Industry Planning Meeting at Georgia Tech – November 5, 2025
The meeting will take place in the CHOA conference room of the Krone Engineered Biosystems Building (EBB) on the Georgia Tech campus. The Krone Building address is 950 Atlantic Drive NW, Atlanta, GA 30332.
Latest Update: November 4, 2025
| 8:00 am – 8:30 am | Registration & Networking |
| 8:30 am – 8:45 am | Opening Remarks & Industry Introductions Ghassan AlRegib (Georgia Tech) |
| 8:45 am – 9:15 am | Vision and Value Proposition · Raju Gottumukkala (UL Lafayette) · Aron Culotta (Tulane University) · Joel Harley (University of Florida) · Ghassan AlRegib (Georgia Tech) |
| 9:15am – 10:00 am | Success Stories from Past Collaborations (7-8 min each) · Will Labar (CGI) · Elizabeth Anne Sprouse (Emory Healthcare) · Nick Duesbery (Ochsner) · Elvira Osuna-Highley (MathWorks) · Francis LaRossa (Piedmont) |
| 10:00 am – 10:30 am | Welcome Remarks Timothy Lieuwen, Executive Vice President for Research, Regents’ Professor (Georgia Tech) |
| 10:30 am – 10:45 am | BREAK |
| 10:45 am – 12:15 pm | Faculty Presentations (15 mins each) 1. May Wang (Georgia Tech) Translating AI for Shared Health Decision-Making 2. Gourav Modanwal, (Emory U. & Georgia Tech) Opportunistic use of Cardiac-CT for Cardio- Metabolic Risk 3. Maribeth Gandy Coleman (Georgia Tech) The IPaT Way: People-centered Approach to the Design and Translation of Foundational Research 4. Joel Harley, (University of Florida) AI-Driven Personalized Musculoskeletal Models of Patients from Clinically Accessible Data 5. Mohit Prabhushankar (Georgia Tech) To use, and how to use, multi-modal data: Imaging, Health Records, and Language 6. Aron Culotta (Tulane University) Community-Guided AI for Culturally Adaptive Patient Education on Diabetes-related Risks |
| 12:15 pm – 1:00 pm | LUNCH |
| 1:00 pm – 2:30 pm | Guided Tour of the Kendeda Building (To maximize the benefits, watch this video before the event) |
| 2:30 pm – 2:45 pm | BREAK |
| 2:45 pm- 4:45 pm | Industry Workshop: Facilitated by Will LaBar (CGI), Tariq Adwan (Helix Labs and TaliaGen), Russ Johnson (Piedmont Health), and Joe Shepley (Alvarez & Marshal/ Triad) · Nitu Kashyap, (Emory Healthcare) · Elizabeth Sprouse (Emory Healthcare) · Paula Edwards (Emory Healthcare) · Jason Hill (Ochsner Health) · Nick Duesbury (Ochsner Health) · Sarah Boyd (Ochsner IO) · Francis LaRossa (Piedmont Healthcare) · Davis Burgess (Grady Health System) · Nick Yatisky (Wellstar (formerly)) · Elvira Osuna-Highley (MathWorks) · Alyssa Silverman (Mathworks) · Robert Rupard (Pro-GenX Labs) · Matt Baldwin (Triad) · Suleima Salgado (Global Partnership for Telehealth) · Bob Klein (Digital Scientists) · Tamer Mehyar (Ishraq Eyecenter) · Tayo Ogunmakin (AWS Amazon) · Janeine Charpiat (Philips) · Dana Weeks (MedTrans Go) Vipul Kashyap (MedHive) · Greg Jungles (ATDC) · Maribeth Gandy Coleman (Georgia Tech) · Muhammed Idris (Morehouse School of Medicine) · Rima Gibbings (University of N. Georgia) · Ravi Parikh (Emory U.) · Richard Starr (Georgia Tech IPaT) · Jocelyn Campbell (Health ComplyAI) |
| 4:45 pm – 5:00 pm | Next Steps · Raju Gottumukkala (UL Lafayette) · Joel Harley (University of Florida) · Aron Culotta (Tulane University) · Ghassan AlRegib (Georgia Tech) Closing Remarks · Ghassan AlRegib (Georgia Tech) |
| 5:00 pm – 6:00 pm | Students Poster Session and Light Finger Food 1. Metaverse for Healthcare 2. MLM for Shared Decision-Making 3. Personalization and Generalization in Heterogeneous Learning for Disease Diagnosis 4. Congruent Multi-Modal Learning for Multifarious Healthcare Applications 5. In-Home Daily Activity Monitoring for Long-term Patient Care 6. Fusion is not all you need: Redundancy and Conditional Informativeness in Multimodal Learning 7. Multi-level and Multi-modal Action Anticipation 8. Targeting Personalization and Generalization in Federated Learning 9. Value-Based Pricing of Tislelizumab as First-Line Treatment for Advanced Esophageal Cancer: A Markov Microsimulation Analysis 10. Subject Invariant Contrastive Learning for Human Activity Recognition 11. Understanding Attention: How Medical Features are Attended in Transformer Models 12. Scale-Aware Self-Supervised Learning for Segmentation of Small and Sparse Structures 13. Deep Learning tools for Ophthalmology: Expanding Quantification and Information Density in Ophthalmic Research |