March 16-18, 2021
The College of Arts & Sciences is pleased to announce USD’s first Artificial Intelligence (AI) Symposium to be held on March 16-18, 2021.
USD’s AI Symposium aims to unite academia, industry and government AI & Data Engineering experts around current issues and areas of collaboration where AI-driven tools are in demand due to big data issues. All attendees will be able to participate in Symposium sessions led by established professionals in AI. The symposium will also provide networking opportunities for participants during the event. Participants will learn how AI-driven and data engineering tools/techniques are used solving current challenges in various applications such as healthcare, cyberthreats, quantum computing, sustainable agriculture and risk management
Tuesday, March 16, 2021
|9:15 – 9:30 a.m.||Welcome|
|9:30 – 10:30 a.m.||How to improve reporting, increase transparency, and reduce failures in machine learning applications in healthcare
Oge Marques, Ph.D. (Florida Atlantic University)
|10:45 – 11:45 a.m.||Searching for Dark Matter with Machine Learning
Scott Kravitz, Ph.D. (Lawrence Berkeley National Laboratory)
Wednesday, March 17, 2021
|8:40 – 8:55 a.m.||Welcome|
|8:55 – 9:55 a.m.||A Human-in-the-Loop AI with Eye-Tracking, Sparse Attentional Model, and Deep Learning
Ullas Bagci, Ph.D. (Northwestern University)
|10:00 – 11:00 a.m.||The role of machine learning in the search for nanoporous materials for storing and separating gases
Cory Simon, Ph.D. (Oregon State University)
Thursday, March 18, 2021
|9:15 – 9:30 a.m.||Welcome|
|9:30 – 10:30 a.m.||Using machine learning to better understand natural resource dynamics
Stephen P. Boyte (U.S. Geological Survey, EROS)
|10:45 – 11:45 a.m.||The role of AI in decision-making
Marzieh Khakiﬁrooz, Ph.D. (Tecnológico de Monterrey)
About the Speakers
Ullas Bagci, Ph.D., is an Associate Professor at the Northwestern University's Radiology and Biomedical Engineering Department at Chicago, and courtesy professor at the Center for Research in Computer Vision (CRCV), department of computer science, University of Central Florida (UCF). His research interests are artificial intelligence, machine learning and their applications in biomedical and clinical imaging. Dr. Bagci has more than 230 peer-reviewed articles in these topics. Previously, he was a staff scientist and lab co-manager at the National Institutes of Health’s radiology and imaging sciences department, center for infectious disease imaging. Dr. Bagci holds two NIH R01 grants (as Principal Investigator) and serves as a steering committee member of AIR (artificial intelligence resource) at the NIH. Dr. Bagci also serves as an area chair for MICCAI for several years and he is an associate editor of top-tier journals in his fields such as IEEE Trans. on Medical Imaging, Medical Physics and Medical Image Analysis. Prof. Bagci teaches machine learning, advanced deep learning methods, computer and robot vision and medical imaging courses. He has several international and national recognitions including best paper and reviewer awards.
Stephen P. Boyte is a Research Geographer at the U.S. Geological Survey, Earth Resources Observation & Science Center (EROS), Sioux Falls, S.D. Mr. Boyte has worked with remote sensing applications to support natural resource management for almost 15 years. He applies data mining approaches to develop ecological models that help to map ecosystem performance and the presence and percent cover of invasive grasses in rangelands of the arid and semiarid western U.S. Boyte’s work includes separating weather-related vegetation variations from management and disturbance effects for terrestrial monitoring and improving ecosystem understanding. His most recent work includes using 30-meter harmonized Landsat / Sentinel-2 satellite data to develop moderate scale maps of annual invasive grasses in near-real-time. He has worked at the EROS Center since 2009. Mr. Boyte earned a B.A. in geography from California State University, Chico, and an M.S. in geography, with an emphasis in remote sensing applications, from South Dakota State University.
Marzieh Khakiﬁrooz, Ph.D., is an assistant professor at the School of Science and Engineering, Tecnológico de Monterrey, Monterrey, Mexico. Dr. Khakiﬁrooz has outstanding practical experience from her various global consultancies for high-tech industries. Her research interests include the application of optimization in smart manufacturing, Industry 4.0, decision making and machine teaching. She has a Ph.D. in Industrial Engineering and Engineering Management and an M.S. degree in Statistics from the National Tsing Hua University (NTHU), Hsinchu, Taiwan.
Scott Kravitz, Ph.D., is a postdoctoral fellow in the Physics Division at Lawrence Berkeley National Laboratory. His research focuses on new detector technologies and analysis methods to improve the sensitivity of searches for dark matter. Through approaches focused on interpretability and reliability, such as data-driven training, anomaly finding, and uncertainty quantification, he aims to bring machine learning to meet the uniquely stringent requirements of particle physics. He is a recipient of the Owen Chamberlain Fellowship at LBNL and received his Ph.D. in Physics from Stanford University.
