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May 30 - June 1, 2024
hosted by University of Wisconsin-Eau Claire
Eau Claire, Wisconsin 54702-4004, USA


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Keynote Speakers

Thursday, May 30, 2024
6:00 PM

Dr. Michael Carney
Interim Provost and Vice Chancellor for Academic Affairs
University of Wisconsin-Eau Claire

Dr. Michael Carney is in his 24th year at UW-Eau Claire and currently serves as interim Provost and Vice Chancellor for Academic Affairs. Prior to his Provost role, he served as Assistant Chancellor for Strategic Partnerships and Program Development (2021-2023) where he managed UW-Eau Claire’s strategic partnerships with the Mayo Clinic Health System and H ewlett Packard Enterprise and helped develop new partnerships and academic programs aligned with the university's strategic plan, including several new degree programs in STEM and bio-health related fields.
In 2021, he co-wrote and now co-manages a three-year, $9.4 million Workforce Innovation Grant that was awarded to UW-Eau Claire by the Wisconsin Economic Development Corporation. This multi-pillared grant is addressing immediate-, medium-, and long-term economic and workforce challenges in rural regions that were disproportionately impacted by the COVID-19 pandemic.
He also served as Associate Vice Chancellor for Academic Affairs (2013-2021), Chemistry Department chair (2010-2013), and has been a faculty member in the chemistry department since 2000. Prior to joining UW-Eau Claire, he enjoyed a 10-year research and development career in petrochemical and polymer industries.

Thursday, May 30, 2024
7:00 PM

Dr. Madjid Fathi
Professor and Director
University of Siegen, Germany

Madjid Fathi is a professor and Head of KBS & KM (Knowledge Based System & Knowledge Management) institute at the EECS Department at the University of Siegen, Germany. He obtained his M.Sc. degree in Computer Science and Ph.D. degree (Dr.-Ing.) both from the University of Dortmund, Germany, in 1986 and 1991, respectively. Accordingly, he obtained Habilitation degree (Post-Doctorate) at the University of Ilmenau, Germany, in 1998. Before he got the Professor at the Department of Electrical Engineering and Computer Science at the University of Siegen he was visiting scholar at Florida State University and from 2003 at LMM (Lab for Micromechanics - Prof. Garmestani) Georgia Institute of Technology.
Since 2004, he is in Siegen. He was Visiting Scholar with Professor Zadeh father of Fuzzy Logic at U.C. Berkeley dept. of EECS joined the BISC (Berkeley Initiative of Soft Computing) from Sep/2012 to Sept/2013. As head of KBS he leads a large academic team of researchers and educators which has, thus far, resulted in over 90 theses. His research interests are focused on AI, Knowledge Based Systems (KBS), knowledge management and their applications in medicine and engineering, knowledge transfer, organizational learning, and knowledge discovery from text (KDT).
He is the editor of "Integration of Practice-Oriented Knowledge Technology" (2013) and "Integrated Systems, Design and Technology" (2011) published by Springer, as well as three text books (the last one has been published in October 2019 with the title: Computer-Aided Writing by Springer) and five edited books. He, with his students, has published with more than 270 publications including 30 Journal publications, and obtained four paper awards. He got the European Award Cut-e prize 2015. He is a senior member of IEEE as well as member of editorial board of five respective journals. He is the founder of Alzheimer Knowledge Platform.

Modern Concepts and Solutions for Healthcare: From AI in Rescue Operations to Decision-Support using Knowledge Graph and Digital Twin
Modern medicine faces the challenge of developing innovative concepts to make medical care more efficient and effective. In this context, various artificial intelligence (AI) approaches are becoming increasingly important. Current research results and applications show that AI can be successfully used in the rescue services. By using knowledge graphs and AI algorithms on a wearable device in the project KIRETT, emergency services can respond faster and more precisely to emergencies and carry out life-saving measures more effectively.
Another promising concept for expanding the existing research is the Digital Twin (DT), which opens up a new dimension in medical care. This approach makes it possible to create virtual models of patients and emergency situations that support personalized and preventive medicine. The use of DT in various medical applications, the combination with existing research on the knowledge-based and AI-integrated emergency services and their potential to improve patient care will be presented. Finally, the importance of visualization and simulation with these techniques to support data-driven decision-making processes in healthcare will be highlighted. Through visualization techniques using knowledge graphs and knowledge fusion, complex medical data can be presented in an understandable way, helping doctors and medical staff to make informed decisions. Simulations with the treatment pathways of the emergency services and DT enable testing of various treatment scenarios and analysis of effects before the implementation in practice.

