جست‌وجوی استاد و پژوهشگر

Peter Lushingمشاهده پروفایل

استاد

• Legal Evidence
• Judicial Proof
• Criminal Law
• +۴ مورد دیگر

Peter Lushing is a Professor of Law at the Benjamin N. Cardozo School of Law, Yeshiva University. Previously, he served as a trial attorney in the Legal Aid Society, administrative assistant district attorney in New York County, chief of the Appeals Bureau, and law clerk to Judge Wilfred Feinberg of the U.S. Court of Appeals for the Second Circuit. He has also held roles in private practice, academia, and legal organizations such as the New York Council of Defense Lawyers. Education: B.A., 1962 LL.B., 1965 (Columbia University) Research focuses on judicial proof dynamics, evidentiary inference, and the intersection of law with probabilistic reasoning. Notable themes include Bayesian analysis in legal contexts, the role of computation in adjudication, and the tension between quantitative methods and traditional legal principles. His work critiques adversarial systems while proposing frameworks for improving factual evaluation in courts. Scientific Awards: Kent Scholar (Columbia Law Review, 1st Year) Harlan Fiske Stone Scholar (twice awarded) Advising and grants: No formal students or grants listed, but his career includes extensive mentorship through legal practice and organizational leadership. Active in legal education and reform through think tanks and bar associations. Labs/Teams: No dedicated research labs, but collaborates with interdisciplinary groups on AI applications in legal evidence and judicial proof systems.

• Neuroscience
• Brain Mapping
• Medical Imaging
• +۳ مورد دیگر

Katrin Amunts is a University Professor at the Cécile and Oskar Vogt Institute for Brain Research of Heinrich-Heine-University Düsseldorf and heads the Institute for Neuroscience and Medicine (INM-1) at Forschungszentrum Jülich. Her research focuses on creating multi-level brain atlases through cytoarchitectonic, molecular, and fiber architecture analysis. Director of the Cécile and Oskar Vogt Institute for Brain Research Head of INM-1 at Forschungszentrum Jülich Pioneered the BigBrain ultra-high-resolution 3D human brain model Research Interests: Combining image analysis, high-performance computing, and big data analytics to map brain structure-function relationships, intersubject variability, and interspecies differences. Her work underpins the Human Brain Project. Key Publications: Her research output includes probabilistic cytoarchitectonic atlases, comparative studies of avian and human brain circuits, and ultra-high-resolution models. These focus on cortical organization, receptor mapping, and neuroethical frameworks.

Christian Lüscherمشاهده پروفایل

استاد

• Neuroscience
• Synaptic Plasticity
• Drug Addiction Mechanisms
• +۳ مورد دیگر

Professor Christian Lüscher is a leading neuroscientist at the University of Geneva, focusing on the cellular and synaptic mechanisms underlying drug addiction. His research explores how addictive substances alter neuroplasticity in the mesolimbic dopamine system, particularly in the ventral tegmental area (VTA). Key Research Areas: Synaptic plasticity in addiction, µ-opioid receptor dynamics, neuroadaptive changes, and developing novel addiction therapies through optogenetics and neuroimaging. Techniques: Electrophysiology, genetic and pharmacological tools, in vivo imaging, and behavioral models. Recent Publications: His 2024-2025 work includes groundbreaking studies on fentanyl reinforcement mechanisms, sweat-based biosensors for substance abuse monitoring, and insights into overeating neurobiology. Earlier articles focus on cocaine-induced synaptic changes, NMDA receptor roles, and circuit models for addiction. Collaborations: Dr. Lüscher works with international experts in psychiatry, biomedical engineering, and neuroimaging, including colleagues at University of Geneva's CMU (Center for Medical Education) and institutions in the U.S. and Europe.

