Resume
Academic Achievements
- - 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.
Miscellany
- Can be reached on LinkedIn.