Dr. Kewei Sha is an Associate Professor at the University of North Texas (UNT) in the College of Information. Previously, he held positions as Associate Professor and Department Chair at Oklahoma City University (OCU) and the University of Houston-Clear Lake (UHCL). He earned a Ph.D. and M.S. from Wayne State University and a B.S. from East China University of Science and Technology. His research focuses on Data Analytics, Edge Computing, Security and Privacy, Blockchain, and IoT, with over $5M secured in research grants from NSF, NASA, and other institutions. He has published over 70 peer-reviewed papers and serves as an Associate Editor for IEEE IoT Journal, Smart Health, and Computing. Dr. Sha has received awards including the UHCL President’s Outstanding Research Award and IEEE Outstanding Leadership Award, and holds senior membership in ACM and IEEE. He chairs conferences like IEEE MOST 2024 and ACM/IEEE SEC 2023. His recent work includes securing NSF funding for edge-based multi-robot systems and NSF Noyce Scholarships for STEM teacher training. Key projects involve EdgeBrain for edge video analytics and ElasticEdge for live video processing. He has pioneered frameworks for data governance, secure cloud storage, and disaster rescue UAV systems. Dr. Sha also develops educational tools like AR therapy systems for autistic children and network forensics labs for teaching institutions.
Panagiotis Toulis (Panos) serves as Associate Professor of Econometrics and Statistics and John E. Jeuck Faculty Fellow at the University of Chicago Booth School of Business, where his research focuses on model-agnostic causal inference methods for complex networked environments using randomization techniques that provide robustness beyond classical statistical approaches. His academic credentials include: Ph.D. in Statistics from Harvard University (2016) under Edo Airoldi, David Parkes, and Don Rubin Master’s in Computer Science from Harvard University (2011) under David Parkes M.Eng. in Electrical and Computer Engineering from Aristotle University of Thessaloniki, Greece (2006) Toulis pioneers randomization and permutation tests for causal inference under network interference, developing methods applicable to experimental design in complex domains where traditional model-based approaches fail. His work bridges statistics , optimization , and economics , with significant contributions to invariance-based inference for observational studies and machine learning integration through stochastic gradient descent frameworks. This research has practical applications in business analytics, vaccine distribution, and economic policy. Analysis of his 15 most recent publications (2023-2025) reveals dominant trends in extending randomization tests to handle network spillover effects, two-sided market experiments, and temporal dynamics with treatment habituation. A growing emphasis on machine learning assistance for complex treatment effect detection demonstrates his innovative fusion of statistical rigor with computational scalability, consistently providing finite-sample theoretical guarantees uncommon in asymptotic-focused literature. Toulis has earned recognition through prestigious awards: Arthur P. Dempster Award from Harvard University’s Department of Statistics LinkedIn Economic Graph Challenge award 2012 Google United States/Canada PhD Fellowship in statistics He teaches core Booth courses including Applied Regression Analysis (41100), Causal Inference for Business Applications (41207), and the Econometrics and Statistics Colloquium (41600), while maintaining active industry connections through prior software engineering roles at Google and Greek startups. His research appears in top journals including Journal of the Royal Statistical Society and Annals of Statistics, addressing critical methodological challenges in modern data science. No dedicated laboratory structure is documented, but his work inherently involves collaborative research networks across statistics, economics, and computer science disciplines through the Booth School’s academic infrastructure.
