{"689306":{"#nid":"689306","#data":{"type":"event","title":"PhD Defense by Ben Tamo","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp;\u003C\/strong\u003ECausal Inference and Evidence-Grounded Language Models for Trustworthy Personalized Clinical Decision Support\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate:\u0026nbsp;\u003C\/strong\u003EThursday, April 9th, 2026\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime:\u0026nbsp;\u003C\/strong\u003E4:00 - 6:00 PM EST\u003C\/p\u003E\u003Cp\u003ELocation: Zoom Link - \u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/97794499684\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/97794499684\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBen Tamo\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EMachine Learning Candidate\u003C\/p\u003E\u003Cp\u003ESchool of Electrical and Computer Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/p\u003E\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EMay D. Wang, PhD\u003C\/strong\u003E, Professor of Biomedical Engineering, Electrical and Computer Engineering, and Computational Science and Engineering\u0026nbsp;at Georgia Tech and Emory University, Director of Biomedical Big Data Initiative, and Georgia Distinguished Cancer Scholar.\u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003ECassie Mitchell, PhD\u003C\/strong\u003E, Associate Professor of Biomedical Engineering at Georgia Tech and Emory University. \u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003EDavid Anderson, PhD\u003C\/strong\u003E, \u0026nbsp;Professor in the School of Electrical and Computer Engineering at Georgia Tech\u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003ELarry Heck, PhD\u003C\/strong\u003E, Professor with a joint appointment in the Schools of Electrical and Computer Engineering\u0026nbsp;and Interactive Computing at the Georgia Institute of Technology.\u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003EB. Randall Brenn, MD\u003C\/strong\u003E, Chief of Anesthesia, Shriners Hospital for Children, Philadelphia, Associate Professor of Anesthesia and Critical Care at Kennett Square, Pennsylvania.\u003C\/li\u003E\u003C\/ol\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EClinical decision support systems (CDSS) are increasingly expected to function not merely as predictive tools, but as active partners in clinical reasoning. However, most existing machine learning approaches remain limited to population-level risk estimation, lacking the ability to personalize decisions, ensure reliability, and provide verifiable justification. This thesis addresses these limitations through a unifying framework organized around three fundamental questions: \u003Cem\u003E\u003Cstrong\u003ECan we personalize decisions?\u003C\/strong\u003E\u003C\/em\u003E \u003Cem\u003E\u003Cstrong\u003ECan we trust those decisions?\u003C\/strong\u003E\u003C\/em\u003E\u0026nbsp;And \u003Cem\u003E\u003Cstrong\u003Ecan we ensure those decisions are evidence-grounded and reasoned?\u003C\/strong\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ETo address the first question, \u003Cem\u003E\u003Cstrong\u003Ecan we personalize decisions?\u003C\/strong\u003E\u003C\/em\u003E\u0026nbsp;This thesis develops causal machine learning approaches that move beyond population-level prediction toward individualized decision-making. By combining latent patient representations with counterfactual modeling, we enable estimation of heterogeneous treatment effects and extend these capabilities to real-time surgical settings.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThe second question, \u003Cem\u003E\u003Cstrong\u003Ecan we trust those decisions?,\u0026nbsp;\u003C\/strong\u003E\u003C\/em\u003Efocuses on reliability in high-stakes environments. We address key failure modes of clinical AI, bias, overconfidence, and opacity, through fairness-aware learning, uncertainty quantification, and reliability metrics that assess consistency and evidence alignment.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EFinally, the third question,\u003Cem\u003E\u003Cstrong\u003E\u0026nbsp;can we ensure those decisions are evidence-grounded and reasoned?\u003C\/strong\u003E\u003C\/em\u003E, unifies personalization and trustworthiness through structured, evidence-guided reasoning. We develop methods to constrain model outputs using clinical knowledge and introduce training frameworks that directly optimize for evidence adherence, ensuring decisions are both traceable and verifiable.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ETogether, these contributions advance CDSS from passive prediction systems to transparent, personalized, and evidence-aligned reasoning frameworks. By integrating causal personalization, reliability-aware modeling, and evidence-grounded reasoning, this thesis establishes a foundation for trustworthy personalized clinical decision support that can meaningfully augment clinical decision-making in high-stakes care.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ECausal Inference and Evidence-Grounded Language Models for Trustworthy Personalized Clinical Decision Support\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Causal Inference and Evidence-Grounded Language Models for Trustworthy Personalized Clinical Decision Support"}],"uid":"27707","created_gmt":"2026-04-01 13:47:17","changed_gmt":"2026-04-01 13:47:55","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-04-09T16:00:00-04:00","event_time_end":"2026-04-09T18:00:00-04:00","event_time_end_last":"2026-04-09T18:00:00-04:00","gmt_time_start":"2026-04-09 20:00:00","gmt_time_end":"2026-04-09 22:00:00","gmt_time_end_last":"2026-04-09 22:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ZOOM","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}