Matthew Holden is an Associate Professor in the School of Computer Science at Carleton University. He holds a PhD (2018) and MSc (2014) from Queen's University and a BScH (2012) from Western University. His research focuses on Surgical Data Science, applying machine learning to surgical time-series data from operating rooms and simulations to improve patient outcomes and surgical training. Key areas include real-time decision support, performance assessment, and surgical efficiency through domain-knowledge integration. Research interests emphasize machine learning for surgical workflows, skill assessment via sensor data (e.g., motion tracking, EEG), and computer-assisted interventions. Notable work includes automated proficiency evaluation in cataract surgery, ultrasound-guided procedures, and neurosurgical training. His contributions span medical robotics, surgical education, and clinical decision support systems. Publications highlight advancements in surgical workflow anticipation, tool detection, and skill metrics across domains like ophthalmology, emergency medicine, and neurology. Holden advocates for interdisciplinary approaches combining computational methods with clinical expertise to enhance healthcare delivery.
Huazhen Fang is an Associate Professor in the Department of Mechanical Engineering at the University of Kansas School of Engineering, where he joined in 2014. He leads the Information & Smart Systems Laboratory (ISSL) and holds a courtesy appointment in the Department of Electrical Engineering & Computer Science. His research focuses on enabling intelligence for complex systems through information-driven approaches. Dr. Fang received his Ph.D. in Mechanical Engineering from the University of California, San Diego in 2014, following an M.Sc. from the University of Saskatchewan and a B.Sc. in Computer Science & Technology from Northwestern Polytechnic University in China. He was a Visiting Faculty Fellow at Mitsubishi Electric Research Laboratories in 2022. His research interests span Systems and Control, Advanced Battery Management, Energy Storage Systems, and Robotics, with particular focus on system modeling, estimation, control design, machine learning and numerical optimization. Dr. Fang's work has significant applications in energy management, cooperative robotics, and environmental observing systems. His research has been supported by the National Science Foundation, Department of Energy, Army Research Laboratory, and Mitsubishi Electric Research Laboratories. His extensive publication record shows a clear trend toward increasingly sophisticated integration of physics-based modeling with machine learning approaches, particularly in battery management systems and autonomous vehicle control. Recent work demonstrates a growing emphasis on Bayesian inference methods, distributed control architectures, and safety-critical applications of intelligent control systems. Faculty Early Career Award from National Science Foundation (2019) University Scholarly Achievement Award (2024) Miller Professional Development Award (2022) Miller Faculty Scholar Award (2018, 2019, 2023) Wesley G. Cramer Outstanding Mechanical Engineering Faculty Award (2016) Big XII Faculty Fellowship (2015) IEEE Transactions on Transportation Electrification Prize Paper Award (2024) Dr. Fang has successfully mentored numerous graduate students through the Information & Smart Systems Laboratory, with many receiving awards for their research. His research has attracted significant funding from prestigious organizations including the National Science Foundation, Department of Energy, Army Research Laboratory, and Mitsubishi Electric Research Laboratories. He currently serves as an Associate Editor for multiple prestigious journals including Information Sciences, IEEE Transactions on Industrial Electronics, and IEEE Control Systems Letters. The Information & Smart Systems Laboratory (ISSL) under Dr. Fang's leadership has established itself as a center for cutting-edge research in information-driven smart systems. The lab focuses on pushing the frontiers of information extraction, analysis and exploitation for dynamic systems to deal with system complexity and enable system intelligence. The lab actively collaborates with industry partners and local communities, emphasizing research that serves societal needs.