Connor Esterwood serves as Assistant Professor in the Department of Technology, Information Systems, and Analytics at Wayne State University's Mike Ilitch School of Business, joining the faculty in August 2025. His interdisciplinary work bridges human-robot interaction, artificial intelligence, and organizational behavior to optimize human-machine collaboration in professional settings. His educational foundation includes: Ph.D. in Information (Human-Robot Interaction concentration), University of Michigan, 2025 M.S. in User Experience and Assessment, University of Tennessee, Knoxville, 2018 B.A. in International Relations, University of North Carolina at Asheville, 2013 Dr. Esterwood's research centers on the lifecycle of trust in robotic systems, specializing in trust repair mechanisms following violations through strategies like apologies, denials, and expectation management. His work examines how individual differences and contextual factors influence trust dynamics in collaborative environments, with significant contributions to meta-analytic methods in human-robot interaction. This research provides critical design principles for preventing over-trust in automated systems while enhancing safety in human-machine teams. His 30+ publications since 2019 demonstrate consistent focus on trust repair meta-analyses (2023-2025), embodied robotics comparisons (2025), and security/autonomous vehicle applications (2023-2024). Recent work emphasizes experimental rigor through open-source platforms like the Warehouse Robot Interaction Sim, addressing replicability challenges while exploring trust dynamics across diverse modalities and risk scenarios. Dr. Esterwood has secured international research funding supporting his investigations into ethical trust manipulation and safety-critical human-robot collaboration. His teaching portfolio includes graduate courses on emerging technologies and business information systems, with anticipated mentorship of master's students in human-centered AI design. Current initiatives involve developing standardized reporting frameworks for HRI studies and extending trust models to essential worker contexts, particularly in warehouse automation and security robotics.
Henrik Walter is a Full Professor (W3) of Psychiatry with a focus on Psychiatric Neuroscience and Neurophilosophy at Charité – Universitätsmedizin Berlin. He serves as Director of the Mind and Brain Research Division and Deputy Medical Director (Research) at the Department of Psychiatry and Psychotherapy, Charité Campus Mitte. Walter is also a faculty member at the Berlin School of Mind and Brain , a faculty member of the Bernstein Computational Center Berlin , and a principal investigator at the Berlin Center for Advanced Neuroimaging . Clinical expertise: Schizophrenia and affective disorders Empirical research: Working memory, volition, reward mechanisms, emotion regulation, mentalization, imaging genetics, connectomics Philosophical research: Philosophy of mind, neurophilosophy, neuroethics, philosophy of psychiatry Research trends in his 15 most recent publications (2009-2024) emphasize connectome-based machine learning , dynamic network reconfiguration , self-control mechanisms , and predictive models for psychiatric relapse . His work explores the intersection of neural network organization , emotional processing , and philosophical frameworks in mental health. Walter oversees major projects including environMENTAL (data harmonization in large cohorts) and FOR5187 PREACT (personalized psychotherapy). His team actively trains students and researchers through internships, theses supervision, and doctoral programs.
Pooya Hatami is an Associate Professor in the Department of Computer Science and Engineering at Ohio State University's College of Engineering. He joined the CSE department in 2019 after completing postdoctoral research at UT Austin (hosted by David Zuckerman) and spending two years as a postdoctoral researcher at the Institute for Advanced Study at Princeton and DIMACS at Rutgers University. He earned his Ph.D. from the University of Chicago in 2015 under the supervision of Alexander Razborov and Madhur Tulsiani. His research interests focus on Theoretical computer science , with particular emphasis on Randomness and Pseudorandomness, Communication Complexity, Analysis of Boolean Functions, Additive Combinatorics, and Learning Theory . His work often bridges theoretical computer science with mathematical concepts, developing rigorous frameworks for understanding computational complexity and designing efficient algorithms. His research has been supported by NSF grant CCF-1947546, demonstrating the significance and impact of his contributions to the field. An analysis of his recent publications reveals a strong focus on the intersection of learning theory and computational complexity, with particular attention to replicability in machine learning, communication complexity lower bounds, and the theoretical foundations of Boolean functions. His work on the Implicit Graph Conjecture and Borsuk-Ulam applications to learning theory demonstrates innovative approaches to longstanding problems in theoretical computer science. Scientific Awards: Best Paper Award at ICALP 2023 for "Online Learning and Disambiguations of Partial Concept Classes" Professor Hatami actively mentors graduate students, currently advising Pushen Wang and Yuting Fang in Computer Science and Engineering, and Chavdar Lalov and Sivan Tretiak in Mathematics. His involvement in program committees for major conferences like SODA 2024 and FOCS 2025 highlights his standing in the theoretical computer science community. His research has established important connections between pseudorandomness, communication complexity, and learning theory, contributing significantly to our understanding of fundamental computational limits.