• Algorithms
• Combinatorics
• Complexity Theory
• +۳ مورد دیگر

Sundar Vishwanathan is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay (IIT Bombay), where he has established himself as a leading researcher in theoretical computer science. His academic career spans over three decades with consistent contributions to algorithms, combinatorics, and complexity theory. His research interests form a cohesive body of work focused on theoretical foundations of computing: Algorithms (particularly approximation algorithms, online algorithms, and randomized algorithms) Combinatorics and extremal set theory Complexity theory and circuit lower bounds Graph theory and graph algorithms Combinatorial optimization problems Vishwanathan's publication record demonstrates remarkable consistency and depth, with publications spanning from 1990 to 2024. His recent work focuses on graph algorithms (particularly maximum matching problems), approximation algorithms for combinatorial optimization, and circuit complexity. He has developed innovative techniques using linear algebra, combinatorial methods, and probabilistic analysis to solve challenging theoretical problems. His approach often bridges theoretical insights with practical algorithmic considerations. His collaborative work spans multiple research groups, with frequent co-authorships with researchers such as Ashish Chiplunkar, Sumedh Tirodkar, and Sreyash Kenkre. His research has evolved from foundational work in online graph coloring in the 1990s to more specialized topics in combinatorics and recently to circuit lower bounds, showing both depth in core areas and adaptability to evolving research landscapes.

Tilo Schwalgerمشاهده پروفایل

استادیار

• Computational Neuroscience
• Theoretical Neuroscience
• Multiscale Modeling
• +۴ مورد دیگر

Tilo Schwalger is an Assistant Professor in Computational Neuroscience and Data Assimilation at the Institut für Mathematik , Technische Universität Berlin . He leads a junior research group affiliated with the Bernstein Network Computational Neuroscience (BCCN) and the Einstein Center Digital Future (ECDF) . His work bridges experimental neuroscience with theoretical models, focusing on multiscale modeling of brain activity. PhD in Theoretical Physics (2013, Max Planck Institute Dresden & Humboldt-Universität Berlin) Diploma in Physics (2006, Humboldt-Universität Berlin) DAAD-funded studies at Lomonosov Moscow State University (2003-2004) His research spans the mathematical modeling of neural activity across scales, from microscopic spiking neurons to mesoscopic neuronal populations and macroscopic brain dynamics . Using tools from statistical physics , stochastic processes , and nonlinear dynamics , he develops frameworks like mean-field theories , coarse-graining techniques , and stochastic delay differential equations to analyze neural variability, synchronization, and information processing. Key applications include understanding cortical circuits , hippocampal replay , and associative memory mechanisms . Recent publications highlight his focus on multiscale modeling , including finite-size networks , stochastic neural dynamics , and short-term synaptic plasticity effects . His work enables rapid simulations of large-scale brain activity through low-dimensional firing-rate models consistent with microscopic spiking neuron dynamics. He has held postdoctoral positions at the University of Pittsburgh , Columbia University , and the École Polytechnique Fédérale de Lausanne (EPFL) , and actively collaborates with experimental and theoretical labs globally.

Viresh Patelمشاهده پروفایل

مدرس

• Extremal combinatorics
• Probabilistic combinatorics
• Graph polynomials / partition functions
• +۵ مورد دیگر

Dr. Viresh Patel is a Lecturer in Optimisation at the School of Mathematical Sciences, Queen Mary University of London. His research focuses on extremal and probabilistic combinatorics, graph polynomials, phase transitions, and approximation algorithms. University: Queen Mary University of London School: School of Mathematical Sciences Email: viresh.patel@qmul.ac.uk Research Interests: Patel’s work bridges combinatorics, graph theory, and statistical physics. Key areas include Hamiltonian cycles in dense graphs, algorithmic approaches to graph polynomials, and complexity analysis of physical models. Publication Trends: Recent articles address structural graph theory (cycle/path decomposition), computational complexity (Potts/Ising models), and approximation algorithms using probabilistic and Taylor series methods. Collaborations: Regular co-authorship with researchers like Guus Regts, Allan Lo, and Matthew Jenssen indicates strong interdisciplinary engagement.

• Cognitive Neuroscience
• Habit Formation
• Addiction (Gambling Disorder, Alcohol Use Disorder)
• +۵ مورد دیگر

Dr. Tim van Timmeren is an Assistant Professor at Utrecht University's Faculty of Social and Behavioural Sciences within the Department of Social, Health and Organisational Psychology (SHOP). He is affiliated with the Goal Lab and Self-regulation Lab, focusing on translating basic cognitive neuroscience findings to applied human research on habits and addiction. His work combines experimental paradigms with neuroimaging and computational modeling. Master's in Cognitive Neuroscience (Donders Institute, Radboud University) PhD in Psychiatry (Amsterdam UMC) His research explores habit mechanisms in addiction disorders, behavioral flexibility, and digital disconnection. Recent publications analyze smartphone-based interventions for habitual behaviors, cortico-striatal connectivity, and lay beliefs about self-control. He collaborates across institutions including University of Amsterdam, Radboud University, and Amsterdam UMC. Key publication trends include: 2024-2025 work on self-control beliefs and digital disconnection frameworks 2023 studies linking white-matter connectivity to goal-directed control 2022 research on implementation intentions and working memory effects He contributes to courses in behavioral regulation, health psychology, and research methods while maintaining active external collaborations. His work aligns with UU research themes 'Dynamics of Youth' and 'Governing the Digital Society'.

