Research

Projects

Ongoing and completed research projects across our wings.

Project list

Recalibrating AlphaFold2 Confidence Metrics for Dynamic Peptide Ensembles Using NMR Experimental Data
ComBio

Recalibrating AlphaFold2 Confidence Metrics for Dynamic Peptide Ensembles Using NMR Experimental Data

AlphaFold2 (AF2) has transformed protein structure prediction by producing highly accurate three-dimensional structures alongside residue-level confidence estimates (pLDDT). However, pLDDT reflects…

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KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging
ComPAs

KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging

Jet tagging, the task of identifying the initiating particle of a jet produced in high-energy collisions, is essential for precision measurements and new-physics searches at the Large Hadron…

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TRoPE-TUL: Trajectory-User Linking via Spatio-Temporal Rotary Position Embedding
AI/ML

TRoPE-TUL: Trajectory-User Linking via Spatio-Temporal Rotary Position Embedding

Trajectory-user linking identifies the likely user behind an anonymous mobility trace, but existing methods often lose fine-grained spatial, temporal, and semantic information or scale poorly.…

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Where the Preference Signal Lives: Constructed Negatives, Token Weighting, and Why Confidence Misses the Decisive Tokens
AI/ML

Where the Preference Signal Lives: Constructed Negatives, Token Weighting, and Why Confidence Misses the Decisive Tokens

This project studies how to make the reasoning produced by medical LLMs more factually reliable, not just their final answers. We construct controlled incorrect rationales with known clinically…

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How Good Must a Detector Be? Graded Semantic-Error Detection in Multi-Agent LLM Systems
AI/ML

How Good Must a Detector Be? Graded Semantic-Error Detection in Multi-Agent LLM Systems

This project studies when error detection and intervention can reliably improve multi-agent LLM systems, where mistakes from one agent may propagate through later interactions and affect the team’s…

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pFedBBN: A Personalized Federated Test-Time Adaptation with Balanced Batch Normalization for Class-Imbalanced Data
AI/ML

pFedBBN: A Personalized Federated Test-Time Adaptation with Balanced Batch Normalization for Class-Imbalanced Data

Personalized Federated Test-Time Adaptation studies how decentralized models can adapt to changing domains while also remaining robust to severe class imbalance across clients. Existing federated…

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FedPoisonTTP: A Threat Model and Poisoning Attack for Federated Test-Time Personalization
AI/ML

FedPoisonTTP: A Threat Model and Poisoning Attack for Federated Test-Time Personalization

Federated Test-Time Adaptation (FTTA) studies how decentralized models can adapt to client-specific distribution shifts at test time using unlabeled local data, without sharing raw data. This project…

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FedRAPP: Federated Test-Time Prompt Tuning for Vision-Language Models with Reliability-Aware Prompt Personalization
AI/ML

FedRAPP: Federated Test-Time Prompt Tuning for Vision-Language Models with Reliability-Aware Prompt Personalization

Source-Free Federated Test-Time Prompt Tuning (SF-FTTPT) studies how vision-language models can collaboratively adapt prompts at test time when clients have private, heterogeneous data with no access…

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Cross-Lingual Concept Steering and Interpretability in Language Models
AI/ML

Cross-Lingual Concept Steering and Interpretability in Language Models

Large language models serve speakers of hundreds of languages, yet most of what is known about their inner workings comes from English. Recent interpretability research shows that abstract concepts,…

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Semantic and Contextual Conditioning for Flow-Matching Speech Tokenization
AI/ML

Semantic and Contextual Conditioning for Flow-Matching Speech Tokenization

Modern spoken language systems convert raw audio into compact discrete representations that downstream models generate from and reason over. The central constraint is compression: as few tokens per…

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Speech-to-Motion: From Next-Token to Next-Gesture Prediction
AI/ML

Speech-to-Motion: From Next-Token to Next-Gesture Prediction

Large language models learn to predict sequences from nothing but text, yet human communication was never just words. Every sentence is accompanied by gesture: the hands, the shoulders, the tilt of a…

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Reliable Multimodal Document Intelligence for Low-Resource Languages
AI/ML

Reliable Multimodal Document Intelligence for Low-Resource Languages

This project develops a unified framework for reliable multimodal document understanding in low-resource and multilingual settings It consists of four complementary parts that progressively test…

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