Namshik Han is a computational drug discovery scientist currently serving as Head of Computational Research & AI at the Milner Therapeutics Institute and Associate Faculty at the Cambridge Centre for AI in Medicine, both within the University of Cambridge. He also holds an Adjunct Professor position at Yonsei University College of Medicine. His work bridges academic research with industry applications through co-founding two startups: KURE.ai Therapeutics in the USA (focused on immune-oncology NK cell therapy) and CardiaTec Biosciences in the UK (dedicated to cardiovascular disease drugs), as well as contributing to the establishment of Storm Therapeutics. Dr. Han's research centers on developing and applying specialized Artificial Intelligence technologies to analyze complex multi-modal biomedical datasets. His lab at the Milner Therapeutics Institute focuses on enhancing AI technology for therapeutics research through machine learning, statistical analysis, and mathematical techniques applied to multi-omics and drug discovery. Key research areas include identifying novel therapeutic targets across diseases, drug repositioning, predicting drug efficacy and safety, and patient stratification for personalized medicine. His work leverages both publicly available big data and purposefully generated experimental data from academic collaborators and pharma/biotech partners. Analysis of Dr. Han's recent publications reveals a strong trend toward applying AI methodologies across diverse biomedical challenges. His work spans RNA methylation pathway prediction, drug-induced liver injury detection through NLP, cancer immunotherapy target identification using CRISPR screens, and SARS-CoV-2 pathway analysis for drug repurposing. These publications demonstrate his expertise in translating computational approaches to address specific therapeutic challenges across multiple disease areas. Dr. Han actively facilitates access to state-of-the-art AI technology for partner organizations within the Milner Consortium while developing novel computational methods. His entrepreneurial activities through startup companies indicate significant engagement with translational research and commercialization of academic discoveries. His laboratory utilizes interdisciplinary approaches combining computational methods with experimental validation across therapeutic areas. The lab has developed innovative approaches such as utilizing Artificial Neural Networks to predict the Mode of Action of drugs by simulating drugs on protein-protein interaction networks, as published in Science Advances (2021). This work exemplifies their strategy of bridging virtual simulations with real-world biological validation to advance therapeutic discovery.
Joo Won Park is an Associate Professor of Music Technology at Wayne State University, creating experimental electroacoustic compositions through electronics, toys, and everyday objects. His work has been featured at international festivals (ICMC, SEAMUS) and released across multiple labels (MIT Press, PARMA). He received the Kresge Arts Fellowship (2020) and Knight Arts Challenge Detroit (2019). Teaches courses like Theories of Electronic Music and Advanced Synthesis Director of Electronic Music Ensemble of Wayne State (EMEWS) which toured nationally Park's research focuses on making mundane sounds musical through creative coding and hardware manipulation. His 100 Strange Sounds YouTube series and Overundertone album archive his exploration of sonic possibilities in everyday environments. He emphasizes portability, affordability, and replicability in electronic instrument design. His electroacoustic works explore themes like human-machine interaction , game controller performance , and playful sound manipulation . The PS Quartet No.1 exemplifies his interest in video game muscle memory and controlled randomness in live performance. Scientific awards include: Knight Arts Challenge Detroit (2019, 2021) Kresge Artist Fellowship ($25,000, 2020) New Music USA Grant (2019) Active in the electroacoustic community as: Board member, Society for Electroacoustic Music in the United States Editorial member, Korean Electroacoustic Music Society Associate Director, Third Practice Electroacoustic Music Festival
