Andrew C. Shin is an Assistant Professor at Texas Tech University within the Department of Nutritional Sciences . His work bridges metabolic health , neuroendocrinology , and neurodegenerative diseases , with a focus on how the brain regulates glucose homeostasis , BCAA metabolism , and bariatric surgery mechanisms . Ph.D. in Neuroscience, Michigan State University (2008) Postdoctoral Fellow, Pennington Biomedical Research Center and Icahn School of Medicine at Mount Sinai Dr. Shin’s research explores the neural pathways involved in appetite regulation , nutrient partitioning , and metabolic resistance . His NIH-funded projects investigate insulin signaling in POMC neurons , BCAA dynamics , and nicotine’s metabolic effects . Recent work highlights the role of the autonomic nervous system in BCAA regulation and its implications for obesity and diabetes . His 15 most recent publications reflect a focus on AI applications in nutrition , BCAA-related pathologies , Alzheimer’s disease , and metabolic surgery outcomes . Key themes include neuroendocrine control , nutritional interventions , and environmental impacts on metabolism . Scientific Awards NIH K01 Award Dr. Shin directs the Mouse Metabolic Phenotyping Facility and collaborates on synbiotic trials for cognitive aging . His work spans basic science and translational research , addressing metabolic disorders and their neurological consequences .
Rong Xu is a Professor of Biomedical Informatics at Case Western Reserve University School of Medicine, where she also serves as Director of the Center for AI in Drug Discovery. She is a member of the Cancer Genomics and Epigenomics Program at the Case Comprehensive Cancer Center. Dr. Xu's research focuses on developing innovative computational approaches including artificial intelligence, natural language processing, data mining, machine learning, and knowledge representation to advance biomedical discovery. Her work spans both computer science and biomedical science domains. Her computer science research interests include Artificial Intelligence, Natural Language Processing, Machine Learning, Deep Learning, Systems Biology, Data Mining, Graph Theory, and Ontology. Her biomedical science interests encompass Drug Discovery, Drug Repositioning, Disease Gene Discovery, Gene-Environment Interactions, Human Gut Microbiome, Drug Target Discovery, Drug Toxicity Prediction, Cancer Drug Toxicity, Drug Addiction, and Neuroscience Informatics. Dr. Xu's recent publications demonstrate a strong focus on applying AI and computational methods to drug discovery, particularly for neurological conditions, diabetes-related complications, and substance use disorders. Her work prominently examines the effects of GLP-1 receptor agonists like semaglutide on various health outcomes, including Alzheimer's disease, opioid use disorder, and cancer. Fellow of American College of Medical Informatics (FACMI) 2020 American College of Medical Informatics Research Scholar 2016 American Cancer Society New Investigator Award 2015 American Medical Informatics Association AACR INNOVATOR Award 2015 Landon Foundation Director's Innovator Award 2014 National Institutes of Health Siebel Scholar 2004 Dr. Xu directs the Center for AI in Drug Discovery and has received significant grant funding for her research, including a $1.4 million grant from NIDA for developing AI technologies to identify potential medications for cocaine use disorder. Her work bridges computational science with clinical applications, focusing on translating AI discoveries into practical healthcare solutions.
Laura Balzer, PhD, MPhil is an Associate Professor of Biostatistics at the University of California, Berkeley . Her research focuses on methodological and applied work in causal inference , machine learning , and messy real-world data , particularly in the context of HIV prevention and global health in East Africa. PhD – Biostatistics, University of California, Berkeley (2015) MPhil – Computational Biology, University of Cambridge (2009) BS – Applied Mathematics, University of Vermont (2008) Dr. Balzer specializes in the design and analysis of cluster randomized and pragmatic trials , addressing challenges like differential measurement , complex dependence , and missing data . Her work integrates epidemiologic methods with machine learning to enhance rigor in real-world studies. Recent publications emphasize community-based HIV interventions , dynamic choice models , and causal inference frameworks for global health applications in Kenya and Uganda. Her methodological contributions include Two-Stage TMLE for handling sub-sampling and non-independent units , while applied studies examine HIV-tuberculosis interactions , hypertension care models , and social network effects on health outcomes. Dr. Balzer’s role as a Primary Statistician for East African studies underscores her commitment to translating academic advances into public health impact .
