Soham Gadgil

Hi! I am a third year CSE PhD student at University of Washington in the AIMS Lab, co-advised by Dr. Su-In Lee and Dr. Linda Shapiro. Prior to starting my Ph.D, I spent a year as a data engineer at Microsoft in the Windows Experience team.

I completed my Masters from Stanford in Computer Science with a depth in AI, where I was a research asistant in the Computational Neuroimage Science Lab (CNS^LAB), advised by Dr. Kilian Pohl. I was also part of the AI for Healthcare (AIHC) bootcamp in the Stanford ML Group, advised by Dr. Pranav Rajpurkar and Dr. Andrew Ng. I finished my Bachelor's at Georgia Institute of Technology, with a major in Computer Engineering and a minor in Computer Science.

I am also a part-time instructor at Persolv, teaching AI fundamentals to high school students. In my spare time, I like playing tennis, hiking, exploring different cuisines, and watching movies (especially legal thrillers).

Research

The research problems I want to work on lie at the interesection of Artificial Intelligence and Healthcare. Some of my current research interests include:

Clinical AI: Using multi-modal data (images, text, etc.) with deep learning to perform diagnoses and treatment of various ailments.
Explainability: Developing AI techniques to increase model interpretability in settings where features are not trivial to obtain (like Emergency Medicine), auditing AI models to make them more trustworthy by exploring causal relationships between inputs and predictions.

Achievements

Accepted into the ORS (Opportunity Research Scholars) program for undergraduate research at Georgia Tech.
Awarded Faculty Honors by Georgia Tech for all semesters in my undergraduate degree.
Recipient of the Electrical and Computer Engineering (ECE) Senior award for highest academic average.
Won the best final paper award at Stanford for CS 238 (Decision Making Under Uncertainty)
Part of the teaching team that developed and first offered the Trustworthy Machine Learning (CS 329T) course at Stanford.

Papers

	Transformer-based Time-Series Biomarker Discovery for COPD Diagnosis Soham Gadgil , Joshua Galanter, Mohammadreza Negahdar NeurIPS 2024 TSALM Workshop
	Discovering mechanisms underlying medical AI prediction of protected attributes Soham Gadgil ^, Alex J. DeGrave ^, Roxana Daneshjou, Su-In Lee CVPR 2024 DCAMI Workshop (Oral, Best Paper Runner-Up Award) [Paper]
	Estimating Conditional Mutual Information for Dynamic Feature Selection Soham Gadgil ^, Ian Covert ^, Su-In Lee ICLR 2024 [Paper] [Code]
	Data Alignment for Zero-Shot Concept Generation in Dermatology AI Soham Gadgil, Mahtab Bigverdi ICLR 2024 DPFM workshop [Paper]
	Fostering transparent medical image AI via an image-text foundation model grounded in medical literature Chanwoo Kim, Soham Gadgil, Alex J. DeGrave, Zhuo Ran Cai, Roxana Daneshjou, Su-In Lee Nature Medicine 2024 [Paper] [Code] [Press] [Allen School News]
	CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation Soham Gadgil ^, Mark Endo ^, Emily Wen ^, Andrew Y. Ng, Pranav Rajpurkar MIDL 2021* [Paper] [Code]
	Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis Soham Gadgil ^, Qingyu Zhao ^, Adolf Pfefferbaum ^, Edith V. Sullivan, Ehsan Adeli, Kilian M. Pohl MICCAI 2020* [Paper] [Code]
	Solving The Lunar Lander Problem under Uncertainty using Reinforcement Learning Soham Gadgil, Yunfeng Xin, Chengzhe Xu IEEE SouthEastCon 2020 [Paper] [Code]

Other Experiences

Reviewer: NeurIPS 2024, MIDL 2022, ICLR 2024 DPFM Workshop
President of Georgia Tech IEEE, leading the largest IEEE student branch in the US with over 800 members.
International Liasion for the Student Alumni Association at Georgia Tech.
Peer leader in freshmen and senior student dormitories.

Teaching Assistantships

University of Washington

Introduction to AI (CSE 473)

The course covers principal ideas and developments in artificial intelligence: Problem solving and search, game playing, knowledge representation and reasoning, uncertainty, machine learning, natural language processing. I held weekly office hours and assisted in preparing/grading the homework assignments.

Stanford

Computer Organization and Systems (CS 107)

TA for CS 107, one of the largest introductory undergraduate courses at Stanford with over 150 students. I led two hour-long lab sessions each week along with office hours and assisted the professor in grading homework and desiging exams. Topics included the C programming language, data representation, machine-level code, computer arithmetic, elements of code compilation, optimization of memory and runtime performance, and memory organization and management.

Trustworthy Machine Learning (CS 329T)

TA for the first course offering of CS 329T. I co-developed and led the lab sections with ~25 students. I also helped the instructors design some of the lecture slides, homework assignments, and the final project.

Georgia Tech

	Linear Algebra (MATH 1554) As a TA for linear algebra, I led two 50 minute recitation sessions with 25 students each week. Concepts ranged from eigenvalues, eigenvectors, applications to linear systems, least squares, diagonalization, quadratic forms.
	Computer Architecture (CS 3056) Guided over 60 students with homeworks and projects in computer architecture. Held weekly office hours, exam review sessions, and collaborated with the instructor for grading and project ideation. Topics included the basic organizational principles of the major components of a processor - the core, memory hierarchy, and the I/O subsystem.

(Design and CSS courtesy: Jon Barron and Amlaan Bhoi)