- About
- Events
- Calendar
- Graduation Information
- Cornell Learning Machines Seminar
- Student Colloquium
- BOOM
- Fall 2024 Colloquium
- Conway-Walker Lecture Series
- Salton 2024 Lecture Series
- Seminars / Lectures
- Big Red Hacks
- Cornell University - High School Programming Contests 2024
- Game Design Initiative
- CSMore: The Rising Sophomore Summer Program in Computer Science
- Explore CS Research
- ACSU Research Night
- Cornell Junior Theorists' Workshop 2024
- People
- Courses
- Research
- Undergraduate
- M Eng
- MS
- PhD
- Admissions
- Current Students
- Computer Science Graduate Office Hours
- Advising Guide for Research Students
- Business Card Policy
- Cornell Tech
- Curricular Practical Training
- A & B 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
- Travel Reimbursement Guide
- 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, October 9, 2020 - 11:30 to 12:45
Streaming via Zoom
Host:
Thorsten Joachims
Categories:
Accelerating Scientific Discovery in the Biological Sciences using Artificial Intelligence (via Zoom)
Abstract: One of the long-standing promises of artificial intelligence (AI) has been to accelerate discovery in modern fields of science, particularly in biology. This talk will explore several research problems in this area. For the core of the talk, I will discuss how AI can help accelerate science by automatically synthesizing knowledge from the academic literature. I will present GWASkb, the largest automated curation effort aimed at studies linking genetics with human traits. GWASkb finds genetic associations that human curators miss and makes them available for disease risk prediction and basic research. Afterwards, I will briefly discuss at a high-level a few additional problems that I am personally interested in; these include the application of modern generative models to reduce the cost of scientific experiments, models for learning the structure of genetic variation across human genomes, and methods for predicting traits from genetic data. Overall, I hope this talk will encourage us to think about applications of AI to new, impactful problems and potentially start new collaborations.
Bio: Volodymyr Kuleshov is an Assistant Professor at the Jacobs Technion-Cornell Institute at Cornell Tech and in the Computer Science Department at Cornell University. He obtained his bachelor’s in Mathematics and Computer Science from McGill University, and his Ph.D. in Computer Science from Stanford University, where he was the recipient of the Arthur Samuel Best Thesis Award.
Kuleshov’s research interests are in the field of machine learning and its applications in scientific discovery, health, and sustainability. His work has been featured in Nature Biotechnology, Nature Medicine, Nature Communications and Scientific American, and was awarded an NSERC Post-Graduate Fellowship and a Stanford Graduate Fellowship. He is the co-founder and Chief Technologist at Afresh, a startup focused on automating the food supply chain using AI in order to reduce food waste