Stephen Hughes is a Full Professor in the Department of Physics, Engineering Physics and Astronomy at Queen’s University, Canada. He holds appointments in the Faculty of Arts and Science and is affiliated with the Centre for Nanophotonics. His research focuses on theoretical and computational nanophotonics, quantum optics, and light-matter interactions. He has co-founded Lumerical Solutions, a leading photonics software company acquired by ANSYS in 2020, and has held postdoctoral positions in Germany, Japan, and the U.S. Education: PhD (Heriot-Watt University, Edinburgh), Postdoctoral work at NTT Basic Research Labs (Japan) and others. Industry Experience: Co-founder of Lumerical and Galian Photonics. Research interests include quantum photonics, nanophotonics, semiconductor optics, photonic crystals, cavity-QED, quantum dots, and topological photonics. His group explores applications in quantum technologies, optomechanics, and metamaterials. Collaborations span global institutions, focusing on both fundamental physics and applied nanophotonics. Labs/Groups: The Hughes Group (Theoretical Quantum and Nanophotonics) operates a state-of-the-art computational lab funded by CFI and MRI Ontario. Current projects involve inverse design techniques, ultrastrong coupling regimes, and quantum trajectory simulations.
Theodora Varvarigou is a Professor in the Department of Electrical and Computer Engineering at the National Technical University of Athens (NTUA). She holds a B.Eng. from NTUA and M.Eng. and Ph.D. degrees from Stanford University. Her career includes research at AT&T Bell Labs and roles at the Technical University of Crete. From 2008-2012, she served as director of NTUA's 'Technoeconomic Systems' postgraduate program. Her research focuses on Cloud Computing, Multimedia Content Processing, Social Networking Technologies, and emerging areas like blockchain, edge computing, and cybersecurity. She has published over 200 papers and led numerous European research projects, emphasizing scalable systems, data management, and smart infrastructure applications. Her work spans technical innovations such as intrusion detection systems, edge resource optimization, and blockchain-based solutions for IoT, healthcare, and smart cities. Recent publications highlight advancements in AI-driven resource allocation, privacy-preserving blockchain designs, and predictive analytics for edge computing environments. Professor Varvarigou has contributed to interdisciplinary initiatives, including cohort data harmonization in biomedical research and social media analytics for urban planning. Her teaching includes courses on digital systems, network programming, and fault-tolerant systems.
Rikky Muller is an Associate Professor of Electrical Engineering and Computer Sciences at UC Berkeley, holding the S. Shankar Sastry Professorship in Emerging Technologies. She is Co-director of the Berkeley Wireless Research Center (BWRC), a Core Member of the Center for Neural Engineering and Prostheses (CNEP), and an Investigator at the Chan-Zuckerberg Biohub. Her research focuses on implantable/wearable medical devices, low-power wireless systems, and neurotechnology for neurological applications. Education: PhD (2013), UC Berkeley; BS and M.Eng. (2004), MIT, all in EECS. Prior roles include IC designer at Analog Devices and co-founder of Cortera Neurotechnologies (acquired). Research interests include neural interfaces, closed-loop neuromodulation, and biomedical microelectronics. Notable contributions include Neural Dust (ultrasonic implants), wireless EEG systems, and seizure prediction hardware. Awards: MIT TR35 Innovator, NAE Gilbreth Lectureship, NSF CAREER Award, IEEE SSCS New Frontier Award Grants: Bakar Fellows, Hellman Fellowship, NSF CAREER Labs: Muller Lab (UC Berkeley EECS), Chan-Zuckerberg Biohub collaborations
Dr. Chad J. Brenner is an Associate Professor in the Department of Otolaryngology-Head and Neck Surgery and Pharmacology at the University of Michigan Medical School. He also directs the U-M Program in Cellular and Molecular Biology, the Otolaryngology Clinical Laboratory (CLIA), and the Head & Neck Oncology Program. His research focuses on developing liquid biopsy tools for cancer detection and precision therapy, particularly in HPV-driven head and neck cancers. Key roles include membership in the Rogel Cancer Center, Kresge Hearing Research Institute, and Center for Computational Medicine & Bioinformatics. Education: B.S. in Biomedical Engineering, M.S. in Bioelectrical Engineering, and Ph.D. in Cellular and Molecular Biology from the University of Michigan, with doctoral work on prostate cancer mechanisms. Research Interests: HPV integration and cancer heterogeneity Urine- and blood-based liquid biopsies for real-time cancer monitoring Combination immunotherapy strategies for improving checkpoint inhibitor responses Genetic engineering to identify cancer vulnerabilities Clinical trial innovation for adaptive therapies Recent Work Highlights: Development of the MyHPVscore blood test for HPV-related head and neck cancer detection, and exploration of tumor-immune interactions through PD-L1 and T-cell profiling. Ongoing projects include PET-guided radiotherapy optimization and multi-omics analyses of tumor heterogeneity. Labs & Teams: Leads the Michigan Otolaryngology and Translational Oncology (MiOTO) lab and collaborates with interdisciplinary teams across computational medicine, immunology, and clinical oncology.