Mustafa Bilgic is a Professor and Chair of the Computer Science Department at Illinois Institute of Technology, where he also directs the Master of Artificial Intelligence program and the Machine Learning Laboratory. His research focuses on machine learning, active learning, explainable AI, and probabilistic graphical models, with applications in healthcare, social media analysis, and biomedical engineering. He has received funding from NSF, NIH, and Samsung, among others. Education: PhD in Computer Science, University of Maryland at College Park (2010) M.S. in Computer Science, University of Maryland at College Park (2006) B.S. in Computer Science, University of Texas at Austin (2004, with High Honors and Special Honors) Research Highlights: Dr. Bilgic's work emphasizes AI ethics, algorithm transparency, and interactive machine learning systems. Notable projects include analyzing political news engagement dynamics and developing frameworks for eliminating explanation noise in AI models. His lab explores tools like OrganoID for tracking organoid growth and IDGI for improving model interpretability. Awards: NSF CAREER Award (2014) ACM SIGKDD Best Student Paper Award (2008) Illinois Tech College of Computing Teaching Excellence Award (2021) Teaching and Leadership: Bilgic teaches advanced courses in AI, machine learning, and data mining. He leads initiatives to bridge AI theory and practical applications, emphasizing interdisciplinary collaboration. His administrative roles include overseeing the AI master’s program and fostering innovation in computing education.
Kalaichelvi Saravanamuttu is an Associate Dean in the Faculty of Science and a Professor in the Department of Chemistry and Chemical Biology at McMaster University. Her research focuses on optochemical self-organization in soft materials, nonlinear optics, and photonics, with applications in light capture, waveguide architectures, and all-optical computing. She holds a PhD in Chemistry from McGill University (2001) and conducted postdoctoral research at the University of Oxford (2001-2003). Her work combines polymer chemistry, photochemistry, and optical physics to develop functional materials like photoresponsive hydrogels and waveguide-encoded lattices. Key research themes include light-induced structural changes in soft matter, dynamic optical systems, and bio-inspired optical devices. Teaching includes courses on equity in science (SCIENCE 2AR3/4AR6) and advanced materials (CHEM 4W03). She has received funding from NSERC, the Canadian Foundation for Innovation, and the US Army Research Office. Her research group collaborates widely, with recent studies exploring electroactive hydrogels and switchable self-trapped light beams.
Dr. Alexander Paulus serves as a Researcher at the Chair of High-Frequency Engineering within the Department of Electrical Engineering at the Technical University of Munich (TUM), School of Computation, Information and Technology. Working under Prof. Dr.-Ing. Thomas Eibert, he contributes to advanced electromagnetic research and measurement systems development at TUM's Arcisstr. 21 campus in Munich. Research Expertise His core specialization lies in near-field antenna measurement and transformation techniques, with significant contributions to phase retrieval algorithms, inverse source methods, and UAV-based electromagnetic field measurements. He addresses critical challenges including probe correction with unknown antennas, sparse sampling for directive antennas, and electromagnetic modeling of environmental effects like rain attenuation. His work bridges theoretical electromagnetics with practical antenna characterization solutions. Publication Trends From 2014-2025, Paulus has published 25+ papers focusing on near-field to far-field transformations, particularly in phaseless and multi-probe scenarios. Recent work (2023-2025) demonstrates innovation in spectral filtering, sparse reconstruction, and UAV-based systems for defect localization and wet antenna modeling. His research increasingly integrates computational techniques to solve complex inverse problems in antenna measurements. Scientific Recognition No formal awards documented in available information Academic Contributions Student Mentoring: No advisees listed in provided materials Research Funding: Grant details not specified in source text Research Environment Paulus operates within TUM's Chair of High-Frequency Engineering facilities, which include advanced near-field measurement ranges, UAV-based electromagnetic characterization systems, and laboratories for metamaterials research and electromagnetic compatibility testing. His work supports applications in 5G/6G communications, aviation navigation systems, and precision antenna diagnostics.
