Daniel Wolpert is a Professor of Neuroscience at Columbia University , where he is also Vice-Chair of the Department of Neuroscience and a key member of the Zuckerman Mind Brain and Behavior Institute . Additionally, he holds a part-time position as Director of Research at the Department of Engineering, University of Cambridge, and is a Fellow of the Royal Society and the Academy of Medical Sciences . Education: Medical Doctor (1989), D.Phil. in Physiology from the University of Oxford (1992) Previous Positions: Lecturer at Sobell Department of Motor Neuroscience (Institute of Neurology), Professor of Engineering at University of Cambridge (2005–2018) Wolpert is a world leader in sensorimotor control , combining computational neuroscience , Bayesian inference , and robotic/virtual reality technologies to reverse-engineer how the brain generates movements. His work emphasizes the brain's role in reducing sensorimotor uncertainty through predictive modeling and has implications for understanding disorders like autism and Parkinson’s disease. His awards include: Royal Society Ferrier Medal (2020) Minerva Foundation Golden Brain Award (2010) Royal Society Francis Crick Prize Lecture (2005) Daniel Wolpert actively contributes to public science communication, including a 2011 TED Talk on the computational role of the brain in movement, and leads the Wolpert Lab at Columbia, which investigates the neural basis of decision-making , motor learning , and reinforcement learning in both healthy and clinical populations.
Mohammad Modarres is the Nicole J. Kim Eminent Professor at the University of Maryland within the A.J. Clark School of Engineering. He serves as Director of the Center for Risk and Reliability (CRR) and is a Professor of Nuclear Engineering in the Department of Mechanical Engineering. Dr. Modarres co-founded the world's first degree-granting graduate curriculum in reliability engineering at the University of Maryland and has established himself as an international expert in reliability and risk analysis. Dr. Modarres received his educational credentials from prestigious institutions: B.S. in Mechanical Engineering from Tehran Polytechnic M.S. in Mechanical Engineering from MIT M.S. and Ph.D. in Nuclear Engineering from MIT Dr. Modarres' research spans multiple critical areas in engineering risk and reliability. His primary interests include probabilistic risk assessment, uncertainty analysis, probabilistic physics of failure, and probabilistic fracture mechanics. His work encompasses both experimental investigations and sophisticated probabilistic model development. He has made significant contributions to materials degradation science, prognosis and health management systems, and nuclear safety analysis. His research bridges theoretical developments with practical applications in complex engineering systems, particularly in nuclear power and aerospace sectors. Analysis of Dr. Modarres' recent publications reveals a strong trend toward integrating advanced data science techniques with traditional reliability engineering. His work increasingly incorporates machine learning, deep learning, and entropy-based approaches to solve complex problems in prognostics and health management. There's a clear focus on multi-unit systems, particularly in nuclear power applications, and a growing emphasis on data-driven methodologies for remaining useful life estimation and failure prediction. His research maintains a strong foundation in probabilistic methods while embracing cutting-edge computational approaches. Dr. Modarres has received numerous prestigious honors and awards throughout his distinguished career: Nicole Y. Kim Eminent Professorship in Engineering Minta Martin Professorship in A.J. Clark School of Engineering University of Maryland Distinguished Scholar-Teacher (2019) Tommy Thompson Award for outstanding lifetime contributions to nuclear safety (American Nuclear Society) Fellow, American Nuclear Society Fellow, Institute of Electrical and Electronics Engineers (IEEE) Life Fellow of IEEE 1996 Maryland Inventor of the Year Award (in Information Sciences) FDA Commissioner Special Citation for Contributions to Risk Assessment Methods (2004) 2008 International Research Leadership Award (Society for Reliability Engineering, Quality and Operations Management) Honorary Doctorate from Universidad Da Vinci de Guatemala As the founding director of the Center for Risk and Reliability, Dr. Modarres has built a world-renowned program that has awarded over 500 Ph.D. and master's degrees. His research has been supported by significant grants from government agencies and industry partners, particularly in nuclear safety, aerospace reliability, and critical infrastructure protection. He has mentored numerous students who have gone on to become leaders in reliability engineering across various industries. His center collaborates extensively with the International Atomic Energy Agency (IAEA) and other international organizations on risk assessment methodologies. The Center for Risk and Reliability (CRR), which Dr. Modarres directs, serves as a hub for multidisciplinary research in risk and reliability engineering. The center has recently renovated its facilities to enhance collaboration among researchers from different engineering disciplines. CRR maintains strong partnerships with industry leaders including Amazon Lab126 (as evidenced by their collaboration on device durability research) and has been instrumental in advancing probabilistic risk assessment tools used in nuclear power plant safety analysis. The center hosts regular seminars, workshops, and international conferences, positioning itself at the forefront of risk and reliability research globally.