Friday, May 31, 2024
8:30 AM

Dr. Chris Hasse
Chief Administrative Officer
Mayo Clinic Health System

Christopher (Chris) Hasse, PhD, MBA, FACHE, FACMPE is chief administrative officer (CAO) of the Mayo Clinic Health System (MCHS). As CAO, Chris partners closely with the MCHS President to lead 17,000 employees serving 16 community hospitals and 53 multispecialty clinics throughout Minnesota, Wisconsin, and Iowa. He is also an Assistant Professor of Health Care Administration and Health Care Systems Engineering at Mayo Clinic College of Medicine and Science. Chris serves on several boards, including the Vizient Upper Midwest CEO Board and the American Hospital Association Regional Policy Board (Region 6).
Chris previously served in various leadership roles across Mayo Clinic, including enterprise Chair of Advanced Care at Home & Practice Strategy and Associate Administrator supporting the Clinical Practice Committee (CPC), hospital operations, surgical/procedural practices, and ancillary services at Mayo Clinic in Florida. Prior to joining Mayo Clinic, Chris worked for Truman Medical Centers (now University Health) in Kansas City, Missouri. A Minnesota native, Chris’ healthcare administration career started in his hometown at Immanuel St. Joseph’s Hospital, now known as Mayo Clinic Health System in Mankato.
Chris received his bachelor's and master's (MBA) degrees from the Helzberg School of Management at Rockhurst University (Kansas City, MO). He earned his doctorate degree (PhD) at Florida Atlantic University (Boca Raton, FL) and completed a graduate certificate in public health (CFPH) from the University of Michigan. Chris has served as an adjunct professor at the University of North Florida (Jacksonville, FL) and is a certified six sigma black belt (CSSBB) through the American Society for Quality. He holds fellowship status and board certifications within the American College of Healthcare Executives (FACHE) and the American College of Medical Practice Executives (FACMPE) through the Medical Group Management Association.
Chris and his wife, Katie, have three children and live in the Greater La Crosse Community (Wisconsin).

OR Insights: Leveraging Predictive Analytics to Optimize Operating Room Capacity and Improve Health System Performance