• Stochastic Modeling
• Statistical Physics
• Non-Equilibrium Statistical Physics
• +۴ مورد دیگر

Cristian Spitoni is an Assistant Professor in the Mathematical Modeling group at the Mathematical Institute within the Faculty of Science at Utrecht University. His office is located in the Hans Freudenthal Building at Budapestlaan 6, Room 510, 3584 CD Utrecht. He maintains an active research profile spanning mathematical physics, statistics, and interdisciplinary applications in medical informatics and neuromorphic computing. His primary research interests include Stochastic Modeling, Statistical Physics, Non-Equilibrium Statistical Physics, and Survival Analysis. Dr. Spitoni's work demonstrates a remarkable interdisciplinary range, bridging theoretical mathematics with practical applications in healthcare and computing. His research trajectory shows a fascinating evolution from fundamental statistical physics problems to medical applications and more recently to neuromorphic computing. Analysis of his recent publications (2023-2025) reveals three major research thrusts: 1) Theoretical work on probabilistic cellular automata and metastability in statistical physics; 2) Medical statistics applications focusing on ICU infections, sepsis, and survival analysis; and 3) Cutting-edge research in neuromorphic computing, particularly on memristors, fluidic circuits, and brain-inspired computing architectures. His work increasingly shows interdisciplinary convergence, with mathematical techniques from statistical physics being applied to both medical informatics and neuromorphic engineering problems. Dr. Spitoni has established productive collaborations with researchers across multiple disciplines, including medical researchers at Utrecht University Medical Center and physicists working on novel computing architectures. His research has been published in high-impact journals spanning physics, mathematics, medical informatics, and computer science, demonstrating the breadth and significance of his contributions. His teaching responsibilities include courses such as Interacting Particle Systems in the Lattice and Continuum, Introduction to Complex Systems, and Mathematical Statistics, reflecting his expertise in both theoretical and applied mathematical modeling.

Brenden M. Lakeمشاهده پروفایل

دانشیار

• Computational cognitive modeling
• Machine learning
• Structured probabilistic models
• +۱۶ مورد دیگر

Brenden M. Lake is an Associate Professor of Psychology and Data Science at New York University, transitioning to Princeton University in Fall 2025 as an Associate Professor of Computer Science and Psychology. His work bridges human cognition and machine intelligence , focusing on computational models that explain cognitive phenomena while advancing AI capabilities. Research Themes: Few-shot concept learning, neuro-symbolic modeling, grounded language acquisition from child input, systematic generalization in neural networks, and human-inspired inductive biases. Lab Affiliation: Human & Machine Intelligence Lab at Princeton (formerly NYU’s Human & Machine Learning Lab), utilizing child-centric data and meta-learning frameworks. Scientific Recognition: MIT Technology Review 35 Under 35 (2019) Society of Mathematical Psychology Outstanding Paper Award (2018) Academic Contributions: Pioneering research on few-shot learning, program induction, and systematic generalization in humans and machines. His Nature (2023) and Science (2024) publications demonstrate how human cognitive mechanisms can enhance AI systems.

Sofiène Taharمشاهده پروفایل

استاد

• Formal methods
• Hardware verification
• Theorem proving
• +۸ مورد دیگر

Sofiène Tahar is a Professor and Tier I Concordia Research Chair in the Department of Electrical and Computer Engineering at Concordia University. His academic career focuses on formal methods for hardware and system verification. Education: Ph.D. from the University of Karlsruhe (1994) Research Interests: Formal methods and theorem proving Hardware verification (digital, analog, and mixed-signal systems) Cyber-physical systems analysis Reliability, probabilistic, and statistical modeling Scientific Recognition: Tier I Concordia Research Chair Teaching Activities: COEN311: Computer Organization and Software COEN6741: Computer Architecture and Design COEN6551: Formal Hardware Verification Thesis Supervision: Currently supervising PhD students in Electrical and Computer Engineering.

