Jason Altschuler

• Jason Altschuler and Kunal Talwar (2025), Resolving the mixing time of the Langevin Algorithm to its stationary distribution for log-concave sampling, SIAM Journal on the Mathematics of Data Science.
• Jason Altschuler and Sinho Chewi (2025), Shifted Composition I: Harnack and reverse transport inequalities, IEEE Transactions on Information Theory.
• Jason Altschuler and Sinho Chewi (2025), Shifted Composition II: Shift Harnack Inequalities and Curvature Upper Bounds, IEEE Transactions on Information Theory.
• Jinho Bok and Jason Altschuler (2025), Accelerating Proximal Gradient Descent via Silver Stepsizes, COLT.
• xufeng cai, Jason Altschuler, jelena diakonikolas (2025), Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm, Journal of the ACM.
• Jason Altschuler and Pablo Parrilo (2025), Acceleration by Stepsize Hedging: Multi-Step Descent and the Silver Stepsize Schedule, Journal of the ACM.
• Jason Altschuler and Pablo Parrilo (2024), Acceleration by stepsize hedging: Silver Stepsize Schedule for smooth convex optimization, Mathematical Programming.
• Jason Altschuler, Jinho Bok, Kunal Talwar (2024), On the privacy of Noisy Stochastic Gradient Descent for convex optimization, SIAM Journal on Computing , 53 (4), pp. 969-1001.
• Jinho Bok, Weijie Su, Jason Altschuler (2024), Shifted Interpolation for Differential Privacy, ICML.
• Jason Altschuler and Pablo Parrilo (2023), Kernel approximation on algebraic varieties, SIAM Journal on Applied Algebra and Geometry , 7 (1), pp. 1-28.
• All Research from Jason Altschuler »

Past Courses

• AMCS5999 - Independent Study

  Independent Study allows students to pursue academic interests not available in regularly offered courses. Students must consult with their academic advisor to formulate a project directly related to the student’s research interests. All independent study courses are subject to the approval of the AMCS Graduate Group Chair.

• AMCS9950 - Dissertation

  Allows for a PhD student to be enrolled full-time to work exclusively on research, writing and preparing his/her doctoral thesis and defense. All required coursework (20 CUs) must be completed, and the student must have passed his/her thesis proposal/oral candidacy examination prior to being enrolled.

• AMCS9990 - Masters Thesis

  For students writing a Master's Thesis to fulfill the program's requirements. All required coursework (8 CUs) must be completed prior to being enrolled.

• AMCS9999 - Ind Study & Research

  Study under the direction of a faculty member.

• STAT4300 - Probability

  Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.

• STAT9910 - Sem in Adv Appl of Stat

  This seminar is for graduate students who wish to learn about current research frontiers. It covers advanced topics in probability, statistical theory and methods, applied statistics, data science and artificial intelligence. Specific topics vary from year to year and emphasize both theoretical foundations and applications.

• STAT9912 - Sem in Adv Appl of Stat (OPT)

  This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

• STAT9950 - Dissertation

  Dissertation

• SIAM Early Career Prize for Optimization, 2026
• AFOSR Young Investigator Program (YIP) Award, 2026
• ICS Paper Prize, 2025
• Sloan Research Fellowship in Mathematics, 2025
• A.W. Tucker Finalist Prize, 2024
• George M. Sprowls Award for Best MIT PhD Thesis in Artificial Intelligence & Decision Making, 2023
• Two Sigma PhD Fellowship, 2020
• James Hayes-Edgar Palmer Prize in Engineering, Princeton University, 2016
• NSF Graduate Fellowship, 2016
• Accenture Prize in Computer Science, Princeton University, 2015

Knowledge @ Wharton

• Is AI Killing User Experience?, Knowledge @ Wharton - 06/02/2026
• Building AI Products That Last: Lessons From SXSW, Knowledge @ Wharton - 06/02/2026
• How Personalized AI Tutors Can Help Students Learn, Knowledge @ Wharton - 06/02/2026
• AI Stocks, Oil Prices, and the Fed’s Next Move, Knowledge @ Wharton - 05/29/2026
• Federal Reserve Leadership Transition Amid Economic and Political Pressures, Knowledge @ Wharton - 05/27/2026
• How Baseball Analytics Is Reshaping Hall of Fame Conversations, Knowledge @ Wharton - 05/27/2026
• Rethinking Political Division and Media Segmentation, Knowledge @ Wharton - 05/26/2026
• Why Human Agency Matters for Quantum AI, Knowledge @ Wharton - 05/26/2026
• Why AI Pricing Doesn’t Always Drive Prices Higher, Knowledge @ Wharton - 05/26/2026
• Brands and Identity in the Age of Social Media, Knowledge @ Wharton - 05/22/2026