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The figure addresses the AI grand challenge of accelerating science requires concerted advances across all areas of AI. CENSAI brings together interdisciplinary teams of scientists with expertise in AI and in specific scientific domains to accelerate science through advances in AI.

ICDS associate director’s work driven by unanswered fundamental questions in AI

UNIVERSITY PARK, Pa. — The work of Vasant Honavar, the Dorothy Foehr Huck and J. Lloyd Huck Chair in biomedical data sciences and artificial intelligence (AI) and a professor of data science in the College of Information Sciences and Technology professor of data science, is driven by answering fundamental questions using machine learning.

Honavar first came to Penn State in 2013 as the Edward Fryomoyer Chair of Information Sciences and Technology and later became a Penn State Institute for Computational and Data Sciences (ICDS) associate director. In this role, Honavar was instrumental in expanding the institute’s focus beyond high-performance computing into data sciences and AI. As part of this expansion, Honavar launched the Penn State Center for Artificial Intelligence Foundations and Scientific Applications (CENSAI), which he currently leads.

“I call myself an accidental computer scientist,” Honavar said. “I started out interested in physics and math. I was in India at the time and couldn’t really make a career out of it, so I was pushed into engineering and got a degree in electrical engineering.”

While attending Drexel University and working on his master’s thesis on image processing, Honavar developed an interest in AI.

“My adviser at Drexel encouraged me to apply to [doctoral] programs where I could pursue AI and I ended up at Wisconsin to work with Professor Leonard Uhr,” Honavar said. “Len was a pioneer in AI — he was among the dozen or so participants in the workshop organized at Dartmouth by John McCarthy in 1956 to develop ideas about thinking machines. Len was an amazing mentor who shaped my approach to science in general, particularly AI. He taught me to look at the world through a computational lens. Everywhere I look, I find computation and information. You understand intelligence when you have algorithms or programs that display intelligence. I am interested in understanding intelligence… What does it mean to learn? What does it mean to reason?”

Honavar’s research has spanned machine learning, causal inference, knowledge representation and scientific applications of AI, especially life sciences and more recently, health sciences and materials sciences. His current research focuses on AI for science; design and analysis of algorithms for predictive modeling from very large, high-dimensional, richly structured, multimodal, longitudinal data; elucidation of casual relationships from disparate experimental and observational studies and from relational, temporal and temporal-relational data; algorithms for continual learning and casual inference; closed-loop integration of data, knowledge, simulations and experiments for materials discovery, design and synthesis.

“My research includes both fundamental advances in AI and machine learning and practical applications of AI,” Honavar said.

His current work aims to identify health disparities and predict health risks using clinical data, such as electronic health records. Researchers from the Penn State College of Medicine in Hershey, Penn State Research Innovation with Scientists and Engineers team and the National Institutes of Health (NIH)-funded Penn State Clinical and Translational Sciences Institute, which Honavar leads, work on this project together. Honavar’s work on new machine learning algorithms needed for predictive modeling from high-dimensional, sparsely and irregularly sampled longitudinal data is funded by the U.S. National Science Foundation (NSF).

“In collaboration with the NIH-funded Observational Health Data Sciences and Informatics (OHDSI), we built the data infrastructure — the Penn State Digital Collaboratory for Precision Health Research (DCPHR) — which allows Penn State researchers to initiate or participate in multi-set studies,” Honavar said.

This infrastructure significantly lowers the barriers to the use of electronic health record data, which is anonymized, in data-intensive, AI-powered biomedical research.

Aside from his current research works, Honavar has been involved in the development of AI centers at Penn State, furthering the enhancement of interdisciplinary research.

“The formation of teams with the right combination of expertise and interests is crucial for successfully advancing AI and accelerating scientific discoveries,” Honavar said.

CENSAI was established to position Penn State as a leader in progress science through advances in AI. Under Honavar’s leadership, CENSAI has brought together more than 40 researchers from across multiple colleges and disciplines at Penn State.

“Accelerating science presents a grand challenge for AI,” Honavar said. “Addressing this challenge requires concerted advances across all areas of AI. CENSAI is organized around addressing this challenge using life sciences, material sciences and health sciences as testbeds. We’re building AI research capacity needed to pursue ambitious interdisciplinary projects. CENSAI has already had successes.”

Collaborations that have come out of CENSAI were instrumental in Penn State’s success in securing a $20 million NSF grant to establish the National Synthesis Center for Emergence in Molecular and Cellular Sciences (NCEMS) under the leadership of Edward O’Brien, Eberly College of Science professor of chemistry and ICDS co-hire. Honavar serves as the associate director for AI and strategic initiatives for NCEMS.

“The [NCEMS] really aligns with the mission of CENSAI and the basic idea that in order to accelerate science, you need to be able to integrate multiple sources and build collaborative human and AI teams. In some sense, we have achieved what we have set out to achieve; now, it is looking forward and continuing to grow that.” Honavar said.

There are multiple collaborative projects that are in the works at various stages of development. Honavar and his colleagues are working, with the support from an NSF AI institute planning grant, with materials scientists to develop an Institute for AI-Enabled Materials Design, Discovery and Synthesis.

“I would like to see ICDS and Penn State become a national leader in advancing science through advances in AI,” Honavar said. “This fits in with the interdisciplinary research mission of ICDS.”

In addition to research, Honavar is committed to expanding AI and data sciences education at Penn State. He has co-led the development of new degree programs in data sciences and AI, taught undergraduate and graduate courses in AI, machine learning and causal interference. In the fall, Honavar is looking forward to teaching a new general education course, “Artificial Intelligence — The very idea,” to undergraduates across the university.

“I am looking forward to being part of the next phase of development of AI and data sciences at Penn State,” Honavar said.

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