Daniel Wilhelm is a Professor of Statistics and Econometrics at LMU Munich, with a courtesy appointment in the Department of Economics. His research focuses on econometric theory, nonparametric methods, measurement error modeling, and statistical inference. He leads the Statistics and Econometrics Group at LMU and holds affiliations with the Centre for Microdata Methods and Practice (CeMMAP), Institute for Fiscal Studies (IFS), and the Centre for Research and Analysis of Migration (CReAM). Wilhelm’s work includes groundbreaking contributions to NPIV estimation, robust statistical testing, and the development of R and Stata packages for rank inference and econometric analysis. His recent publications address topics like rank-based inference, measurement error detection, and high-dimensional independence testing. He organizes academic events such as the Munich Econometrics Seminar and the LMU-Todai Econometrics Workshop. His research emphasizes methodological rigor and practical applications, with a focus on improving statistical techniques for social science and policy analysis.
Professor Karl Peter Giese holds the position of Professor of Neurobiology of Mental Health and Co-Head of the Basic & Clinical Neuroscience Department at King's College London's Institute of Psychiatry, Psychology & Neuroscience (IoPPN). His research focuses on memory mechanisms in health and disease, particularly Alzheimer's pathology, synaptic dysfunction, and aging effects. He leads projects funded by Alzheimer's Research UK and other institutions, investigating molecular and cellular bases of memory storage. His work bridges experimental models (e.g., mice) with translational insights for clinical applications. He has over 140 publications, including high-impact studies on CYFIP proteins in dementia and CaMKII in synaptic plasticity. Collaborations include researchers at King's College London and international partners. His lab explores mechanisms linking amyloid-beta, tau, and synaptic proteins to cognitive decline, with recent work applying computational methods to model aging brains. Education: PhD from ETH Zurich (1992), MSc Chemistry from Ruhr-University Bochum (1989). Current grants include Alzheimer's Research UK Network Centres and studies on MNK inhibition for Alzheimer's therapies. Projects span protein synthesis dysregulation, thalamic amyloid pathology, and intellectual disability genetics. His research has been featured in Nature Neuroscience , Brain , and Neuron . He advises on translational neuroscience initiatives and mentors early-career researchers.
Professor Hakan Ali Çırpan is a distinguished faculty member at Istanbul Technical University's Faculty of Electrical and Electronics Engineering, where he serves as Professor in the Department of Electronics and Communication Engineering. He also holds the position of Vice Dean at Istanbul Technical University since 2021. With over three decades of academic experience, Professor Çırpan has established himself as a leading researcher in signal processing and communications. His educational background includes: PhD from Stevens Institute of Technology (1993-1997) Master's degree in Electrical-Electronic Engineering (with thesis) from Istanbul University (1989-1992) Bachelor's degree in Electrical and Electronic Engineering from Uludağ University (1985-1989) Professor Çırpan's research spans multiple domains within signal processing and communications. His primary interests include wireless communications, radar systems, machine learning applications in communications, and electronic warfare. His work on channel estimation, orthogonal frequency division multiplexing, and maximum likelihood methods has been particularly influential. He has pioneered research in areas such as source localization, spectrum sensing, and physical layer security. His recent work focuses on 5G/6G networks, AI-enhanced communications, and integrated sensing and communication systems. Analysis of his recent publications (2023-2025) reveals a strong focus on next-generation wireless technologies, particularly 5G/6G networks, AI integration in communications, and electronic warfare applications. His research demonstrates a consistent pattern of addressing fundamental challenges in signal processing while adapting to emerging technological needs. A significant portion of his recent work involves machine learning applications for spectrum management, optimization techniques for radar systems, and novel approaches to network slicing and resource allocation. His notable scientific achievements include: ASELSAN ACADEMY THESIS COMPETITION WINNER (2020) Professor Çırpan has supervised 59 theses throughout his career, mentoring numerous graduate students in the fields of signal processing and communications. He has secured significant research funding, including the "AI-Enhanced 5G/6G Networks with Integrated Camera and ISAC Systems" project (2023-2024) and the "Railway Vehicle Infrastructure New Generation Secure Communication Systems" TÜBİTAK project with a budget of ₺955,000. His research has practical applications in defense systems, railway communications, and next-generation wireless networks. His laboratory work focuses on wireless communications systems, radar signal processing, and AI-enhanced communication technologies. Professor Çırpan leads research teams working on projects related to 5G/6G networks, electronic warfare countermeasures, and secure communication systems. His group collaborates with industry partners like ASELSAN and conducts research with practical applications in national defense and critical infrastructure.
