Medha Sawhney
Computer Science Ph.D. Student at Virginia Tech
Greetings! I am a PhD student in the Computer Science department at Virginia Tech under the guidance of my advisor Dr. Anuj Karpatne.
I am part of the Knowledge Guided Machine Learning Lab, where my research lies at the intersection of machine learning and scientific discovery. Currently, my focus is on solving inverse problems in physics through generative AI, leveraging diffusion models and physics-informed neural networks. My earlier work explored the intersection of biology and computer vision, particularly studying cell behaviors and bacterial motility.
During my PhD, I have interned as a Machine Learning Engineer Intern (MLE) at Twitter and NVIDIA, where I gained valuable experience applying cutting-edge techniques in AI. Prior to starting my PhD, I worked as a full-time ML Engineer at HP, Bangalore, and hold a Bachelor’s in Electronics and Communication Engineering from Manipal Institute of Technology, MAHE, Manipal, India.
Feel free to get in touch—I’m excited to collaborate and connect!
News
| Jun 03, 2025 | Our work on “Scientific Foundation models” has been accepted at ICML 2025 Workshop - Toward Scientific Foundation Models for Aquatic Ecosystems! |
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| May 29, 2025 | Our paper on “Open World Scene Graph Generation using Vision Language Models” got accepted at CV in Wild workshop at CVPR 2025! |
| May 22, 2025 | Two posters accepted at CVPR 2025 Workshop - 1) “Physics-guided Diffusion Neural Operators for Solving Forward and Inverse PDEs” , 2) “Scientific Equation Discovery using Modular Symbolic Regression via Vision-Language Guidance” |
| Jan 22, 2025 |  I finally have an ICLR paper! Thrilled to share that our paper, “A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations”, has been accepted to ICLR 2025! [ paper] [ project page] |
| Oct 26, 2024 |  Excited to announce that our paper, “VLM4Bio: A New Benchmark Dataset for Vision-Language Models”, has been accepted to NeurIPS 2024! This work introduces a comprehensive benchmark tailored for evaluating Vision-Language Models in the context of biological data. [ paper] [ code] |
| Oct 15, 2024 |  New preprint available “A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations.” [preprint] |
| May 20, 2024 |  Excited to be interning at NVIDIA as a Deep Learning Automation Intern this Summer! |
| Feb 16, 2024 |  I’ll be delivering a lightning talk at the Imageomics workshop, AAAI’24 Conference in Vancouver, Canada! |
| Jan 17, 2024 | Our paper “MEMTRACK: A Deep Learning-Based Approach to Microrobot Tracking in Dense and Low-Contrast Environments was accepted to Advanced Intelligent Systems”! [paper] [code] |
| Oct 18, 2023 | New preprint available “MEMTRACK: A Deep Learning-Based Approach to Microrobot Tracking in Dense and Low-Contrast Environments.” [preprint] [code] |
| May 20, 2023 | Excited to have received the opportunity to present my work on Bacteria Tracking at CVPR 2023 as part of the CV4Animals Workshop!  [poster] [workshop website] |
| Apr 06, 2023 |  Embarked on a Ph.D. Journey at Virginia Tech! |
| Oct 25, 2022 | New preprint available “Deep Learning Enabled Label-free Cell Force Computation in Deformable Fibrous Environments.” [preprint] |
| Jun 21, 2022 | Interning at Twitter as a Machine Learning Engineering Intern this Summer! |
| Jun 06, 2022 |  Honored to be the recipient of the Grace Hopper Celebration (GHC) 2022 Student Scholarship awarded by CS@VT and AnitaB.org! |
| Aug 21, 2021 |  Joined the MS in Computer Science Program at Virginia Tech |