جست‌وجوی استاد و پژوهشگر

Brian Dentonمشاهده پروفایل

استاد

• Operations Research
• Data Science
• Healthcare Systems
• +۳ مورد دیگر

Brian Denton is the Stephen M. Pollock Collegiate Professor of Industrial and Operations Engineering at the University of Michigan College of Engineering, with a courtesy appointment as Professor of Urology. His research centers on data-driven sequential decision making and optimization under uncertainty, applied to healthcare delivery, public health, and chronic disease management. His educational background includes: Ph.D. in Management Science from McMaster University (2001) M.Sc. in Physics from York University (1996) B.Sc. in Chemistry and Physics from McMaster University (1994) Denton develops mathematical models for sequential decision making under uncertainty, including stochastic programming and Markov decision processes. These are applied to medical decision making for cancer, cardiovascular disease, and diabetes, as well as optimization of scheduled systems in manufacturing. His work bridges operations research, data science, and clinical medicine to improve patient outcomes and system efficiency. Recent publications demonstrate integration of machine learning with robust optimization for healthcare applications, particularly in cancer detection and chronic disease management. His group develops novel methods for multi-model decision processes and dynamic prediction models addressing clinical data uncertainty. His accolades include: NSF CAREER Award (2009) INFORMS Daniel H. Wagner Prize (2005) IISE Outstanding Publication Award (2005, 2020) INFORMS Service Section Best Paper Prize (2010) Distinguished Mentor Award (2018) Distinguished Educator Award (2017) INFORMS Fellow IISE Fellow Dr. Denton secured NSF grants CMMI 1462060 for chronic disease treatment optimization and CMMI 1536444 for cancer screening strategies. He mentors PhD students including Weiyu Li and collaborates with clinical departments across the University of Michigan and Mayo Clinic. He leads a research group in healthcare decision analytics affiliated with the University of Michigan Cancer Center and Institute for Healthcare Policy and Innovation (IHPI), working with urology, internal medicine, and pathology departments on translational research projects.

• Machine Learning
• Contact/Symplectic Geometry
• Computational Geometry
• +۶ مورد دیگر

Maia Fraser is an Associate Professor at the University of Ottawa , cross-appointed to the School of Electrical Engineering and Computer Science and affiliated with the Brain and Mind Research Institute . Her research bridges Machine Learning , Symplectic Geometry , and Computational Geometry , with interdisciplinary collaborations in Neuroscience and Mathematical Biology . PhD in Computer Science (University of Chicago, 2013) PhD in Mathematics (Stanford University, 1994) Postdoctoral work: University of Toronto Industry experience: Supercomputing Systems AG (Zurich) Her recent work explores AI safety through the lens of living systems, combining mathematical principles with societal implications. She examines temporal abstraction in continual learning, reinforcement learning limits, and geometric constraints in AI. Publications span topology , biological navigation , and neural time scales . Scientific contributions include: Co-organizing Fields Medal Symposium (2022) Co-moderating New Paradigms session at Fields Institute (2024) Leading INTER-MATH-AI (IMA) NSERC-CREATE program (2022) She investigates AI's impact on mathematical research and is writing a book on mathematical frameworks for AI safety .

Henri Hurkmansمشاهده پروفایل

پژوهشگر

• Rehabilitation Medicine
• Stroke Rehabilitation
• Orthopedic Rehabilitation
• +۷ مورد دیگر

