Shamanth Kuthpadi S.
skuthpadi[at]umass[dot]edu
Shamanth Kuthpadi, data science, python, causal inference, machine learning, academic porfolio, CV
AI/ML for Healthcare
My research focuses on developing advanced AI systems to deepen our understanding of complex biological processes and drive impactful advancements in healthcare. By leveraging causal learning and inference on multimodal data — including the detailed modeling of biological networks like brain connectomes — I aim to reveal fundamental structures and relationships that can guide more accurate diagnostics and innovative therapeutic approaches.
I am a computer science graduate student at the Manning College of Information and Computer Sciences in UMass Amherst. This summer I am a ML research intern at IOMICS Corporation, where I focus on building automated casual discovery/estimation pipelines and researching Kolmogorov-Arnold Networks (KANs) for their applicability in the unsupervised and supervised domains.
Publications & Current Research
A Smart Electrode-Integrated Cooling Patch for Motion-Robust ECG Monitoring and Real-Time 3D Facial Animation
Authors: T. Q. Trung, S. K. Seethakantha, Z. Lei, A. Radmehr, P. Nguyen, D. Ganesan
Venue: To be submitted to a journal
Description: Developed a smart electrode-integrated system for robust biosignal acquisition and designed a deep learning pipeline to reconstruct real-time 3D facial animations in VR from physiological signals.
Assess-and-Evolve: Scalable Generation of Preference Tuning Data for Alleviating Hallucinations in Medical Summaries
Authors: S. K. Seethakantha, D. Thai, S. Tiwari, V. P. Gudi, S. Mohan, S. Sairaj, W. Zhao, A. Mitra, A. McCallum
Venue: Almost finished, to be submitted in AAAI 2026
Description: We propose a detect-and-revise framework paired with Direct Preference Optimization (DPO) using automatically generated silver data to reduce hallucinations in medical summarization.
Modeling & Analyzing Structural Brain Connectomes
Authors: S. Kuthpadi Seethakantha Venue: Academic Project, UMass Amherst
Description: This study used pre-processed brain connectome data to apply machine learning and spectral graph theory, developing a classifier that maps node-level features to brain regions.
News
August 2025: Finally “finished” creating my personal website.
February 2025: Working under the supervision of Prof. Deepak Ganesan and with Wireless and Sensor Systems Lab to create a multi-modal system for real-time 3D facial animation from physiological signals.
January 2025: Working with Mendel.ai to mitigate hallucinations in clinical summarization using synthetic data generation and preference tuning.
January 2025: Starting my Master’s in Computer Science at the University of Massachusetts Amherst
December 2024: Graduated with a Bachelor’s degree in Computer Science from the University of Massachusetts Amherst
September 2024: Began my Independent Study in modeling and analyzing structural brain connectomes under the supervision of Cameron Musco
June 2024: Started working as a Machine Learning Research Intern at IOMICS Corporation
September 2021: Started my undergraduate degree in Computer Science at the University of Massachusetts Amherst