جست‌وجوی استاد و پژوهشگر

Kaushik Mallikمشاهده پروفایل

استادیار

• Controller Synthesis for Dynamical Systems
• Combinatorial Games on Graphs
• Algorithmic Fairness in AI
• +۴ مورد دیگر

Kaushik Mallik is an Assistant Professor at the IMDEA Software Institute in Madrid, Spain. His research focuses on formal methods for ensuring safety and trustworthiness of AI systems, combining game theory, control theory, and machine learning. He holds a PhD in Computer Science from the Max Planck Institute for Software Systems (2022), an M.Tech in Systems and Control from IIT Roorkee (2015), and a B.Tech in Electrical Engineering from Meghnad Saha Institute of Technology (2012). His work addresses challenges in controller synthesis for cyber-physical systems, bidding games, and algorithmic fairness monitoring. Education: PhD in Computer Science, Max Planck Institute for Software Systems, Germany (2022) M.Tech in Systems and Control, IIT Roorkee (2015) B.Tech in Electrical Engineering, Meghnad Saha Institute of Technology (2012) Research Interests: Formal synthesis of safe AI systems Algorithmic fairness in decision-making algorithms Bidding games and stochastic control Symbolic control for cyber-physical systems His current projects include runtime fairness monitoring tools and theoretical advancements in auction-based scheduling algorithms. Key Achievements: Winner of the 2023 ETAPS Doctoral Dissertation Award Recipient of the DAAD IIT Master-Sandwich Programme Scholarship Lead developer of tools like Mascot-SDS and BOCoSy for symbolic control Ongoing research explores resilient abstraction-based controllers and fairness watchdog systems for AI applications.

• Machine Learning
• Computer Science
• Medical Informatics
• +۷ مورد دیگر

Yan Li is a researcher with extensive contributions across interdisciplinary domains including Machine Learning , Signal Processing , and Environmental Science . Affiliated with institutions such as the University of Southern Queensland , Hebei University , and Shandong University , Li's work spans applications in Medical Informatics , Remote Sensing , and Operations Research . Recent publications highlight expertise in Deep Learning (e.g., hyperspectral classification, image fusion), Stochastic Modeling (e.g., chemotaxis models), and Federated Learning (e.g., vertical federated fuzzy clustering). Collaborative projects include 3D Reconstruction , Smart Grid Security , and Landslide Monitoring using satellite data. Li's 2025 work demonstrates a focus on Medical Imaging (segmentation algorithms with dual-frequency decoupling), AI in Education (ChatGPT adoption), and Industrial IoT (GPU-accelerated vessel trajectory visualization). While no explicit academic rank is provided, their prolific publication record suggests a Researcher role.

• Uncertainty Quantification
• Bayesian Inverse Problems
• Data Science
• +۷ مورد دیگر

Dr. Aretha Teckentrup is a Lecturer in the Mathematics of Data Science at the University of Edinburgh's School of Mathematics. Her research focuses on integrating mathematical models with observational data, particularly in areas like uncertainty quantification, Bayesian inverse problems, and computational methods for partial differential equations (PDEs). She holds a PhD in Mathematics from the University of Bath and has held postdoctoral positions internationally, including in Florida. Her work emphasizes interdisciplinary approaches, blending statistics, numerical analysis, and applied mathematics. Notably, she has contributed to advancing Gaussian processes, multilevel Monte Carlo techniques, and sparse grid methods for high-dimensional problems. Her academic journey reflects a strong commitment to bridging theoretical foundations with practical applications. She has published extensively on topics such as probabilistic numerical methods, error estimation in Bayesian inference, and adaptive sampling strategies. Dr. Teckentrup is an active member of the SIAM community, having received the prestigious SIAG/UQ Early Career Prize in recognition of her contributions to uncertainty quantification. Her research continues to explore innovative solutions for data-driven modeling challenges in science and engineering. Dr. Teckentrup’s work often addresses the growing importance of combining data with physical models, exemplified by her development of numerical methods for weather prediction and stochastic dispersion modeling. She advocates for collaboration across disciplines and emphasizes the transformative potential of integrating computational tools with real-world data. Her career trajectory underscores the dynamic and collaborative nature of modern academic research in applied mathematics and data science.

