Eight Penn State faculty teams awarded seed grants for AI research

Eight Penn State faculty teams awarded seed grants for AI research

UNIVERSITY PARK, Pa. — Multi-Disciplinary Research Grants were recently awarded to eight research groups from across Penn State’s colleges and campuses conducting research related to artificial intelligence (AI) and machine learning (ML). In collaboration with numerous research institutes and colleges, these grants are funded in concert with the 2020 industryXchange, an annual University-wide event hosted by the College of Engineering. 

“The collaborative nature of these research projects is truly inspiring,” said Justin Schwartz, Harold and Inge Marcus Dean of Engineering. “We had an astounding number of high-quality submissions, and we are confident that these interdisciplinary teams will make an impact by further advancing applications of AI and machine learning.” 

These one-year seed grants will support research on application-specific development in AI and ML in the areas of cybersecurity, energy, health care, manufacturing and transportation. Please note that the announcement of these grant awardees was delayed due to COVID-19.  

With the principal investigators listed first, the grants were awarded to the following teams:  

  • Youakim Badr, associate professor of data analytics; Partha Mukherjee, assistant professor of data analytics; Raghu Sangwan, associate professor of software engineering; and Satish Srinivasan, assistant professor of information sciences, all at Penn State Great Valley, for “Managing Risks in AI Systems: Mitigating Vulnerabilities and Threats Using Design Tactics and Patterns.” 

  • Rebecca Napolitano, assistant professor of architectural engineering, and Wesley Reinhart, assistant professor of materials science and engineering, for “Enabling Energy-Efficient Building Envelopes: Artificial Intelligence for Nondestructive Evaluation.” 

  • Ankit Maheshwari, staff physician of PSHHVI electrophysiology at Penn State Health Milton S. Hershey Medical Center, and Vasant Honavar, professor and Edward Frymoyer Chair of Information Sciences and Technology, for “Prediction of Subclinical Atrial Fibrillation in Patients with Cryptogenic Stroke using Point of Care Artificial Intelligence Guided Interpretation of Sinus Rhythm Electrocardiograms.” 

  • Tarasankar Debroy, professor of materials science and engineering, and Todd Palmer, professor of engineering science and mechanics and materials science and engineering and director of Center for Innovative Sintered Products, for “Machine learning based quality improvement of additively manufactured metallic components.” 

  • Yiqi Zhang, assistant professor of industrial engineering, and Fenglong Ma, assistant professor of information sciences and technology, for “Modeling of Driver Performance in Connected Vehicle Systems with Machine Learning Algorithms.” 

  • Jinchao Xu, Verne M. Willaman Professor of Science, and John Yilin Wang, associate professor of petroleum and natural gas engineering, for “Advanced and fast simulation technologies for modeling shale gas wells.” 

  • Michael Lanagan, professor of engineering science and mechanics; Prasenjit Mitra, professor of information sciences and technology; and Ram Narayanan, professor of electrical engineering, for “5G Infrastructure and Materials Optimization Through Artificial Intelligence.” 

  • Penelope Kay Morrison, assistant professor of biobehavioral health at Penn State New Kensington; Kristin Sznajder, assistant professor of public health sciences at Penn State Health Milton S. Hershey Medical Center; Esther Obonyo, associate professor of engineering design and architectural engineering and director of the Global Building Network; and Soundar Kumara, Allen E. Pearce and Allen M. Pearce Professor of Industrial Engineering, for “Healthy Residents 2.0: An exploratory pilot study of air quality and psychosocial well-being.” 

The 2020 industryXchange, originally scheduled for this past spring but postponed due to COVID-19, has been rescheduled for Dec. 7-8.  

“We encourage faculty to attend this year’s event to develop new collaborations with federal program directors, national lab leaders and industry attendees,” said Priya Baboo, interim senior director of corporate and industry engagement.  

Faculty that plan on attending this year’s event should submit a quad blocker for their research to Priya Baboo at pzb104@psu.edu by Oct. 30. The template is available here

Please visit the industryXchange webpage for updates, registration and more information on the event.  

(Media Contacts)

Megan Lakatos

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Last Updated October 16, 2020

Published at Fri, 16 Oct 2020 21:00:00 +0000

Experts introduce AI-RADS to help bolster radiologists’ proficiency in artificial intelligence

Artificial intelligence is poised to have a monumental impact on the future of medicine and imaging in particular. And yet, many medical schools pay little mind to this burgeoning piece of the healthcare delivery system.

Wanting to close the knowledge gap, experts with Dartmouth College have developed a novel curriculum to bolster physicians’ know-how in this space. AI-RADs, as it’s called, has shown early promise, with residents rating it as a 9.8 out of 10 and significant gains in comprehension when reading AI articles after lectures, experts reported Friday in Academic Radiology.

“AI-RADS was well-received amongst trainees at our institution,” Alexander Lindqwister, with the New Hampshire-based college’s Geisel School of Medicine, and colleagues wrote Oct. 16. “From our metrics of quality, trainees overwhelmingly feel that the content depth of the AI-RADS lecture series is ideal, and the examples used are helpful vehicles to understand key concepts in artificial intelligence.”

Lindqwister et. al built their course around a sequence of foundational algorithms, presented as logical extensions of each other and based around familiar examples, such as movie recommendations. They also incorporated secondary lessons on topics such as pixel mathematics, since most residents have little to no computational background. They further built out the program with a journal club, exploring the algorithm discussed in the most recent lecture, along with study guides that helped cut through “intimidating technical descriptions.”

Published at Fri, 16 Oct 2020 20:48:45 +0000

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