F. Javier Arsuaga is a Professor in the Department of Molecular and Cellular Biology at the University of California, Davis, with affiliations in the Mathematics Department and multiple graduate groups including Biochemistry, Molecular, Cellular and Developmental Biology; Biostatistics and Statistics; and Mathematics. Position: Professor of Molecular and Cellular Biology Institution: University of California, Davis Office: Briggs Hall 0009 and MSB 2115 Email: jarsuaga@ucdavis.edu Graduate Group Affiliations: Biochemistry, Molecular, Cellular and Developmental Biology; Biostatistics, Statistics; Mathematics Dr. Arsuaga received his BS in Mathematics from Universidad de Zaragoza, Spain in 1993 and his PhD in Mathematics from Florida State University in 2000. His academic journey reflects a unique interdisciplinary path that bridges pure mathematics with molecular biology. His research focuses on developing mathematical and computational methods to address questions related to the 3D structure of chromosomes. The three-dimensional organization of the genome plays a critical role in cellular processes such as transcription, replication, and repair. His work has particular relevance to understanding DNA packaging in bacteriophages, mitochondrial DNA in trypanosomes (kDNA), and yeast. Dr. Arsuaga employs tools from low-dimensional topology, computational knot theory, random knotting, algebraic topology, persistence homology, combinatorics, statistics, and Monte Carlo methods. His laboratory, the Topological Molecular Biology Lab, is deeply committed to promoting diversity in mathematics and the sciences. An analysis of his recent publications reveals a consistent focus on applying topological methods to biological problems, particularly in three main areas: DNA topology in viral systems, kinetoplast DNA network analysis in trypanosomes, and cancer genomics. His work demonstrates how mathematical approaches can provide novel insights into genome organization, with applications ranging from understanding basic biological mechanisms to identifying patterns in cancer genomes. 2018 Plenary speaker: Abel Symposium, Geiranger, Norway 2018 Plenary speaker: Encuentros en Algebra Computacional y Aplicaciones (EACA), Zaragoza, Spain 2013-2014 Long Term Visitor of the Institute of Mathematics and Its Applications (IMA), Minneapolis, MN 2012 Plenary speaker: Conference in Computational Physics, Kobe, Japan 2011 Research selected by NSF for the NSF highlights As an advisor, Dr. Arsuaga has mentored numerous students including Maxime Pouokam and Rachael Phillips who completed Master's theses under his guidance. His laboratory has been supported by multiple National Science Foundation grants including DMS1519375, DMS1057284, and DMS0920887. He co-organizes the Biology and Mathematics in the Bay Area (BaMBA) conference, which encourages dialogue between researchers from different disciplines. His lab is known for being interdisciplinary and vertically integrated, bringing together mathematicians, biologists, and computational scientists to tackle complex biological problems. The Topological Molecular Biology Lab conducts research at the interface of mathematics and molecular biology, focusing on applications of topological methods to understand genome organization, study genome rearrangements in cancer, and model enzymatic actions such as those of recombinases and topoisomerases. The lab is known for developing innovative mathematical approaches to analyze complex biological structures and has made significant contributions to understanding DNA topology in various biological contexts.
Christian Scheideler is a Professor at the Institute for Computer Science (Theory of Distributed Systems) within the Faculty of Electrical Engineering, Computer Science, and Mathematics at the University of Paderborn. He serves as institute director, advisory committee member of SPAA, and associate editor for Journal of the ACM and Journal of Computer and System Sciences . His editorial and leadership roles extend to managing Journal of Interconnection Networks and steering committees for conferences like DISC, SIROCCO, and ALGOSENSORS. Director, Institute for Computer Science Advisory Committee, SPAA Associate Editor, Journal of the ACM and Journal of Computer and System Sciences Managing Editor, Journal of Interconnection Networks Steering Committee member for DISC, SIROCCO, ALGOSENSORS His research focuses on distributed algorithms, data structures, security in distributed systems, randomized algorithms, stochastic processes, network theory, and discrete mathematics . Notably, he investigates hybrid programmable matter systems, self-stabilizing overlay networks, and robust distributed protocols under adversarial conditions. Recent publications (2021-2025) highlight work on 3D programmable matter shape formation , low-diameter graph decompositions , reconfigurable circuits in amoebot models , and blockchain lightweight state replication . Topics span distributed computing, computational geometry, and network security. Scientific awards include the 2022 Edsger W. Dijkstra Prize in Distributed Computing for foundational work in self-stabilization and overlay networks. He has served as PC Chair (DISC 2022) and organized numerous conferences (SPAA, SSS, SIROCCO).
