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Brett J. BorghettiView profile

Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +12 more

Dr. Brett J. Borghetti is a Professor of Computer Science in the Department of Electrical and Computer Engineering at the Air Force Institute of Technology (AFIT), Graduate School of Engineering and Management, Wright-Patterson AFB, OH. He was promoted to Professor in July 2022, following prior appointments as Associate Professor (2017) and Assistant Professor (2008/2013). His expertise lies in artificial intelligence, machine learning, deep learning, cybersecurity, and human-machine teaming. Education: Ph.D. in Computer Science, University of Minnesota, Twin Cities (2008) M.S. in Computer Systems, Air Force Institute of Technology (1996) B.S. in Electrical Engineering, Worcester Polytechnic Institute (1992) Dr. Borghetti's research focuses on applying machine learning to physical science sensors (hyperspectral, seismic, RF), cybersecurity, and enhancing human-machine team performance. He teaches graduate courses in machine learning, AI, data security, and algorithm design, and advises numerous MS and PhD students in areas such as sensor exploitation, cognitive workload, and cyber situational awareness. His recent publications demonstrate strong trends in deep learning for multimodal sensor fusion, nuclear security, and neuroergonomics. Scientific Awards: AETC Educator of the Year (2021, Civilian) AFIT Ezra Kotcher Teaching Award (2021) AFIT Teaching Excellence Award (2019) AF STEM Outstanding Science and Educator Award (2015) Multiple Eta Kappa Nu Outstanding Instructor Awards Air Force Meritorious Service Medal and other military honors Dr. Borghetti has advised numerous graduate students and led research projects with significant funding and applications in defense and national security. He has directed research in AI-driven sensor analysis, cyber defense systems, and adaptive automation. His work often involves collaboration with national labs and DoD agencies. He has contributed to major research initiatives in human factors, cyber intruder detection, and machine learning for operational environments. Labs and Research Teams: His work is associated with AFIT's research in cyber security, sensor exploitation, and human-machine systems. He collaborates with teams working on the Cyber Intruder Alert Testbed (CIAT), neuroergonomic modeling, and machine learning for defense applications.

Aidong ZhangView profile

Professor

• Machine Learning
• Data Mining
• Bioinformatics
• +3 more

Aidong Zhang is the Thomas M. Linville Professor of Computer Science at the University of Virginia, with joint appointments in Biomedical Engineering and the School of Data Science. Her research focuses on machine learning, interpretable AI, federated learning, and generative AI applications in healthcare and bioinformatics. She holds a Ph.D. in Computer Science from Purdue University. Dr. Zhang has been honored with prestigious awards including the ACM Fellow (2017), IEEE Fellow (2009), and the 2025 Distinguished Researcher Award from UVA. Her work bridges computational methods with biomedical challenges, emphasizing fairness, robustness, and explainability in AI systems. Key research areas include federated learning frameworks, concept-based models, and large language models for scientific hypothesis generation. Dr. Zhang leads a lab offering PhD positions in machine learning, bioinformatics, and health informatics. Notable grants include NSF projects on explainable AI platforms and hardware-software co-design for extreme-scale machine learning. Education: Ph.D., Computer Science, Purdue University Affiliations: School of Engineering and Applied Science, School of Data Science Grants: NSF-funded projects on federated learning, multimodal analysis, and biomedical AI Labs/Teams: Zhang's Research Group focusing on interpretable machine learning and healthcare applications

Brenton FaberView profile

Professor

• Healthcare Delivery Systems
• Medical Communication
• Pre-Hospital Care
• +3 more