Ares J. Rosakis is the Theodore von Kármán Professor of Aeronautics and Mechanical Engineering at the California Institute of Technology (Caltech), where he served as Chair of the Division of Engineering and Applied Science from 2009-2015 and previously as Director of the Graduate Aerospace Laboratories (GALCIT). He has held numerous prestigious visiting professorships including at Nanyang Technological University, Northwestern University, Columbia University, Oxford University, and École Normale Supérieure in Paris. Rosakis earned his B.A. and M.A. in Engineering Science from Oxford University in 1978, followed by his Sc.M. (1980) and Ph.D. (1982) in Engineering (Solid Mechanics) from Brown University. He joined Caltech as an Assistant Professor in 1982, was promoted to Associate Professor in 1988, and to full Professor in 1993. In 2004, he was named the Theodore von Kármán Professor, one of Caltech's most distinguished named chairs. Rosakis is globally recognized as the foremost expert in dynamic failure mechanics of solid materials. His pioneering contributions span the dynamic failure of metals, composites, and interfaces. He invented Coherent Gradient Sensing (CGS) interferometry, a novel optical method sensitive to gradients of optical path differences that has been widely adopted in fracture mechanics and thin film stress measurements. His research encompasses dynamic shear-dominated rupture of heterogeneous materials, rupture mechanics of crustal earthquakes (where he experimentally discovered 'intersonic' or 'supershear' ruptures), and reliability of thin films and in-situ wafer level metrology. His work bridges engineering science, materials mechanics, and geophysics with remarkable interdisciplinary impact. His recent publications demonstrate a strong focus on earthquake mechanics and laboratory simulations of seismic events, particularly supershear earthquake ruptures. The research connects fundamental fracture mechanics with real-world geophysical phenomena, revealing how laboratory-scale experiments can illuminate the physics of large-scale earthquakes. His work has established critical links between theoretical models, experimental observations, and geological field evidence. Rosakis has received numerous prestigious awards including: 2024 Foreign Member of the Royal Society, UK 2023 Honorary PhD from National Technical University of Athens 2023 Honorary Degree of Doctor of Engineering from University of Illinois 2021 Zdeněk P. Bažant Medal for Failure and Damage Prevention 2018 Timoshenko Medal from ASME 2016 Elected to the National Academy of Sciences 2011 Elected to the National Academy of Engineering Throughout his distinguished career at Caltech, Rosakis has mentored numerous graduate students and postdoctoral researchers, many of whom have become leaders in their fields. His research has been continuously supported by major grants from the National Science Foundation, Department of Energy, and other federal agencies, focusing on dynamic fracture, earthquake mechanics, and advanced optical measurement techniques. He has served on numerous editorial boards and advisory committees for major scientific organizations. At Caltech, Rosakis leads research in the Graduate Aerospace Laboratories (GALCIT), where he has established world-class experimental facilities for studying dynamic fracture and earthquake mechanics. His laboratory features high-speed imaging systems capable of millions of frames per second, infrared diagnostics for temperature field measurements, and specialized equipment for simulating earthquake ruptures at laboratory scale. His research group combines experimental, theoretical, and computational approaches to address fundamental questions in solid mechanics and their applications to geophysics and materials engineering.