Ricardo Eiris is an Assistant Professor at Arizona State University's School of Sustainable Engineering and the Built Environment. His research integrates emerging technologies to enhance human performance in construction workforce training and education. Education Ph.D. in Design, Construction, and Planning, University of Florida (2020) M.S. in Digital Arts and Science, University of Florida (2020) M.S. in Construction Management, University of Florida (2016) M.S. in Civil Engineering, University of Florida (2016) B.S. in Civil Engineering, Universidad Metropolitana - Venezuela (2013) Research Focus His work spans three interconnected domains: (1) AI-Enabled Metaverse for workforce education transformation, (2) Extended Reality (AR/VR/MR) for hazard visualization, and (3) Drone Operations for inspection training. He develops immersive environments like iVisit and DroneSim to address safety and training gaps. Scientific Contributions 2024: 360-degree digital twins for task detection 2023: Social equity in PPE accessibility 2022: Multiuser VR site visit frameworks 2021: Drone flight visualization systems 2020: Comparative VR vs traditional safety methods Honors & Awards ASC VDC Faculty Boot Camp Fellowship (2024) ASC International Best Conference Paper Award (2023) ASC Mechanical & Electrical Faculty Fellowship (2022) ASCE ExCEEd Fellowship (2021) National Academies STEM Education Competition Winner (2020) Teaching & Leadership He teaches courses in construction technology and advanced research methods. Leads the Human-Centered Technology in Construction (HCTC) Research Group, focusing on technology-driven safety and workforce development.
Mårten Björkman is a Professor at the School of Electrical Engineering and Computer Science , Royal Institute of Technology (KTH) , specializing in Robotics, Perception and Learning . His research bridges 3D computer vision with multimodal sensor fusion, including force-torque sensing, to enhance human-robot interaction. Recent work focuses on modeling sequential data for robotic control, human movement prediction, and power systems. Key research areas include: Reinforcement learning for robotic systems Human movement analysis and prediction Implicit regularization in neural networks Online learning for dynamical systems Power system control with AI 3D perception through multi-view vision His recent publications span deep learning for assembly inspection , EEG-based brain-robot interfaces , safe reinforcement learning , and multimodal motion generation . The work intersects robotics, machine learning, and human-centric system design. The Robotics, Perception and Learning division at KTH hosts his research, equipped with state-of-the-art facilities for robotic vision and human interaction studies.
Brian Anthony Kelch is a Professor in the Department of Biochemistry and Molecular Biotechnology at UMass Chan Medical School, affiliated with the Morningside Graduate School of Biomedical Sciences. He holds additional roles in the Interdisciplinary Graduate Program and MD/PhD Program. Kelch earned his BS in Biochemistry & Molecular Biology from Pennsylvania State University and his PhD in Biochemistry & Molecular Biology from the University of California, San Francisco. His research focuses on structural and mechanistic studies of macromolecular machines , with emphasis on viral assembly mechanisms and DNA replication/repair complexes. The Kelch Lab employs cryo-EM, biochemical assays, and biophysical approaches to investigate clamp loaders, viral packaging motors, and DNA-protein interactions. Key areas include: Conformational dynamics in bacteriophage assembly Eukaryotic vs. prokaryotic clamp loader mechanisms Thermostable viral capsid engineering DNA damage signaling pathways Kelch's publications demonstrate consistent focus on structural virology (45%), DNA replication machinery (35%), and enzyme mechanisms (20%), with recent work exploring neurodevelopmental implications of viral proteins. His lab actively recruits postdoctoral researchers and collaborates with institutions like UCSF and the International Vaccine Institute.
