Brice Kuhl is a Professor in the Department of Psychology at the University of Oregon , where he leads the Kuhl Lab. His research focuses on the cognitive neuroscience of memory formation, retrieval, and forgetting , utilizing advanced neuroimaging techniques like fMRI and EEG combined with machine learning algorithms to analyze distributed neural activity patterns. His work explores mechanisms of memory interference resolution , forgetting , and neural representation transformation . Recent publications emphasize spaced learning , temporal memory dynamics , and memory-cognitive control interactions . The lab has received attention for decoding perceptual content from neural activity and reconstructing face images based on brain states. Current lab members include graduate students Anisha Babu , Tongle Cai , and America Romero , alongside postdocs like Soroush Mirjalili and Yoonjung Lee . The lab frequently presents at conferences like CNS and SFN , and maintains active collaborations in memory research and neuroimaging methodology . Notable projects include investigations into hippocampal pattern differentiation and parietal cortex roles in memory . The lab also contributes to open science initiatives with publicly available experimental codes and data .
Oliver Schmitz is a Professor in the Department of Nuclear Engineering & Engineering Physics at the University of Wisconsin-Madison, where he leads research in plasma edge physics for magnetic confinement fusion and next-generation particle accelerators. His work bridges experimental plasma science, computational modeling, and diagnostic development with applications in both tokamaks and stellarators. Education: PhD (2006), Heinrich-Heine-Universität Diploma (2003), Rheinische Friedrich-Wilhelms-Universität Professor Schmitz's research focuses on 3D plasma edge transport phenomena, plasma-wall interactions, and helicon plasma generation for wakefield accelerators. His group employs advanced computational tools like EMC3-EIRENE for 3D plasma edge modeling and develops active spectroscopic diagnostics to measure plasma parameters through atomic emission analysis. Key themes include resonant magnetic perturbation effects in tokamaks, inherent 3D physics in stellarators, and high-density plasma sustainment for accelerator applications. He actively develops atomic models to interpret spectroscopic data and operates helicon plasma test stands for fundamental process studies. Recent publications reveal strong emphasis on experimental-computational integration for fusion boundary physics, with significant contributions to ITER divertor solutions, stellarator exhaust optimization, and plasma-facing materials. The work shows growing focus on wakefield accelerator diagnostics through helicon plasma sources and advanced spectroscopy, alongside persistent innovation in 3D modeling of plasma-material interfaces. Scientific Awards: 2020 Thomas and Suzanne Werner Chair Professorship 2018 UW Madison Teaching Academy Fellow 2017 ITER Science Fellowship & Vilas Mid-Career Award 2015 DOE Early Career Award & NSF CAREER Award 2011 Torkil Jensen Award (General Atomics) 2007 Günther-Leibfried-Preis (Jülich) Professor Schmitz directs multiple DOE/NSF-funded research programs including his UW Madison laboratory and AWAKE project contributions at CERN. He mentors graduate students through NE 890/990 thesis research courses and has developed nationally recognized K-12 outreach including the "Plasma Show" for elementary schools and "Plasma Academy" for high-school educators developing AP Physics curriculum modules. His leadership extends to university governance through the Kaufman seminar on academic leadership. His research group operates helicon plasma test stands and computational facilities for EMC3-EIRENE simulations, with current efforts focused on high-density plasma sources for accelerators and resilient divertor solutions for stellarators. The group maintains strong international collaborations with ITER, CERN, and major fusion facilities worldwide.
Zhao Guoying is an Academy Professor at the Academy of Finland and holds a tenured Full Professorship at the University of Oulu, Finland. His research focuses on human behavior understanding, emotion AI, and computer vision. He has held visiting positions at institutions including Stanford University and Aalto University. He earned his PhD (2005) in Computer Science from the Chinese Academy of Sciences. His work has led to pioneering contributions in facial expression analysis, micro-expression recognition, and remote physiological signal measurement. Zhao has secured over €19.8 million in research grants as PI, including the prestigious Academy Professor Grant (2021-2026) and Profi-7 Hybrid Intelligence funding. He has supervised 22+ PhD students and 16+ postdocs, many of whom hold academic and industry leadership roles. His awards include IEEE Fellow (2022), IAPR Fellow (2020), and Finland’s Most Publishing AI Researcher (2017). His research interests span machine learning, affective computing, and feature representation. Notable contributions include the first systems for spontaneous micro-expression analysis, novel methods for face anti-spoofing, and remote health monitoring via video. He actively organizes conferences (e.g., Arctic AI Days) and chairs committees such as the Finnish AI Society board.