Dr. Henri Hurkmans is an active Researcher at Erasmus University Medical Center in Rotterdam, Netherlands, affiliated with the Erasmus School of Medicine and the Department of Orthopedics and Sports Medicine. His research program focuses on developing and validating technological interventions for rehabilitation, particularly in stroke recovery and orthopedic rehabilitation settings. Dr. Hurkmans' research interests center around the application of sensor technology and feedback systems to improve rehabilitation outcomes. His work spans multiple domains including stroke rehabilitation where he has pioneered mirror therapy protocols, orthopedic rehabilitation with focus on weight-bearing techniques, and sensor-based monitoring of patient movements in hospital settings. His research demonstrates how technological innovations can provide objective measurements and real-time feedback to enhance patient recovery. Analysis of his publication history reveals a clear progression from foundational work on mirror therapy and visual illusions for stroke patients (2012-2015) toward more recent applications of wearable sensor technology for monitoring hospitalized patients (2023-2024). His research consistently bridges clinical rehabilitation practice with technological innovation, with particular emphasis on validating measurement tools and developing practical clinical applications. Dr. Hurkmans has established productive collaborations with key researchers including Bussmann, Verhaar, Selles, and Ribbers across multiple publications. His work has demonstrated significant impact, with his 2015 mirror therapy article accumulating 59 Scopus citations and his 2014 visual illusion study receiving 47 citations, indicating substantial influence in the neurorehabilitation field.

Leslie Kaelblingمشاهده پروفایل

استاد

• Robotics
• Machine Learning
• Artificial Intelligence
• +۲ مورد دیگر

Leslie Kaelbling serves as the Panasonic Professor in MIT's Department of Electrical Engineering and Computer Science (EECS) and Director of Research for the MIT Quest for Intelligence, while also holding an investigator position at the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her academic background includes a BA in philosophy and PhD in computer science, both earned at Stanford University. Professor Kaelbling's research centers on building intelligent robots capable of operating in uncertain environments through advanced learning, state estimation, and planning techniques. Her work bridges theoretical AI foundations with practical robotics applications, focusing on creating systems that adapt to complex real-world scenarios through probabilistic reasoning and decision-making under uncertainty. Analysis of her recent publications reveals a strong trajectory toward integrating foundation models with robotic planning systems, particularly leveraging vision-language models for constraint inference and diffusion models for task-motion planning. Her research increasingly emphasizes uncertainty quantification, neuro-symbolic representations, and sample-efficient learning methods for long-horizon manipulation tasks. Key scientific recognitions include: Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) Founder and inaugural Editor-in-Chief of the Journal of Machine Learning Research As Director of Research for the MIT Quest for Intelligence, she leads strategic initiatives advancing human and machine intelligence research, fostering cross-laboratory collaborations between CSAIL, neuroscience, and cognitive science groups to develop next-generation AI systems. Her leadership extends to shaping research directions in embodied intelligence and foundational learning algorithms.

Ömür Arslanمشاهده پروفایل

استادیار

• Robotics
• Motion Planning
• Control Theory
• +۵ مورد دیگر

Ömür Arslan is an assistant professor in the Department of Mechanical Engineering at the Eindhoven University of Technology (TU/e) , affiliated with the Robotics Group and the EAISI Mobility and EAISI High Tech Systems institutes. His research focuses on the algorithmic foundations of robotics, particularly safe adaptive motion planning algorithms for robots operating around humans. Education: PhD in Electrical and Systems Engineering (2016), University of Pennsylvania MSc in Electrical Engineering (2012), University of Pennsylvania MSc in Electrical & Electronics Engineering (2009), Bilkent University BSc in Electrical & Electronics Engineering (2007), Middle East Technical University His research interests span robotics, motion planning, control theory, sensor networks, dynamical systems, optimization, machine learning , and machine perception . Recent work includes safe unicycle control, probabilistically safe corridors, and statistical coverage control of mobile sensor networks. He has received nominations for best paper awards at ICRA 2018 and WAFR 2016 . Ömür has supervised student projects such as Hancheng Min's MSc thesis on statistical coverage control and Vincent Pacelli's work on local geometry in motion planning .