Prof. Dr. Harald Reiterer is a leading researcher in Human-Computer Interaction at the University of Konstanz, where he has served as Professor since 2009. His academic journey includes a Ph.D. (1991) and habilitation (1995) from the University of Vienna, followed by roles including Senior Researcher at Fraunhofer FIT and Associate Professor at Konstanz. He currently holds multiple leadership roles: Dean of the Faculty of Sciences , Senator of Section 1 , and Consulting Dean . Ph.D. in Computer Science (University of Vienna, 1991) Venia Legendi (Habilitation) in HCI (University of Vienna, 1995) His research focuses on: Interaction Design for mixed reality environments Information Visualization in immersive contexts Hybrid User Interfaces combining physical and virtual elements 3D Object Manipulation in handheld AR Behavioral Analytics through mHealth interventions Recent work explores: Avatar representation in Augmented Reality (2024) Node selection efficiency in Virtual Reality (2024) Peripheral vision toolkits for Head-Mounted Displays (2023) Hybrid interface optimization for Mixed Reality (2023) Smartphone AR extensions for Spatial Memory (2023) Key scientific contributions: Landeslehrpreis 2021 for interdisciplinary exhibition design Development of Colibri cross-reality toolkit (2023) Foundational work on Re-locations for remote collaboration (2022) He leads numerous projects including: SMARTACT (Smart Mobility, 2015-2023) SFB TRR 161 (2009-2027) on XR interface measurement Blended Library (2011-2015) for future library design
Philip Pugh is a Senior Lecturer in the Faculty of Science and Engineering at Anglia Ruskin University, specializing in Life Sciences. He has extensive experience in Antarctic biogeography, cladistics, and multivariate analysis, with a focus on biodiversity and environmental biology. His teaching includes the BSc (Hons) Marine Biology with Biodiversity and Conservation course. BSc Zoology, University of Wales Swansea, 1980 PhD Marine Biology, University of Wales Swansea, 1985 PGCE Biology with Integrated Science, University of Cambridge, 1988 Philip's research spans Antarctic biogeography, cladistics, and multivariate analysis. He investigates the origins of Antarctic fauna, human impacts on ecosystems, and develops computational methods for biogeography. His work integrates ecological modeling, taxonomy, and conservation biology. Recent research trends include Antarctic ecology, biodiversity analysis using multivariate techniques, and the ecological consequences of climate change and tourism. His publications address topics like tardigrade population dynamics, seabird entanglement, and land snail evolution in sub-Antarctic regions. Fellow, Linnean Society of London Fellow, Zoological Society of London Fellow, Royal Entomological Society of London Philip supervises graduate students in Antarctic ecology, taxonomy, and microbial environmental interactions. He is affiliated with the Applied Ecology Research Group and collaborates on projects involving seabirds, fungal pathogens, and conservation strategies.
Professor Tim Denison FREng holds a joint appointment in the Department of Engineering Science and Nuffield Department of Clinical Neurosciences at the University of Oxford, where he serves as the Royal Academy of Engineering Chair in Emerging Technologies and an MRC Investigator. His research focuses on the fundamentals of physiologic closed-loop systems and developing next-generation neural interface technologies for treating chronic neurological diseases. Professor Denison received his A.B. in Physics from The University of Chicago, followed by M.S. and Ph.D. degrees in Electrical Engineering from MIT. He later completed an MBA at The University of Chicago, where he was named a Wallman Scholar. His research spans neural engineering, closed-loop neuromodulation systems, and computational neuroscience, with particular emphasis on deep brain stimulation, neural oscillations, and adaptive neurostimulation techniques. His work integrates engineering principles with clinical neuroscience to develop innovative treatments for neurological disorders. Professor Denison's approach combines computational modeling with experimental validation to optimize brain stimulation parameters for individual patients. Professor Denison has received numerous prestigious awards, including membership in the Bakken Society (2012, Medtronic's highest technical honor), the Wallin leadership award (2014), election to the College of Fellows for the American Institute of Medical and Biological Engineering (2015), and recognition as a Fellow of the Royal Academy of Engineering (FREng). As a former Technical Fellow at Medtronic PLC and Vice President of Research & Core Technology for the Restorative Therapies Group, Professor Denison brings significant industry experience to his academic work. His research group focuses on developing advanced neurostimulation technologies that incorporate chronobiology principles and adaptive algorithms to improve treatment outcomes for neurological conditions.