Fernando De la Torre is a Courtesy Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, with an affiliation to the Robotics Institute where he has been a research faculty member since 2005. He holds a Ph.D. in Electronic Engineering from Ramon Llull University (2002). His research focuses on machine learning and computer vision, with applications in human health, augmented/virtual reality, generative models, and data-centric methodologies. He directs the Human Sensing Laboratory, which explores technologies for human behavior analysis and health monitoring. Notable contributions include founding FacioMetrics LLC (acquired by Meta), advancing facial recognition and 3D human digitization, and developing frameworks for robust visual models. His work bridges theory and practice, with over 225 peer-reviewed publications and editorial roles, including Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence. Recent research trends emphasize generative AI applications in satellite imagery analysis, VR/AR rendering optimizations (e.g., Gaussian splatting), and clinical motion recognition for healthcare. His projects often intersect with industry, addressing challenges in wearable health monitoring and immersive technologies. His lab collaborations span academia and industry, focusing on scalable solutions for 3D human modeling, adversarial robustness, and multimodal data fusion. Key achievements include pioneering work on 3D face animation from speech and garment reconstruction from single images.
Karen Panetta is a Professor at Tufts University School of Engineering with appointments in Electrical and Computer Engineering, Computer Science, Mechanical Engineering, and Academic Services. She currently serves as Dean of Graduate Education for the School of Engineering and holds the title of Distinguished Professor. Ph.D. in Electrical Engineering, Northeastern University M.S. in Electrical Engineering, Northeastern University B.S. in Computer Engineering, Boston University Dr. Panetta's research focuses on developing efficient algorithms for simulation, modeling, and signal and image processing for security and biomedical applications. Her work brings together artificial intelligence, machine learning, and visual sensing systems to create solutions for robot vision and biomedical imaging. She develops algorithms inspired by the human visual system to enable machines to 'see' like humans, with applications in homeland security, biomedicine, facial recognition, and search and rescue operations. Her research has significant humanitarian applications, addressing global challenges facing women and children. Dr. Panetta has received numerous prestigious awards including induction into the National Academy of Engineering (2023), the Presidential Award for Science and Engineering Education and Mentoring (2011), and the IEEE Award for Distinguished Ethical Practices (2013). She is a fellow of multiple prestigious academies including the National Academy of Inventors, European Academy of Sciences and the Arts, and IEEE. Member, National Academy of Engineering (2023) Presidential Award for Science and Engineering Education and Mentoring (2011) IEEE Award for Distinguished Ethical Practices (2013) Fellow, National Academy of Inventors Fellow, European Academy of Sciences and the Arts Fellow, Asia-Pacific Artificial Intelligence Association As an educator and mentor, Dr. Panetta founded the nationally acclaimed Nerd Girls program to promote engineering to young students, particularly women. She previously served as worldwide director for IEEE Women in Engineering and editor-in-chief of the IEEE Women in Engineering magazine. Her approach to graduate education emphasizes the importance of building strong collaborative relationships between faculty and students, with a focus on proactive communication and documentation of research progress. Dr. Panetta's humanitarian research applies engineering solutions to global challenges, including developing technology to help doctors find cancerous tumors, security screeners find concealed weapons, and law enforcement agencies find criminals and missing children. Her work demonstrates a commitment to 'Doing The Right Thing' by addressing issues affecting populations with limited resources or 'voice' in society.