Rong Chenمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Machine Learning
• Neuroimaging
• +۵ مورد دیگر

Rong Chen is an Associate Professor in the Department of Diagnostic Radiology and Nuclear Medicine at the University of Maryland School of Medicine. He serves as Associate Vice Chair of AI and leads the Biomedical Data Mining Laboratory, focusing on integrating machine learning, computational neuroscience, and neuroimaging to decode brain-behavior relationships. His work spans clinical and translational research for disorders like Alzheimer’s, Parkinson’s, autism, and HIV, and he develops open-source software (GAMMA suite, Advanced Connectivity Analysis) for neuroimaging data analysis. Education: BS in Biomedical Engineering, Southeast University, China (1996) MS in Electrical Engineering, The Graduate School of Chinese Academy of Sciences (1999) PhD in Electrical and Computer Engineering, Washington State University (2003) Postdoctoral Researcher in Radiology, University of Pennsylvania (2005) MTR in Translational Research, University of Pennsylvania (2012) Research Interests: Computational modeling of neural activity and behavior Development of machine learning frameworks for neuroimaging Brain-inspired AI and therapeutic concepts Longitudinal analysis of brain disorders Distributed data mining for heterogeneous databases Software tools for biomarker detection and functional connectivity Scientific Contributions: 20+ years of advanced modeling and algorithm development Two open-source neuroimaging software packages (GAMMA suite, ACA) NIH and BRAIN initiative-funded research Editorial roles in journals like Frontiers in Computational Neuroscience Honors: Senior Member of IEEE Labs & Collaborations: Dr. Chen collaborates with institutions like NIH and Oracle, and his lab has developed tools used in studies on sickle cell disease, autism, and traumatic brain injury.

• Blockchain technologies
• Quantum Computing
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Ernestas Filatovas is a Senior Researcher and Chief Researcher in the Project at Vilnius University's Institute of Data Science and Digital Technologies (formerly Institute of Mathematics and Informatics), where he has been affiliated since 2013. He leads the Blockchain and Quantum Technologies Group, focusing on cutting-edge research at the intersection of quantum computing, blockchain, and artificial intelligence. Previously, he served as an Associate Professor and Lecturer at Vilnius Gediminas Technical University's Faculty of Fundamental Sciences from 2013 to 2019. Dr. Filatovas earned his Doctor of Technology in Computer Science Engineering from Vilnius University Institute of Mathematics and Informatics in 2012. His dissertation, supervised by Prof. Dr. Olga Kurasova, focused on the interactive solution of multi-criteria optimization problems. His research spans multiple high-impact domains, with particular expertise in blockchain technologies, quantum computing, artificial intelligence, and machine learning. He has pioneered work in quantum blockchain implementations, reproducibility of AI research through blockchain verification, and quantum machine learning applications. His research bridges theoretical computer science with practical applications in financial markets, healthcare, and distributed systems. His extensive publication record—over 50 scientific papers, with more than 25 in Clarivate Analytics-indexed journals—demonstrates consistent productivity and international collaboration. Recent work shows a clear trajectory toward quantum-enhanced AI systems, blockchain-based research verification frameworks, and quantum algorithms for practical problems. Laureate of the 4th LMA Young Scientists' Conference (2014) INFOBALT scholarship 2nd place winner (2014) Lithuanian State Science and Studies Foundation funding recipient (2009, 2010) Recognized as one of Lithuania's most active doctoral students Master's degree with honors (2006) Dr. Filatovas leads multiple significant research projects, including the 2021-2024 project 'Solving the problems of reproducibility of scientific research in the field of artificial intelligence using blockchain technologies' as team leader, and the 2023-2027 project 'Development and validation of quantum machine learning methods using prepared datasets' as Chief Researcher. He has also contributed to international collaborations such as the Spanish-funded 'High Performance Solutions for Modern Scientific Computing Challenges' (2019-2021). His popular science contributions, including the VU news portal article 'Quantum Computing: Who and Why?', demonstrate his commitment to science communication. As a key member of Vilnius University's Blockchain and Quantum Technologies Group, Dr. Filatovas contributes to Lithuania's growing reputation in quantum computing research and blockchain innovation, working closely with international collaborators across Europe.