Brenton Faber is a Humanities & Arts Professor affiliated with the Department of Biomedical Engineering at Worcester Polytechnic Institute (WPI) . He holds a B.A. in Political Science & English from the University of Waterloo (1992), an M.A. in English from Simon Fraser University (1993), and a Ph.D. in English from the University of Utah (1998). His research focuses on healthcare delivery for uninsured populations, medical writing, and the application of allostasis theory to healthcare systems. He volunteers as a practicing paramedic with rural ambulance squads and urban clinics. Education: B.A. (University of Waterloo), M.A. (Simon Fraser), Ph.D. (University of Utah) Faber’s lab investigates human factors in medical diagnosis and patient care, emphasizing pre-hospital care and clinical report analysis. His work integrates allostasis concepts to address chronic health conditions and patient decision-making. Current projects include a free urban clinic’s electronic database initiative and a 'food is medicine' program targeting hypertension/diabetes patients. He advises an undergraduate research team and co-founded the 'Medical Mystery' series in WPI’s student newspaper. His recent publications span healthcare logistics, emergency medicine, and discourse analysis. Notable works include The End of Genre (2022) and studies on community paramedicine optimization (2023). While no formal awards are listed, his contributions to medical communication and patient advocacy are central to his scholarly identity. Advising focuses on interdisciplinary teams addressing public health challenges. Collaborative grants are not explicitly mentioned, but his work with free clinics and paramedic squads highlights community-oriented engagement. His lab’s activities emphasize translational research bridging humanities and biomedical engineering disciplines.

• Health Psychology
• Stress and Psychoneuroendocrinology
• Fatigue and Decision Fatigue
• +7 more

Daniel Powell is a Senior Lecturer in Health Psychology and Programme Director of the MSc Health Psychology at the University of Aberdeen, School of Medicine, Medical Sciences and Nutrition. He is a core member of the Aberdeen Health Psychology Group and the interdisciplinary Centre for Labour Market Research. He holds a PhD from the University of Southampton and became a Fellow of the Higher Education Academy in 2019. His educational background includes: BSc (Hons) Psychology – University of the West of England, 2007 MSc Health Psychology – University of Southampton, 2009 PhD Psychology – University of Southampton, 2014 Daniel's research focuses on health psychology, particularly using intensive longitudinal methods such as ecological momentary assessment (EMA) to study stress, fatigue, self-regulation, and decision-making in real-world contexts. His work spans chronic illness (e.g., multiple sclerosis, diabetes), healthcare professionals (e.g., doctors, nurses), and occupational settings (e.g., fly-in fly-out workers). He leads the Stress and Health Research Theme and convenes regular workshops to support health psychology researchers. His methodological expertise includes real-time data collection, psychophysiology (e.g., heart rate variability, cortisol), and interdisciplinary collaboration with health economics, primary care, and bioengineering. His recent publications (2024–2025) reveal a strong trend in investigating decision fatigue in healthcare, stress and recovery patterns in medical professionals using biometric monitoring, and the psychosocial impact of shift and rotation work. He frequently employs EMA and systematic reviews to explore behavioral patterns in context. His work also extends to sustainable clinical research and digital health tools for pandemic response. His scientific recognition includes: Stan Maes Early Career Award, European Health Psychology Society (2019) Rosemary Anne Price Student Award, MS Society (2013) Daniel actively supervises five PhD students on topics including decision fatigue in healthcare, quality of life after limb loss, stress in medical and dental students, and low-carbon clinical trials. He teaches across postgraduate programs, coordinates the PU5053 course on Stress, Personality & Health, and co-founded an annual Summer School in Intensive Longitudinal Methods. He has no indication of part-time status and is actively engaged in research, teaching, and leadership. He is affiliated with several professional organizations, including the British Psychological Society (Chartered Psychologist), Division of Health Psychology, European Health Psychology Society, and UK Society for Behavioural Medicine. His research lab is embedded within the Aberdeen Health Psychology Group, which fosters interdisciplinary collaboration and methodological innovation in health behavior research.

Robert J.K. JacobView profile

Professor

• Human-Computer Interaction (HCI)
• Brain-Computer Interfaces (BCI)
• User Interface Design
• +5 more

