- About
- Events
- Calendar
- Graduation Information
- Cornell Tech Colloquium
- Student Colloquium
- BOOM
- Spring 2023 Colloquium
- Conway-Walker Lecture Series
- Salton Lecture Series
- Seminars / Lectures
- Big Red Hacks
- Cornell University High School Programming Contests 2023
- Game Design Initiative
- CSMore: The Rising Sophomore Summer Program in Computer Science
- Explore CS Research
- Research Night
- Cornell Junior Theorists' Workshop
- People
- Courses
- Research
- Undergraduate
- M Eng
- MS
- PhD
- Admissions
- Current Students
- Computer Science Graduate Office Hours
- Business Card Policy
- Cornell Tech
- Curricular Practical Training
- Exam Scheduling Guidelines
- Fellowship Opportunities
- Field of Computer Science Ph.D. Student Handbook
- Graduate TA Handbook
- Field A Exam Summary Form
- Graduate School Forms
- Instructor / TA Application
- Ph.D. Requirements
- Ph.D. Student Financial Support
- Special Committee Selection
- Travel Funding Opportunities
- The Outside Minor Requirement
- Diversity and Inclusion
- Graduation Information
- CS Graduate Minor
- Outreach Opportunities
- Parental Accommodation Policy
- Special Masters
- Student Spotlights
- Contact PhD Office
Speaker
Friday, May 3, 2019 - 12:15 to 13:15
Gates Hall 122 (Overflow 405)
Host:
Deborah Estrin
Categories:
Related Research Areas:
Title: Predictive Modeling with Complex Healthcare Data
Abstract: The recent years have witnessed a surge of interests on healthcare data analytics. Predictive modeling, which aims at building computational models to predict the likelihood of specific outcomes (e.g., mortality or onset of specific condition), is one fundamental and challenging problem. In this talk I will introduce the research that has been done by my group in recent years on predictive modeling with complex healthcare data. Here the complexities can come from either input features or predicted outcomes. Specifically, I will talk about tensor based modeling for input complexity and heterogeneous target regression for outcome complexity. I will also talk about other potential issues or challenges for healthcare data modeling and discuss about future directions.
Bio: Fei Wang is an Associate Professor in Department of Healthcare Policy and Research at Weill Cornell Medicine. His research interest lies in the intersection between data mining and computational medicine. He has extensive publications on top CS venues such as NIPS, ICML, KDD, AAAI and biomedical venues such as JAMA Internal Medicine. His h-index is 50. Dr. Wang is the winner of 2016 Parkinson's Progression Markers Initiative data challenge organized by Michael J. Fox Foundation and 2017 NIPS/Kaggle challenge on Personalized Medicine organized by Memorial Sloan Kettering Cancer Center. Dr. Wang is the recipient of NSF CAREER award in 2018, and his research has been supported by NSF, NIH, ONR, PCORI, AHA, MJFF and industries such as Amazon.