Sacha Sokoloskiمشاهده پروفایل

استادیار

• Computational Neuroscience
• Machine Learning
• Probabilistic Modeling
• +۳ مورد دیگر

Dr. Sacha Sokoloski serves as Group Leader for Neuronal Modeling in the Department of Data Science at the Hertie Institute for AI in Brain Health, University of Tübingen, where he develops methods for understanding biological neural circuits through rigorous probabilistic modeling and large-scale machine learning. His academic background includes a PhD in Informatics from the Max Planck Institute for Mathematics in the Sciences, Leipzig (2019), a Master's in Computational Neuroscience from the Bernstein Centre for Computational Neuroscience, Berlin (2013), and a Bachelor's in Cognitive Science & AI, Philosophy, and Statistics from the University of Toronto (2009). His research integrates computational neuroscience with machine learning, focusing on neural circuit modeling through probabilistic frameworks and deep reinforcement learning applications for studying ecological adaptations in visual systems. This work bridges theoretical neuroscience with scalable AI methodologies to decode biological computation. He leads the Neuronal Modeling research group at the Hertie Institute, though specific details about student supervision, grant funding, or laboratory infrastructure are not provided in available sources.

• Robotics
• Electronics
• Autonomous Vehicles
• +۷ مورد دیگر

María Soledad Escudero Hernanz is a Full Professor in the Electronics Department at the University of Alcalá's School of Engineering. With extensive experience in both teaching and research, she has been actively contributing to the university community since at least 2005. Her research focuses on several cutting-edge areas within robotics and electronics, with particular emphasis on service robotics and transportation safety applications. Professor Escudero Hernanz is an active member of the 'Robótica de Servicios y Tecnologías para la Seguridad Vial - Service Robotics and e-Safety' research group, where she contributes to multiple research lines including autonomous driving systems, intelligent control for robotics, driver monitoring technologies, and mobile robot navigation. Her scholarly output shows a consistent pattern of research focused on practical applications of robotics and computer vision technologies. The analysis of her publications reveals a strong emphasis on transportation safety systems, with numerous projects related to driver monitoring, distraction detection, and advanced driver assistance systems. She has also made significant contributions to assistive robotics and multi-robot navigation systems. Professor Escudero Hernanz has been involved in numerous research projects funded by various entities including: Spanish Ministry of Economic Affairs and Digital Transformation Community of Madrid University of Alcalá Spanish Foundation for Science and Technology (FECYT) Her work demonstrates a strong commitment to both fundamental research and practical applications, particularly in the areas of transportation safety and assistive technologies. As an educator, she teaches Circuit Electronics and Digital Electronics across multiple engineering programs, contributing to the development of future engineers in various specializations. Her collaborative approach is evident through her long-standing research partnerships with colleagues at the University of Alcalá, particularly with Rafael Barea Navarro, María Elena López Guillén, and Luis Miguel Bergasa Pascual, with whom she has worked on multiple projects spanning more than a decade.

Thomas W. Repsمشاهده پروفایل

استاد

• Program Analysis
• Abstract Interpretation
• Model Checking
• +۴ مورد دیگر

Thomas W. Reps is the J. Barkley Rosser Professor & Rajiv and Ritu Batra Chair Emeritus at the University of Wisconsin-Madison , where he has been a faculty member since 1985. He is also President of GrammaTech, Inc., and a co-founder of the company. Education: Ph.D. in Computer Science from Cornell University (1982), winner of the 1983 ACM Doctoral Dissertation Award. Reps’s research spans program analysis , abstract interpretation , model checking , and computer security . His recent work focuses on quantum computing verification , probabilistic program analysis , and symbolic methods for static analysis . His publications (over 225) include foundational contributions to program slicing (1988 paper with Horwitz and Binkley, cited >1,780 times), machine-code analysis (ETAPS Best-Paper Awards in 2004 and 2008), and programming environments (co-author of The Synthesizer Generator ). Key awards include the ACM SIGPLAN Programming Languages Achievement Award (2017) , Guggenheim and Packard Fellowships , and ACM Fellow (2005) . Students: Mentored award-winning graduates like Akash Lal (SIGPLAN Outstanding Dissertation) and Venkatesh Srinivasan (Outstanding Graduate Student Research Award).