Lyndia Wu is an Assistant Professor in the Department of Mechanical Engineering at the University of British Columbia's Faculty of Applied Science, where she holds the prestigious Canada Research Chair in Wearable Brain Injury Sensing. She leads the SimPL (Sensing in Biomechanical Processes Lab) and maintains an active research program focused on biomechanics and medical device development. Her educational background includes: B.A.Sc. from the University of Toronto M.S. from Stanford University Ph.D. from Stanford University Postdoctoral Fellowship from Stanford University Dr. Wu's research program centers on developing novel sensing and data analytics technologies to study human biomechanics in health and disease states. Her primary research areas encompass brain injury or concussion biomechanics using advanced sensing, modeling, and machine learning approaches, as well as the development of innovative sensors and algorithms for studying sleep disorders like obstructive sleep apnea. She specializes in wearable sensors for brain health monitoring, traumatic brain injury mechanisms, and AI applications in healthcare settings. Analysis of her recent publications reveals a strong focus on sports-related head impacts (particularly in soccer), EEG monitoring following impacts, and sleep monitoring after concussions. Her work demonstrates interdisciplinary collaboration across biomechanical engineering, neuroscience, and clinical medicine, with publications spanning biomechanics, neurotrauma, biomedical instrumentation, and signal processing domains. Dr. Wu has received significant recognition for her work, including: Scholar Award from the Michael Smith Foundation for Health Research (2019) Junior Faculty Teaching Award from UBC Mechanical Engineering (2022) She actively supervises graduate students in Mechanical Engineering programs (MASc and PhD) and collaborates extensively across disciplines. Dr. Wu is affiliated with multiple research centers including the Institute for Computing, Information and Cognitive Systems (ICICS), Origins of Balance Deficits and Falls, and SmarT Innovations for Technology Connected Health (STITCH), reflecting her interdisciplinary approach to solving complex biomedical challenges. As director of the SimPL lab, she leads a research team developing cutting-edge sensing solutions for biomechanical processes with particular emphasis on brain injury prevention, monitoring, and recovery assessment through innovative engineering approaches.
Maria Virvou serves as Professor and Chair of the Department of Informatics at the University of Piraeus, where she also directs the Graduate Program in Informatics and leads the Research Laboratory 'Software Technology'. She holds significant institutional leadership roles including membership in the University Senate and has chaired the Department of Informatics for multiple terms. As Editor-in-Chief of Springer book series 'Learning and Analytics in Intelligent Systems' and 'Artificial Intelligence-Enhanced Software and Systems Engineering', she maintains substantial academic influence across international scholarly platforms. Dr. Virvou earned her PhD in Computer Science and Artificial Intelligence from the University of Sussex with a scholarship from the State Scholarships Foundation, a Master of Science in Computer Science from University College London, and her undergraduate degree from the Department of Mathematics at the National and Kapodistrian University of Athens. Her educational background in both mathematics and computer science has provided a strong foundation for her interdisciplinary research approach. Professor Virvou's research spans Software Technology, Artificial Intelligence, Educational Software and Games, User Modeling, and Human-Computer Interaction. She has pioneered work in personalized interactive software systems, applying fuzzy logic and machine learning techniques to create adaptive educational environments. Her recent work demonstrates a strategic expansion into AI applications for healthcare, with significant contributions to medical diagnostics using large language models and multimodal AI systems. She has also made notable advances in smart tourism applications through personalization techniques. With over 400 publications to her name, Professor Virvou's scholarly output shows a clear progression from foundational work in user modeling toward increasingly sophisticated AI applications across multiple domains. Her publication trends reveal a strategic focus on explainable AI, multimodal systems, and practical implementations that bridge theoretical advances with real-world applications, particularly in healthcare and education sectors. Ranked #1 worldwide in 'User Modelling' publications (147,450 total publications) according to Scopus Ranked #1 worldwide in 'Educational Software' publications according to both Scopus and Microsoft Academic Search Recognized among the top 2% of most influential Artificial Intelligence scientists worldwide by Stanford University General Co-Chair at the 14th IISA Conference 2023 Invited Keynote Speaker at the 35th IEEE International Conference on Software Engineering Education and Training (CSEE&T 2023) As Director of the Research Laboratory 'Software Technology', Professor Virvou has built a robust research team focused on AI applications across multiple domains. She co-founded and co-chairs the IEEE Intelligent Information Systems and Applications international conference series, creating a significant platform for scholarly exchange. Her leadership extends to editorial roles with major academic publishers and active participation in international research collaborations that have secured substantial funding for innovative projects in AI and software engineering.
