The Doctoral Program (Ph.D.) in Computer and Information Science (CIS) welcomes candidates in disciplines related to computer science, information processing, and computing. Our curriculum is designed to develop the intellectual skills essential for the rapidly changing character of research and to meet the demands of academe and industry. Students develop their own advanced study focus, working with faculty mentors on topics ranging from the core computer science discipline to diverse scholarly interactions within the School of Engineering and the University.
Doctoral studies in the CIS department offer the opportunity for rewarding exploration and research. Research opportunities span a wide range of theoretical and application topics including algorithms, bioinformatics, databases, graphics, machine learning, programming languages, robotics, security, software engineering, systems, vision, as well as interdisciplinary collaborations with fields such as biology, electrical engineering, genetics, linguistics, and mathematics.
Our research laboratories offer myriad possibilities for exploration. Seminars hosting outstanding leaders in their fields at our departmental and laboratories’ colloquia provide rigor, breadth, and relevance to the research and education experience. The University of Pennsylvania’s schools and research centers create an academic environment whose synergy informs research and education in the CIS department.
Our faculty prepare our doctoral students to be tomorrow’s innovators, leaders, and visionaries. The CIS department is an exciting place to be, and we invite you to join us. Read more about our research areas and highlights of projects and activities.
Detailed application instructions including the application URL are available from https://gradadm.seas.upenn.edu/how-to-apply/
The application will be open from September 14, 2020 through December 14, 2020 (11:59 PM Eastern Standard Time).
You will need to provide the following as part of your application:
- Biographical Information (as part of the online application)
- Personal Statement (up to two pages in readable font/size)
- Unofficial electronic Transcripts
- Contact information of 3 recommenders
- TOEFL/IELTS scores for international applicants and non-US Citizens/Permanent Resident applicants for whom English is not the native language
- $80 non-refundable application fee
If you are a US Citizen or Permanent Resident who identifies as an under-represented minority (as defined here), a first-generation student, or a member of an engineering honor society like Tau Beta Pi, you may be eligible to receive an application fee waiver. The waiver will be automatically applied upon submission of your graduate application.
Submitting your GRE score is optional for the upcoming admissions cycle. If you are able to take the GRE exam and have valid scores to report, we encourage you to do so. If you are not able to take the GRE exam, we will evaluate your application holistically. Your candidacy will not be negatively impacted if you do not report GRE scores.
The department and university are strongly committed to diversity, equity, and inclusion. The Applicant-Support Program will offer assistance to applicants from under-served or under-represented communities in the admissions process. The program will connect each applicant with a current PhD student who will serve as a buddy/mentor to help navigate the process, give feedback on the resume and personal statement, and provide any other reasonable assistance necessary. Interested applicants should apply by filling this form by November 1, 2020 AOE (Anywhere On Earth). Note that applying to this program does not guarantee a buddy/mentor.
New Concentration in “Machine Learning + X”
Recognizing the integration of machine learning into all specializations of computer science: starting in Fall 2020, the department is offering applicants the opportunity to specify a new concentration called “Machine Learning + X” as the primary concentration, where X is any of several existing specializations in computer science that intersect with machine learning. Such applicants must identify one or two of these specializations as their 2nd and 3rd concentrations. The department has exciting research projects at the intersection of machine learning and these specializations. These specializations along with faculty with interest in machine learning are as follows:
- Algorithmic Fairness and Data Privacy
Michael Kearns, Aaron Roth, Kristian Lum
- Algorithms and Computational Complexity
Aaron Roth, Sampath Kannan, Sanjeev Khanna, Anindya De
- Computational Social Science
Michael Kearns, Aaron Roth, Kristian Lum, Lyle Ungar, Sharath Guntuku, Duncan Watts
- Programming Languages / Compilers and Program Analysis / Formal Methods and Logic
Rajeev Alur, Mayur Naik, Osbert Bastani, Steve Zdancewic, Benjamin Pierce, Stephanie Weirich
- Computer Architecture
André DeHon, Joseph Devietti, Benjamin Lee, Jing (Jane) Li
- Computer Graphics, Animation, and Computational Physics
- Computational Biology and Biomedical Informatics
Lyle Ungar, Yoseph Barash, Sharath Guntuku, Konrad Kording, Joshua Plotkin, Jim Weimer
- Data Science Platforms and Databases
Susan Davidson, Zack Ives, Boon Thau Loo
- Distributed Systems, Networks, and Operating Systems
Sebastian Angel, Andreas Haeberlen, Vincent Liu, Boon Thau Loo, Linh Phan, Jonathan Smith
- Real-Time, Cyber-Physical, and Autonomous Systems
Rajeev Alur, George Pappas, Insup Lee, Rahul Mangharam, Nikolai Matni, James Weimer
Frequently Asked Questions
- How do my choices of up to 3 concentrations and up to 2 research advisors impact my chances of admission?
These choices are used to facilitate the review of your application by the most relevant faculty. Given our rising applicant numbers in recent years, we will ensure that your application is reviewed by the following faculty in decreasing order of priority:
– the 2 faculty you specify as research advisors,
– all the faculty listed in the primary concentration you specify, and
– all the faculty in the 2nd and 3rd concentrations you specify.
- Can I select “Machine Learning + X” as my 2nd or 3rd concentration?
No. This concentration is used to facilitate review of your application by the faculty listed in the specializations above, based on your choice(s) of the 2nd and 3rd concentrations.
- If I select “Machine Learning + X” as my primary concentration, can I also select a specialization not in the above list (e.g. Robotics) as my 2nd or 3rd concentration?
You must pick your 2nd concentration from the above list, but your 3rd concentration may be any specialization.
- Where can I find faculty listed in each concentration?
Faculty and their concentrations are listed at https://highlights.cis.upenn.edu/cis-research-areas/
- What is the difference between the concentrations “Machine Learning and Artificial Intelligence” and “Machine Learning + X”?
The main difference is that your application will be reviewed by faculty listed in the corresponding concentrations.
- Will selecting “Machine Learning + X” increase my chances of admission?
In general, you should pick the concentrations based on your research interests and the faculty listed in the concentrations. Note that there is very limited overlap between faculty in the “Machine Learning and Artificial Intelligence” concentration and the “Machine Learning + X” concentrations. The latter has been newly introduced to cater to the growing fraction of applicants in recent years who list the “Machine Learning and Artificial Intelligence” concentration but cannot be accommodated by faculty in that concentration.
- Does selecting both “Machine Learning + X” and “Machine Learning and Artificial Intelligence” increase my chances of admission?
The only way to do so is to specify the latter concentration as your 3rd, which would give a low chance that your application will be reviewed by faculty in that concentration.
- If I am accepted to the program, am I required to study the concentrations I list on my application?
First-year PhD students go through a matching process by taking an independent study (typically one each in the Fall and Spring semesters) with potential research advisors. While there are exceptions, faculty will typically agree to supervise an independent study only with students who applied in their concentration.
- I have a question not answered in the FAQ.
Please contact the Graduate Coordinator, Britton Carnevali at firstname.lastname@example.org.
Your CIS Contacts:
Graduate Coordinator for on-campus MCIT, CIS/MSE and CGGT programs
Office: 308 Levine
Graduate Coordinator for DATS and SCMP
Office: 308 Levine
Graduate Coordinator for PhD program
Office: 310 Levine
Graduate Coordinator for ROBO
Office: 459 Levine
Liz Wai-Ping Ng
Associate Director for Embedded Systems MSE program
Office: 313 Levine