• Applied Mechanics
• Robotics
• Vehicle Dynamics
• +۳ مورد دیگر

Giacomo Mantriota is a Full Professor at the Department of Mechanics, Mathematics & Management (DMMM) within Politecnico di Bari, Italy. His research spans multiple domains in applied mechanics, focusing on robotics and vehicle dynamics. Key research themes include tracked robot mobility, vacuum grasping mechanisms, and energy-efficient vehicle systems. Academic rank: Full Professor Department: Mechanics, Mathematics & Management Primary research areas: Robotics, Vehicle Dynamics, Energy Recovery Systems Email: giacomo.mantriota@poliba.it Current research initiatives involve obstacle negotiation for tracked robots, deformable tyre dynamics, and nonlinear control systems for electric vehicles. His work on vacuum grippers explores underactuation and partial contact loss in industrial automation contexts. Scientific publications demonstrate consistent output across 2022-2025, with recent emphasis on: Stair-climbing robotics Multi-actuator vehicle control Energy recovery in prosthetic joints Compound transmission efficiency

Bruno Gaujalمشاهده پروفایل

استاد پژوهشی

• Performance Evaluation
• Optimization
• Discrete Event Dynamic Systems
• +۵ مورد دیگر

Bruno Gaujal is a Research Professor at Inria Grenoble-Rhône-Alpes, affiliated with Université Grenoble Alpes. He obtained his PhD from the University of Nice in 1994 under François Baccelli's supervision and has held positions at AT&T Bell Labs, INRIA, and École Normale Supérieure de Lyon. He previously led the MESCAL (now POLARIS) research group focused on large-scale computing until 2015. His research interests center on performance evaluation, optimization, and control of discrete event dynamic systems with stochastic inputs. Specific areas include: Markov Chains and Markov Decision Processes Reinforcement Learning and stochastic optimization Queueing theory and scheduling algorithms Energy-efficient computing in distributed systems Game-theoretic approaches in network optimization Gaujal's recent publications show strong emphasis on reinforcement learning applications in queueing networks, energy optimization for real-time systems, and scalable algorithms for Markov Decision Processes. His work bridges theoretical frameworks like Whittle indices with practical implementations in cloud computing and distributed systems. He has supervised numerous PhD students including Nicolas Gast (now Inria researcher), Anne Bouillard (Huawei researcher), and Emmanuel Hyon (Paris Nanterre professor). Current students include Hélène Arvis and Romain Cravic. Gaujal co-founded RTaW, a startup specializing in real-time network design tools. At Inria, he leads research in the POLARIS group, focusing on optimization methods for large-scale distributed computing infrastructures. His work involves collaborations with 85+ co-authors across institutions globally.

Pieter Vansteenwegenمشاهده پروفایل

استاد

• Public Transportation Optimization
• Metaheuristics
• Demand-Responsive Transport Systems
• +۴ مورد دیگر

Prof. Pieter Vansteenwegen is a full professor at KU Leuven's Faculty of Engineering Science, leading the Centre for Industrial Management/Traffic and Infrastructure (CIB) and chairing the KU Leuven Institute for Mobility (LIM). With a PhD in Operations Research (2008), he works on robust public transport systems, metaheuristics for logistics, and demand-responsive mobility solutions. Chair, KU Leuven Institute for Mobility Program Director, Master of Mobility and Supply Chain Engineering Board member, International Association of Railway Operations Research His research integrates transportation engineering , logistics optimization , and smart mobility systems , focusing on: Demand-responsive public transportation Metaheuristic algorithm development Inventory routing problems Smart waste collection strategies Multi-robot scheduling Recent publications demonstrate expertise in variable neighborhood search , Benders decomposition , and genetic algorithms applied to public transport, satellite scheduling, and circular economy logistics. His work has been cited over 9,400 times (Google Scholar H-index 51). Scientific recognition includes: Multiple IAROR best paper awards Best Reviewer European Journal of Operational Research BIVEC-GIBET PhD awards (2009, 2021, 2023) RASIG-INFORMS Student Paper Prizes He directs KU Leuven's Planning and Operational Research subdivision , serves on faculty councils, and founded spin-off dyNAVic for electronic tourist guides. His teaching portfolio includes courses in Public Transportation Design , Distribution Logistics , and Operational Management .

