Scholar
Lokesh Boominathan
Google Scholar ID: hASf9bUAAAAJ
Carnegie Mellon University
Citations & Impact
Citations
570
H-index
4
i10-index
2
Publications
6
Co-authors
0
Publications
Resume
Academic Achievements
- Paper on 'Phase transitions in when feedback is useful' accepted at NeurIPS, 2022.
- Presented talk at TEX2022 conference held at SISSA - International School for Advanced Studies, Italy (video).
- Developed a deep learning based phase retrieval algorithm for Fourier Ptychographic Microscopy that is fast and requires fewer acquisitions than traditional phase retrieval algorithms (BMVC 2018).
- Developed a deep learning algorithm for estimating crowd density from static images of highly dense crowds (ACM Multimedia 2016, 500+ citations in Google Scholar).
- Developed an algorithm using deep neural networks and Bayesian optimization to compensate for large in-plane rotations present in photographs (ICVGIP 2016).
Research Experience
- Before joining Rice, worked as a Research Assistant with Dr. Kaushik Mitra at IIT Madras and Dr. R. Venkatesh Babu at IISc, focusing on deep learning algorithms for computer vision and computational imaging.
Education
- Ph.D. student in the Electrical and Computer Engineering Department at Rice University, advised by Dr. Xaq Pitkow at the Laboratory for the Algorithmic Brain.
Background
- Research interests: reinforcement learning, computational neuroscience, deep learning, and machine learning. Current research focuses on applying reinforcement learning to model animal foraging.
Miscellany
- Website credits to Jon Barron.