Research Experience

8+ years of research experience in computer science, focusing on graphs, HPC, computer architecture and AI.

Timeline

University of Cambridge

2017 - 2019

Post-doctoral research focusing on Graph databases, machine learning and artificial intelligence applications.

Ph.D. Research

2013 - 2017

Research in computer architecture and high-performance computing, with a focus on fault-tolerant processor design.

IBM Research

2010 - 2013

Led research in machine learning and graph processing at IBM Research, India. Focused on developing efficient algorithms for large-scale graph processing and machine learning applications.

  • Developed novel graph algorithms for social network analysis
  • Created efficient implementations for large-scale data processing
  • Published research papers in top-tier conferences
  • Contributed to IBM's graph processing frameworks

Research Areas

Computer Architecture

  • Fault Tolerant Processors
  • Circuit Simulation
  • Performance Optimization

High Performance Computing

  • GPU Computing
  • Parallel Algorithms
  • Graph Processing

Machine Learning

  • Graph Neural Networks
  • Large Language Models
  • Drug Discovery

Research Highlights

Fault Tolerant Processor Design

Developed novel architectures for fault-tolerant processors using Gem5 simulator, contributing to more reliable computing systems.

GPU-Accelerated Graph Processing

Pioneered early work in GPU-based graph algorithms, publishing multiple papers on efficient implementations of graph theoretical applications.

Drug Discovery Applications

Led research in applying machine learning to drug discovery, developing protein VAE models and drug-protein interaction prediction systems.

Research Impact

  • Multiple publications in top-tier conferences and journals
  • Early contributions to CUDA development and GPU computing
  • Practical applications in pharmaceutical research and drug discovery
  • Innovations in fault-tolerant computing and circuit simulation

Research Interests

Machine Learning

  • Deep Learning
  • Reinforcement Learning
  • Neural Networks

Artificial Intelligence

  • Natural Language Processing
  • Computer Vision
  • AI Ethics

Applications

  • Healthcare AI
  • Autonomous Systems
  • Robotics