George D Konidarisمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۲ مورد دیگر

George D Konidaris serves as Associate Professor of Computer Science at Brown University, where his research bridges artificial intelligence, machine learning, and robotics with emphasis on autonomous decision-making systems. His work focuses on developing algorithms that enable robots and AI agents to learn hierarchical structures, discover reusable skills, and operate effectively in complex environments. Education: 2010: PhD, University of Massachusetts, Amherst 2003: MS, University of Edinburgh 2001: BS, University of the Witwatersrand 2000: BS, University of the Witwatersrand His research spans reinforcement learning , robotic motion planning , and hierarchical abstraction , with significant contributions to skill discovery, temporal abstraction, and model-based methods. Current work integrates visuo-haptic perception for manipulation tasks and explores language-guided robotics using large language models. His approach emphasizes creating systems that learn compact world representations for efficient long-horizon planning in partially observable environments. Analysis of his 2025 publications reveals strong trends in model-based reinforcement learning with focus on memory mechanisms, uncertainty quantification, and hierarchical skill composition. Key themes include temporal abstraction for planning efficiency, visuo-haptic fusion for robotic manipulation, and language grounding for task specification. His work increasingly connects cognitive science concepts like theory of mind with AI capabilities. Teaching responsibilities include CSCI 1410 (Artificial Intelligence) and CSCI 2951X (Reintegrating AI), where he bridges theoretical foundations with practical robotics applications.

Stephanie Paradeمشاهده پروفایل

دانشیار

• Child Development
• Developmental Psychopathology
• Child Maltreatment
• +۷ مورد دیگر

Stephanie Parade is an Associate Professor of Psychiatry and Human Behavior at Brown University and Director of Early Childhood Research at E. P. Bradley Hospital. She serves as Associate Director of the Initiative on Stress, Trauma, and Resilience and Co-Director of the T32 Childhood Stress, Trauma, and Resilience training program, mentoring postdoctoral fellows in developmental psychopathology and intervention science. Brown University (2010–present): Associate Professor of Psychiatry and Human Behavior E. P. Bradley Hospital (2010–present): Director of Early Childhood Research Initiative on Stress, Trauma, and Resilience (2015–present): Associate Director Education: PhD, University of North Carolina at Greensboro (2010) MS, University of North Carolina at Greensboro (2007) BS, University of Connecticut (2005) Research Interests focus on biopsychosocial mechanisms linking childhood adversity to lifespan health outcomes. Key areas include: Biological consequences of early adversity (epigenetics, mitochondrial DNA, cortisol) Community interventions to prevent child maltreatment (home visiting programs, evidence-based practices) Parent-child relationship dynamics in maltreated populations Transgenerational effects of trauma on infant health Her scientific collaborations span departments at Brown including Pediatrics, Behavioral and Social Sciences, Emergency Medicine, and Obstetrics. She works with RI state agencies and family service organizations. Funded Projects include: NHLBI UG3/UH3 HL163508: Cardiovascular health in home visiting programs CDC R01 CE003103: Mental health consultation in prenatal home visiting NICHD R01 HD095837: Epigenetic studies in maltreated children NIDA R61 DA059785: Substance use prevention in child welfare youth HRSA R40 MC28318: Impact of parental ACEs on program efficacy Methodological Expertise includes observational assessments, DNA methylation analysis, and community-partnered research. She actively trains clinicians and researchers in evidence-based practices through seminars and the Community College of RI Early Childhood Education program.

