I am a computer scientist and a research lead in the Machine Intelligence group at Lawrence Livermore National Laboratories. My experience spans developing AI/ML solutions for computer vision, healthcare, graph analysis and multimodal learning.

Topics of interest: Representation learning, ML robustness/safety, domain adaptation/generalization, uncertainty quantification, OOD detection, generative models, multimodal learning, few-shot learning and inverse problems.

Here is a research statement that summarizes my experience and vision!

If you are looking for postdoc opportunities or staff scientist roles in ML/AI (unsupervised learning, safe ML, knowledge-aware AI, design optimization and scientific machine learning), reach out to me!

Some exciting projects that I have been part of recently

AI-Powered Cognitive Simulations to Accelerate Scientific Discovery (High-Energy physics, Material Science, Computational Biology, Epidemiology)

Uncertainty Quantification in Deep Neural Networks to Advance Black-Box Optimization and to Improve ML Safety

Knowledge-Integrated Learning to Build Data-Efficient and Well Grounded AI Solutions for Biology and Situational Awareness

Diagnostic AI Tools for Traumatic Brain Injury, Cancer and Patient Health Monitoring (Human Connectomes, Multimodal Imaging Data, Electronic Health Records)

Advanced Algorithms for Robust ML, Domain Generalization, Test-Time Adaptation, Few shot learning and Transfer Learning

Explainability Tools to Support Human-Centric Analysis of Deep Neural Networks

Solving Inverse Optimization for Ill-posed Image Recovery, Audio Restoration and High-Dimensional Parameter Space Exploration

High-dimensional Sampling for Experiment Design (Additive Manufacturing) and Analyzing Generalization of ML Models