Seminar Archives

Smiling man in a blue shirt and dark blazer standing in front of a brick wall with trees and buildings in the background.

Date: November 21, 2025

Joel Harley, PhD
Associate Professor, Kent and Linda Fuchs Faculty Fellow, University of Florida; Director, SmartData Lab

A professional headshot of a smiling man with short dark hair, wearing a white shirt, set against a blurred background.

Aron Culotta, PhD
Associate Professor, School of Science & Engineering, Director, Center for Community-Engaged Artificial Intelligence, Tulane University

A man wearing glasses and a black suit, posing against a decorative background.

Date: February 20, 2026

Ravi Teja Bhupatiraju, MBBS, PhD, Health Informatics Research Scientist, Center for Applied Artificial Intelligence, University of Louisiana at Lafayette ravi-teja.bhupatiraju@louisiana.edu.

Professional headshot of a smiling man in a suit against a plain background

Topic: Data Science Approaches to Primary Care/Precision Medicine
Date: Friday, March 20, 2026
Guest Speaker: Lizheng Shi, PhD, MsPharm, M, School of Public Health and Tropical Medicine, Tulane University

View Seminar

Biography: Dr. Shi is the founding director of Tulane’s Health Systems Analytics Research Center (HSARC). He leads the research team that receives awards from professional organizations, including the 2025 Hartzema Distinguished Speaker Award, Best Research Paper Award from the Patient Access Network Foundation, and American Journal of Managed Care. He advised students for three of the best research presentation awards in the International Society for Pharmacoeonomics and Outcomes Research (2013, 2015, and 2024). He has published more than 300 papers in peer-reviewed journals and served as principal investigator and co-PI for more than 40 research grants and contracts from AHRQ, CDC, NIH, PCORI, and other public and private funding sources. Dr. Shi is dedicated to disseminating and translating population health knowledge at the local, national, and international levels. He is the associate editor of Value In Health and co-editor-in-chief for Pharmacoeconomics and Policy.
Dr. Shi’s current health services research interest focuses on innovative health technologies to improve healthcare quality, access, and cost of patient-centered care from the equity perspective, using pharmaco-economics, health technology assessment, health analytics, and policy evaluation. Dr. Shi has used data science tools (artificial intelligence and machine learning) to improve policy evaluation and analytics. He has conducted several projects on goal optimization and data analytics to support diabetes management including the Building, Relating, Assessing, and Validating Outcomes (BRAVO) diabetes simulation. The BRAVO model is the first American-based patient-level microsimulation model. The improvements in the BRAVO model included the US-based model fitting for the US general population of diabetes, global calibration to other countries, and the adaptation module for its application in the electronic medical records system. He has worked on several projects using big data analytics to further optimize treatment for diseases with multiple goals (e.g., HbA1c, lipid, and blood pressure). He has fostered extensive and inclusive partnerships with state government agencies, community nonprofit organizations, patients, health systems, federally qualified health centers, and payers.
Learn more

A woman wearing a black hijab and glasses, with a neutral expression, set against a gray background.

Topic: Ethical Sensemaking in AI Mental Health Chatbots
Date: Friday, April 17, 2026
Guest Speaker: Beenish Chaudhry, PhD, Associate Professor, School of Computing and Informatics, University of Louisiana at Lafayette

View Seminar

Biography: Dr. Chaudhry’s work focuses on human-computer interaction (HCI) and artificial intelligence (AI), exploring the intersection of technology, design, and user experience (UX). She is passionate about designing ethical, user-centered AI tools and improving the integration of AI into creative workflows and healthcare. She currently teaches courses on system design and analysis, HCI, and UX principles. She prefers hands-on techniques in her teaching, fostering an interactive and practical learning environment for her students. Her work continues to push the boundaries of AI in design and healthcare, ensuring that technology is accessible and beneficial to all users.

Abstract

As AI adoption expands, mental health chatbots are increasingly used for emotional support and self-management, yet their roles and responsibilities remain ethically ambiguous in everyday use. Prior research often treats these boundaries as externally defined, overlooking how users interpret and negotiate them in practice.

We analyze a large corpus of app store reviews of four widely used mental health chatbots using topic modeling and qualitative analysis. We identify three recurring processes: interactional role inference, ethical negotiation during success and breakdown, and collective boundary contestation through reviews.

We show that users infer roles from conversational cues and regulate trust as a bounded form of delegation. Breakdowns act as ethical inflection points, while reviews externalize judgments into collective boundary work and vernacular governance.

These findings demonstrate that ethical boundaries are actively constructed through interaction and platform participation, shifting design toward supporting ethical sensemaking as an ongoing, socially distributed process.