Omar Rifki is an Associate Professor (Maître de Conférences) specializing in combinatorial optimization and artificial intelligence applications. His research bridges theoretical computer science with practical logistics challenges, focusing on routing problems, process mining, and machine learning integration for complex decision systems. His core research interests include phase transitions in NP-hard problems, vehicle routing optimization under time constraints, and healthcare process modeling. Rifki's work demonstrates a consistent pattern of integrating reinforcement learning with traditional optimization techniques to solve large-scale real-world problems in transportation and logistics, with particular emphasis on spatio-temporal data effects and collaborative systems. Analysis of his 15 publications (2019-2025) reveals three dominant research thrusts: (1) Fundamental studies of combinatorial problem hardness using phase transition frameworks, (2) Practical applications of deep reinforcement learning in vehicle routing and taxi assignment, and (3) Healthcare process optimization through advanced process mining techniques. His work consistently addresses scalability challenges in real-world implementations while maintaining theoretical rigor. No scientific awards were documented in the provided materials. His collaborative work with researchers like Christine Solnon and Thierry Garaix indicates active participation in European operations research communities, though specific grant details remain unreported. Rifki's research shows increasing integration of graph theory and machine learning in transportation applications, particularly evident in his Lyon City case studies on autonomous ride-sharing systems.
Dr. Gary Glover is a Professor of Radiology (Radiological Sciences Lab) at Stanford University , with courtesy appointments in Psychology and Electrical Engineering. His work focuses on the physics and mathematics of MRI, particularly rapid scanning methods using spiral k-space trajectories for functional brain imaging and multimodal neuroimaging (fMRI/EEG/fPET/fNIRS) combined with neuromodulation techniques like TMS and transcranial ultrasound. Academic Appointments: Radiology, Psychology, Electrical Engineering Professional Affiliations: Bio-X, Stanford Cancer Institute, Wu Tsai Neurosciences Institute Research Interests include: Development of blood oxygen level-dependent (BOLD) and viscoelastic contrast in MRI Functional MR Elastography for brain activation mapping Optimization of MR-ARFI for transcranial ultrasound guidance Automated spinal cord segmentation (EPISeg) using machine learning Scientific Awards : National Academy of Engineering (2013) Gold Medal, ISMRM (2000) Steinmetz Award, General Electric (1985) Lauterbur Lecture, ISMRM (2018) Recent Publications analyze: Fast fMRI sampling and spurious signal correction Dissociated patterns in default mode network anti-correlations Neural correlates of collaborative behavior in triadic fMRI Salience network contributions to depression pathophysiology
Professor Ashish Sharma is a Professor of Hydrology and Water Resources in the School of Civil and Environmental Engineering at the University of New South Wales, Sydney, Australia. With a PhD in Civil Engineering from Utah State University and extensive experience in hydrological research, he has established himself as a leading expert in his field. Dr. Sharma's research focuses on hydrological uncertainty, with particular emphasis on the impact of climate change and variability on hydrological practice. His work spans multiple areas including remote sensing applications, stochastic hydrological modeling approaches, development of hydrological models, and addressing key hydrology challenges such as design flood estimation and water resources management. He has made significant contributions to understanding how climate change affects hydrological extremes and water availability. His publications reveal a strong trend toward advanced modeling techniques for climate change impact assessment, with recent work focusing on spectral transformation methods, multivariate bias correction in climate models, flood forecasting improvements, and the relationship between temperature and precipitation extremes. His research increasingly integrates remote sensing data with hydrological modeling to address challenges in data-scarce regions. Professor Sharma has held significant leadership positions including President of the International Commission of Hydrologic Sciences (IAHS) Commission on Statistical Hydrology (STAHY) since 2016, service on the Australian Research Council's College of Experts twice, and participation on the Technical Committee for the Australian Rainfall and Runoff Design Flood Estimation guidelines (ARR2016). In addition to his research leadership, Professor Sharma actively mentors students and collaborates with researchers globally, as evidenced by his extensive publication record across top hydrology and climate journals. His work bridges theoretical hydrology with practical applications for water resources management under changing climate conditions.
