Anuran Makur
Anuran Makur is an Assistant Professor in the Department of Computer Science and the Elmore Family School of Electrical and Computer Engineering at Purdue University, West Lafayette, IN, USA. He received the B.S. degree with highest honors (summa cum laude) from the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (UC Berkeley), CA, USA, in 2013, and the S.M. and Sc.D. degrees from the Department of Electrical … ↓More
Joined department: Fall 2021
Research Areas
- Artificial Intelligence, Machine Learning, and Natural Language Processing
- Theory of Computing, Algorithms, and Quantum Computing
Education
Sc.D., Massachusetts Institute of Technology, Electrical Engineering and Computer Science (2019)
S.M., Massachusetts Institute of Technology, Electrical Engineering and Computer Science (2015)
B.S., University of California, Berkeley, Electrical Engineering and Computer Sciences (2013)
Anuran Makur is an Assistant Professor in the Department of Computer Science and the Elmore Family School of Electrical and Computer Engineering at Purdue University, West Lafayette, IN, USA. He received the B.S. degree with highest honors (summa cum laude) from the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (UC Berkeley), CA, USA, in 2013, and the S.M. and Sc.D. degrees from the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, in 2015 and 2019, respectively. From 2019 to 2021, he was a postdoctoral researcher at the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society, MIT. His research interests include information theory, theory of machine learning, and applied probability. He was a recipient of the Arthur M. Hopkin Award from UC Berkeley in 2013, the Jacobs Presidential Fellowship from MIT in 2013, the Ernst A. Guillemin Master's Thesis Award from MIT in 2015, the Jin Au Kong Doctoral Thesis Award from MIT in 2020, the Thomas M. Cover Dissertation Award from the IEEE Information Theory Society in 2021, and the CAREER Award from the National Science Foundation in 2023.
Selected Publications
D. Lee, W. Lu, and A. Makur, "On the oracle complexity of interpolation-based gradient descent," IEEE Transactions on Automatic Control, pp. 1-16, April 2026.
D. Lee, W. Lu, A. Makur, and J. Singh, "Doeblin curves," IEEE Transactions on Information Theory, pp. 1-42, March 2026.
A. Makur and J. Singh, "Minimax hypothesis testing for the Bradley-Terry-Luce model," IEEE Transactions on Information Theory, vol. 71, no. 12, pp. 9163-9202, December 2025.
A. Makur and J. Singh, "Hypothesis testing for generalized Thurstone models," in Proceedings of the 42nd International Conference on Machine Learning (ICML), Vancouver, BC, Canada, July 13-19 2025, pp. 42730-42764.
W. Lu and A. Makur, "Permutation capacity region of adder multiple-access channels," IEEE Transactions on Information Theory, vol. 70, no. 7, pp. 4693-4720, July 2024.