Marcelo Epstein is a Professor in the Department of Mechanical and Manufacturing Engineering at the University of Calgary's Schulich School of Engineering. He also holds an adjunct position in the Faculty of Humanities, focusing on Classics. His academic journey includes degrees from the University of Buenos Aires, Technion-Israel Institute of Technology, and the University of Calgary. Epstein is renowned for contributions to Continuum Mechanics, Differential Geometry, and Biomechanics, with over 189 peer-reviewed articles and several authored/edited books. His research spans material defects, growth mechanics, and geometric theories in solids. Notable recognitions include the Frank Spragins Technical Award (2020), Tullio Levi-Civita award (2014), and CANCAM Medal (2009). Epstein has advised 25 graduate students and pioneered courses like ENME 653 (Continuum Mechanics) and LATI 205/207 (The Latin of Science). He has held visiting roles at institutions worldwide, including Oxford, Paris VI, and Ben-Gurion University. Epstein’s work bridges engineering and humanities, exemplified by his book The Latin of Science , blending scientific texts with classical language studies. His research on material evolution, dislocations, and biomechanical modeling has significantly influenced applied mechanics and interdisciplinary education.
Walter Roberts is an Assistant Professor in the Psychiatry Department at Yale School of Medicine with a secondary appointment in Biomedical Informatics & Data Science. A licensed clinical psychologist specializing in substance use treatment, particularly alcohol and tobacco use disorders, he combines clinical expertise with technological innovation in his research program. Dr. Roberts received his PhD in Clinical Psychology from the University of Kentucky in 2016. His research program employs multiple methodologies to identify the causes and consequences of substance use in humans, with a focus on developing mobile biosensor platforms that can passively detect and predict substance use behaviors. These systems, built using machine learning and informatics techniques, aim to improve assessment and targeted delivery of interventions in clinical and research settings. He also investigates risk factors for hazardous alcohol use using clinical and epidemiological data and maintains an active interest in research ethics related to substance use research. Dr. Roberts' publication record reveals a strong focus on alcohol use disorders, with increasing integration of digital phenotyping and AI technologies in recent years. His work spans from traditional clinical studies to cutting-edge investigations using wearable technology and machine learning. The research shows a clear trajectory toward developing objective, real-time assessment methods for substance use behaviors, with particular attention to sex and gender differences in alcohol-related outcomes. Recent publications demonstrate growing interest in ethical considerations surrounding digital technologies in substance use research. MD2K Training Institute Summer Scholar (2023) Early Career Scientist Award from CHADD (2015) Enoch Gordis Award Finalist from Research Society on Alcoholism (2013) Dr. Roberts serves as a Sub Investigator on multiple clinical trials related to alcohol addiction and mental health, including studies examining the effects of acute stress and inflammation on drinking behavior, combining varenicline and guanfacine for smoking cessation, and investigating whether propranolol attenuates stress-induced drinking. His collaborative network includes frequent co-authors such as Sherry McKee, Terril Luce, MacKenzie Peltier, Yasmin Zakiniaeiz, and Bubu Banini. His research has been featured in YaleNews, highlighting work on using smartwatches to better understand psychiatric illness.
