor, Biomedical Engineering & Neuroscience Institute Carnegie Mellon University Mellon Institute 115N [email protected]

Program Director (Pitt)

Gelsy Torres-Oviedo, Ph.D.                   Associate Professor, Bioengineering University of Pittsburgh Schenley Place, Room 115 [email protected]  

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Fully Funded PhD Programs in Machine Learning

Last updated October 30, 2022

Next in my series on How to Fully Fund Your PhD , I provide a list below of universities that offer full funding for PhD Programs in Machine Learning. Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. A PhD in Machine Learning can provide pathways for careers in technology, research, and academia.

“Full funding” is a financial aid package for full-time students that includes full tuition and an annual stipend or salary for living expenses for the three to six-year duration of the student’s doctoral studies. Funding is typically offered in exchange for graduate teaching and research work that is complementary to your studies. Not all universities provide full funding to their doctoral students, which is why I recommend researching the financial aid offerings of all the potential Ph.D. programs in your academic field, including small and lesser-known schools both in the U.S. and abroad.

You can also find several external fellowships in the  ProFellow database for graduate and doctoral study, as well as dissertation research, fieldwork, language study, and summer work experience.

Would you like to receive the full list of more than 1000+ fully funded programs in 60 disciplines? Download the FREE Directory of Fully Funded Graduate Programs and Full Funding Awards !

Carnegie Mellon University, School of Computer Science

(Pittsburgh, PA): They committed to providing your full tuition and stipend support for the coming academic year as long as you continue to make satisfactory progress in our program. Students who do not have external financial support will be funded via graduate assistantships, awarded for a nine-month period. Also, provide a dependency allowance.

Johns Hopkins University, Department of Computer Science

(Baltimore, MD): All Computer Science, PhD students at JHU are guaranteed full funding for tuition, stipend, and health insurance, through a mix of research and teaching assistantships. The Department of Computer Science’s core research areas include theory and algorithms; security, privacy, and cryptography; computational biology and medicine; and machine learning and data-intensive computing.

University of Cambridge, PhD in Advanced Machine Learning

(Cambridge, UK & Tübingen, Germany): Available funding for up to two PhD Fellowships covering university tuition fees (at Cambridge EU rates) and a stipend of approximately 17,000 Euros.

University College London, PhD in Theoretical Neuroscience and Machine Learning

(London, United Kingdom): Students at the Gatsby Unit study toward a PhD in either machine learning or theoretical neuroscience. Gatsby Ph.D. studentships cover the cost of tuition at the appropriate rate and include a tax-free stipend of £26,000 per annum. Full funding is available to all students, regardless of nationality.

Stanford University, Department of Computer Science

(Stanford, CA): Most Computer Science PhD students are supported by a research or teaching assistantship in Computer Science or the School of Engineering (SOE), or by a fellowship, or by an approved assistantship through a collaborating research organization. The SOE’s PhD program is full-time and requires full tuition, most or all of which is normally covered by such support.

Harvard University, PhD in Computer Science includes Machine Learning

(Cambridge, Massachusetts): The financial aid program features guaranteed funding for the first five years to all Ph.D. students and a variety of funding options and fellowships for other students. This includes tuition, fees, and a cost-of-living stipend.

Looking for graduate funding? Sign up to discover and bookmark more than 2,400 professional and academic fellowships in the ProFellow database .

© Victoria Johnson 2020, all rights reserved.

Related Posts:

• Fully Funded PhD Programs in the United Kingdom
• 6 Artificial Intelligence Fellowships For All Career Levels
• Fully Funded PhD Programs in Neuroscience
• Fully Funded PhD Programs in Mathematics
• Fully Funded PhDs in Teaching English as a Second Language

Computer Science Fellowships , Fully Funded PhD Programs , Machine Learning Fellowships , PhD in Machine Learning

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Machine Learning - CMU

Machine learning graduate programs rankings.

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Globally Known in AI and Machine Learning

Cs rankings.

AI : 1  Machine learning and data mining : 1 

CS Rankings ranks Carnegie Mellon University as the top university for machine learning and AI programs.

Ph.D. Program : AI: 1  Machine learning and data mining: 1 

Master's Program : AI: 1  Machine learning and data mining: 1 

Towards AI ranks the Machine Learning Department as the best educational research institution for machine learning graduate programs, both for Ph.D. and master's in machine learning.

U.S. News Report

AI: 1  

U.S. News Report ranks Carnegie Mellon University as the best institution for artificial intelligence (AI) programs.

Analytics India Magazine

Master's Program: Machine learning and data mining: 1 

AIM ranks the Machine Learning Department as the best institution for machine learning master's programs.