Swati Aggarwal is a Professor in Artificial Intelligence at the Faculty of Logistics, Molde University College (HiMolde). Her research focuses on AI applications in healthcare, ethics, cognitive development, and neural networks. She holds a PhD in Neutrosophic Neural Networks, a Master's in Information Technology, and a Bachelor's in Computer Science and Engineering. Previously, she was a Marie Curie Postdoc Fellow at NTNU, working on AI models for cognitive assessment in infants (AIM_COACH project). Research Interests - AI in Health/Medicine - Ethics in AI and Societal Impact - EEG/BCI for Cognitive Assessment - Machine Learning and Deep Learning Publications Her recent work spans AI ethics, BCI applications, adversarial attacks, and healthcare diagnostics. Notable contributions include EEG-based infant perceptual monitoring (2025) and malaria detection via EfficientNet (2023). She also explores cross-lingual adversarial robustness and blockchain in hospitality systems. Labs/Teams - ABC-AI: Applied, Basic, and Conscientious AI Group - Virtual Technologies and Learning Research Group
Ian Bradley is an Assistant Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo, State University of New York. His research focuses on creating sustainable biological processes to address needs in engineered and natural systems for water and wastewater treatment and resource recovery. Education: PhD in Environmental Engineering, University of Illinois at Urbana-Champaign (2017) MS in Environmental Engineering, University of Illinois at Urbana-Champaign (2011) MS in Civil Engineering (Structures), University of Illinois at Urbana-Champaign (2010) Research Interests: Dr. Bradley specializes in microalgal-based nutrient recovery, wastewater surveillance for public health monitoring, PFAS degradation using nanomaterials, and sustainable resource recovery systems. His work integrates biological processes with environmental engineering to optimize wastewater treatment efficiency and develop predictive models for water quality and health outcomes. Publications: His recent research includes advancements in microalgal cultivation (EcoRecover process), wastewater-based epidemiology for SARS-CoV-2 tracking, and computational enzyme design for PFAS remediation. These studies demonstrate interdisciplinary expertise spanning environmental engineering, biotechnology, and public health analytics.
Dr James Herbert-Read is an Associate Professor and Whitten Lecturer in Marine Biology at the Department of Zoology, University of Cambridge. He serves as Deputy Head of Department (Postgraduate Education) and leads the Marine Behavioural Ecology Group. His research focuses on understanding how animals, particularly marine organisms, collect and process information from their environments to make behavioral decisions, with emphasis on social interactions, adaptation mechanisms, and ecological constraints. His group employs theoretical frameworks, controlled experiments, and quantitative field studies to investigate behavioral diversity in marine species. Key themes include collective behavior, predator-prey dynamics, camouflage strategies, and the impacts of environmental stressors on animal decision-making. Recent publications highlight work on lionfish vocalization mechanisms, cuttlefish camouflage, citizen science applications in marine research, and behavioral responses to visual and acoustic noise. Scientific awards and affiliations include: Whitten Lecturer in Marine Biology Associate Professor, University of Cambridge He has supervised research projects on topics such as: Social attraction in invasive fish species Evolution of coordinated movement Neurophysiological basis for leadership in shoals Maternal effects on offspring exploration
Sharon Abrahams is a Professor of Neuropsychology and holds a Personal Chair at the University of Edinburgh's School of Philosophy, Psychology and Language Sciences. She leads research in clinical and experimental neuropsychology, focusing on neurodegenerative diseases like motor neurone disease/ALS and frontotemporal dementia. Her work integrates neuroimaging, cognitive assessment, and clinical intervention. She is affiliated with the Euan MacDonald Centre for Motor Neurone Disease, the Centre for Clinical Brain Sciences, and Edinburgh Neuroscience. Current PhD students include Monica de Icaza Valenzuela, Debbie Gray, and Caroline McHutchison. Abrahams has secured over 17 research grants since 2008, including projects on cognitive screening in MND, apathy impact, and the ECAS tool. Her research emphasizes cognitive and behavioral changes in ALS, with a focus on early detection, staging, and patient quality of life. Notable contributions include the Edinburgh Cognitive and Behavioural ALS Screen (ECAS), validated across multiple languages and populations.