Dr. Gloria Roberts is a Research Fellow at the Black Dog Institute, affiliated with the University of New South Wales' Faculty of Medicine, School of Psychiatry. Her research focuses on identifying predictors of bipolar disorder development in high-risk populations, with particular emphasis on neural mechanisms of executive functioning and emotional processing. Location: Black Dog Institute, Hospital Road, Prince of Wales Hospital, Randwick NSW 2031 Contact: +61 2 9382 8324 | ORCID: https://orcid.org/0000-0002-1966-5120 Education Background: B.Sc in Applied Psychology (University College Cork, Ireland, 2002) M.Sc in Neuropharmacology (National University of Ireland Galway, Ireland, 2003) Diploma in Statistics (Trinity College Dublin, Ireland, 2006) PhD in Neuroscience (Trinity College Dublin, Ireland, 2008) Dr. Roberts' research program centers on the neural basis of emotional dysregulation characteristic of mood disorders, employing structural and functional Magnetic Resonance Imaging as her primary research tool. Her work integrates advanced neuroimaging analysis techniques including diffusion tensor imaging tractography, dynamic causal modeling, graph theory, and machine learning approaches. She maintains active collaborations with Queensland Institute of Medical Research (Brisbane), Neuroscience Research Australia (Sydney), and the Centre for Healthy Brain Ageing (Sydney). Analysis of Dr. Roberts' publication record (94 journal articles, 2 book chapters, 25 conference papers) reveals a consistent research trajectory focused on neurocognitive patterns in bipolar disorder. Her recent work increasingly incorporates machine learning techniques to identify predictive biomarkers, with a growing emphasis on longitudinal studies tracking high-risk populations. The interdisciplinary nature of her research bridges neuroscience, psychiatry, and computational methods to address fundamental questions about mood disorder development. Scientific Contributions: Extensive publication record across multiple formats (journal articles, book chapters, conference presentations) Development of innovative neuroimaging analysis techniques for bipolar disorder research Establishment of multi-institutional collaborations across Australia Integration of machine learning approaches with traditional neuroimaging methods Dr. Roberts actively mentors junior researchers and contributes to the broader scientific community through peer review activities and participation in research networks focused on mood disorders. Her work has significant implications for early intervention strategies and the development of novel therapeutic approaches for bipolar disorder.
Elijah Van Houten is a Full Professor at Université de Sherbrooke , Canada, specializing in Biomedical Engineering and Medical Imaging . His research focuses on Magnetic Resonance Elastography (MRE) , inverse problems, and nonlinear optimization for tissue mechanical property characterization. He is affiliated with the Centre de recherche du CHUS and leads international collaborations, including the Netherlands-funded VICI project Seismology of the Brain and NIH-funded High-Resolution, Anisotropic MR Elastography of the Brain . Doctorate in Engineering Science, Dartmouth College (2001) Bachelor's in Music and Mechanical Engineering, Tufts University (1997) Van Houten’s work integrates computational modeling, finite element analysis, and advanced imaging to study brain and liver tissue mechanics. Recent publications highlight applications of transversely isotropic models , poroelasticity , and multi-frequency MRE for improved diagnostic imaging. His research has been supported by grants from NIH , NWO , CIHR , and NSERC , totaling over $7 million. Scientific awards include the ISMRM Summa Cum Laude award . He has supervised projects on diabetic foot ulcers , breast cancer detection via wearable technology , and ultrasound-based liver disease diagnostics . Van Houten collaborates with institutions in the Netherlands, USA, France, and Mexico, advancing MRE techniques for clinical applications.
Miroslav Bulíček is an Associate Professor at the Mathematical Institute of the Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic. He has been with Charles University since 2006, progressing from Researcher to Senior Assistant Professor (2012-2021) and currently serving as Associate Professor since January 2022. He is also a Senior Researcher at the University Center for Mathematical Modeling, Applied Analysis and Computational Mathematics (MathMac) since 2014. His educational background includes a habilitation in Mathematics-Mathematical Analysis from Charles University (2021), a Ph.D. in Mathematical and Computational Modeling from Charles University (2003-2006), and a Master's degree in Mathematical modeling in physics and technology from Charles University (1998-2003). Bulíček's research focuses on Partial differential equations, Continuum thermodynamics, and Mathematical modelling . His work primarily addresses the mathematical analysis of nonlinear systems describing flows of incompressible fluids, with particular emphasis on thermodynamically compatible models, implicit constitutive relations, and viscoelastic rate-type fluids. He has made significant contributions to the understanding of existence, uniqueness, and regularity of solutions to complex fluid models, especially those describing far-from-equilibrium systems in continuum thermodynamics. His recent publications demonstrate a strong focus on advanced mathematical analysis of fluid models with applications in material sciences. The research trends show increasing sophistication in handling non-Newtonian fluids, stress-diffusion phenomena, and thermodynamically consistent models. His work bridges pure mathematical analysis with practical applications in continuum mechanics, particularly in the analysis of viscoelastic rate-type fluids with stress diffusion. NEURON Fund for Support of Science Award (2012) for the project "Qualitative analysis of incompressible Navier-Stokes-Fourier equations" Czech Mathematical Society Award for young researchers (2014) for publications during 2009-2013 Bulíček has successfully supervised multiple PhD students including Mark Dostalík, Michael Zelina, Michal Bathory, and Tomáš Los. He serves as principal investigator for the GAČR project 20-11027X "Mathematical analysis of partial differential equations describing far-from-equilibrium open systems in continuum thermodynamics" (2020-present). His research has been supported by various grants including GAČR project 18-12719S, GAČR 16-03230S, and ERC-CZ no. LL1202, demonstrating sustained funding for his research program. He is actively involved in the University Center for Mathematical Modeling (MathMac) and has organized several international conferences and workshops, including the "Modelling, partial differential equations analysis and computational mathematics in material sciences" conference in Prague (2024) and the "Mathematical Aspects of Fluid Flows" EMS School in Kácov (2024), contributing significantly to the mathematical community in fluid dynamics and partial differential equations.