Aris Karastergiouمشاهده پروفایل

استاد

• Pulsar Astronomy
• Radio Astronomy
• Fast Radio Bursts
• +۳ مورد دیگر

Professor Aris Karastergiou serves as Professor of Astrophysics at the University of Oxford and holds a Fellow by Special Election in Astrophysics position at St Edmund Hall, where he is also a College Lecturer in Physics. His research leverages next-generation radio telescopes including the Square Kilometre Array (SKA), LOFAR, and MeerKAT to advance pulsar science and transient astronomy. Specializing in pulsar astrophysics, Karastergiou investigates emission mechanisms, timing properties, and interstellar medium effects through observational and computational techniques. His work emphasizes real-time processing systems for transient detection and large-scale data analysis from international collaborations, with significant contributions to understanding neutron star physics and fast radio bursts. Analysis of his 15 most recent publications (2023-2025) reveals concentrated focus on the Thousand-Pulsar-Array programme using MeerKAT, yielding insights into pulsar polarization, timing variability, Galactic magnetic fields, and pulse profile evolution. These studies frequently address millisecond pulsars, fast radio bursts, and computational methods for handling massive radio astronomy datasets. Fellow by Special Election in Astrophysics (St Edmund Hall) As principal investigator of the ARTEMIS project, Karastergiou leads real-time pulsar and fast radio burst discovery initiatives, securing research funding for advanced instrumentation including FPGA-based accelerators. His leadership in major international collaborations like SKA and MeerKAT demonstrates significant mentorship roles despite limited student listings in available records. He actively contributes to the Square Kilometre Array, LOFAR telescope network, and MeerKAT operations, participating in key working groups for pulsar timing arrays and transient science. His research group develops specialized pipelines for real-time data processing across multiple observatories in southern Africa and Western Australia.

Erik Lindstromمشاهده پروفایل

استاد

• Financial Mathematics
• Statistical Inference
• Stochastic Processes
• +۲ مورد دیگر

Erik Lindstrom is a Professor of Mathematical Statistics and Head of Division at Lund University's Centre for Mathematical Sciences within the Faculty of Science. He additionally serves as assistant Director for the Industrial Engineering and Management program and directs the statistical consulting service for the Science faculty. His educational background includes: M.Sc. in Engineering Physics (2000) from Lund University B.Sc. in Economics and Business (2000) from Lund University M.Sc. in Economics and Business (2001) from Lund University Ph.D. in Mathematical Statistics (2004) from Lund University Lindstrom's research bridges theoretical statistics with real-world applications in finance and energy systems. His work focuses on parameter estimation algorithms for discretely observed diffusion processes, financial mathematics including option valuation under uncertainty, energy finance, power system stability, and statistical testing methodologies. The 2015 book "Statistics for Finance" synthesizes key aspects of his financial statistics research. Recent publications (2023-2025) reveal a concentrated research trajectory applying statistical jump models to cryptocurrency markets and financial options, alongside advanced time series techniques for building energy load forecasting. This demonstrates his consistent interdisciplinary approach connecting stochastic processes with practical financial and energy sector challenges. His scientific recognition includes: Excellent Teaching Practitioner (ETP) award (2013) Lindstrom actively mentors doctoral researchers including current PhD students Carl Åkerlindh and Peter Nystrup, and postdoctoral fellows Josef Höök and Sidi Mohamed Aly. His teaching portfolio spans Financial Statistics (MSc), Non-linear Time Series (MSc/PhD), and Statistical Inference for Partially Observed Stochastic Processes (PhD), supported by research grants in financial mathematics and energy applications. As head of division and director of the statistical consulting service, he leads initiatives providing statistical methodology support across the Science faculty, fostering cross-disciplinary research collaboration through structured consultation frameworks.