Robert J.K. Jacob is a Professor of Computer Science at Tufts University, affiliated with the School of Engineering's Department of Computer Science. His research focuses on Human-Computer Interaction (HCI), particularly implicit brain-computer interfaces (BCI) using fNIRS and EEG technologies. He has held visiting positions at University College London, Université Paris-Sud, and MIT Media Lab. Education: Ph.D. in Computer Science from Johns Hopkins University. Research Interests : Jacob's work explores novel interaction techniques, adaptive interfaces, and BCI applications. Current projects emphasize real-time fNIRS-based systems for effortless user input, cognitive workload assessment, and neuroadaptive technologies. His lab investigates how brain signals can enhance user interfaces in domains like music learning, gaming, and urban design. Recent Trends in Articles : Recent publications highlight advancements in BCI design, neuroadaptive systems, and interdisciplinary applications of fNIRS. Work spans theoretical frameworks (e.g., NeuroCHI ethics) to practical tools like the Tufts fNIRS dataset. Key themes include improving BCI calibration, exploring AI's role in urban environments, and integrating affective computing into artistic interfaces. Awards : ACM Fellow (2016) ACM CHI Academy Membership (2007) Best Paper Award at CHI 2016 Advising & Grants : Supervised 15+ Ph.D. alumni in HCI and BCI. Served as Vice-President of ACM SIGCHI and co-chair of UIST/CHI conferences. Active in editorial roles for Human-Computer Interaction and ACM Transactions on Computer-Human Interaction . Labs & Teams : Directs the Tufts HCI Lab in the Joyce Cummings Center. Collaborates with interdisciplinary teams on projects like the Marble Track Audio Manipulator and Reality-Based Interaction Framework.

Abdol-Hossein EsfahanianView profile

Professor

• Graph Theory
• Computer Networks
• Algorithm Design
• +3 more

Abdol-Hossein Esfahanian is a Professor and Chairperson of the Computer Science and Engineering (CSE) Department at Michigan State University (MSU), part of the College of Engineering. He joined MSU in 1983 and has held leadership roles, including Graduate Director for 10 years and Associate Chair. His research focuses on applying graph theory to computer networks, algorithm design, and fault-tolerant computing. He has published extensively in journals like IEEE Transactions on Computers and Discrete Applied Mathematics, and serves as an editor for professional journals. Education: Ph.D. in Electrical Engineering and Computer Science from Northwestern University (1983), M.S. in Computer, Information, and Control Engineering from the University of Michigan (1977), and B.S. in Electrical Engineering from the University of Michigan (1975). Research interests include graph theory applications in network design, distributed systems, and fairness-aware algorithms. Notable awards include the Withrow Teaching Excellence Award (2005, 2015) and recognition as an IEEE Senior Lifetime Member. Teaching includes courses like CSE 835 (Algorithmic Graph Theory). He has contributed to curriculum development, emphasizing computational competencies for engineering students. His work integrates theoretical foundations with practical applications in networking and distributed systems.

Karin JacobsView profile

Professor

• Soft Matter Physics
• Condensed Matter Physics
• Surface Physics
• +8 more

Karin Jacobs is a Professor in the Department of Physics at Saarland University, where she leads the research group for soft matter physics within the Faculty of Natural Sciences and Technology. Her work bridges experimental physics and applied materials science, focusing on interfacial phenomena, thin films, and functional materials. Research Interests: Her group investigates the stability of coatings, properties of simple and complex fluids, and the adhesion of biomolecules on surfaces. Using advanced experimental techniques such as atomic force microscopy (AFM), ellipsometry, surface plasmon resonance spectroscopy, optical microscopy, and ultra-high vacuum (UHV) methods like photoelectron spectroscopy, her team probes nanoscale and microscale interactions at solid-liquid and solid-gas interfaces. The research spans fundamental and applied domains, including the synthesis and characterization of graphene and boronitrene, production of water-in-water vesicles using hydrophobins, and bacterial adhesion studies. These investigations are often linked to industrial applications in the paint, semiconductor, and biomedical sectors. Publication Trends: Over the past 15 years, her publications reflect a consistent focus on surface physics and soft matter. Key themes include graphene synthesis via liquid precursor deposition (including unconventional sources like fingerprints), interfacial rheology, biopolymer adsorption, and quantitative imaging analysis. The interdisciplinary nature of her work is evident in the combination of physics, chemistry, and biological interfaces. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: As head of an active research group, Prof. Jacobs supervises graduate students and postdoctoral researchers, though specific names are not listed. Her collaborations with theoretical groups and external institutions (e.g., University of Augsburg) suggest participation in joint grants and funded projects, particularly in nanomaterials and surface science. The applied orientation of her research indicates engagement with industry partners in coatings and semiconductor technologies. Labs and Teams: The Jacobs Group operates a well-equipped experimental laboratory at Campus E2 9, Saarland University, specializing in surface analysis and soft matter characterization. The team includes researchers working on biofilms, microfluidics, and functional materials, supported by technical and administrative staff.

