The Wicklow AI in Medicine Research Initiative, WAMRI, provides researchers the opportunity to focus on utilizing artificial intelligence models to advance critical medical research across areas including oncology, cardiology and neurology. WAMRI, located at USF's Data Institute in downtown San Francisco, is launching with five joint projects.
The researchers and faculty at USF’s Data Institute have a high impact record of conducting multidisciplinary research in collaboration with leading medical research institutions such as UCSF, Harvard Medical School and Boston Children’s Hospital. WAMRI was funded through a gift from Wicklow Capital to support research at the intersection of machine learning, AI and medicine.
Working with WAMRI
We would love to help you analyze your organization's medical data, whether it be imaging, text, tabular, genomic, or any other type (or a mixture of types) by developing a collaborative project. We can provide the data science expertise from our faculty and mentors; we would need you to provide the medical data and medical expertise, and to work closely with our data scientists. Projects are generally academic in nature - that is, the end product will be an academic paper or similar.
Jeremy Howard - WAMRI Chairman, Founder FAST.ai
Jeremy Howard is an entrepreneur, business strategist, developer, and educator. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He is also a faculty member at the University of San Francisco, and CSO at doc.ai and platform.ai.
Jeremy’s most recent startup, Enlitic, was the first company to apply deep learning to medicine. He was previously the President and Chief Scientist of the data science platform Kaggle, where he was the top ranked participant in international machine learning competitions 2 years running. He was the founding CEO of two successful Australian startups (FastMail, and Optimal Decisions Group–purchased by Lexis-Nexis). Before that, he spent 8 years in management consulting, at McKinsey & Co, and AT Kearney.
He has many television and other video appearances, including as a regular guest on Australia’s highest-rated breakfast news program, a popular talk on TED.com, and data science and web development tutorials and discussions.
david uminsky - WAMRI Board Member, USF Director MS in Data Science & Data Institute
David Uminsky is the director for the MS in Data Science program. His research interests are in applied mathematics. He is interested in unsupervised machine learning, data clustering, algebraic signal processing, as well as pattern formation, dynamical systems and fluids. David was selected in 2015 by the National Academy of Sciences (NAS) as a Kavli Frontiers of Science Fellow. Each year, 100 researchers under the age of 45 are selected by the academy, and the 20% of the current NAS were previous Kavli Fellows. He is also the founding director of the Bachelor's program in Data Science at University of San Francisco. Before joining USF, he was a combined National Science Foundation and UC President's Fellow at UCLA, where he was awarded the Chancellor's Award for post-doctoral research. This award is given to approximately top 20 post-docs out of over a thousand who qualify for consideration. He holds a PhD in Mathematics from Boston University and a BS in Mathematics from Harvey Mudd College.
Fred monroe - RESEARCH AND SUPPORT
If you would like to collaborate with WAMRI, please email firstname.lastname@example.org - Fred will be on the other side.
Fred Monroe is a machine learning researcher, serial entrepreneur, software developer, trader and investor with over twenty five years of experience working at the forefront of technology. He is working to apply advances in deep learning and artificial intelligence (AI) to important real world problems like those studied at WAMRI.
Fred also works for Wicklow Capital, a family office which makes investments across a wide range of early stage companies. Fred helps Wicklow assess investments and donations, particularly when related to artificial intelligence.