Christoph Müller is a Full Professor of Energy Science and Engineering at ETH Zürich's Department of Mechanical and Process Engineering. He leads the Laboratory of Energy Science and Engineering, focusing on sustainable energy generation, heterogeneous catalysis, and granular systems. His research integrates experimental methods like Magnetic Resonance Imaging (MRI) and Discrete Element Modelling (DEM) with mathematical modeling to address industrial energy challenges. Education: Dipl.-Ing. from Technical University of Munich (2004), PhD in Chemical Engineering from the University of Cambridge (2008). Notable awards include the Danckwerts-Pergamon Prize (2009) and DAAD Scholarship (2005). He teaches courses such as Thermodynamics I and Thermo- and Fluid Dynamics. Research interests span CO₂ capture via chemical looping, catalytic hydrogenation, and granular flow dynamics. Recent work explores catalyst design for propane dehydrogenation, MXene-based ammonia synthesis, and MgO-based CO₂ sorbents. His lab employs advanced techniques like operando X-ray absorption spectroscopy to study catalyst behavior under reaction conditions. Key achievements include developing stable PtGa propane dehydrogenation catalysts and advancing understanding of Na₂CO₃-promoted CO₂ sorbents. His work on fluidized bed hydrodynamics via MRI contributes to reactor design optimization. Müller's interdisciplinary approach bridges fundamental science and industrial application, addressing global energy sustainability challenges.
Pietro Liò is Full Professor in the Department of Computer Science and Technology at the University of Cambridge, where he leads research in Artificial Intelligence and Computational Biology as part of the AI group and the Cambridge Centre for AI in Medicine. He holds additional affiliations as Fellow and Council member of Clare Hall College, member of Ellis (European Lab for Learning & Intelligent Systems), and member of Academia Europaea. Professor Liò earned dual PhDs in Complex Systems and Non Linear Dynamics from the University of Florence and in Theoretical Genetics from the University of Pavia, Italy. His educational background bridges theoretical computer science with biological sciences, forming the foundation for his interdisciplinary research approach. His research focuses on developing Artificial Intelligence and Computational Biology models to understand disease complexity and advance personalized medicine. Current work emphasizes Graph Neural Network modeling for integrating multi-scale, multi-omics, and multi-physics data; combining deep learning with mechanistic approaches; explainability in medical AI; and developing AI-based medical digital twins and personal decision support systems. His work spans from fundamental algorithm development to clinical applications, with particular emphasis on translating computational advances into medical solutions. Analysis of his recent publications reveals strong activity in geometric deep learning , explainable AI for healthcare , and multi-omics integration , with increasing focus on clinically applicable tools that maintain both predictive power and interpretability. Member of Academia Europaea Listed among Top Italian Scientists by VIA-Academy Professor Liò has mentored over 40 PhD students and postdoctoral researchers, including notable names such as Petar Velickovic, David Buterez, and Chaitanya Joshi. His research is supported through collaborations with the Cambridge Centre for AI in Medicine and various international partnerships. He serves on departmental committees including Student Complaints and Postdoc Mentoring, and has completed equality and diversity training essentials. He leads research within the Artificial Intelligence group at Cambridge, focusing on creating computational frameworks that bridge biological complexity with clinical applications through advanced machine learning techniques.
Chris Russell is the Dieter Schwarz Associate Professor of AI, Government & Policy at the Oxford Internet Institute (OII), University of Oxford. His work bridges computer vision, machine learning, and ethical AI governance. He leads the Governance of Emerging Technologies programme, focusing on algorithmic accountability, transparency, and fairness. Prior roles include Group Leader at the Alan Turing Institute and Reader at the University of Surrey. Russell’s research has been recognized with awards, including the ICRA Best Paper Prize for autonomous driving mapping work. Research interests span algorithmic fairness, explainable AI, and responsible AI design. Notable projects include collaborations with the British Antarctic Foundation on climate modeling and causal approaches to algorithmic fairness. His work with Sandra Wachter and Brent Mittelstadt informs GDPR guidelines and tools like TensorFlow’s 'What-if Tool.' Current projects include advancing medical machine learning for inflammatory arthritis prediction and governance frameworks for emerging tech. Russell advises PhD students on socio-technical AI evaluations and teaches courses in machine learning and AI ethics. Recent publications address deepfake proliferation, LLM regulation, and fairness in foundational models. He co-leads initiatives like the Digital Good Network to align tech development with societal benefit.