Alexander M. PetersenView profile

Associate Professor

• Science of Science
• Complex Systems
• Computational Social Science
• +7 more

Alexander M. Petersen is an Associate Professor and Graduate Chair in the Management of Complex Systems (MCS) department and the Ernest and Julio Gallo Management of Innovation Sustainability and Technology (MIST) graduate group at the University of California Merced (UCM). He is affiliated with the School of Engineering and serves as a key faculty member driving research and academic programs in complex systems and innovation management. His educational background includes a Ph.D. (2011) and M.A. (2008) from Boston University, and a B.S. (2003) in physics and mathematics from the University of Utah. Prior to joining UC Merced, he was a tenure-track faculty member at the IMT Institute for Advanced Studies Lucca in Italy. Petersen's research focuses on the evolution of large multiscale socio-economic systems by applying concepts and methods from complex systems, statistical physics, management, and innovation science. His work spans Science of Science, Computational Social Science, Complex Systems, Quantitative Finance, and Sports Analytics. He has made significant contributions to understanding research collaboration networks, innovation dynamics, convergence science, and the impact of socio-economic shocks on various systems. His publication record demonstrates a clear trend toward increasingly complex interdisciplinary work, particularly in convergence science and network analysis. His most recent publications (2024-2025) focus on global science structure, disruption metrics, national park systems, and the impact of crises on economic behavior. These works often integrate multiple disciplinary perspectives and leverage large-scale data analysis. NSF award #1738163 for research on convergence science Petersen has advised numerous graduate students, including current PhD student Andrea del Pilar Montaño Ramirez and former students Felber J. Arroyave Bermudez (PhD in Environmental Systems) and Dr. Dong Yang (MCS Postdoctoral Scholar). His research has been supported by competitive grants and has resulted in publications in high-impact journals including Nature Communications, Science Advances, and Research Policy. He is actively involved in developing new academic programs, including upcoming B.A. majors in Management of Innovation, Sustainability & Technology (2025) and Data Science & Analytics (Fall 2024). His lab focuses on complex systems analysis, with current projects examining university digital media networks, disruption metrics in science, and the integration of regional innovation systems. The research group maintains an active presence in the science of science community, regularly presenting at conferences like the Science of Team Science conference (INSciTS).

• Photonics
• Laser Technology
• Machine Learning
• +5 more

Professor Robert Eason is a leading academic at the University of Southampton, specializing in photonics and laser technology. His research spans interdisciplinary areas combining Machine Learning , Medical Diagnostics , and Microfluidics . Research Interests : Eason focuses on AI-driven laser applications, including deep learning for phototherapy , autonomous laser machining , and low-cost paper-based diagnostic devices . His work bridges photonics with biomedicine and advanced manufacturing. Recent Publications : His 2025 article in Scientific Reports explores AI simulations for psoriasis treatment, while 2024-2022 works address laser-controlled microfluidics, deep learning in microscopy, and reinforcement learning for laser machining. Supervision : He supervises PhD student Georgia Mourkioti in laser-based research projects. External Roles : Eason has served as a speaker at international conferences including the International Symposium on Laser Precision Microfabrication (2018), LAISER (2019), and Deep Learning for Control of Light-Matter Interactions (2022).

