I am a Visiting Assistant Professor at the Department of Computer Science at Purdue University. Before that, I was a Postdoctoral Research Associate at NSF Center for Science of Information (CSoI) based at Purdue University and hosted by Wojciech Szpankowski. I received my Ph.D. in Electrical Engineering at the University of Michigan (2019). I was fortunate to have Sandeep Pradhan as my advisor. I hold my M.Sc. in Applied Mathematics from the University of Michigan, in 2017. Prior to U of M, I received my M.Sc. and B.Sc. in Electrical Engineering at Sharif University of Technology, Iran.
I will be joining the Department of Computer Science at Indiana University as an Assistant Professor in Fall 2022.
My research interests lie in theoretical machine Learning, quantum computing and algorithms, classical and quantum information theory. My recent work has focused on adapting analytical tools (e.g., Fourier analysis) for developing and analyzing various learning methods both in classical and quantum frameworks. See my CV for more details.
Most Recent News:
(04/2022): Sufficiently Informative and Relevant Features: An Information-Theoretic and Fourier-Based Characterization, with Jithin Sreedharan, Gil Shamir and Wojciech Szpankowski, accepted to IEEE Transactions on Information Theory, 2022.
- (02/2022): our paper Toward Physically Realizable Quantum Neural Networks is selected for oral presentation in AAAI ‘22.
(02/2022) new submission: Regret for Online Regression with General Log-Type Losses, with Philippe Jacquet and Wojciech Szpankowski.
(10/2021): Faithful Simulation of Distributed Quantum Measurements with Applications in Distributed Rate-Distortion Theory, with Touheed Anwar Atif and Sandeep Pradhan, accepted to IEEE Transactions on Information Theory, 2021.
(10/2021) new submission: Quantum State Classification via Quantum Fourier, with Wojciech Szpankowski.
(9/2021) new submission: L2 Polynomial Regression is Agnostic PAC Learner for k-Juntas, with Wojciech Szpankowski.
(4/2021) talk: Purdue CS Theory Seminar, Learning from Classical and Quantum data: A Fourier Perspective.
- (4/2021) talk: University at Albany, SUNY, Learning from Classical and Quantum data: A Fourier Perspective.
- (4/2021) talk: Imperial College London, Learning from Classical and Quantum data: A Fourier Perspective.