Dr. Youngchan Kim is a Lecturer in Quantum Biology at the University of Surrey , serving as Director of the Quantum Biology Doctoral Training Centre (QB-DTC). He is affiliated with multiple departments including the School of Biosciences, Advanced Technology Institute, and Quantum Sciences Group. PhD in Physics (2011), Korea Advanced Institute of Science and Technology MSc in Physics (2008), KAIST BSc in Physics (2006), Chung-Ang University Graduate Certificate in Learning and Teaching (2022), Advance HE His research focuses on quantum phenomena in biological systems at physiological temperatures, particularly using femtosecond optical spectroscopy and genetically engineered fluorescent proteins to explore evolutionary adaptations and develop quantum-bio-inspired technologies like room-temperature single-photon sources. The 15 most recent publications span quantum biology, biophotonics, and optical spectroscopy, with particular emphasis on quantum coherence in biological systems , terahertz birefringence , fluorescent protein dynamics , and biomedical imaging innovations . These works demonstrate his interdisciplinary approach bridging physics, biology, and medical applications. As QB-DTC Director, he leads transdisciplinary initiatives fostering collaboration between quantum physics and biosciences. His technical expertise includes time-correlated single-photon counting , common-path interferometry , and ultrafast fluorescence depolarization techniques.
João Paulo Costeira is an Associate Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), Lisbon. He holds a PhD in Electrical and Computer Engineering from IST (1995) and was a Visiting Scientist at Carnegie Mellon University's Robotics Institute (1991–1995). His research focuses on Computer Vision, 3D Reconstruction, and Structure from Motion, with contributions to object recognition, robotics, and multimedia analysis. Education: PhD in Electrical and Computer Engineering, IST (1995); Visiting Scientist, CMU Robotics Institute (1991–1995). Roles: Coordinator of the Signal and Image Processing Group (SIPg), Co-director of the Carnegie Mellon|Portugal Dual PhD Program in ECE and Robotics (2007–2018), and Scientific Director of Carnegie Mellon|Portugal (2014–2018). Research Interests: João's work emphasizes 3D reconstruction from video, rigid and non-rigid motion analysis, and applications in robotics and urban surveillance. He has pioneered methods for motion segmentation and robust correspondence problems in computer vision. Publications: His recent work includes advancements in apple counting systems, rotation averaging for robotics, and domain adaptation for traffic density estimation. These contributions highlight his expertise in real-world computer vision challenges. Awards: None explicitly listed. However, his extensive publication record and academic leadership reflect significant scholarly impact. Advising & Grants: Supervised 13 PhD students, many co-advised with CMU faculty. Active in projects like CityCam (vehicle counting) and MultiDrone (robotics collaboration). Funded by FCT, EU, and industry partnerships. Labs/Teams: Leader of the Signal and Image Processing Group (SIPg) at ISR. Involved in NETSyS program for networked systems and robotics.
Ali Bilgin is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Arizona's College of Engineering. He also holds associate professor appointments in Biomedical Engineering, the BIO5 Institute, and Medical Imaging, and is a member of the Graduate Faculty. His work bridges engineering and medical applications, particularly in signal and image processing. His educational background includes: PhD in Electrical Engineering, University of Arizona, 2002 MS in Electrical Engineering, San Diego State University, 1995 BS in Electronics and Telecommunications Engineering, Istanbul Technical University, 1992 Dr. Bilgin's research focuses on signal and image processing , with key applications in image and video coding, data compression, and magnetic resonance imaging (MRI) . His work integrates theoretical advances with practical biomedical applications. Teaching interests include digital signal processing, linear algebra, probability theory, and machine learning in image processing. With over 250 research papers and 13 granted patents, his scholarly output reflects sustained contributions to engineering and imaging sciences. Though specific articles are not listed, his editorial roles and publication volume indicate leadership in signal and image processing domains, particularly in compression and medical imaging. His scientific recognition includes multiple teaching awards from the UA College of Engineering, notably being named Most Supportive Senior Faculty . Most Supportive Senior Faculty, UA College of Engineering Dr. Bilgin has served as an associate editor for several top IEEE journals, including IEEE Signal Processing Letters (2010–2012), IEEE Transactions on Image Processing (until 2014), and IEEE Transactions on Computational Imaging (2014–2019), reflecting his standing in the academic community. While no specific grants or students are listed, his extensive publication record and interdisciplinary affiliations suggest active mentorship and funded research. He is affiliated with the BIO5 Institute, indicating participation in collaborative, interdisciplinary research teams focused on health and bioscience innovation.