• Physiology
• Sports Science
• Molecular Biology
• +3 more

Tommy Lundberg is a Senior Lecturer and Docent in Physiology at Karolinska Institutet. He works at the Department of Laboratory Medicine, Division of Clinical Physiology. His email address is tommy.lundberg@ki.se, and his postal address is H5 Laboratoriomedicin, H5 Klinisk Fysiologi Gustafsson, 141 52 Huddinge. Lundberg is affiliated with the university library and has held positions since 2022. Research Interests: Lundberg's research focuses on skeletal muscle mass and function adaptation, particularly in athletic performance, disease contexts, aging, and transgender individuals undergoing hormone therapy. He investigates molecular, metabolic, morphological, and functional responses to resistance and aerobic exercises. His work also explores biological maturity selection biases in youth sports, bio-banding applications in soccer and ice hockey, and the impact of anti-inflammatory drugs on muscle hypertrophy. Article Trends: Lundberg's recent publications (2025–2024) cover topics like sex differences in disc golf, muscle atrophy in space exposome, longitudinal hormone therapy effects in transgender individuals, and bio-banding in youth sports. Earlier works delve into molecular pathways in muscle hypertrophy, concurrent training effects, mitochondrial function, and imaging techniques (CT/MRI) for muscle assessment. Scientific Recognition: Most prominent young researcher in Sport Science, Swedish Central Association for Sport Promotion (SCIF), 2017 Teaching and Editorial Roles: Lundberg teaches human physiology and sports science in nursing, physiotherapy, and biomedical analytics programs. He leads a contract education course in advanced exercise physiology and contributes to a PhD course on scientific writing. He is an Associate Editor for Frontiers in Physiology - Exercise Physiology (2022) and a member of the editorial board for Translational Exercise Biomedicine (2024). Collaborations and Expertise: He collaborates with the Swedish Football Association and Swedish Ice Hockey Association on bio-banding studies. Lundberg served as an invited speaker at the ACSM Annual Meeting (2024) on transgender athletes and as an expert panelist for World Rugby's transgender workshop (2020). His supervision includes Andrea Tryfonos (2021) and thesis evaluations at Mid Sweden University and Linköping University.

• Embedded Systems
• Artificial Neural Networks
• Signal Processing
• +3 more

Magdalena Szymczyk is a Lecturer in the Department of Biocybernetics and Biomedical Engineering at AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering. Her work bridges embedded systems, biomedical signal processing, and geophysical data analysis. Research focuses on energy-efficient sensor networks, neural networks for GPR data classification, and mathematical transforms in signal analysis Expertise in parallel computing, real-time systems, and biomedical engineering applications Her publications (2015–2025) demonstrate a trajectory from parallel neural networks and S-transform/GPR methodologies to recent work on MicroPython in embedded systems. Key themes include energy optimization in distributed architectures and AI-driven signal processing across biomedical and geophysical domains. She has authored works on deterministic chaos in simulations, GPU image processing, and cybersecurity in microcontroller systems. Her current research emphasizes embedded systems security, medical signal diagnostics, and computational methods for geological analysis. She utilizes tools like OpenCL for GPU acceleration and MATLAB for parallel computing implementations.

• Machine learning
• Artificial intelligence
• Big data analytics
• +8 more

Gianluca Setti is a Full Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin, where he has been serving since 2017. He previously held positions at the University of Ferrara from 1997 to 2017. His institutional roles include being the Contact Person for the Research Quality Evaluation process, Member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, and Member of the University Quality Assurance Committee. He serves as Editor-in-Chief of the Proceedings of the IEEE, the first non-US editor to hold this position. Dr. Setti's research spans multiple interdisciplinary fields including machine learning, artificial intelligence, big data analytics, Internet of Things, biomedical signal processing, power electronics, and electromagnetic compatibility. His work bridges theoretical foundations with practical applications, particularly focusing on compressed sensing, neural networks, and circuit design for specialized applications. His research has significant implications for healthcare, sustainable infrastructure, and next-generation electronics. His publication record reveals a consistent trajectory from foundational work in chaotic systems and neural networks to contemporary applications in AI, IoT, and edge computing. The most recent publications demonstrate his focus on anomaly detection at the edge, neural oracles for biosignal processing, and power electronics innovations. His work shows strong integration between theoretical signal processing and practical circuit implementation. 1998 Caianiello prize (best Italian Ph.D. thesis on Neural Networks) IEEE Fellow (2006) IEEE Circuits and Systems Society Distinguished Lecturer (2004, 2015) 2004 IEEE CAS Society Darlington Award 2013 IEEE CAS Society Meritorious Service Award 2013 IEEE CAS Society Guillemin-Cauer Award 2019 IEEE Transactions on Biomedical Circuits and Systems best paper award Multiple best paper awards at major conferences including ECCTD2005, EMCZurich2005, ISCAS2011, PRIME2019, and EMCCOMPO2019 Dr. Setti has supervised numerous PhD students across various research domains including electromagnetic compatibility, signal and power integrity, communication networks, mechatronics and robotics. His research is supported by significant funding including national PRIN projects, EU-funded JTI-ECSEL initiatives, and commercial contracts. He leads the VLSILAB Group at DET, focusing on circuit architectures, embedded systems, and AI applications. His current projects include DECORI (anomaly detection), StorAIge (embedded storage for AI), PROGRESSUS (energy infrastructure), CONNECT (smart grid), and CONVERGENCE (wearable healthcare applications).

