[Mobilizeplans-starstudents] Apr 5th Mobilize Center Seminar, Dragutin Petkovic

Wed Apr 4 13:57:52 PDT 2018

The next Mobilize Center Seminar is scheduled TOMORROW, Thursday, April 5th.  It features Dragutin Petkovic, Computer Science Department, San Francisco State University.  He will be presenting “Toward Explainable Machine Learning - RFEX: Improving Random Forest Explainability”.

The Mobilize Center seminars are held once a month.  Please check Mobilize Events<http://mobilize.stanford.edu/events/> for updates.

We look forward to seeing you tomorrow!

TITLE:
Toward Explainable Machine Learning - RFEX: Improving Random Forest Explainability

WHEN:
Thursday, April 5th
noon - 1 pm

WHERE:
Y2E2 300, Stanford University

Abstract:
Machine Learning (ML) methods are now influencing major decisions about patient care, new medical methods, drug development and their use and importance are rapidly increasing in all areas.  However, these ML methods are inherently complex and often difficult to understand and explain resulting in barriers to their adoption and validation. We define explainability in ML as easy to use information explaining why and how the ML approach made its decisions. We believe that much greater effort is needed to address the issue of ML explainability because of the ever increasing use and dependence on ML in many applications and the need for increased adoption by non-ML experts.

In our talk, we will 1) summarize a workshop discussion on ML explainability organized jointly with Profs. L. Kobzik and C. Re at the 2018 Pacific Symposium on Biocomputing (PSB) and 2) describe our work on Random Forest Explainability (RFEX) (joint work with Prof. R. Altman, M. Wong and A. Vigil). RFEX provides easy-to-interpret explainability summary reports from trained RF classifiers to improve the explainability for users who are often non-experts. We tested RFEX with the FEATURE program to predict functional sites in 3-D molecules based on their electrochemical signatures (features). Through formal usability testing with expert and non-expert users, we found the RFEX explainability report significantly increased explainability and user confidence in RF classification.

The workshop and RFEX research were supported by NIH grant R01 LM005652, Stanford Mobilize Center and SFSU Center for Computing for Life Sciences.
Bio:
Dr. Dragutin Petkovic obtained his Ph.D. at UC Irvine in biomedical image processing. He spent over 15 years at IBM Almaden Research Center as a scientist and in various management roles. His contributions ranged from the use of computer vision for inspection, to multimedia and content management systems. He is the founder of IBM's well-known QBIC (query by image content) project, which significantly influenced the content-based retrieval field. Dr. Petkovic received numerous IBM awards for his work and became an IEEE Fellow in 1998 and IEEE LIFE Fellow in 2018 for his leadership in content-based retrieval.  In 2003 Dr. Petkovic joined San Francisco State University’s (SFSU) Computer Science (CS) Department as a Chair. He founded SFSU’s Center for Computing for Life Sciences in 2005, where he continues to serve as the Director. Currently the Associate Chair of the department, Prof. Petkovic’s research and teaching now focuses on Machine Learning with an emphasis on explainability, software engineering, and the design and development of easy-to-use systems.
To keep up-to-date on upcoming speakers and the dates, visit Mobilize Events<http://mobilize.stanford.edu/events/>.
Diane Bush
Assistant to Professor Scott Delp
NMBL, Mobilize Center, OpenSim
Stanford University
dbush1 at stanford.edu<mailto:dbush1 at stanford.edu>

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