- Dissertation: AI for Science: Graph Machine Learning as an Instrument for Understanding, Controlling, and Creating Physical Systems
- Project: Graph Generation via Adaptation
- Project: Graph Partition Learning (Published in NeurIPS 2023 Workshop)
- Project: Graph Structure Learning (Developed during internship at MIT-IBM Watson AI Lab)
- Project: Adapting biomedical segmentation models for accelerator loss deblending (Published in NAPAC'22)
- Project: Language models as PID controllers
Research Experience
- Worked on various projects in drug discovery, manufacturing, organic materials research, particle physics, medical imaging, quant finance, and social good.
- Collaborated with AbbVie to deploy a novel molecular manipulation method in an active drug discovery project, yielding novel compounds that were physically synthesized and tested.
Education
Received his PhD from Northwestern University in 2024, where he worked on machine learning for scientific discovery, with projects in drug discovery and particle physics.
Background
Working at the intersection of AI and physical sciences. Currently, he is working on a startup that's modernizing chemical hazard assessment.