David L. Henann serves as the James R. Rice Associate Professor of Solid Mechanics in the Department of Engineering at Brown University's School of Engineering. His research focuses on continuum-level constitutive modeling of engineering materials, with particular expertise in granular materials, viscoelastic foams, and bubble dynamics in soft solids. Henann leads an active research group developing computational frameworks for material behavior prediction through numerical simulation. PhD, Massachusetts Institute of Technology (2011) SM, Massachusetts Institute of Technology (2008) BS, State University of New York at Binghamton (2006) Henann's research spans constitutive theory development and computational implementation for complex material systems. His group pioneers nonlocal continuum models for granular flows, large-deformation viscoelastic theories for elastomeric foams, and high-strain-rate characterization of microcavitation phenomena. Current projects include modeling size segregation in granular media, bubble dynamics in viscoelastic hydrogels, and electromechanical instabilities in dielectric elastomers. His publication record reveals consistent focus on material instability phenomena , constitutive model validation , and experimental-computational synergy . Henann frequently collaborates with experimental groups to validate theoretical frameworks, particularly in soft matter mechanics and cavitation dynamics. Eshelby Mechanics Award for Young Faculty (2020) NSF CAREER Award (2016) Pi Tau Sigma Gold Medal (ASME, 2016) Brown University Teaching Awards (2015-2016) Henann maintains an active teaching portfolio covering continuum mechanics, solid mechanics, and plasticity at both undergraduate and graduate levels. His research group operates a computational mechanics laboratory with extensive Fortran-based simulation capabilities, evidenced by multiple open-source repositories on GitHub for granular rheology, foam modeling, and dielectric elastomer analysis. Current work focuses on extending nonlocal granular models to industrial applications and developing predictive frameworks for soft material failure under extreme loading conditions.
Markus Heilig is a Professor of Psychiatry and Head of the Department of Biomedical and Clinical Sciences (BKV) at Linköping University. He serves as Director of the Center for Social and Affective Neuroscience (CSAN), integrating basic and clinical neuroscience to address addictive and affective disorders. His work bridges neuropharmacology, molecular psychiatry, and clinical trials. Linköping University (Current) Center for Social and Affective Neuroscience (CSAN) (Director) Wallenberg Center for Molecular Medicine (Collaborator) His research focuses on alcohol dependence mechanisms, stress-neurotransmitter interactions (e.g., CRF, substance P, endocannabinoids), and personalized medicine approaches. Using animal models and human neuroimaging (fMRI), his lab explores epigenetic changes in frontal lobe and amygdala neurons that drive compulsive alcohol use. Recent publications highlight his work on endocannabinoid modulation for PTSD treatment, neural correlates of reward choices, and pharmacological interventions targeting GABA and opioid receptors. His studies often combine brain imaging, behavioral analysis, and pharmacology. Scientific awards include the 2019 Wallenberg Clinical Scholar grant and 2017 CAN/Nordic Drugs prizes. Heilig's research is supported by Vetenskapsrådet (SEK 57 million center funding) and international donations like the Andrew J. Bock Memorial Fund.