Andrea Ianiro is a Full Professor in the Aerospace Engineering Department at Universidad Carlos III de Madrid (UC3M), where he leads research in fluid dynamics, turbulence, and heat transfer. His work bridges experimental techniques and machine learning applications for flow analysis and control. He serves as Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) and directs the EFM Lab (Experimental Fluid Mechanics Laboratory) at UC3M. Professor Ianiro's research focuses on turbulence characterization, boundary layer flows, and the application of machine learning to fluid mechanics problems. His work spans experimental techniques including Particle Image Velocimetry (PIV), infrared thermography, and advanced data processing methods. Recent research emphasizes data-driven approaches for flow field reconstruction, turbulence control, and heat transfer optimization in wall-bounded flows. His projects often combine theoretical, experimental, and computational approaches to address complex fluid mechanics challenges. The analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional fluid mechanics. His work increasingly focuses on using deep learning techniques (particularly CNNs and GANs) for flow field prediction from limited measurements, developing meshless computational methods for flow analysis, and applying optimization techniques (including genetic algorithms) to heat transfer enhancement. His research maintains a strong experimental foundation while embracing data-driven approaches to tackle turbulence modeling challenges. Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) Professor Ianiro leads multiple significant research projects including SPANDRELS (SParse AND paRsimonious Event-based fLow Sensing, 2025-2030), HumanIC (Human-Centric Indoor Climate for Healthcare Facilities, 2024-2027), and EXCALIBUR (Extraction of machine learning strategies for turbulent flow control, 2023-2026). His work has attracted funding from the European Commission, Spanish National Research Agency, and industry partners including Airbus. He has supervised numerous theses on topics including AI-based sensing of turbulent flows, convective heat transfer control, and turbulent boundary layers. At UC3M, Professor Ianiro directs the Experimental Fluid Mechanics Laboratory (EFM Lab), which focuses on advanced measurement techniques for fluid flow and heat transfer characterization. The lab specializes in PIV/PTV techniques, infrared thermography, and the development of novel experimental approaches for turbulence research. Current research directions include machine learning applications for flow field reconstruction, plasma-based flow control, and heat transfer optimization in complex flow configurations.
Michal Assaf, MD, is a faculty member at the Yale School of Medicine, affiliated with the Department of Psychiatry. His research focuses on the neural mechanisms underlying social-emotional regulation and cognitive deficits in psychiatric disorders, particularly schizophrenia. Utilizing advanced neuroimaging techniques such as functional MRI, Dr. Assaf investigates brain-wide connectivity patterns during naturalistic tasks and electrophysiological markers like frontal alpha asymmetry. His recent work highlights changes in brain connectivity during emotional processing and their relationship to social functioning in schizophrenia. These studies contribute to a deeper understanding of the neurobiological basis of psychiatric conditions and may inform future therapeutic interventions. Dr. Assaf has published original research in high-impact journals such as Cerebral Cortex and Journal of Psychiatric Research in 2025, demonstrating an active research trajectory in clinical and cognitive neuroscience. His work often involves collaborative, multidisciplinary teams across institutions. Research Interests: Functional brain connectivity Neuroimaging in psychiatry Social cognition in schizophrenia fMRI and EEG biomarkers Clinical neuroscience Emotional regulation While no formal mentorship or grant activities are detailed in the available text, his publication record suggests active involvement in funded research and academic collaboration. There is no indication of part-time status, retirement, or former affiliation.
