Powerful new computational methodologies are being developed for analyzing today’s large and complex data sets. For example, machine learning methods provide insight in biomedicine by recognizing, classifying and exploiting complex patterns and probabilistic models for reasoning under uncertainty. Cutting edge research in the data science and machine learning communities seeks to create novel ways of discovering new knowledge from data, presenting discovered information to users, facilitating associations and guiding decision-making in clinical settings.
Researchers often work collaboratively on problems that span sequencing and imaging to data mining of medical records, comparative analysis of drug efficacy and personalized treatments for cancer. Our 2018 conference will focus on recent advancements in data science and machine learning that impact biomedicine and the new challenges that lie ahead.
Confirmed speakers include:
Payel Das, Manager and Technical Lead
AI Solutions, IBM Watson Research Center
Casey Greene, Assistant Professor
Systems Pharmacology & Translational Therapeutics, University of Pennsylvania
Jennifer Neville, Associate Professor
Computer Science and Statistics, Purdue University
Lucila Ohno-Machado, Professor and Chair
Biomedical Informatics, UC San Diego
Christopher Ré, Associate Professor
Computer Science, Stanford University
This annual conference highlights emerging areas of quantitative biomedical science important to our community and provides an opportunity to showcase, via poster session, the diverse research of trainees affiliated with GCC/Keck Center training programs and other institutional programs. This year, the conference focus is on Data Science and Machine Learning for Biomedicine.
Lydia Kavraki, Rice University, Chair
Jim Briggs, University of Houston
Chris Jermaine, Rice University
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