Dr. Samuel Cheng is an Associate Professor at the Gallogly College of Engineering , University of Oklahoma , specializing in Electrical and Computer Engineering . He holds a Ph.D. in Electrical Engineering from Texas A&M University (2004), preceded by M.S. and M.Phil. degrees from the University of Hawaii and Hong Kong University of Science and Technology. Education: B.S. (University of Hong Kong, 1995), M.Phil. (HKUST, 1997), M.S. (University of Hawaii, 2000), Ph.D. (Texas A&M, 2004) Professional Experience: Senior Research Engineer at Advanced Digital Imaging Research (2004-2005), prior internships at Microsoft Asia and Panasonic Technologies His research focuses on Information Theory , Signal and Image Processing , and Pattern Recognition , with applications in remote sensing, urbanization analysis, and disaster monitoring. His publications span topics including urban impervious surface mapping , nighttime light analysis , and machine learning for environmental data . His work often integrates multi-source datasets (e.g., Landsat, LiDAR, social media) for spatiotemporal modeling. Technical Expertise: Spectral unmixing, machine learning, thermal remote sensing, GIS integration Key Applications: Power outage detection, vegetation-crime correlation, PM2.5 estimation, smart meter data fusion Dr. Cheng holds three US patents in digital watermarking and is affiliated with IEEE, Sigma Xi, and AAAS. His recent articles demonstrate a trend toward leveraging AI for remote sensing challenges and analyzing urbanization impacts on ecosystems.
Xenophon Papademetris is a Professor of Biomedical Informatics & Data Science and Radiology & Biomedical Imaging at Yale School of Medicine. He serves as Associate Director of Biomedical Imaging Data Sciences at Yale Biomedical Imaging Institute and directs the Medical Software and Medical Artificial Intelligence Certificate Program. PhD in Electrical and Information Sciences from Yale University (2000) BA from Cambridge University (1994) Postdoctoral Fellowship at Yale University (2002) His research focuses on medical image analysis, machine learning, and biomedical software development. He has developed tools like BioImage Suite Web and contributed to standards committees at the Association for the Advancement of Medical Instrumentation (AAMI). His work spans modalities including MRI, CT, PET, and optical imaging. Recent publications emphasize neuroimaging analysis, explainable AI in healthcare, and multimodal data integration across species. He leads NIH-funded research under the BRAIN Initiative (R24 MH114805) and has authored a textbook on Medical Software published by Cambridge University Press. IEEE Senior Member Yale Brown-Coxe Postdoctoral Fellowship Harding Bliss Prize for Excellence in Engineering He directs the BioImage Suite Project, creating web-based image analysis tools using JavaScript and WebAssembly. His teaching includes both academic courses and a Coursera program on Medical Software with over 14,000 enrollments.
Søren Munch Kristiansen is an Associate Professor in the Department of Geoscience at Aarhus University, Faculty of Natural Sciences. His interdisciplinary research bridges geoscience and archaeology, focusing on the complex interactions between soil, water, and human societies across time. He is actively engaged in transdisciplinary projects involving geoarchaeology, groundwater, and public health. Research Interests: His work centers on how soil and groundwater have shaped human prehistory and continue to influence modern life. Key areas include safe drinking water from a lifelong perspective, geoarchaeological prospection, Viking Age settlements, and the application of geophysical and geochemical methods in archaeological contexts. He is particularly interested in novel, interdisciplinary methodologies. Recent Research Trends: His recent publications reveal a strong focus on integrating geophysical data (e.g., GPR, borehole databases) with archaeological interpretation, especially in 3D urban modeling and landscape change. Themes include Viking Age sites, interglacial deposits, and anthropogenic soil modifications. His work increasingly employs machine learning and large-scale data synthesis. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: While specific students are not listed, he leads and participates in multiple funded research projects, indicating an active supervisory role. His grants span from 2016 to 2024, including projects like TITAN, SAGA, and 'Fingerprinting displaced molecular substances,' highlighting sustained funding and leadership in interdisciplinary geoscience-archaeology research. Labs and Teams: He is involved in collaborative networks such as the Soil Science & Archaeo-geophysics Alliance (SAGA), suggesting leadership in interdisciplinary research teams. His work often involves multi-proxy analyses and international collaborations, particularly in Scandinavian archaeological geophysics.
