With an initial commitment of $10M spread over five years, Param Hansa Philanthropies is committed to long-term investment in the innovative partnership between The University of Texas at Austin’s Machine Learning Lab, Oden Institute for Computational Engineering and Sciences and Dell Medical School that aims to accelerate the pace of discovery in cancer research and transform clinical care for cancer patients.
Drawing on the breadth and depth of UT Austin's expertise in computational science, machine learning, and clinical and medical research, this collaborative multidisciplinary effort is expected to deliver high-impact work that advances the ability to model and predict cancer, imagine new computational tools to aid diagnosis and decision-making, and translate research into improved patient outcomes.
Project Leadership
Professor, Department of Computer Science, UT Austin
Director, Machine Learning Laboratory
Director, National AI Institute for the Foundations of Machine Learning
Director, Oden Institute for Computational Engineering and Sciences
Associate Vice President for Research
Professor, Department of Aerospace Engineering and Engineering Mechanics, UT Austin
W. A. "Tex" Moncrief, Jr. Chair in Computational Engineering and Sciences IV - Computational Oncology
Director, Center for Computational Oncology, Oden Institute
Vice Dean of Research, Dell Medical School
Robert E. Askew, Sr. M.D. Chair in Oncology, Department of Oncology
Professor, Department of Oncology
Key Implementation Strategies
Two Pandey Endowed Faculty Fellowships in Computational Oncology, one each at the Oden Institute for Computational Engineering and Sciences and Dell Medical School. These positions will promote collaborative research across UT Austin's campus to further our search for maximally effective cancer treatments.
The Param Hansa Philanthropies Endowed Research Fund in the Machine Learning Lab, which will provide support for priority research in computational medicine in perpetuity.
The Pandey Graduate Fellowship in the Oden Institute, which will provide graduate students with expertise and support to execute an ambitious research agenda.
The Param Hansa Philanthropies Computational Oncology Research Fund, which sustains collaborative investigations focusing on high impact, translational research that will improve the lives and care of cancer patients in real time.
Permanent enhancement of the existing Pandey Trinity Graduate Fellowship, which provides graduate fellowships or graduate research assistantships for students working in Artificial Intelligence at the Machine Learning Lab.
Initiatives Launched
Funds from the Param Hansa Philanthropies Computational Oncology Research Fund have been deployed to create a new joint postdoctoral fellow program between UT Austin and MD Anderson Cancer Center. The goal is to build deep and sustainable collaborations in computational oncology, with a particular focus on connecting UT Austin’s computational expertise with MD Anderson’s clinical expertise to achieve high impact and translational research. Fellows are co-advised by one co-advisor from UT Austin and one co-advisor from MD Anderson. The fellowship appointment includes 50% salary support, with the co-advisors providing the other 50%. The fellowship also provides funds to facilitate travel to Austin/Houston, with the expectation that Austin-based postdocs will spend up to one week per month (on average) at MD Anderson (and vice versa for the MD Anderson-based postdocs). The initial cohort of fellows includes the following two UT Austin-based postdocs:
Integrating Quantitative Imaging Biomarkers into Computational Modeling to Improve Therapy Response Prediction in High Grade Glioma: An Investigative Study
Pandey Postdoctoral Fellow: Dr. Bikash Panthi
Mentors: Dr. Caroline Chung (MD Anderson) & Dr. David Hormuth (Oden Institute)
Multi-vendor Rapid and Motion-robust MRI for Assessment of Prostate Cancer and Other Applications
Pandey Postdoctoral Fellow: Dr. Yamin Arefeen
Mentors: Dr. Jon Tamir (Oden Institute) & Ken-Pin Hwang (MD Anderson)
Ongoing Research Projects
Machine Learning Guided Enzyme Engineering Dramatically Improves Expression Yield of Potential Cancer Therapeutic Enzyme
Post-Doc and Graduate Research Assistant: Ebru Cayir, PhD; Ladan Mashouri
Lead Principal Investigator: Everett M. Stone
Building Deep Networks that can Predict Mutations for Improving Thermal Stability, Binding Affinity, and Activity of Enzymes and Antibodies.
The project aim is to build AI models that can efficiently 'learn' the underlying chemistry of proteins, especially at the interface. Core applications include (1) improved cancer therapeutics for binding serine dehydratase, and (2) identifying destabilizing mutations that result in increased interaction with intra-cellular chaperons, a key step in identifying cancer-causing mutations.
Post-Doc: Danny Diaz
Project Updates
Conquering Breast Cancer using Supercomputers, Data, and Mathematical Modeling
Stampede2, Lonestar6, Corral systems help advance tumor models, treatment options.
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