Dr. Andrei A Korostelev is a Professor at UMass Chan Medical School, with primary appointments in the RNA Therapeutics Institute and the Department of Biochemistry and Molecular Biotechnology at the T.H. Chan School of Medicine. He also holds positions in multiple graduate programs at the Morningside Graduate School of Biomedical Sciences, including Biochemistry and Molecular Biotechnology, Biophysical Chemical and Computational Biology, Interdisciplinary Graduate Program, MD/PhD Program, and RNA Therapeutics and Biology Program. Dr. Korostelev received his BS and MS in Chemistry from Moscow State University, followed by a PhD in Chemistry & Biochemistry from Florida State University. His educational background provided the foundation for his current research in structural biology and molecular mechanisms of protein synthesis. Dr. Korostelev's research focuses on the structure and function of the ribosome, the cellular machine responsible for decoding genetic information and synthesizing proteins. His laboratory employs X-ray crystallography and cryo-electron microscopy to obtain high-resolution snapshots of different functional states of the ribosome during translation. Through biochemical approaches, his team tests hypotheses concerning the mechanisms and dynamics of the ribosome and translation factors. This research not only expands fundamental knowledge of this essential molecular machine but also aids in developing new drugs that target ribosomes, with potential applications in treating bacterial infections and other diseases. Dr. Korostelev's scientific contributions have been recognized with several prestigious awards, including the Soros Academic Fellowship from Moscow State University (1994-1995), the I.V. Berezin Young Scientist Award from Moscow State University (1996-1997), the RNA Society/Scaringe Young Scientist Award (runner-up, 2010), the Earl and Thressa Stadtman Scholar Award from ASBMB (2018), and the Early Career Award from the RNA Society (2018). As an active researcher and mentor, Dr. Korostelev supervises graduate students through rotation projects that apply X-ray crystallography and biochemical methods to understand ribosome mechanisms. His laboratory offers opportunities to investigate translation via mutagenesis/biochemical assays, crystallize and determine structures of translation factors and ribosome complexes, and improve computational methods for macromolecular structure determination. His extensive publication record demonstrates significant contributions to understanding translation termination, ribosome rescue mechanisms, frameshifting, and the structural dynamics of protein synthesis. Dr. Korostelev leads an active research laboratory focused on ribosome structure and function. His team combines structural biology techniques with biochemical approaches to investigate the molecular mechanisms of translation. Through collaborations with other researchers at UMass Chan Medical School and beyond, his laboratory continues to make significant contributions to our understanding of this fundamental biological process and its implications for human health and disease.
Dr. Romy Lorenz is a Max Planck Research Group Leader at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, where she leads the Cognitive Neuroscience & Neurotechnology research group. She previously held postdoctoral positions at the University of Cambridge, Stanford University, and the Max Planck Institute for Human Cognitive & Brain Sciences from 2018 to 2023 as a Sir Henry Wellcome Postdoctoral Fellow. Dr. Lorenz's educational background includes: BSc in Psychology from Leuphana University (2009) MSc in Human-Machine Interaction from TU Berlin (2012) PhD in Neurotechnology from Imperial College London (2017) Her research focuses on understanding the frontoparietal brain network mechanisms that underpin high-level cognition and adaptive behavior. She employs an interdisciplinary approach combining subject-specific brain-computer interface technology, fMRI at standard and ultrahigh magnetic field strengths (3T, 7T, and 9.4T), EEG, non-invasive brain stimulation, computational modeling, and machine learning techniques. A key innovation in her work is the development of neuroadaptive Bayesian optimization methods that allow for real-time, closed-loop experimental design in cognitive neuroscience. Dr. Lorenz's recent publications demonstrate a clear trend toward investigating brain function at increasingly