Kay Severin is a full professor at the Laboratory of Supramolecular Chemistry (LCS) within École Polytechnique Fédérale de Lausanne (EPFL) , Switzerland. His research focuses on the design and reactivity of metal-ligand assemblies, including coordination cages, metalloligands, and supramolecular receptors. He has pioneered the use of metalloligands for constructing heterometallic architectures and developed systems for anion extraction and stimuli-responsive hydrogels. Key funder: Swiss National Science Foundation (FNS) Collaborative work with Rosario Scopelliti and Farzaneh Fadaei Tirani Research Interests: Severin's work spans supramolecular chemistry, organometallic synthesis, and functional materials. Recent projects include: Dynamic palladium-based hydrogels with anion-responsive crosslinks Gold(I)-driven nano-onion structures via π-stacking Triazene-derived ligands for Sandmeyer-type reactions Metalloligand assembly of Fe/Pd/Au heterotrimetallic cages Publication Trends: Over 300 publications since 1994, with recent emphasis on: Coordination-driven self-assembly (2024: 6 articles) Triazene and diazoolefin reactivity (2025: 4 articles) Metal-ligand interactions in nanogels and vesicles (2024-2025: 3 articles) Environmental applications in anion extraction (2025: 1 article)
Marco Pedersoli serves as an Assistant Professor at École de technologie supérieure (ETS) in Montreal since February 2017, where he leads research in computer vision and machine learning. His work focuses on reducing computational costs and annotation requirements for deploying vision algorithms on embedded devices, positioning ETS at the forefront of Montreal's AI ecosystem. His academic journey includes: Ph.D. from Autonomous University of Barcelona (UAB) under Jordi Gonzàlez and Juan José Villanueva Post-doctoral research at INRIA Grenoble with Cordelia Schmid and Jakob Verbeek (2015-2016) Research at KU Leuven with Tinne Tuytelaars (2012-2015) Dr. Pedersoli's research tackles deep learning bottlenecks through weakly-supervised methodologies and computational efficiency innovations . His three core projects address: Reduced Supervision : Developing weakly/semi-supervised learning for images, video, audio and text Exploration Learning : Optimizing data selection in unstructured environments Efficient Computation : Accelerating deep learning training and inference These efforts enable vision algorithms to run on resource-constrained portable devices. Publication trends (2014-2022) reveal consistent focus on weak supervision (60% of works) and computational efficiency (30%), with recent expansion into medical imaging and multimodal emotion recognition. Key venues include CVPR, ICCV, NeurIPS and ECCV. His accolades include: Best Paper Award at ICIAR 2019 NVIDIA Titan X Pascal hardware donation Dr. Pedersoli actively mentors 18 graduate students across PhD and MSc programs, with notable placements at Huawei and Radio Canada. His lab secures competitive tax-free funding for projects with international collaborations, including Element AI and European institutions. Current openings emphasize Python/C++ proficiency and deep learning expertise. He leads a dynamic research group at ETS developing open-source tools for Roi-Pooling, weakly-supervised detection, and 3D object recognition, maintaining active GitHub repositories with community contributions. Recent WACV 2023 acceptances demonstrate ongoing productivity following medical leave.
Duncan Astle is the Gnodde Goldman Sachs Professor of Neuroinformatics at the Department of Psychiatry, University of Cambridge. He serves as a Programme Leader at the Medical Research Council's Cognition and Brain Sciences Unit (MRC CBU) and is a Fellow of Robinson College. Astle heads the 4D Lab (Development, Dynamics, Disorders, Data Science), which provides a research home for approximately 15 Early Career Researchers working at the intersection of developmental cognitive neuroscience and advanced data science methodologies. Astle's research focuses on understanding childhood development through innovative analytical approaches. His work employs transdiagnostic methods to study children with attention, learning, and memory difficulties, moving beyond traditional diagnostic categories. He investigates how neural systems develop in childhood, how they relate to developmental disorders, and how they respond to intervention. His research integrates network science, machine learning, and generative modeling to capture the complexity of neurodevelopmental diversity, examining how cognitive skills, literacy, numeracy, and mental health interrelate over developmental time. His publication record reveals a strong focus on brain connectivity and organization across development. Recent work explores structural and functional neurodevelopmental trajectories, brain wiring economics, and the impact of environmental factors on neural development. Astle's research frequently employs advanced data science techniques to identify sub-populations of children with different cognitive or brain profiles, regardless of diagnosis, and to map non-linear relationships between brain organization and cognitive difficulties. His work has increasingly focused on transdiagnostic approaches to understanding developmental disorders and the application of computational models to developmental neuroscience. Astle actively supervises PhD students and has built a substantial research group that contributes to major projects including the Centre for Attention Learning and Memory (CALM) and Resilience in Education and Development (RED). His work has been supported by prestigious funding bodies including the Royal Society, the British Academy, the Medical Research Council, and the Economic and Social Research Council, as well as multiple charitable foundations. The 4D Lab, under Astle's leadership, utilizes state-of-the-art facilities at the University of Cambridge, including on-site magnetic resonance imaging and magnetoencephalography scanners. The lab contributes to building specialist cohorts such as CALM (800 children with cognitive difficulties plus 200 comparison children) and RED, which study children's development, resilience, and educational outcomes. Astle's team explores how growing up in adverse environments affects children's brains, behavior, and mental health, with the aim of identifying early markers of risk and resilience.