Patrick Lin is a Professor in the Philosophy Department at California Polytechnic State University (Cal Poly), where he serves as Director of the Ethics + Emerging Sciences Group, a non-partisan organization established at Cal Poly in 2007 to focus on the risk, ethical, and social impact of emerging sciences and technologies. He is frequently quoted in national publications on topics including ethics of autonomous vehicles, artificial intelligence, robotics, outer space, Arctic frontiers, military and policing applications, virtual and augmented reality, and smart cities. Lin received his Ph.D. and M.A. from the University of California, Santa Barbara, and his B.A. from the University of California, Berkeley. His academic appointments include Affiliate Scholar at Stanford Law School's Center for Internet and Society, Fulbright Specialist at the University of Iceland's Centre for Arctic Policy Studies (2018), and Visiting Senior Research Fellow at the Centre for Applied Philosophy and Public Ethics in Australia (2010-2016). Lin's research spans technology ethics broadly, with specific expertise in AI ethics, robotics ethics, autonomous vehicle ethics, space ethics, cybersecurity ethics, and military ethics. His work bridges philosophical theory with practical application, examining how emerging technologies challenge traditional ethical frameworks. His research demonstrates consistent themes across different technological domains: examining risk assessment methodologies, developing ethical frameworks for emerging technologies, analyzing social and political implications of technological adoption, and providing practical guidance for developers, policymakers, and users. His publication record shows a progression from early work on nanotechnology ethics to current focus areas including space cybersecurity, AI kitchens, and ethical frameworks for autonomous systems. The articles reflect his interdisciplinary approach, combining insights from philosophy, law, engineering, and policy studies to address complex ethical challenges in emerging technologies. Cal Poly/Academic Senate, Distinguished Scholarship Award (2017) American Philosophical Association's Public Philosophy Op-Ed Award (2015) Cal Poly/College of Liberal Arts, Outstanding Scholarship Award (2009) Lin has secured significant grant funding from organizations including the National Science Foundation, US Department of Defense, and Canadian Institute for Advanced Research for research on military AI risk assessment, AI kitchens and robot cooks, outer space cybersecurity, and autonomous vehicles. He has advised numerous students through his teaching and research activities at Cal Poly, where he teaches courses including Philosophy of Technology, Ethics of Science and Technology, and Introduction to Philosophy. Lin directs the Ethics + Emerging Sciences Group at Cal Poly, which serves as a hub for interdisciplinary research on technology ethics. He also participates in several other research initiatives including his role as Research Director for the Consortium for Emerging Technologies, Military Operations, and National Security (CETMONS) and as a member of the Emerging Technologies of National Security and Intelligence initiative at the University of Notre Dame.
Edward Delp is the Charles William Harrison Distinguished Professor of Electrical and Computer Engineering at Purdue University's College of Engineering. He holds affiliations with both the Department of Electrical and Computer Engineering and the Department of Biomedical Engineering. His research spans computer vision, medical imaging, and data forensics with a focus on synthetic media detection, deep learning applications, and healthcare technologies. Education: Not explicitly listed in the provided text. His work includes developing algorithms for speech forensics, microscopy image analysis, and food/nutrition assessment systems. He leads projects on synthetic speech detection, medical image segmentation, and automated crop disease measurement using RGB imaging. Delp collaborates across disciplines, integrating machine learning with healthcare and agricultural challenges. Recent work emphasizes ethical AI through fairness in synthetic media detection and explainable artifacts in biomedical imaging. He contributes to large-scale datasets like MetaFood3D and 3D nuclear segmentation frameworks for microscopy analysis. His grants and advising focus on interdisciplinary applications, though specific grant details are not provided. Delp is affiliated with the Purdue School of Biomedical Engineering and maintains active collaborations in medical imaging, computer vision, and aerospace anomaly detection.
Junhong Chen is the Crown Family Professor of Molecular Engineering at the University of Chicago's Pritzker School of Molecular Engineering and Lead Water Strategist at Argonne National Laboratory. His research focuses on hybrid nanomaterials, 2D materials, sensors for chemical/biological molecules, and energy devices. He has pioneered innovations in real-time water sensing and energy storage, with applications in environmental sustainability and healthcare. Chen holds a PhD from the University of Minnesota (2002) and a postdoc from Caltech (2003). He previously directed the NSF Industry-University Cooperative Research Center on Water Equipment & Policy and served as a NSF program director. Education: PhD in Mechanical Engineering (2002, University of Minnesota), Postdoc in Chemical Engineering (2002–2003, Caltech) Research Interests: Nanomaterials, Sensors, Energy Storage, Water Pollution Control Awards: Fellow of National Academy of Inventors, ASME, IAAM Medal, Wisconsin Innovation Award (2016) Chen's lab group develops nanosensors and energy devices using molecular engineering, with a focus on scalable manufacturing and AI integration. Recent work includes graphene-based sensors for real-time water monitoring and novel battery technologies. His research also addresses global challenges like PFAS contamination and sustainable manufacturing.
