352 citations · 1k across the 42 of their papers we have counts for
52 papers
AegisVox: Factorized Privacy and Clinical Preservation for Long-Form Speech
Praphul Singh, Akshat Agarwal, Ratul Ghosh +5
Clinical speech reveals identity through both spoken identifiers and voice, yet its pauses, delivery, and speaker roles can also carry useful clinical evidence. Protecting that spe…
CheXmix: Unified Generative Pretraining for Vision Language Models in Medical Imaging
Ashwin Kumar, Robbie Holland, Corey Barrett +8
Recent medical multimodal foundation models are built as multimodal LLMs (MLLMs) by connecting a CLIP-pretrained vision encoder to an LLM using LLaVA-style finetuning. This two-sta…
From Attribution to Abstention: Training-Free Attention-Based Auditing for Clinical Summarization
Qianqi Yan, Huy Nguyen, Sumana Srivatsa +3
Deploying multimodal large language models (MLLMs) for clinical summarization demands not only fluent generation but also transparency about where each statement originates-and a m…
Balancing Safety and Helpfulness in Healthcare AI Assistants through Iterative Preference Alignment
Huy Nghiem, Swetasudha Panda, Devashish Khatwani +3
Large Language Models (LLMs) are increasingly used in healthcare, yet ensuring their safety and trustworthiness remains a barrier to deployment. Conversational medical assistants m…
OG-Rank: Learning to Rank Fast and Slow with Uncertainty and Reward-Trend Guided Adaptive Exploration
Praphul Singh, Corey Barrett, Sumana Srivasta +3
Clinicians need ranking systems that work in real time and still justify their choices. Motivated by the need for a low-latency, decoder-based reranker, we present OG-Rank, a singl…
JEDA: Query-Free Clinical Order Search from Ambient Dialogues
Praphul Singh, Corey Barrett, Sumana Srivasta +4
Clinical conversations mix explicit directives (order a chest X-ray) with implicit reasoning (the cough worsened overnight, we should check for pneumonia). Many systems rely on LLM…
Optimizing Long-Form Clinical Text Generation with Claim-Based Rewards
Samyak Jhaveri, Praphul Singh, Jangwon Kim +2
Automating clinical documentation with large language models requires precise alignment with priorities such as completeness and factual grounding. We present an evaluation-integra…
RadAgents: Multimodal Agentic Reasoning for Chest X-ray Interpretation with Radiologist-like Workflows
Kai Zhang, Corey D Barrett, Jangwon Kim +3
Agentic systems offer a potential path to solve complex clinical tasks through collaboration among specialized agents, augmented by tool use and external knowledge bases. Neverthel…
Permissioned LLMs: Enforcing Access Control in Large Language Models
Bargav Jayaraman, Virendra J. Marathe, Hamid Mozaffari +2
In enterprise settings, organizational data is segregated, siloed and carefully protected by elaborate access control frameworks. These access control structures can completely bre…
RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification
Praphul Singh, Charlotte Dzialo, Jangwon Kim +4
Ensuring clinical data privacy while preserving utility is critical for AI-driven healthcare and data analytics. Existing de-identification (De-ID) methods, including rule-based te…
Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
Bo Ni, Zheyuan Liu, Leyao Wang +17
Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC). By integrating context retriev…
Mastering the Craft of Data Synthesis for CodeLLMs
Meng Chen, Philip Arthur, Qianyu Feng +13
Large language models (LLMs) have shown impressive performance in \emph{code} understanding and generation, making coding tasks a key focus for researchers due to their practical a…
Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey)
Krishnaram Kenthapadi, Mehrnoosh Sameki, Ankur Taly
With the ongoing rapid adoption of Artificial Intelligence (AI)-based systems in high-stakes domains, ensuring the trustworthiness, safety, and observability of these systems has b…
Measuring Distributional Shifts in Text: The Advantage of Language Model-Based Embeddings
Gyandev Gupta, Bashir Rastegarpanah, Amalendu Iyer +2
An essential part of monitoring machine learning models in production is measuring input and output data drift. In this paper, we present a system for measuring distributional shif…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
Designing Closed-Loop Models for Task Allocation
Vijay Keswani, L. Elisa Celis, Krishnaram Kenthapadi +1
Automatically assigning tasks to people is challenging because human performance can vary across tasks for many reasons. This challenge is further compounded in real-life settings…
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
Ana Lucic, Sheeraz Ahmad, Amanda Furtado Brinhosa +7
When using medical images for diagnosis, either by clinicians or artificial intelligence (AI) systems, it is important that the images are of high quality. When an image is of low…
Visual Auditor: Interactive Visualization for Detection and Summarization of Model Biases
David Munechika, Zijie J. Wang, Jack Reidy +4
As machine learning (ML) systems become increasingly widespread, it is necessary to audit these systems for biases prior to their deployment. Recent research has developed algorith…
A Human-Centric Take on Model Monitoring
Murtuza N Shergadwala, Himabindu Lakkaraju, Krishnaram Kenthapadi
Predictive models are increasingly used to make various consequential decisions in high-stakes domains such as healthcare, finance, and policy. It becomes critical to ensure that t…
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi +2
We show that deep networks trained to satisfy demographic parity often do so through a form of race or gender awareness, and that the more we force a network to be fair, the more a…
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
Deborah Sulem, Michele Donini, Muhammad Bilal Zafar +6
Data-driven methods that detect anomalies in times series data are ubiquitous in practice, but they are in general unable to provide helpful explanations for the predictions they m…
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks
Fan Wu, Linyi Li, Chejian Xu +5
As reinforcement learning (RL) has achieved near human-level performance in a variety of tasks, its robustness has raised great attention. While a vast body of research has explore…
Designing Closed Human-in-the-loop Deferral Pipelines
Vijay Keswani, Matthew Lease, Krishnaram Kenthapadi
In hybrid human-machine deferral frameworks, a classifier can defer uncertain cases to human decision-makers (who are often themselves fallible). Prior work on simultaneous trainin…
Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness
Kate Donahue, Alexandra Chouldechova, Krishnaram Kenthapadi
Much of machine learning research focuses on predictive accuracy: given a task, create a machine learning model (or algorithm) that maximizes accuracy. In many settings, however, t…