Friday, May 31, 2024
9:15 AM

Dr. Rajeev Chaudhry
Clinical Informaticist Internist
Mayo Clinic

Hannah Johnson
Operations Administrator
AI & Biomedical Informatics
Mayo Clinic

Rajeev Chaudhry, M.B.B.S., M.P.H., is the Medical Director of Artificial Intelligence and Informatics for Mayo Clinic Health System and for the NWWI, Eau Claire, MCHS. He is also the Medical Director of Patient Cohort Intelligence Solutions and Regulatory for the Center for Digital Health-Data Analytics, Mayo Clinic, Medical Director of AI for Center for Women’s Health and AI & Informatics advisor to the Department of Medicine, Mayo Clinic Florida.
Dr. Chaudhry is a consultant in Community Internal Medicine at Mayo Clinic Health System in Eau Claire, Wisconsin, and Mayo Clinic in Rochester with joint position in the department of Cardiology.
He is Professor of Medicine, Mayo Clinic College of Medicine and Science.
Dr. Chaudhry joined Mayo Clinic in 1995. During his tenure, he has led work that has affected the practice transformations for the entire institution with innovations in both technology and processes. This work includes partnering with VitalHealth Software, a Mayo Clinic startup, to develop a first-of-its-kind, web-based decision support system, developing and implementing e-Consults for the enterprise and affiliated practice network and developing population management applications for primary care and specialty practices for the Mayo Clinic enterprise.
He along with his team currently work to identify and enable new models of care delivery using intelligent electronic health record and digital applications. This includes identifying opportunities for automating repetitive tasks that are protocol driven. He also partners with the primary care and specialty practices for the transformation of care through design and development of new applications, their appropriate implementation and scientific evaluation.
Ms. Hannah Johnson currently holds the role of Operations Administrator – Surgical Services and Anesthesiology for Mayo Clinic Health System in Wisconsin. She also provides leadership support for AI & Bioinformatics for the Mayo Clinic Health System. Hannah has served as the site director for the MCHS-NWWI administrative fellowship since 2020. Hannah previously held the OA role for the NWWI Neurosciences Center and OM oversight of Population Health and Primary Care. She has also worked externally at Allina Health as an Operations Manager for General and Plastic Surgery. Hannah completed her Administrative Fellowship at the Mayo Clinic Health System in 2015-2016. Hannah earned her Master of Healthcare Administration from the University of Minnesota and a Bachelor of Business Administration in Healthcare from UW-Eau Claire.

Practice Applications of AI and Informatics that Drive Transformation of Healthcare Delivery in Community Settings

Friday, May 31, 2024
12:00 PM

Bill Mannel
Senior Director
Hewlett Packard Enterprise (HPE)

Bill Mannel is Senior Director of the Chief Technology Office and the Higher Education Sales Organization for HPC & AI, Hewlett Packard Enterprise (HPE), in North America. He received his B.S. degree in Mechanical Engineering and Materials Science from Duke University and MBA from San Jose State University. Bill was a uniformed officer in the United States Air Force and after his service was a flight control systems engineer for NASA on the X-29A advanced fighter program, NASA Dryden Flight Research Facility, Edwards Air Force Base, California. He then moved to Silicon Valley to work for Silicon Graphics (SGI), where he was a technical instructor, managed the customer education function, become a graphics product line manager, and ended his career at SGI as the VP and GM of the High-Performance Compute and Storage division. Bill then moved to HPE as the VP & General Manager of the HPC & Big Data division, a position he held until 2022, when he moved into his current sales role with the HPE North American HPC & AI organization.

History and Progress of AI, from Expert Systems to Large-Language Models, and its Changing Technology Foundation
Artificial Intelligence is not new, the term having been coined in the early 1950's by a working group at Dartmouth University. It has allowed use cases such as video analytics, prescriptive medicine and predictive maintenance. However, with the recent invention of Large-Language Models (LLM's) and Generative AI, significant new use cases are possible, and AI is capable of touching all individuals in society. This discussion will focus on definitions of AI, the progress in AI over the last few decades, use cases for AI, the need for supercomputing for many new use cases with Gen AI and LLM's, and the technology being brought to bear to reduce the cost and environmental impact of running large supercomputers dedicated to task of training and inferencing of LLM's.

Friday, May 31, 2024
7:00 PM

Professor Mohammad Shahidehpour
IEEE Fellow
Galvin Center for Electricity Innovation
Illinois Institute of Technology

Dr. Mohammad Shahidehpour is a University Distinguished Professor, Bodine Chair Professor of Electrical and Computer Engineering, and Director of the Robert W. Galvin Center for Electricity Innovation at Illinois Institute of Technology (IIT). He has over 40 years of experience with power system operation, planning, and control and has completed several major projects for the electric energy sector. His project on Perfect Power Systems has converted the entire IIT Campus to an islandable microgrid. Dr. Shahidehpour was the recipient of several technical awards including of the IEEE Burke Hayes Award for his research on hydrokinetics, IEEE/PES Outstanding Power Engineering Educator Award, IEEE/PES Ramakumar Family Renewable Energy Excellence Award, IEEE/PES Douglas M. Staszesky Distribution Automation Award, and the Edison Electric Institute's Power Engineering Educator Award. He has co-authored 6 books and over 800 technical papers on electric power system operation and planning, and served as the founding Editor-in-Chief of the IEEE Transactions on Smart Grid. Dr. Shahidehpour is the recipient of the 2009 honorary doctorate from the Polytechnic University of Bucharest. He is a Fellow of IEEE, Fellow of CSEE (China), Fellow of the American Association for the Advancement of Science (AAAS), Fellow of the National Academy of Inventors (NAI), and an elected member of the US National Academy of Engineering (NAE). He is also listed as a highly cited researcher on the Web of Science (ranked in the top 1% by citations demonstrating significant influence among his peers).