Anahita Khojandiمشاهده پروفایل

دانشیار

• Decision Making Under Uncertainty
• Reinforcement Learning
• Markov Decision Processes
• +۹ مورد دیگر

Anahita Khojandi is an Associate Professor in the Department of Industrial and Systems Engineering at the University of Tennessee, Knoxville, where she also holds the Heath Endowed Faculty Fellow in Business & Engineering title. She is also a Joint Associate Professor at the Bredesen Center for Interdisciplinary Research and Graduate Education and served as the Director of the Reliability and Maintainability Engineering Program from 2021 to 2024. Her interdisciplinary research spans healthcare, transportation, environmental engineering, and advanced manufacturing. Education: PhD in Industrial Engineering, University of Pittsburgh MS in Industrial Engineering, University of Pittsburgh BS in Industrial Engineering, Sharif University of Technology, Tehran, Iran Her research focuses on decision-making under uncertainty, reinforcement learning, Markov decision processes, and predictive analytics. These methodologies are applied to critical domains such as sepsis prediction, Parkinson’s disease treatment using wearable sensors, intelligent transportation systems, nuclear safety, and green infrastructure resilience. She integrates machine learning with stochastic modeling to improve real-time decision-making in complex systems. The recent publications highlight a strong trend toward AI-driven solutions in healthcare and infrastructure, with emphasis on explainability, anomaly detection, and real-time adaptation. Her work frequently appears in high-impact journals such as Management Science , IEEE Transactions , and Nuclear Technology , reflecting both methodological rigor and practical relevance. Scientific Awards and Honors: AAAS Rapid Response AI Fellow (2024) NIH AIM-AHEAD Fellowship in Leadership (2023) First Place, Harvey J. Greenberg Research Award (INFORMS, 2022) Best Paper Award, OMEGA Journal (2020, 2021) INFORMS Senior Member (2024) Teaching Award, IISE DAIS Division (2024) Khojandi has advised numerous graduate students and postdoctoral researchers, many of whom are co-authors on her publications. Her research has been supported by the National Science Foundation (NSF), National Institutes of Health (NIH), Department of Energy (DOE), and other federal and institutional grants. She actively contributes to professional service, including leadership roles in INFORMS, where she served as Chair of the Diversity, Equity, and Inclusion Committee and currently serves as Vice President of Membership and Professional Recognition. She leads research initiatives at the intersection of industrial engineering and data science, often in collaboration with the Health Innovation Technology & Simulation (HITS) Lab and the Center for Transportation Research. Her lab focuses on developing AI-enabled decision support tools for clinical and operational environments, with future work targeting autonomous systems, personalized medicine, and climate-resilient infrastructure.

• Riemannian Geometry
• Stochastic Processes
• Statistical Models
• +۴ مورد دیگر

Dr. Bui Ngoc Mai is a Lecturer in the Faculty of Business at British University Vietnam , where she has worked since 2023. Her academic journey includes a PhD and postdoctoral research at University College London (UCL) , focusing on inference on Riemannian manifolds and applications in cancer early detection. PhD in Mathematics and Statistical Science (2017–2022), UCL MSc in Mathematics and Statistical Science (2013–2017), UCL A-levels (2011–2013), Chichester College, UK Mai’s research bridges theoretical and applied domains, emphasizing Riemannian geometry , stochastic processes , and multi-state statistical models . Her work explores regression and SDEs on Lie groups, with applications in early cancer detection and financial data analysis. Recent publications highlight her focus on covariance matrix modeling , partially observed diffusions , and Riemannian Ornstein-Uhlenbeck processes . These studies integrate geometric statistics with biomedical and financial applications, reflecting her interdisciplinary approach. Scientific Awards: Costas Goutis Prize (2021) UCL Research Excellence Scholarship (2017) IMA Graduate Prize (2017) Barlett Prize (2016) Nazir Ahmad Prize (2015) Mai has presented her work at international conferences, including the Greek Stochastic Conference (2023) and the Early Detection of Cancer Conference (2022). Her prior roles at UCL spanned research, teaching, and applied health analytics.

Robert Plattمشاهده پروفایل

دانشیار

• Robotics
• Robot Manipulation
• Human-Robot Interaction
• +۳ مورد دیگر

Robert Platt is an Associate Professor at Northeastern University's Khoury College of Computer Sciences, affiliated with the College of Engineering's Mechanical and Industrial Engineering and Electrical and Computer Engineering departments. His research focuses on perception, planning, and control algorithms for robots operating in unstructured environments, particularly in human-centric settings. He leads the Helping Hands Lab, advancing technologies for safe human-robot interaction and contact-rich manipulation. Platt has secured significant grants, including a $750K NSF grant for robotic arm development and an NSF CAREER Award. He advises numerous PhD students and collaborates on projects like Amazon's MARS conference and the Autotrans autonomous transportation system. Key research interests include robotic manipulation, equivariant learning, reinforcement learning, and human-robot collaboration. His work emphasizes symmetry exploitation in algorithms to enhance policy robustness and generalization. Notable contributions include the development of SE(3)-equivariant grasp learning, Fourier Transporter networks for 3D manipulation, and the ThinkGrasp vision-language system for strategic grasping in cluttered environments. Grants: NSF CAREER Award (2018), $750K NSF Grant for Contact-Rich Manipulation (2018) Awards: NSF CAREER Award Students: Advises Ahmed Agha, Haojie Huang, Dian Wang, and others in advanced robotics topics Lab: Helping Hands Lab explores algorithms for real-world robotic applications Collaborations: Amazon MARS Conference, Autotrans Project

