Curriculum Vitae

The detailed PDF verison of my CV can be found here - Curriculum Vitae

Research Interests

I am interested in machine learning and optimization broadly. Particularly in characterizing fundamental quantities, phenomena, and general laws that arise due to scale.

Research Interests - Machine Learning, Large Scale Optimization, Probability theory
Other Learning Interests - Geometry and Analysis

Education

Ph.D. student at Paul G. Allen School of Computer Science & Engineering, 2019 - Present
University of Washington
Advisor: Prof. Sewoong Oh
M.S. in Computer Science and Engineering, 2022
Paul G. Allen School of Computer Science and Engineering, University of Washington
Supervisor: Prof. Sewoong Oh
B.Tech. in Mathematics and Computing, 2013 - 17
Indian Institute of Technology Guwahati

Work experience

September 2019 - Present: Graduate Research Assistant
- Advisor: Prof. Sewoong Oh
- Machine Learning Lab
July 2017 - July 2019: Research Fellow
- Microsoft Research Lab - India
- Mentors: Dr. Praneeth Netrapalli and Dr. Prateek Jain
- Project group: Provable Non-convex Optimization for Machine Learning Problems
Summer 2016: Research Intern
- Microsoft Research Lab - India
- Supervisor: Dr. Sreangsu Acharyya
- Project: Clustered Monotone Transforms for Rating Factorization
Summer 2015: Summer Research Intern
- CAFRAL - RBI
- Supervisor: Dr. N. R. Prabhala
- Project: Economic Policy Uncertainty Index - Intern report

Selected Publications

Complete list at Google Scholar, or dblp.

FireFly: A Synthetic Dataset for Ember Detection in Wildfire

Let’s Roll: Synthetic Dataset Analysis for Pedestrian Detection Across Different Shutter Types

Bridging the Gap Between Spiking Neural Networks & LSTMs for Latency & Energy Efficiency

ViTA: A Vision Transformer Inference Accelerator for Edge Applications

In-Sensor & Neuromorphic Computing Are all You Need for Energy Efficient Computer Vision

Technology-Circuit-Algorithm Tri-Design for Processing-in-Pixel-in-Memory (P2M)

Neuromorphic-P2M: processing-in-pixel-in-memory paradigm for neuromorphic image sensors

Self-Attentive Pooling for Efficient Deep Learning

Enabling ISPless Low-Power Computer Vision

Hoyer regularizer is all you need for ultra low-latency spiking neural networks

P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking

P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications

Towards Energy-Efficient Hyperspectral Image Processing Inside Camera Pixels

Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression

ACE-SNN: Algorithm-hardware co-design of energy-efficient & low-latency deep spiking neural networks for 3d image recognition

Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks?

A High Performance and Robust FIFO Synchronizer-Interface for Crossing Clock Domains in SFQ Logic

Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding

Asd-transformer: Efficient active speaker detection using self and multimodal transformers

Metastability in superconducting single flux quantum (SFQ) logic

Spike-thrift: Towards energy-efficient deep spiking neural networks by limiting spiking activity via attention-guided compression

Modeling and Characterization of Metastability in Single Flux Quantum (SFQ) Synchronizers

Single flux quantum (SFQ) first-in-first-out (FIFO) synchronizers: New designs and paradigms

Two-Phase Asynchronous to Synchronous Interfaces for an Open-Source Bundled-Data Flow

Gourav Datta