Machine Learning Applications in Power System Decision Analyses
Modern power systems are large, distributed, dynamic, uncertain, and complex machines with a wide range of heterogeneous and spatially-distributed electrical components, e.g., distributed energy resources (DERs), electric vehicles (EVs), intelligent switches, and smart meters. With the fast-growing penetration of distributed devices and technologies in electric power systems, advanced communication, computation, and control infrastructures are progressively utilized by stakeholders for substantiating a more efficient, reliable, resilient, sustainable, economic, and secure management of electricity grid. However, a rigorous modeling of complex power system operations is becoming more challenging as distributed, data-oriented, closely-coupled, and highly uncertain components are blended into power systems. With steady advances in communication and computational technologies, e.g., 5G networks and edge-computing, machine learning techniques will evolve as a viable tool to embrace new opportunities and challenges for power system optimization. Machine learning, which is an extension of the artificial intelligence practice in power systems, is portrayed as a data analytic technique that can train computers to complete complicated operation tasks and arrive at credible decisions automatically via a specific learning process. This presentation offers a systematic application of state-of-the-art machine learning techniques in the optimal operation and control of distributed power systems.

Friday, May 31, 2024
8:00 PM

Dr. Tooran Emami
Professor of Electrical Engineering
U. S. Coast Guard Academy (USCGA)


Dr. Tooran Emami is a tenured and full professor of Electrical Engineering in the Department of Electrical Engineering and Computing at the U. S. Coast Guard Academy (USCGA). She was an adjunct faculty member in the Department of Electrical Engineering and Computer Science at Wichita State University for three semesters before joining the Academy. Her research interests are in control systems, particularly in Proportional Integral Derivative (PID) controller design, robust control, time delay, compensator design for continuous-time and discrete-time systems, analog or digital filter design, and hybrid fuel cell power plant design. Dr. Emami received the Center for Advance Study Summer Fellowship Award in 2012 and the 2016 Spirit of The Bear Award at the USCGA. She received numerous awards in her graduate education at Wichita State University, including the Boeing Integrated Defense System Graduate Fellowship, the Spirit Aerosystems, Inc. Graduate Fellowship, the Highest Distinction Graduate Student Award, the Ollie A. and J. O. Heskett Fellowship, the E.L. Cord Foundation Fellowship, the Dr. Michael P. Tilford Graduate Fellowship, and the Outstanding Doctoral Dissertation Award.

Utilizing Frequency Data for PID Controller Design
In industrial processes, maintaining the system performance is critical, particularly when unexpected conditions arise. A robust controller design is pivotal in achieving this goal. The Proportional Integral Derivative (PID) controller has been a popular choice in many industrial systems for more than a century due to its adaptability, computerization, and ease of use. However, designing a PID controller is a complex task that requires selecting coefficients across three degrees of freedom. Recent research has focused on using frequency data to design PID controllers. This approach involves analyzing the frequency response data of a system to design proper PID coefficients. Studies have shown that this refined design approach enhances robustness against perturbation uncertainty and aligns more closely with performance expectations. This presentation showcases the algorithmic developments across multiple platforms to highlight the effectiveness of this methodology. This approach simplifies the robustness of system dynamics and improves overall performance in various applications.