Karen Bandeen-Roche is a Professor and the Hurley-Dorrier Professor and Chair of the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, with joint affiliations in the School of Medicine and the School of Nursing. She is a leading expert in biostatistical methodology, particularly in latent variable models, longitudinal analysis, and multivariate survival methods applied to aging and gerontology. Her research focuses on developing statistical models for unobservable processes such as frailty, resilience, and functional status in older adults. She has made significant contributions to the measurement of aging-related constructs and has extensive collaborative work in ophthalmology and neurology. Her methodological work includes mixture models, measurement error correction, and latent class modeling. The recent publications highlight a strong trend in gerontological biostatistics, with a focus on frailty, dementia risk, resilience, and multisystem physiological responses in aging. Her work integrates complex data from observational cohorts and clinical studies, often employing innovative latent variable frameworks to address measurement challenges in health outcomes. Scientific Awards and Honors: Marvin Zelen Leadership Award in Statistical Science (2016) Fellow of the American Statistical Association (2001) Brookdale National Fellow (1997) Golden Apple Award for Excellence in Teaching (2010) Garland Clay Award (1999) Chair, NIH BMRD Study Section (2006–2008) President, Eastern North American Region, International Biometric Society (2011–2013) Executive Board, International Biometric Society (2015–2022) Board of Directors, National Institute of Statistical Sciences (2020–2023) Karen Bandeen-Roche has been deeply involved in advising and training the next generation of researchers. She co-directs a training program in Biostatistics and Epidemiology of Aging and has received multiple teaching and mentoring awards. She has served on numerous academic committees, including appointments and promotions, faculty senate, and ethics committees at Johns Hopkins. Her grants and collaborative research span aging, dementia, ophthalmology, and cardiovascular health, often supported by NIH and other federal agencies. She leads the Center on Aging and Health and is actively involved in interdisciplinary research initiatives that bridge biostatistics, medicine, and public health. Her lab and research team focus on developing and applying advanced statistical methods to understand the biological and social determinants of healthy aging.
Howard Forman is a Professor of Radiology and Biomedical Imaging at Yale School of Medicine, with secondary appointments in Public Health (Health Policy), Management, and Economics. He is fully joint in the School of Management and holds affiliations with the Institute for Social and Policy Studies. He serves as Director of the MD/MBA Program, the Executive MBA Healthcare Focus Area, and the Health Care Management Program at the Yale School of Public Health. He is also the Faculty Director of Finance in the Department of Radiology and an active clinician at Yale New Haven Hospital’s Emergency Department, where he serves as deputy operational chief for Radiology. Professor, Radiology & Biomedical Imaging, Yale School of Medicine Professor, School of Management Professor, Economics Professor, Health Policy & Management Director, MD/MBA Program Director, Health Care Management Program (YSPH) Faculty Director of Finance, Radiology Department Dr. Forman’s research centers on health economics, healthcare policy, quality improvement, and radiology administration. His interests include healthcare financing, cost analysis, health systems reform, and the application of AI and large language models in radiology reporting. He has extensively studied patient access to imaging reports, clinician staffing, and end-of-life cancer care. His recent work explores how AI can improve reporting accuracy while addressing ethical concerns like racial bias. His recent publications reflect a strong trend in leveraging artificial intelligence to enhance radiology workflows and patient engagement, while maintaining a focus on equity and policy implications. Articles in Radiology , JAMA Network Open , and Clinical Imaging demonstrate his leadership at the intersection of medicine, technology, and policy. Healthcare Track Teaching Award – 2025, 2023, 2017 Regent's Award – 2019 Leah Lowenstein Award – 2019 Distinguished Student Mentoring Award – 2013 Fellow, Society for Advanced Body Imaging – 2003 Dr. Forman is a dedicated educator and mentor, having founded and led multiple interdisciplinary programs. He has been instrumental in shaping health policy curricula and promoting evidence-based public health communication. He co-hosts the popular Health & Veritas podcast with Harlan Krumholz, contributing to public discourse on healthcare. He has served on editorial boards for journals including American Journal of Roentgenology and Clinical Imaging , and is actively involved in professional societies such as the Radiologic Society of North America.