Melissa Hooijmans is an Assistant Professor at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Behavioural and Movement Sciences and the Physiology department. She also holds a position in the AMS - Ageing & Vitality research group. Her research focuses on advancing MRI techniques for studying muscular dystrophies, skeletal muscle biology, and sports-related injuries. Key areas include diffusion tensor imaging, magnetic resonance imaging applications, and muscle fiber analysis. Her work integrates clinical and biomechanical perspectives, with recent studies on Becker muscular dystrophy, hamstring injury recovery, and exercise physiology. She actively contributes to interdisciplinary collaborations and has published extensively in peer-reviewed journals like NMR in Biomedicine and the Journal of Magnetic Resonance Imaging. Dr. Hooijmans teaches advanced courses such as Master Research Projects and Functional Anatomy, emphasizing hands-on research training for graduate students. She leads initiatives in compositional and functional MRI methodologies, aiming to improve diagnostic precision and therapeutic strategies for muscle-related disorders.
Ali Gooya is a Senior Lecturer (Associate Professor) in Machine Learning at the School of Computing Science, University of Glasgow, UK. His research focuses on probabilistic deep learning applied to medical imaging, particularly in cardiology and oncology, emphasizing semi/unsupervised methods due to sparse expert annotations. He holds a PhD in medical image analysis from the University of Tokyo (2007) and has held academic positions at the University of Leeds and Sheffield before joining Glasgow in 2022. Affiliations: Senior Lecturer in Machine Learning, University of Glasgow (2022–present) Lecturer in Computing, University of Leeds (2018–2022) Lecturer in Computing, University of Sheffield (2016–2018) Postdoctoral Researcher, University of Pennsylvania (2008–2011) Research Interests: Deep learning for medical imaging, probabilistic modeling, cardiac and cancer imaging, computational anatomy, and marker discovery. Key applications include motion analysis, segmentation, and predictive modeling in healthcare. Key Achievements: Won prestigious fellowships including Allen Touring Institute (2022), JSPS Short-Term (2020), Marie-Curie IIF (2014), and JSPS-PDRA (2008). Pioneered Bayesian deep learning frameworks for cardiac motion assessment and generative models in medical imaging. Grants & Supervision: EPSRC Impact Acceleration Award (PI) EPSRC New Investigator Grant (EP/S012796/1) Actively supervising PhD students in areas like Bayesian deep atlases for cardiac motion analysis. Labs & Teams: Leads research in medical AI within the School of Computing Science, collaborating on projects integrating imaging and patient metadata for clinical decision support.
Jian Tang is an Assistant Professor at HEC Montreal and the Montreal Institute for Learning Algorithms (MILA), as well as an Associate Professor at the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also affiliated with IVADO (Institut de valorisation des données) as a member. His research spans multiple institutions including collaborations with leading biology labs worldwide and access to extensive computational resources through industry partners. Ph.D. in Computer Science, Peking University (2009-2014) Visiting Ph.D. student, University of Michigan (2011.10-2013.8) B.S. in Mathematics, Beijing Normal University (2005-2009) Professor Tang's research focuses on the intersection of deep learning and graph theory, with particular emphasis on geometric deep learning, knowledge graph reasoning, and applications in drug discovery. His work bridges symbolic and neural approaches to create robust reasoning systems that can handle complex structured data. He has pioneered techniques in graph representation learning that have significantly advanced the field of molecular property prediction and protein design. His publication record shows a clear trajectory toward applying geometric deep learning to biological problems, with a growing emphasis on protein design, molecular conformation generation, and multi-omics analysis. Recent work demonstrates sophisticated integration of 3D geometry with deep learning architectures to model complex biomolecular interactions. Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) Tencent AI Lab Rhino-Bird Gift Fund Amazon Faculty Research Award Microsoft-Mila collaboration grant National Research Council Canada (NRC) Collaborative Research and Development Grant Professor Tang actively mentors doctoral and master's students, with six recent graduates working on cutting-edge topics including graph neural networks for reasoning, protein design, and molecular representation learning. His research is supported by substantial funding from industry partners including Microsoft, Amazon, and Tencent, as well as government agencies like NRC. He collaborates extensively with biology labs worldwide, applying AI to solve real-world biomedical challenges. He leads a research group focused on geometric deep learning for drug discovery, with active projects in protein design using geometric-aware models and large language models for multi-omics analysis. The group has access to thousands of GPUs through industry collaborations, enabling large-scale experiments in molecular simulation and generative modeling.