Professor Alexander Slocum holds the Walter M. May (1939) and A. Hazel May Chair in Emerging Technologies at MIT's Department of Mechanical Engineering within the School of Engineering. A distinguished educator and researcher, Slocum has made significant contributions across precision machine design, medical device innovation, and renewable energy systems. His research interests span precision machine design for medical devices and energy industry applications, with particular focus on offshore renewable energy storage systems and kinematic couplings. Slocum's work bridges theoretical mechanical engineering principles with practical applications that address real-world challenges in healthcare and sustainable energy. His recent publications demonstrate a strong emphasis on bio-inspired engineering, underwater energy storage systems, and medical device innovation. The articles reveal a consistent pattern of applying fundamental mechanical engineering principles to solve problems in healthcare delivery and renewable energy storage, often with a focus on practical implementation in resource-constrained environments. NSF Presidential Young Investigator (1987) MacVicar Faculty Fellow (1999) Massachusetts Professor of the Year Award (2000) Multiple R&D 100 Awards (1994-2010) ASME Leonardo da Vinci Award (2004) ASME Machine Design Award (2008) ASME Ruth and Joel Spira Outstanding Design Educator Award (2018) National Academy of Inventors Fellow (2021) Slocum actively mentors students through MIT's Experimental Study Group (which he directs) and his renowned 2.75/2.750 Precision Machine Design courses. His educational approach emphasizes hands-on learning and real-world problem solving, particularly through medical device design projects developed in collaboration with Boston-area clinicians. His research has been supported by significant grants from the NSF, Department of Energy, and military research agencies. His PERG (Precision Engineering Research Group) lab fosters interdisciplinary collaboration, bringing together mechanical engineers, materials scientists, and medical professionals to develop innovative solutions for healthcare and energy challenges. The lab is particularly known for its work on kinematic couplings, hydrostatic bearings, and bio-inspired engineering solutions.
Jordon Gilmore, Ph.D., is an Associate Professor in the Department of Bioengineering at Clemson University's College of Engineering, Computing and Applied Sciences (CECAS). He leads the S.M.A.R.T. Lab (Intelligent Biomaterials, Biomedical Textiles, Bioinstrumentation) focusing on cutting-edge research in smart wound care, textile-based sensors, and bioprocess data engineering. Ph.D. in Bioengineering from Clemson University (2015) Expertise spanning biosensors, biomedical textiles, and AI applications Develops real-time biomarker sensing systems and infection management strategies His research interests include: Biosensor development for clinical diagnostics Biomedical textile engineering Machine learning in bioprocess modeling Physiological sensing for psychotherapy applications Contact: jagilmo@clemson.edu | Office: 401-3 Rhodes Engineering Research Center | Phone: 864-656-4262
Dr. Md S Hossain is a Professor of Civil Engineering at The University of Texas at Arlington, directing the Solid Waste Institute for Sustainability (SWIS). He holds a B.S. from IIT Bombay, an M.E. from AIT Bangkok, and a Ph.D. from North Carolina State University. His research focuses on sustainable waste management, bioreactor landfills, geotechnical engineering, and slope stabilization, with notable contributions to landfill gas-to-energy and recycled materials utilization. He has over 6 years of professional experience in geotechnical and geoenvironmental projects globally. Research Interests: Sustainable waste management, landfill engineering, slope stabilization, recycled materials, geotechnical site investigations. Key Roles: Director of SWIS, Principal Investigator on numerous grants, and advisor to over 50 graduate students. Dr. Hossain has secured millions in grants from agencies like NSF, EPA, and TxDOT, focusing on waste-to-energy, landfill mining, and slope stabilization using recycled plastics. His awards include the ISWA Leadership Award and Distinguished AIT Alumni recognition. He actively collaborates with international organizations like CCAC and ISWA, advocating for sustainable waste solutions in developing countries. His work spans 50+ projects in Bangladesh, Singapore, Hong Kong, and the U.S., emphasizing practical applications like recycled plastic pins for slope stabilization and ET cover systems for landfills. SWIS, under his leadership, promotes global sustainable waste management through education, research, and policy advocacy.