fine-grained spatial scales, particularly exploring layer-specific processing in the prefrontal cortex using ultrahigh-field fMRI. Her work bridges computational neuroscience, cognitive psychology, and neurotechnology, with applications ranging from basic cognitive science to clinical rehabilitation. A significant portion of her research focuses on closed-loop systems that integrate real-time brain imaging with adaptive experimental design and stimulation protocols. Her notable scientific achievements include: Outstanding Contributions in AI Innovation Award Sir Henry Wellcome Postdoctoral Fellowship Funding from the German Scholar Organisation Fellowships from the Wellcome Trust and Engineering and Physical Sciences Research Council Dr. Lorenz has secured significant research funding for her work, including support from the Wellcome Trust and German funding agencies. She has mentored several students, including Master's students like Pedro who has presented their collaborative work at conferences. Her research group actively recruits postdoctoral researchers and students interested in cognitive neuroscience and neurotechnology. Dr. Lorenz leads the Cognitive Neuroscience & Neurotechnology research group at the Max Planck Institute for Biological Cybernetics, which focuses on developing and applying advanced neuroimaging and computational methods to understand high-level cognitive functions. She has also co-initiated interest groups such as CoCoNUT (Computational Cognitive Neuroscience at the Max Planck Institute) to foster interdisciplinary collaboration. Her lab utilizes cutting-edge technologies including real-time fMRI at ultrahigh field strengths, EEG, and non-invasive brain stimulation to investigate frontoparietal network function.
Guilherme Dias de Melo is a Brazilian-French neurovirologist and Chargé de Recherche (Research Professor) at the Institut Pasteur in Paris, France, within the Unité Lyssavirus, épidémiologie et neuropathologie . Since 2022, he has led research on neurotropic viruses, focusing on SARS-CoV-2 and rabies virus , with a special emphasis on long COVID and viral persistence in the brain. Education: Habilitation à diriger des recherches (HDR), Université Paris Cité, 2025 Ph.D. in Medical and Surgical Pathophysiology, São Paulo State University (UNESP), Brazil, 2015 M.Sc. in Veterinary Medicine, UNESP, Brazil, 2012 D.V.M. (Doctor of Veterinary Medicine), UNESP, Brazil, 2010 Research Interests: Guilherme is deeply invested in understanding how neurotropic viruses such as SARS-CoV-2 and rabies virus invade the central nervous system, persist at low levels, and trigger long-term neurological symptoms like anxiety, memory loss, and depression. His work integrates cellular models , in vitro stem cell systems , and animal models to dissect the molecular mechanisms of viral neuroinvasion and neuroinflammation. His research spans: Neurotropic virus pathogenesis Long COVID and post-viral syndromes Viral persistence in brain tissue Neuroinflammation and immune responses in the CNS Rabies virus as a model for CNS infection Development of antiviral therapies and neutralizing antibodies Research Trends: His recent publications (2022–2025) reveal a strong focus on long COVID neurobiology , including transcriptomic profiling of brainstem changes in hamster models, the role of viral persistence in neurological symptoms, and the development of broad-spectrum neutralizing antibodies for both SARS-CoV-2 and rabies. He has also contributed to spatial transcriptomics , antiviral drug mechanisms , and immune imprinting in the context of viral variants. Scientific Awards & Recognition: While no formal awards are listed, his work has been recognized through high-impact publications and his role in the ANRS-MIE “Action coordonnée Covid long” group, a national initiative in France to address long COVID. Grants & Collaborations: Guilherme has worked across continents, including: Postdoctoral fellowships at Institut Pasteur (2015–2022) Visiting researcher at Institut Pasteur Korea (2018) Visiting scholar at Justus-Liebig University, Germany (2012) Active member of the ANRS-MIE long COVID initiative since 2022 Labs & Teams: He is part of the Lyssavirus, Epidemiology and Neuropathology Unit led by Hervé Bourhy at the Institut Pasteur Paris. The lab is equipped with state-of-the-art tools for studying viral neuroinvasion, including advanced microscopy, stem cell systems, and electrophysiology platforms.