Professor Donna Slonim is a leading computational biologist at Tufts University with dual appointments in the Department of Computer Science and Department of Immunology , and membership in the Genetics, Molecular and Cellular Biology program. She holds a Ph.D. from MIT (1996) and focuses on integrating genomic data with algorithmic approaches to advance disease diagnosis and treatment. Education Ph.D., MIT, 1996 M.S., University of California, Berkeley, 1991 B.S., Yale University, 1990 Research Interests include: Algorithm development for biological network analysis Precision medicine applications in human development Pharmacogenomics and drug discovery Temporal gene expression modeling Machine learning for biomedical data Scientific Awards : Best Student-Led Paper Award at ACM-BCB 2022 Academic Leadership : Teaches courses like Computational Biology , Statistical Bioinformatics , and Biological Networks Co-leads the BCB Group at Tufts On sabbatical during 2025-26 but remains active in research
Jessica D. Rosarda PhD is an Assistant Professor in the Department of Anatomy, Physiology and Genetics at the Uniformed Services University (USU) of the Health Sciences School of Medicine in Bethesda, MD. She leads a research laboratory focused on cellular stress mechanisms in military-relevant disorders. PhD in Chemical and Biological Sciences, The Scripps Research Institute (2023) MSc in Pharmacy, University of Florida (2013) BSc in Biology, Washington and Lee University (2010) Dr. Rosarda's research centers on cellular stress response pathways, particularly how cells respond to stress through signaling mechanisms that can either protect or damage tissues. Her work spans chemical biology, molecular medicine, neuroscience, and molecular/cell biology with a focus on proteostasis, unfolded protein response, and stress signaling dynamics in disease contexts. She investigates how imbalances in these pathways contribute to conditions ranging from retinal degeneration to traumatic brain injury, with particular relevance to military medicine. Analysis of Dr. Rosarda's publication record reveals a strong focus on stress response pathways, particularly the unfolded protein response and integrated stress response. Her work demonstrates how perturbations in proteostasis contribute to diverse pathologies including neurodegeneration, amyloidosis, and inflammatory conditions. She employs chemical biology approaches to develop therapeutic strategies targeting these pathways, with several publications on pharmacological modulators of stress responses. Dr. Rosarda maintains an active research program with numerous publications in high-impact journals including Nature Communications, Cell Chemical Biology, and ACS Chemical Biology. Her work frequently involves collaborations with the Wiseman laboratory and other researchers in the fields of proteostasis and stress response. Dr. Rosarda's laboratory at USU focuses on defining stress pathway signaling dynamics in military-relevant disorders, determining the metabolic factors that govern these stress responses, and identifying novel approaches for resolving toxic stress involved in both acute and chronic conditions.