Saturday, June 1, 2024
8:30 AM

Dr. RJ Nowling
Electrical Engineering and Computer Science
Milwaukee School of Engineering

Dr. RJ Nowling is an assistant professor of Computer Science at the Milwaukee School of Engineering (MSOE). Dr. Nowling earned his Ph.D. in Computer Science & Engineering from the University of Notre Dame with research in numerical simulations for computational chemistry and applications of machine learning and statistical algorithms to genomics. In between his Ph.D. and faculty position, Dr. Nowling was a software engineer at Red Hat with a focus on open-source scalable data-processing infrastructure and data science engineer at AdRoll (now NextRoll) with a focus on building and maintaining ML production systems for digital advertising. Dr. Nowling's research focuses on the application of machine learning and data science to genomics, provided research experiences for 15 undergraduate students, and is heavily collaborative with biologists at Midwestern research universities. Dr. Nowling teaches courses on algorithms, machine learning, and data science.

Moving ML Models into Production: What You Need to Know
Machine learning is primarily taught in an offline context: students learn how to develop and evaluate models on their laptops. The end goal for many companies, however, is to deploy ML models into user-facing services; ML can be used to support features like product recommendations, text suggestions, and fraud detection.
Developing models successfully requires consideration of a couple of challenges. ML models are functions that take vectors as input. Most software systems, however, are going to have data stored as records with heterogeneous fields and potentially multi-level structures. Feature extraction pipelines need to be deployed alongside the corresponding models and care needs to be taken to ensure that the pipelines produce consistent results in both offline training and evaluation and online prediction contexts.
Secondly, real-world data undergo constant change and model performance degrades when not kept up-to-date with data. Monitoring can be used to detect drift and continuous training can be used to automate the process of training and deploying new models using the latest data.
This talk will present key concepts to understand and identify the aforementioned problems and practical solutions. After this talk, participants will be aware of the practical details of deploying and maintaining ML models in production systems.

Saturday, June 1, 2024
12:00 PM

Sami Khorbotly, PhD
Professor and Chair of Electrical & Computer Engineering
Valparaiso University

Dan Maguire, PhD
Assistant Professor Electrical & Computer Engineering
Valparaiso University

Dr. Sami Khorbotly received his Ph. D. in Electrical and Computer Engineering from The University of Akron, in Akron, OH in 2007. He served as an Assistant Professor of Electrical and Computer Engineering at Ohio Northern University from 2007 to 2013. Started his tenure at Valparaiso University as an Associate Professor of Electrical and Computer Engineering in 2013 and was promoted to the rank of Professor in 2021. Currently serving as the Chair of the department of Electrical and Computer Engineering. His areas of expertise include: Design of digital systems, Robotic swarms, and Machine Learning. He is a senior member of IEEE and a member of ASEE.
Dr. Dan Maguire received his Ph. D. in Mechanical Engineering from Purdue University in West Lafayette, IN in 1998. Worked in industry for 25 years prior to beginning teaching - fulfilling roles as varied as programming PLCs for the control of steel mills and designing and programming adaptive active noise control systems for a number of automotive, locomotive, aircraft, and mining vehicle/equipment suppliers and leading many of those teams. Started at Valparaiso University as an Assistant Professor of Electrical and Computer Engineering in 2019. Was recently named the recipient of the 2024-2025 Excellence in Teaching Award. His areas of interest include: Digital Signal Processing, Mixed analog and digital system design, Audio processing, Noise reduction and vibration control, Entrepreneurial Mindset Learning and Undergraduate student research program matching. He is a senior member of IEEE and a member of ASME, SAE, ASEE, and IIAV.

Steganography: Hiding Messages as Payload in Existing File Structures and File Data
If a person or group of interest encrypts a file, those interested in them know that there is information to be discovered. But, what if the information existed in a different, common, and easily overlooked form? We will present Steganography - the act of hiding messages in plain sight. Messages can exist as a block of text in a photograph or recorded speech in a song. Even Word documents can be used to hide messages. These file types are rarely suspected and even more difficult to parse. We will discuss how these files are made and how they might be detected.