• Mathematical statistics
• Analysis
• Probability theory
• +۳ مورد دیگر

Shohei Nakajima serves as an Assistant Professor (without tenure) at Waseda University with dual appointments in the School of International Liberal Studies under the Faculty of International Research and Education, and the School of Fundamental Science and Engineering under the Faculty of Science and Engineering. He teaches undergraduate courses including Introductory Calculus, First Year Seminar, and Stochastic Processes across these institutions. Education: Ph.D. in Fundamental Science and Engineering, Waseda University (2017.04 - 2023.03) Master's degree in Fundamental Science and Engineering, Waseda University (2015.04 - 2017.03) Dr. Nakajima's research program centers on mathematical statistics, analysis, and probability theory with specialized focus on statistical inference for stochastic processes driven by fractional Brownian motion. His theoretical work addresses parameter estimation challenges under various noise conditions, developing asymptotic properties of estimators for both continuous and discrete observation settings. He has made significant contributions to understanding maximum likelihood estimation techniques and least squares methods for complex stochastic systems with non-standard noise structures. His publication pattern reveals a concentrated research trajectory examining statistical properties of estimators for stochastic differential equations with fractional components. The work consistently bridges theoretical mathematical analysis with practical statistical methodology, particularly addressing cases involving small noise asymptotics, multiplicative fractional noise, and non-Lipschitz coefficients that present unique analytical challenges. Dr. Nakajima currently leads a Japan Society for the Promotion of Science (JSPS) funded research project (2024.04-2026.03) titled "Statistical inference theory for stochastic differential equations driven by fractional Brownian motion," which extends his internal special research projects conducted from 2021-2023 on related theoretical frameworks.

• Partial Differential Equations
• Numerical Analysis
• Stochastic Differential Equations
• +۵ مورد دیگر

Anders Szepessy is a Professor in Mathematics and Numerical Analysis at the Department of Mathematics, KTH Royal Institute of Technology. His research centers on partial differential equations and numerical methods for differential equations, with significant contributions to molecular dynamics, quantum mechanics, and stochastic systems. His primary research domains include: Partial Differential Equations Numerical Analysis Stochastic Differential Equations Molecular Dynamics and Quantum Mechanics Optimal Control and Finite Element Methods Machine Learning Applications Recent publications reveal a pronounced shift toward integrating machine learning with computational physics, particularly in neural network approximations for molecular dynamics and quantum systems. His work on adaptive methods for high-dimensional stochastic and partial differential equations demonstrates consistent innovation in numerical analysis, with applications spanning quantum chemistry, fluid dynamics, and energy systems. Professor Szepessy actively contributes to academic instruction as course responsible and examiner for advanced mathematics courses including Analytical and Numerical Methods for Partial Differential Equations, Computational Methods for Stochastic Differential Equations and Machine Learning, and multiple degree projects in mathematics and scientific computing at KTH.

• Catalysis
• Materials Science
• Spectroscopy
• +۴ مورد دیگر

Dr. Asma Tougerti is an Associate Professor and researcher at the University of Lille, France, specializing in heterogeneous catalysis. She is affiliated with the Solid State Catalysis and Chemistry Unit (UCCS - UMR CNRS 8181), specifically within the MODSPEC (Modeling and Spectroscopy) research group under the Heterogeneous Catalysis department. Her office is located in building C3, room 106 at the University's Scientific City campus in Villeneuve d'Ascq. Dr. Tougerti's research focuses on advanced characterization of catalytic materials using spectroscopic techniques, particularly X-ray absorption spectroscopy (XAS/XANES), combined with computational modeling (DFT). Her work spans multiple areas including hydrodesulfurization catalysts, methanol conversion processes, three-way automotive catalysts, and the structural analysis of transition metal oxide catalysts. She has developed expertise in correlating electronic and geometric structures of catalysts with their performance characteristics. Her publication record demonstrates consistent research activity since 2011, with a particular emphasis on in situ and operando characterization techniques that allow observation of catalysts under working conditions. Her work often involves collaborations with researchers across multiple French institutions and occasionally international partners, reflecting the interdisciplinary nature of modern catalysis research. Dr. Tougerti has contributed significantly to understanding the structure-property relationships in various catalytic systems, particularly those involving molybdenum, rhenium, and iron-based materials. Her research has practical applications in environmental protection (emission control), energy production, and chemical manufacturing processes.