Jinjin Gu is a tenure-track Assistant Professor at Sofia University "St. Kliment Ohridski" 's INSAIT (Institute for Computer Science, Artificial Intelligence, and Technology), leading research on visual cognition and intelligence. Her work spans visual perception, processing, generation, and reasoning. Education: Ph.D. in Electrical and Computer Engineering (2024), University of Sydney B.Sc. in Computer Science and Engineering (2020), Chinese University of Hong Kong, Shenzhen Her research focuses on visual cognition , including agentic systems , diffusion models , GAN architectures , model interpretability , super-resolution , and multimodal vision-language systems . She has developed novel paradigms like HYPIR for diffusion-quality restoration at GAN speeds. Recent publications highlight advancements in image/video restoration , generative modeling , and visual reasoning . Her work addresses critical challenges in model generalization , causal interpretation , and real-world application robustness . Scientific Awards: Stanford University's World's Top 2% Scientists (2024) Yunfan Award at World Artificial Intelligence Conference (WAIC) (2023) She has advised students contributing to TPAMI, CVPR, and ICLR publications, and serves as Area Chair for ICLR 2026, NeurIPS 2025, and ICML 2025.
Yogananda Isukapalli is a Teaching Professor and Vice Chair in the Computer Engineering Program at the Electrical and Computer Engineering Department , University of California, Santa Barbara . He joined the faculty in Winter 2017 after a career as a staff scientist at Broadcom (2010–2017), where he designed Wi-Fi chips (11n/11ac/11ax) and worked on underwater wireless communication models during a postdoctoral stint at Scripps Institution of Oceanography (2009–2010). His PhD in Communication Theory and Systems from UC San Diego (2009) forms the basis of his expertise in wireless systems and digital design .
Dr. Patrick J. McNamara is an Associate Professor in the Department of Civil, Construction and Environmental Engineering at Marquette University's College of Engineering. He directs the McNamara Research Group, which focuses on understanding how chemicals from consumer products impact public health and the environment once they pass through water treatment systems. His research bridges environmental engineering and microbiology to address critical water quality challenges facing modern infrastructure. Dr. McNamara's educational background includes: Ph.D., 2012, Civil Engineering, University of Minnesota, Twin Cities M.S., 2008, Environmental and Water Resources Engineering, University of Texas at Austin B.S., 2006, Civil Engineering (Minor - Spanish for the Business Professions), Marquette University His research program investigates how consumer product chemicals impact engineering treatment processes that rely on healthy bacteria to treat water. The McNamara Research Group develops non-traditional treatment processes to remove these chemicals from water and mitigate their environmental effects. His work spans antibiotic resistance in water systems, micropollutant removal technologies, pyrolysis of biosolids, PFAS contamination, and electrochemical treatment processes. Specific areas include the impact of corrosion inhibitors on antibiotic resistance, removal of chemicals via drinking water treatment, environmental antibiotic resistant bacteria, beneficial biosolids reuse, and pyrolysis applications. Dr. McNamara's publication record demonstrates a strong focus on emerging water quality challenges, particularly the intersection of chemical contaminants and antibiotic resistance. His recent work examines corrosion inhibitors' impact on antibiotic resistance in drinking water, PFAS mitigation through advanced treatment processes, and environmental drivers of antibiotic resistance in stormwater systems. His research combines fundamental microbiology with practical engineering solutions to address complex water quality issues. Dr. McNamara has received numerous honors and awards: 2022 OCOE Outstanding Researcher Award from Marquette University Marquette University's Campus 2020 KEEN Rising Star Faculty Scholar Award from Provost Office (2019) Central States Water Environment Association Bill Boyle Outstanding Educator Award (2018) Way Klingler Young Scholar Award (Marquette University, 2018) Excellence in Review Award – Environmental Science & Technology (2017) Dr. McNamara has secured significant research funding as Principal Investigator on multiple projects, including NSF grants focused on mitigating antibiotic resistance in drinking water and studying the environmental impacts of quaternary ammonium compounds. His current research portfolio includes projects on PFAS removal through novel electrocoagulation-peroxidation processes, designing green stormwater infrastructure to combat antibiotic resistance, and removing contaminants from greywater using electrocoagulation technology in collaboration with industry partners like Kohler Company. The McNamara Research Group at Marquette University maintains strong collaborations with researchers across multiple institutions and works closely with water utilities and industry partners to translate research findings into practical solutions for water treatment challenges. Their work addresses critical infrastructure needs while protecting environmental and public health through innovative engineering approaches.