Wojciech Jarosz is an Associate Professor of Computer Science at Dartmouth College, affiliated with the College of Engineering and Computer Science. His research focuses on computer graphics, particularly light transport simulation, rendering algorithms, and digital fabrication. He co-founded the Visual Computing Lab and previously led the rendering group at Disney Research Zürich. Jarosz holds a Ph.D. and M.S. from UC San Diego and a B.S. from the University of Illinois Urbana-Champaign. His educational background includes studies in computer science and engineering, with a strong emphasis on graphics and rendering. Research interests span light transport simulation, Monte Carlo methods, appearance capture, and fabrication. Notable achievements include the Eurographics Young Researcher Award (2013) and the NSF CAREER Award (2019). Jarosz's work integrates theoretical rigor with practical applications, such as real-time rendering techniques and volumetric light transport. His lab develops tools for artistic authoring, including intuitive metaphors for volumetric lighting in animated films. Recent projects explore wave-optics BSDF models, optical heterodyne rendering, and unifying radiative transfer models. Key awards include the SIGGRAPH 2024 Best Paper Award and Neukom Institute prizes. His teaching includes courses on rendering algorithms, computer graphics, and computational photography. Jarosz collaborates with industry (e.g., Disney, NVIDIA) and advocates for diversity in computer graphics research.
Anthony A Gatti is a Postdoctoral Scholar at Stanford University's Wu Tsai Human Performance Alliance and School of Medicine. His research integrates biomechanics , medical imaging , and machine learning to advance musculoskeletal health diagnostics, particularly focusing on knee osteoarthritis and exercise physiology. Education : Ph.D. in Rehabilitation Science (McMaster University, 2021), M.Sc. in Rehabilitation Science (McMaster University, 2015), B.Sc. in Kinesiology (McMaster University, 2013) His research develops automated tools for quantifying knee anatomy and integrating anatomical data with biomechanical models . These methods analyze acute responses to exercise and long-term joint degeneration, leveraging MRI , deep learning , and statistical shape modeling . Recent publications emphasize AI-driven segmentation , exercise-induced cartilage changes , and biomechanical simulations , spanning journals like Magnetic Resonance in Medicine and Arthritis & Rheumatology . Trends include machine learning validation for clinical predictions and open-source tool development for musculoskeletal analysis. Scientific Awards : CIHR Postdoctoral Fellowship (top 1%), Mitacs Accelerate Entrepreneur, Forge Student Start-Up Competition Winner, multiple scholarships from McMaster University He founded NeuralSeg , a company commercializing deep learning-based MRI segmentation technology. Collaborations include Stanford's Digital Athlete Moonshot Project with advisors like Scott Delp and Garry Gold.
Dr. Anna Bobak is a Senior Lecturer in Psychology at the University of Stirling, UK. She holds a PhD from Bournemouth University (2016) and joined Stirling as a Research Assistant on an EPSRC project under Peter Hancock before transitioning to her current role. Her primary research focuses on individual differences in unfamiliar face recognition, particularly developmental prosopagnosia, and the reliability of face-processing assessments. She also investigates neurodiversity in women, emphasizing lived experiences of autism and ADHD, including camouflaging behaviors and societal awareness. Research Interests: Face Recognition: Examines perceptual strategies, diagnostic criteria (e.g., Balanced Integration Score), and technological applications in forensic contexts. Neurodiversity: Explores gender-specific manifestations of autism and ADHD, societal perceptions, and support mechanisms. Cognitive Methodology: Advances psychometric rigor in face-processing studies and critiques measurement validity. Her work bridges theoretical research and real-world applications, such as evaluating automated face recognition technology’s biases and collaborating on initiatives like #ScienceForUkraine to aid displaced academics. She is affiliated with the Cognition in Complex Environments research group and contributes to global security and resilience themes at Stirling.
