296 citations · 435 across the 16 of their papers we have counts for
6 papers · 1 filter
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…
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…
Fairness-Aware Online Personalization
G Roshan Lal, Sahin Cem Geyik, Krishnaram Kenthapadi
Decision making in crucial applications such as lending, hiring, and college admissions has witnessed increasing use of algorithmic models and techniques as a result of a confluenc…
Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned
Sahin Cem Geyik, Qi Guo, Bo Hu +4
LinkedIn Talent Solutions business contributes to around 65% of LinkedIn's annual revenue, and provides tools for job providers to reach out to potential candidates and for job see…