Martin Magnussonمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Autonomous Systems
• +۵ مورد دیگر

Martin Magnusson is a Professor at the Department of Natural Sciences and Technology, Örebro University, leading the Center for Applied Autonomous Sensor Systems (AASS) and the Robot Navigation and Perception Lab . His research focuses on robotics and artificial intelligence , particularly 3D mapping, localization, radar-based navigation, and human-robot interaction . Email: martin.magnusson@oru.se Phone: +46 19 303870 Location: Room T1222 His work addresses fundamental challenges in achieving robust autonomy through innovations like the 3D Normal Distributions Transform (3D-NDT) and methods for scan registration in dynamic environments. Recent research extends to radar-based navigation and heterogeneous map data integration , with ethical implications regarding military applications of autonomous systems. Key research themes include: Autonomous Perception: Radar and lidar sensor fusion for localization Dynamic Mapping: Flow-aware and quality-assessed environmental models Human-Aware Robotics: Predictive modeling for safe shared-space navigation Professor Magnusson teaches Computer Graphics , connecting academic principles (e.g., ray tracing, light scattering) to applied research in radar simulation models and neural rendering . His research projects span DARKO (agile production robots), NiCE (changing environment navigation), and Radarize (underground autonomous vehicles).

• Mathematical Statistics
• Machine Learning
• Kernel Methods
• +۳ مورد دیگر

Prof. Dr. Nicole Mücke is a Professor in the Institute for Mathematical Stochastics at the Carl-Friedrich-Gauss Faculty of Technische Universität Braunschweig. Her research focuses on mathematical statistics, machine learning, kernel methods, and statistical inference. She explores topics such as neural network theory, inverse problems, optimization, and regularization techniques. Her work bridges theoretical foundations with practical applications in areas like distributed computing and uncertainty quantification. Prof. Mücke’s research portfolio includes contributions to empirical risk minimization, neural operator learning, and gradient-based optimization. She investigates the interplay between overparameterization and generalization in machine learning models, as well as the design of efficient algorithms for large-scale problems. Her publications span topics ranging from distributed stochastic gradient descent to localized kernel regression techniques. Her recent work emphasizes theoretical guarantees for learning algorithms, including convergence rates, statistical performance in high-dimensional settings, and the role of regularization in inverse problems. She also explores methodological advancements in spectral methods, algorithm unfolding, and data-splitting strategies to enhance statistical efficiency. Prof. Mücke’s research is characterized by a strong emphasis on rigorously analyzing machine learning algorithms through the lens of statistical theory and functional analysis. Her contributions address challenges in both classical and modern machine learning paradigms, with a focus on bridging the gap between abstract mathematical frameworks and practical implementation.

Erik Van Vleckمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Analysis
• Numerical Methods for PDEs
• +۳ مورد دیگر

Erik Van Vleck is a Professor of Mathematics at the University of Kansas, affiliated with the College of Liberal Arts & Sciences. His research focuses on applied mathematics, numerical analysis, and numerical methods for partial differential equations (PDEs), with particular emphasis on data assimilation and reaction-diffusion systems. His work bridges theoretical advancements and practical applications in computational dynamics, environmental modeling, and ecological systems. Van Vleck’s research interests include multi-scale data assimilation techniques, adaptive mesh methods, and the numerical analysis of traveling wave solutions in discrete and continuous systems. He has contributed to understanding pattern formation in ecological models, particularly in savanna ecosystems and fire-driven dynamics. His methodological innovations span numerical methods for PDEs, reduced-order modeling, and stochastic filtering approaches for complex systems. His recent work highlights trends in developing robust adaptive frameworks for data assimilation, integrating advanced numerical techniques with real-world applications in geophysics and environmental science. This includes projects on metric tensor-based mesh adaptation and projected data assimilation using sliding window methods. His contributions to theoretical foundations include spectral stability analyses for discrete dynamical systems and bifurcation studies in reaction-diffusion equations. Van Vleck has collaborated extensively across disciplines, applying mathematical tools to ecological, climatological, and biomedical systems. His research often addresses computational challenges in high-dimensional spaces, emphasizing efficiency and accuracy in modeling complex phenomena. Despite the breadth of his contributions, no specific grants or advising roles are detailed in the provided materials.