Dr. Gunjan Y. Parikh serves as an Associate Professor in the Department of Neurology at the University of Maryland School of Medicine, where she also functions as the Medical Director of the UMMC Neuro Critical Care Unit. Her clinical practice focuses on the intersection of neurology and critical care medicine, with particular expertise in managing patients with acute neurological emergencies including traumatic brain injury, intracerebral hemorrhage, and subarachnoid hemorrhage. Dr. Parikh completed her undergraduate education with a BA in Biochemistry from The University of Texas at Austin, followed by her MD from Texas A&M College of Medicine. Her clinical training includes an Internal Medicine internship at St. Vincent's Hospital - Manhattan, Neurology residency at Barrow Neurological Institute at Saint Joseph's Hospital and Medical Center (where she also served as Chief Resident), and specialized fellowship training in Neurocritical Care/Vascular Neurology at Columbia University Medical Center/Weill Cornell Medical Center. She further enhanced her research skills through an NINDS Research Fellowship in Neuroimaging at the National Institutes of Health. Her research program centers on identifying biomarkers during the resuscitation phase of patients with acute brain injuries that determine lesion repair, restoration of function, and recovery of consciousness. This work has led to significant contributions in characterizing the MRI signature of primary microvascular injury after head trauma and other acute brain injuries through both in vivo and ex vivo investigations. Her lab integrates multimodality monitoring with advanced neuroimaging techniques to translate findings to clinical care and develop outcome measures for clinical trials targeting currently untreatable aspects of brain injury such as demyelination and neurodegeneration. Analysis of Dr. Parikh's recent publications reveals a strong focus on translational neuroscience with emphasis on neuroimaging techniques, biomarker discovery, and clinical applications for acute brain injury management. Her work spans from basic science investigations of microvascular injury to clinical studies on patient outcomes, with particular attention to traumatic brain injury, intracerebral hemorrhage, and subarachnoid hemorrhage. Recent publications demonstrate increasing integration of data science approaches for predicting neurological deterioration and developing automated clinical assessment tools. Fellow of the Neurocritical Care Society (2024) Member, American Academy of Neurology Member, Neurocritical Care Society Member, National Neurotrauma Society Dr. Parikh serves as Site Co-PI for the DISCOVERY study (Determinants of Incident Stroke Cognitive Outcomes and Vascular Effects on Recovery) under NINDS/NIA U19 NS115388, and as SubK site-PI for the REACH-ICH study (Race/Ethnicity, Hypertension and Prevention of VCID and Stroke after Intracerebral Hemorrhage) under NINDS/NIH 5R01NS093870-08. Her research has been featured in multiple media outlets including MD Edge Psychiatry, News Medical, and SciTech Daily, highlighting her contributions to understanding vascular injury in traumatic brain injury. As Medical Director of the UMMC Neuro Critical Care Unit, Dr. Parikh leads a multidisciplinary team focused on providing high-reliability, quality care with emphasis on patient experience. Her research group actively investigates cerebral hemodynamics, neuroimaging biomarkers, and clinical decision support tools for patients with acute neurological conditions, with particular attention to improving triage and interhospital transfer of neuroscience patients.
Dr. Chris A. Flask is a Professor at the Case Western Reserve University School of Medicine, with joint appointments in Radiology, Pediatrics, and Biomedical Engineering. He serves as Co-Director of the Imaging Research Core and Associate Director of the Medical Scientist Training Program, while also contributing to the Cancer Imaging Program at the Case Comprehensive Cancer Center. Research Focus Quantitative Magnetic Resonance Imaging (MRI) MRI Physics and Pulse Sequence Design Lung Imaging in Cystic Fibrosis Kidney Imaging in Polycystic Kidney Disease, Sickle Cell Disease, and Diabetic Nephropathy Liver Imaging for Inflammation and Fibrosis Scientific Recognition Distinguished Investigator Award 2023 from The Academy for Radiology and Biomedical Imaging Research Reviewer with Distinction for Magnetic Resonance in Medicine Semi-Finalist for ISMRM Young Investigator Award 2013 His recent publications focus on pH-responsive imaging agents, MR fingerprinting techniques, and applications in pediatric and adult diseases. Dr. Flask's work bridges technical MRI innovation with clinical translation across multiple organ systems.