Oge Marques, Ph.D., is a professor of engineering and computer science at Florida Atlantic University, where he is also the advisor for the "AI in Healthcare Interest Group" in the College of Medicine. He is the author of 11 books and more than 120 scholarly publications in the area of Visual Artificial Intelligence. Dr. Marques is a Sigma Xi distinguished speaker, a fellow of the Leshner Leadership Institute of the American Association for the Advancement of Science (AAAS), Tau Beta Pi eminent engineer, and a senior member of both the IEEE (Institute of Electrical and Electronics Engineers) and the ACM (Association for Computing Machinery). Dr. Marques is actively working on the intersection between AI and Radiology with colleagues, medical professionals, researchers, and students from FAU, NIH, Stanford, and other universities and research labs in the US and abroad. He is an associate member of the Radiological Society of North America (RSNA), a corresponding member of the European Society of Radiology (ESR), and a member of the Society for Imaging Informatics in Medicine (SIIM) and the European Society of Medical Imaging Informatics (EuSoMII). He is currently working on the book "AI for Radiology", scheduled to appear later this year. Dr. Marques has more than 35 years of teaching and research experience in different countries (USA, Austria, Brazil, India, Spain, Serbia, France, and the Netherlands) and has won several teaching awards, including the Outstanding Mid-Career Teaching Award, American Society for Engineering Education - Southeastern Section (ASEE-SE) (2011), the Engineering Educator of the Year Award, The Engineers’ Council (2019), and the Excellence and Innovation in Undergraduate Teaching Award, Florida Atlantic University, three times (2018, 2011 and 2004).
Cory Simon, Ph.D., is an assistant professor of Chemical Engineering at Oregon State University. Through molecular simulations, machine learning, and mathematical modeling, his research group aims to computationally design nanoporous materials with optimal adsorption properties for storing, separating, and sensing gases. He is actively collaborating with Xiaoli Fern, Ph.D. (associate professor of computer science at Oregon State University) to use machine learning, such as message passing neural networks and low rank matrix models, to (a) predict the adsorption properties of nanoporous materials and (b) cluster together nanoporous materials with similar structures and properties. Cory teaches a graduate course, "introduction to data science for engineers", using Julia. He earned his Ph.D. in Chemical Engineering from the University of California, Berkeley, studied applied mathematics at the University of British Columbia, and was a data science intern at Stitch Fix. For more info about his research group, visit their website.
- KC Santosh, Ph.D., Chair & Associate Professor, Computer Science, USD
- Dan Van Peursem, Ph.D., Chair & Professor, Mathematical Science, USD
- Meghann Jarchow, Ph.D., Chair & Associate Professor, Sustainability, USD
- Joel Sander, Ph.D., Associate Professor, Physics, USD
- Jeff Wesner, Associate Professor, Biology, USD
- Bess Vlaisavljevich, Ph.D., Assistant Professor, Chemistry, USD
- Pere Miro, Ph.D., Assistant Professor, Chemistry, USD
- Ranjeet John, Ph.D., Assistant Professor Biology/Sustainability, USD
- Sameer Antani, Ph.D., US National Library of Medicine, NIH, MD
- Kara McCormick, Ph.D., South Dakota Department of Health, SD
- Sabina Kupershmidt, Ph.D., Nursing, USD, SD
- Sema Candemir, Ph.D., The Ohio State University Wexner Medical Center, OH
- Szilard Vajda, Ph.D., Central Washinton University, WA
- Mark Montgomery, KYeild Inc.
- Laurent Wendling, Ph.D., University of Paris, France
- Mickael Coustaty, Ph.D., University of La-Rochelle, France
- Alice Othmani, Ph.D., University of Paris, France
- Antoine Vacavant, Université Clermont Auvergne, France
- Mohamed-Rafik Bouguelia, Ph.D., Halmstad University, Sweden
- Lalit Garg, Ph.D., University of Malta, Malta
- Virach Sornlertlamvanich, Ph.D., Musashino University, Japan
- Thanaruk Theeramunkung, Ph.D., SIIT, Thammasat University, Thailand
- Mufti Mahmud, Ph.D., Nottingham Trent University, UK
- Sunil Aryal, Ph.D., Deakin University, Australia
- Xianqing Mao, M.D., Ph.D., University of Luxemburg, Luxemburg
- Do Thanh Ha, Ph.D., VNU University of Science, Vietnam
- Hanan Salam, Ph.D., New York University, Abu Dhabi, UAE
- Geetha Lathkar, Ph.D., MGM’s College of Engineering, India
- Umapada Pal, Ph.D., Indian Statistical Institute, Kolkata, India
- Satish K Singh, Ph.D., Indian Institute of Information Technology, India
- Patrice Boursier, Ph.D., International Medical University, Malaysia
If you are a person with a disability and need a special accommodation to fully participate, please contact Disability Services at 605-658-3745 at least 48 hours before the event.