Shannon Johnson is an Associate Professor in the Department of Psychology and Neuroscience at Dalhousie University, concurrently affiliated with the Departments of Pediatrics and Psychiatry. She serves as Director of Clinical Training and Co-Director of the Dalhousie Centre for Psychological Health, which provides mental health services to underserved populations while training clinical psychology students. Dr. Johnson holds a BA from Kalamazoo College, MSc and PhD from the University of Victoria, and a Postdoctoral Fellowship from Indiana University. Her research focuses on enhancing well-being through nature connection interventions, understanding resilience mechanisms in pediatric populations, and improving diagnostic practices for neurodevelopmental disorders. She investigates the physical and cognitive benefits of nature exposure, barriers to nature connection, and behavioral change strategies. Her work bridges clinical and environmental psychology, with recent studies examining nature-based interventions for stress reduction, pain adaptation in youth with juvenile idiopathic arthritis, and moral foundations in autistic children. She has pioneered the concept of Indoor Nature Exposure (INE) as a health-promotion framework. Dr. Johnson’s lab collaborates with healthcare providers to develop scalable mental health interventions, particularly for underserved communities. Her training programs emphasize evidence-based practices and culturally responsive care. Key contributions include validating the role of nature in cognitive restoration and challenging clinical biases in autism assessment.
Kay James is an Associate Professor of Neuroscience and Education at Teachers College, Columbia University, and serves as Director of the Graduate Program in Neuroscience and Education and the Neurocognition of Language Lab. Their work focuses on neural mechanisms underlying language disorders, second language acquisition, and cognitive processes in schizophrenia. Key affiliations include Biobehavioral Sciences, Neuroscience and Education, Human Development, and Cognitive Science in Education. Research interests emphasize the neural basis of language processing in pathological contexts such as developmental speech disorders and schizophrenia, alongside second language acquisition in adults. Their interdisciplinary approach bridges cognitive neuroscience with clinical and educational interventions. Publications span studies on mismatch negativity in speech disorders, syntactic development in Arabic diglossia, and brain-behavior asymmetry in schizophrenia. Ongoing work explores voice-related cortical potentials and emotional face processing through electrophysiological methods. Labs and teams include the Neurocognition of Language Lab, focusing on language neurobiology and clinical applications. Grants and advising roles are not explicitly detailed in the provided materials.
Xiaohu Guo is a Professor of Computer Science at the University of Texas at Dallas specializing in computer graphics, computer vision, and geometric modeling. His research develops algorithms for 3D/4D reconstruction, virtual reality, medical imaging, and physics-based simulations. Professor Guo has received significant recognition including a Best Paper Award at SIGGRAPH (2023) and an NSF CAREER Award (2012). His current research focuses on dynamic human capture, deformable models, and medical image computation. Education: PhD, Stony Brook University MS, Stony Brook University BS, University of Science and Technology of China Research Funding: Recently secured a $500,000 NSF grant for developing open-source 4D reconstruction frameworks for real-time dynamic human capture (2021). Editorial Roles: Serves on editorial boards of Graphical Models , Computer Animation and Virtual Worlds , and IEEE Transactions on Visualization and Computer Graphics .
Dr. Edouard Boujo is a Scientist and Lecturer at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering (STI) and working in the Institute of Mechanical Engineering (IGM) and Laboratory of Fluid Mechanics and Instabilities (LFMI) . He also teaches in the SGM-ENS department of the School of Engineering. Scientist at EPFL STI IGM LFMI Lecturer at EPFL STI-SGM SGM-ENS His research focuses on Fluid Dynamics with expertise in Flow Stability , Flow Control , Aeroacoustics , Thermoacoustics , Fluid-Structure Interaction , and Coating Flow Dynamics . He employs advanced mathematical modeling and computational methods to study complex fluid behaviors. Recent publications highlight his work on stochastic modeling of fluid instabilities, adjoint-based optimization of flow systems, and nonlinear dynamics of coating flows. His 15 most recent papers cover topics ranging from symmetry-breaking bifurcations to spin coating optimization and noise-induced transitions in fluid systems. Dr. Boujo actively collaborates with institutions across Europe and New Zealand, mentoring PhD student Atharva Lagwankar . He has received research funding from the Swiss National Science Foundation for two PhD theses and contributes to major fluid dynamics conferences like the European Fluid Dynamics Conference and APS Division of Fluid Dynamics meetings. His laboratory work at LFMI involves experimental and computational studies of fluid instabilities, with applications in aerospace, mechanical engineering, and industrial coating processes. He develops adjoint-based control methods for optimizing flow systems and reducing drag in various fluid configurations.