Professor Georg Gottwald is a distinguished academic in the School of Mathematics and Statistics at the University of Sydney, where he has been a faculty member since 2002, progressing from Lecturer to his current position as Professor since 2013. He also holds a Visiting Professor position at the University of Surrey in the UK since 2013. His extensive research career spans dynamical systems theory, geophysical fluid dynamics, and the intersection of machine learning with complex systems. Professor Gottwald's research focuses on dynamical systems theory as an abstract formalism for studying systems evolving in time and space. His work has significant applications across diverse fields including climate modeling, biological systems, and complex networks. He is particularly known for developing methods for model reduction of complex dynamical systems, stochastic modeling approaches, and the application of machine learning techniques to dynamical systems. His research aligns with the Faculty of Science Research Strengths in Understanding the Universe, Fundamental Laws of Nature, Complex Systems, Climate and Environmental Change, Data and Decisions, and National Security. His most recent publications demonstrate a strong trajectory toward integrating machine learning with dynamical systems theory, particularly in developing stable generative models, learning dynamical systems with random feature maps, and combining data assimilation with machine learning for forecasting. His work spans pure mathematical theory to practical applications in climate science, finance, and biological systems, showing remarkable breadth while maintaining deep mathematical rigor. Future Fellowship, 'Stochastic methods in mathematical geophysical fluid dynamics', Australian Research Council, 2010-2014 Australian Research Fellowship, 'Stochastic methods in mathematical geophysical fluid dynamics', Australian Research Council, 2010-2015 (declined) Australian Research Fellowship, 'Geometric methods in geophysical fluid dynamics', Australian Research Council, 2004-2009 Professor Gottwald has successfully supervised numerous PhD and Master's students who have gone on to academic and industry positions worldwide. His current research group includes postdocs and PhD students working on machine learning for dynamical systems, stochastic model reduction, physics-informed machine intelligence, and tensor methods for scientific machine learning. He has secured multiple ARC Discovery Project grants and has been involved in significant international collaborative research projects. He is actively involved with the Sydney Dynamics Group, which he co-founded in 2007, fostering collaboration between the University of Sydney and UNSW. Professor Gottwald maintains strong editorial commitments as Associate Editor for Geophysical and Astrophysical Fluid Dynamics, SIAM Journal of Applied Dynamical Systems, and Journal of Computational Dynamics, and serves on the Editorial Advisory Board for Chaos and the Editorial Board for Physical Review E. His professional activities demonstrate leadership in the dynamical systems community through organizing workshops, seminars, and special journal issues.
Dr. Tae Twomey is a Research Fellow in Imaging Neuroscience at University College London (UCL). Their work focuses on the intersection of cognitive neuroscience and language processing, utilizing advanced brain imaging techniques to investigate how the brain processes language across different populations and writing systems. Dr. Twomey's research interests span multiple areas within cognitive neuroscience: Neuroscience of language processing Brain imaging techniques applied to linguistic studies Comparative studies of language processing across different writing systems (including Japanese and Chinese) Effects of auditory experience on brain function Neural mechanisms underlying reading and visual word recognition Dr. Twomey's publication record demonstrates expertise in examining language processing across diverse populations, including studies comparing deaf and hearing adults, investigations of bilingual processing in Japanese readers, and cross-linguistic studies of dyslexia. Their work frequently employs functional neuroimaging methods to uncover the neural basis of language and reading processes. Dr. Twomey has received recognition for their research, with publications in high-impact journals such as NeuroImage, Journal of Neuroscience, and Brain. As an educator, Dr. Twomey has served as: BSc Seminar Leader, Experimental Psychology, UCL (2020/21) Psychology Writing Lab instructor (2020/21) Module Convenor, MSc Introduction to the Brain and Imaging the Brain (2015/16 & 2017/18) Module Convenor, MSc Neuroscience of Language (2015/16)
Jordan Cotler is an Assistant Professor of Physics at Harvard University, affiliated with the Department of Physics within the Faculty of Arts and Sciences. He holds a BS in physics and mathematics from MIT (2015) and a PhD in physics from Stanford University (2020). Before joining Harvard's faculty, he served as a Junior Fellow at the Harvard Society of Fellows from 2020 to 2024. His research focuses on the intersection of quantum information, computation, and spacetime physics. Key interests include quantum algorithms for analyzing many-body and quantum gravitational systems, information-theoretic frameworks for chaotic dynamics, and non-perturbative methods in quantum cosmology and field theory. Cotler's work has advanced quantum algorithm design for experimental platforms and contributed to understanding black hole microstructure and cosmological spacetimes. He has been recognized with prestigious early-career awards, including his Harvard Society of Fellows Junior Fellowship. His publications span foundational topics such as quantum gravity, holography, computational complexity, and quantum chaos, reflecting a multidisciplinary approach to theoretical physics.