Sharona HoffmanView profile

Professor

• Health Law
• Employment Discrimination
• Health Information Technology
• +4 more

Sharona Hoffman is the Edgar A. Hahn Professor of Law and Professor of Bioethics at Case Western Reserve University , where she also serves as Co-Director of the Law-Medicine Center . Since joining the faculty in 1999, she has become a leading voice in health law, employment discrimination, and the legal and ethical implications of health information technology and artificial intelligence. Education: Doctor of Juridical Science (SJD) in Health Law – Case Western Reserve University Master of Laws (LLM) in Health Law – University of Houston Juris Doctor (JD) – Harvard Law School Bachelor of Arts – Wellesley College Research Focus: Hoffman's scholarship spans health law , employment discrimination , health information technology , AI and the law , public health emergencies , and elder law . She has authored over 70 scholarly articles and two books: Electronic Health Records and Medical Big Data: Law and Policy (Cambridge University Press, 2016) and Aging with a Plan: How a Little Thought Today Can Vastly Improve Your Tomorrow (2nd ed., 2022). Awards & Honors: 2021 CWRU Faculty Distinguished Research Award Elected to the American Law Institute (2017) 9th most-cited health law scholar in the U.S. (2016–2020, Leiter’s Law School Reports) First Year Teacher of the Year – 2011, 2012, 2019, 2024 Grants & Fellowships: Hoffman has held prestigious fellowships including the Robert Wood Johnson Foundation Scholar-in-Residence (2013), and visiting scholar positions at the CDC (2007, 2014), Emory University (2014), and NIH Bioethics Department (2022). Professional Roles: Prior to academia, Hoffman served as a Senior Trial Attorney at the EEOC, litigation associate at O'Melveny & Myers, and judicial clerk for U.S. District Judge Douglas W. Hillman.

Ross KoppelView profile

Adjunct Professor

• Healthcare Information Technology
• Medication Errors
• Technology in Society
• +2 more

Ross Koppel is an Adjunct Professor of Sociology with expertise in healthcare information technology, medication errors, and ethics in social research. His work explores the intersection of technology and societal impacts, focusing on data governance, clinical workflows, and human factors in health IT systems. Research Interests Context-sensitive understanding of medication errors Societal implications of clinical data sharing Ethical challenges in AI and informatics Healthcare cost analysis Scientific Awards Fellow of the American College of Medical Informatics (FACMI)

Maryam GhahramaniView profile

Senior Lecturer

• Human Motion Analysis
• Machine Learning
• Rehabilitation Engineering
• +5 more

Dr. Maryam Ghahramani is a Senior Lecturer in AI & Robotics at the Faculty of Science & Technology, University of Canberra, Australia. She holds a BSc in Electrical Engineering from Shiraz University, Iran, and a PhD in Biometric Gait Analysis from the University of Wollongong, Australia. Biomedical Engineering Researcher Machine Learning Specialist Human Motion Analysis Expert Her research focuses on applying machine learning to human motion analysis for rehabilitation purposes, particularly in three key areas: Parkinson's Disease: Using fNIRS and machine learning for disease detection and motor function assessment Fall Prevention: Analyzing postural sway and risk of falls in older adults Spatial Disorientation: Studying balance in hypoxic aviation environments Recent publications demonstrate her work at the intersection of biomedical engineering, machine learning, and clinical rehabilitation. Current projects include: Young Onset Dementia Detection with 12-week Home-Based Exercise Programs Mild Hypoxia Analysis for Aviation Safety Balancing Mat Performance Evaluation