Xiaoning Qian is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, where he also serves on the Faculty Advisory Committee for the Texas A&M Institute of Data Science (TAMIDS) and the Executive Committee for the Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He holds a joint appointment in the Applied Math group within the Computational Science Initiative at Brookhaven National Laboratory (BNL). Previously, he was an Associate Professor (2018-2022) and Assistant Professor (2013-2018) at Texas A&M, and an Assistant Professor in the Department of Computer Science and Engineering at the University of South Florida (2009-2013). Dr. Qian received his B.S.E. and M.S.E. degrees from Shanghai Jiaotong University, China, and his M.Ph. and Ph.D. degrees in Electrical Engineering from Yale University. Dr. Qian's research focuses on developing mathematical models and computational algorithms in signal processing, machine learning, and Bayesian methods, particularly in learning, uncertainty quantification, and experimental design. His work spans multiple disciplines, with applications in life sciences and materials science. His research group, the Biomedical Imaging, Sensing, and Genomic Signal Processing Group, actively applies probabilistic models and optimization algorithms to solve complex problems in interdisciplinary domains. His research has evolved from foundational work in bioinformatics and biomedical image processing to more recent applications in materials science and broader AI for science initiatives. Dr. Qian has received numerous scientific awards and recognitions including: National Science Foundation (NSF) CAREER Award Segers Family Dean's Excellence Professorship II in the College of Engineering TEES (Texas A&M Engineering Experiment Station) Senior Faculty Fellow Montague-Center for Teaching Excellence Scholar J. T. Oden Faculty Fellow at the University of Texas, Austin Finalist of the 2023 INFORMS QSR Best Paper Faculty Impact Fellow from the Department of Electrical & Computer Engineering As an advisor , Dr. Qian has mentored numerous graduate students through their PhD and MS programs, with many of his alumni securing positions at prestigious institutions and companies including NIH/NCBI, Microsoft, Baidu Research Lab, and Qualcomm. His research has been supported by multiple grants, including an NSF CAREER award and collaborative research funding from the Information Integration and Informatics program. He is actively recruiting postdoc and graduate student research assistants for projects in machine learning and optimization methods with applications in bioinformatics and materials science. Dr. Qian is involved with several research initiatives including the Objective-Based Uncertainty Quantification (ObjectiveUQ) project, which provides a mathematical framework for integrating prior knowledge and data while enabling effective operational and experimental design under uncertainty. He also co-organizes the Bio-Seminar series for the Biomedical Imaging, Sensing & Genomic Signal Processing group at Texas A&M.
Sunny K. Boyd is a Professor in the Department of Biological Sciences at the University of Notre Dame, where she has served since 1987. She also held significant administrative roles, including Associate Vice President for Research (2009–2013) and Director of Graduate Studies (2008–2013). Her research integrates neuroscience, endocrinology, and computational modeling to understand the chemical regulation of animal behavior. Her educational background includes: A.B. in Biology, Princeton University (1981) M.S. in Behavioral Endocrinology, Oregon State University (1984) Ph.D. in Neuroendocrinology, Oregon State University (1987) Dr. Boyd’s research focuses on the interactions of neuropeptides, neurotransmitters, and neurosteroids in controlling social behaviors, particularly in amphibians. Her lab employs a multi-level approach—spanning molecular, cellular, organismal, and computational levels—to investigate mechanisms from synapses to social interactions. Key interests include GABAergic signaling, neurosteroid modulation, and the role of vasotocin in vocal and reproductive behaviors. Her recent publications reflect a strong trend in integrating computational models with empirical data, especially using agent-based simulations to explore mate choice, aggression, and spatial behavior. These studies bridge neuroscience and behavioral ecology, offering insights into both animal and potential human social behaviors. She has received notable scientific recognition, including: Joyce Award for Excellence in Undergraduate Teaching (2007, 1999) Kaneb Faculty Fellow (2003–2004) Editorial roles for General and Comparative Endocrinology and Hormones and Behavior Service on NSF, NIH, and American Heart Association review panels Dr. Boyd is actively involved in mentoring and training students at all levels. She has advised multiple graduate students and currently leads a vibrant research team. Her lab is supported by grants from the National Science Foundation (NSF), National Institutes of Health (NIH), and U.S. Forest Service. She emphasizes interdisciplinary collaboration, notably with computer scientists on modeling projects. She also teaches courses in neuroscience, physiology, and scientific writing, with a focus on training future health professionals. The Boyd Lab operates at the intersection of experimental and synthetic approaches, combining wet-lab neuroscience with computational modeling. Current projects include: Peptide control of vocalization in amphibians GABA and neurosteroid interactions in neural circuits Agent-based modeling of mate choice and social behavior The lab fosters collaboration across biology, psychology, engineering, and computer science, maintaining an interdisciplinary and innovative research environment.
