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New Horizons in Theoretical Computer Science

tentative website - more details coming soon!

Important dates:

See application form below.

New horizons in theoretical computer science is a week-long online summer school which will expose undergraduates to exciting research areas in the area of theoretical computer science and its applications. The school will contain several mini-courses from top researchers in the field. The course is free of charge,and we welcome applications from undergraduates majoring in computer science or related fields. We particularly encourage applications from students that are members of groups that are currently under-represented in theoretical computer science.

Please contact for more information. Please also see this (regularly updated) FAQ page.


Shuchi Chawla (UT Austin) Ran Libeskind-Hadas (Claremont McKenna) Adam Smith (Boston University) Madhur Tulsiani (TTI-Chicago)

The summer course is organized by the Committee for the Advancement of Theoretical Computer Science (CATCS) of the ACM Special Interest Group on Algorithms and Computation Theory (SIGACT). We are grateful for support from SIGACT and the Toyota Technical Institute at Chicago.

We are still looking for teaching assistants! Please fill out this form if you are interested.

Applying to the school

The course is intended for currently enrolled undergraduate students that are majoring in computer science or related fields. Students will be expected to be familiar with the material typically taught in an introductory algorithms and discrete mathematics / mathematics for computer science courses. If you are unsure if you are prepared for the course, please write to us at We particularly encourage applications from students that are members of groups that are currently under-represented in theoretical computer science.

Application form:

To apply to the summer school, please fill out this Google form

To ensure full consideration, please fill out the form no later than April 25, 2022. Please mention one reference in your form. References do not need to write a full letter, but rather a short (one paragraph) email to will suffice. The letter should have the subject “Reference for [full name]”.

Decisions will be communicated to applicants who applied by the deadline no later than May 10, 2022.


We will update the list of instructors as we get more confirmations:

Rediet Abebe(UC Berkeley) Rediet Abebe is an Assistant Professor of Computer Science at the University of California, Berkeley. Her research esearch is broadly in the fields of algorithms and artificial intelligence, with a focus on inequality and distributive justice concerns. Rediet received her Ph.D. from Cornell University in 2019; after that, she was a junior fellow at the Harvard Society of Fellows. Rediet also serves on the Executive Committee for the ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) and was a Program Co-Chair for the inaugural conference. She also co-founded and co-organizes the related MD4SG research initiative as well as the non-profit organization Black in AI.
Antonio Blanca(Penn State University) Antonio Blanca is an Assistant Professor in the CSE Department at Penn State. His research is focused on the design and analysis of randomized algorithms, the Markov chain Monte Carlo method, and more generally, on the computational problems that arise in the study of probabilistic models in machine learning, statistical physics, and theoretical computer science. He received his Ph.D. from UC Berkeley in 2016; after that, he was a Postdoctoral Fellow at the Algorithm and Randomness Center at Georgia Tech.
Yael Kalai (Microsoft Research) Yael Tauman Kalai received her BA (1997) from the Hebrew University in Jerusalem, MA (2001) under the supervision of Adi Shamir at the Weizmann Institute, and PhD (2006) under the supervision of Shafi Goldwasser at MIT. After postdoctoral positions at Microsoft Research and the Weizmann Institute, she is now a Researcher at Microsoft Research New England. Her honors include an outstanding master's thesis prize, and a Sprowls award (co-winner) for best PhD thesis at MIT. Her research focuses on cryptography.
Gillat Kol (Princeton University) Gillat Kol is an assistant professor of computer science at Princeton university. Gillat studies applied math and data science with a focus on the theoretical aspects of computation, how information theory applies to computational complexity, and interactive compression and coding. She earned a PhD and an MSc in Computer Science from the Weizmann Institute, Israel, and joined the joined Princeton the faculty in 2016. She is also the recipient of an NSF CAREER award, and a Sloan foundation fellowship.
Samory Kpotufe (Columbia University) Samory is an associate professor at Columbia University, working in statistical machine learning, with an emphasis on common nonparametric methods (e.g., kNN, trees, kernel averaging). Samory graduated (Sept 2010) in Computer Science at the University of California, San Diego, advised by Sanjoy Dasgupta. He then was a researcher at the Max Planck Institute, and later a Research Assistant Professor at the Toyota Technological Institute at Chicago. Samory also spent 4 years at ORFE, Princeton University as an Assistant Professor.


The course will take place June 6 to June 10, 2022.