Stephen E. Still is a Professor of Practice in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo (UB), affiliated with the Institute for Sustainable Transportation and Logistics. His roles include teaching applied transportation planning and technology courses, advising students, and collaborating across disciplines between the School of Engineering and Applied Sciences and the School of Management. Prior to academia, he served as founder and managing director of Seabury Airline Planning Group and Diio, LLC, specializing in aviation consulting and IT. With over 30 years of industry experience, he held leadership roles at US Airways and United Airlines, focusing on strategic route planning, fleet management, and alliance development. Dr. Still holds a PhD in Civil Engineering and Operations Research from Princeton University, with a focus on transportation systems and economics, and a BS in Engineering (magna cum laude) from UB with a concentration in transportation planning. He has also completed advanced coursework in demand modeling at MIT. His research interests emphasize sustainable transportation systems and logistics, integrating engineering principles with operational efficiency. While his academic contributions primarily reside in transportation engineering, his interdisciplinary work incorporates wearable technology and sensor-based solutions for health monitoring, as evidenced by his extensive publication record in smoking cessation and behavioral health research. Scientific awards and grants are not explicitly mentioned in the provided information. Dr. Still’s advising and teaching focus on fostering student engagement in transportation innovation and real-world problem-solving. His professional experience bridges academia and industry, reflecting a commitment to practical applications of engineering and logistics principles.
Nazli Goharian is a Clinical Professor of Computer Science at Georgetown University and Associate Director of the Information Retrieval Lab. She holds a PhD from Florida Institute of Technology and joined Georgetown in 2010 after industry experience and previous academic positions at Illinois Institute of Technology. Education: PhD Computer Science, Florida Institute of Technology (2001) MSc Computer Science, George Mason University (1995) BSc Computer Science, Dortmund University (1992) Her research spans information retrieval, text mining, and natural language processing with applications in health/medical domains. She focuses on developing computational methods for medical search, mental health analysis from social media, clinical text summarization, and adverse drug reaction detection. Her recent publications (2020-2016) predominantly focus on neural ranking models, transformer architectures for document retrieval, and clinical NLP applications. Notable trends include work on BERT-based re-ranking, zero-shot multilingual retrieval, and ontology-aware medical summarization. Awards & Honors: EMNLP 2017 Best Long Paper Award COLING 2018 Honorable Mention & Area Chair Favorite Julia Beveridge Award for Faculty (IIT, 2009) Multiple Teaching Excellence Awards (2002-2007) Research Leadership: She has supervised 6 PhD students to completion with placements at leading institutions. Secured over $500,000 in research funding from NSF, Adobe, and international partners. Founded the Semi-Annual Graduate Research Presentation Days at Georgetown and served as Program Chair for ECIR 2024. She leads the Information Retrieval Lab which focuses on developing novel algorithms for efficient document retrieval, cross-lingual search, and specialized applications in healthcare text analysis.
Dr. Eric Meyers is an Assistant Professor in the Department of Bioengineering at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He holds a Ph.D. in Biomedical Engineering and dual Bachelor's/Master's degrees in Electrical Engineering from the same institution. His research focuses on closed-loop neurotechnology, neuromodulation, and bioelectronic medicine to enhance recovery from nervous system injuries. Key projects include developing wearable EMG sleeves for stroke rehabilitation and closed-loop neuromodulation systems to restore motor function. Education: B.S. (2012), M.S. (2018), Electrical Engineering; Ph.D. (2017), Biomedical Engineering – all from UTD His research interests span machine learning applications in neurorehabilitation, biomarker discovery for neurological conditions, and clinical translation of bioelectronic therapies. Recent work emphasizes wearable devices for real-time motor function assessment and neuromodulation-driven recovery strategies. Publications highlight advancements in EMG-based neural interfaces, closed-loop algorithms for stroke therapy, and innovative FES systems. His lab actively collaborates on projects funded by NIH and industry partnerships, with a focus on translating technologies to clinical settings.
Erik Scheme is an Associate Professor in the Department of Electrical and Computer Engineering at the University of New Brunswick (UNB), and serves as Associate Director of the Institute of Biomedical Engineering (IBME). He holds a PhD and is a Professional Engineer (PEng). His roles include advising the Dr. J. Herbert Smith Centre for Technology Management and Entrepreneurship, emphasizing innovation in biomedical technologies and healthcare systems. His research focuses on advanced human-machine interaction through biomedical engineering, with a strong emphasis on myoelectric prosthetics, wearable sensors, and machine learning applications. Key areas include improving neuroprosthetic control via incremental learning, gait analysis using underfoot pressure sensors, and developing robust EMG-based gesture recognition systems. His work bridges clinical needs with technological innovation, addressing challenges in rehabilitation, activity monitoring, and user-centric design. Recent publications highlight advancements in adaptive control systems, sensor fusion, and ethical data practices in healthcare. His contributions span both theoretical frameworks (e.g., self-supervised learning models) and applied technologies (e.g., gold-plated 3D-printed electrodes). Dr. Scheme collaborates across disciplines, integrating robotics, signal processing, and clinical validation to create impactful solutions. His lab, affiliated with IBME, actively explores emerging areas like exhaled breath analysis for disease detection and federated learning in healthcare data analytics.