Peter Jan van Leeuwenمشاهده پروفایل

استاد

• Data Assimilation
• Physical Oceanography
• Geophysical Fluid Dynamics
• +۴ مورد دیگر

Peter Jan van Leeuwen is a Professor of Data Assimilation and Physical Oceanography at Colorado State University (since 2018) and a Professor of Data Assimilation at the University of Reading (since 2009). He holds leadership roles, including Director of the Data Assimilation Research Centre (DARC) and former Director of the National Centre for Earth Observation (NCEO). He earned his Ph.D. in Fluid Dynamics from Delft University of Technology (1992) and holds multiple advanced degrees in Theoretical Physics and Astrophysics from Dutch universities. His research focuses on advancing data assimilation techniques (including machine learning), causal discovery in non-intervenable systems, and their applications to geophysical fluids like oceans, atmosphere, and cryosphere. Key areas include ocean dynamics, air-sea interaction, sea-ice prediction, cloud microphysics, and hurricane modeling. He has been funded by NSF, NOAA, NASA, and European agencies like the ERC and NERC. Notable awards include the George T. Abell Outstanding Faculty Research Award (2025) and the ERC Advanced Investigator Award (2016). He has authored over 50 peer-reviewed articles and co-edited the open-access textbook Data Assimilation Fundamentals . Van Leeuwen leads the Center for Nonlinear Data Assimilation, Causal Discovery, and Prediction, emphasizing cutting-edge methods for high-dimensional systems and climate predictability.

Sergey Nadtochiyمشاهده پروفایل

استاد

• Financial Mathematics
• Probability Theory
• Partial Differential Equations
• +۲ مورد دیگر

Sergey Nadtochiy serves as Professor of Applied Mathematics within the College of Computing at Illinois Institute of Technology. His research bridges theoretical mathematics with financial applications through rigorous probabilistic and analytical frameworks. His academic foundation includes: Ph.D. in Operations Research & Financial Engineering from Princeton University (2009), advised by R. Carmona M.A. in Operations Research & Financial Engineering from Princeton University (2008), advised by R. Carmona Specialist (M.Sc.) in Mathematics, summa cum laude, from Moscow State University (2005), advised by A. Shiryaev Nadtochiy's research program centers on Financial Mathematics, Probability Theory, and Partial Differential Equations with applications to market microstructure, optimal contract design, and systemic risk. His work demonstrates deep integration of Stochastic Control and Game Theory to model complex financial phenomena like price impact and limit order book dynamics, while recent publications extend into mathematical physics through Stefan problem analysis. Analysis of his 15 most recent publications reveals a strategic evolution from pure financial mathematics toward interdisciplinary connections with mathematical physics. The 2022-2024 works show increasing focus on the Stefan problem and diffusion-limited aggregation, while maintaining core expertise in market microstructure through studies on price impact concavity, brokerage contracts, and liquidity effects. His scientific recognition includes: Award for excellence (honorific fellowship) from Moscow State University (2001-2005) SIAG/FME Junior Scientist Prize from SIAM (2012) Charlotte Elizabeth Procter Honorific Fellowship from Princeton University (2008-2009) Gordon Y.S. Wu Honorific Fellowship from Princeton University (2005-2009) Nadtochiy has secured significant research funding as sole Principal Investigator on two NSF awards: a CAREER grant (DMS-1855309, 2017-2022) and earlier grant DMS-1411824 (2014-2017). While no formal student advisees are listed in the source materials, his collaborative publications with researchers like M. Shkolnikov and S. Shreve demonstrate active mentorship within research teams focused on probabilistic methods and financial modeling.

Boualem Djehicheمشاهده پروفایل

استاد

• Stochastic Analysis
• Stochastic Control
• Insurance Mathematics
• +۸ مورد دیگر

Boualem Djehiche is a Professor of Mathematical Statistics at the Department of Mathematics, KTH Royal Institute of Technology. He is affiliated with the Digital Futures Faculty and the SCI School at KTH. His research focuses on Stochastic Analysis, including Stochastic Control, Insurance Mathematics, Mathematical Finance, and Game Theory. Djehiche holds editorial roles in journals such as Scandinavian Actuarial Journal and Finance and Stochastics . Education details are not explicitly stated in the provided texts. His teaching responsibilities include courses like Game Theory, Probability Theory, and Financial Mathematics. He advises students in these areas, though explicit student names are not listed. His research explores advanced topics such as mean-field games, time-inconsistent optimal control, and applications in finance and economics. Recent publications address topics like zero-sum Dynkin games, generative AI outcomes as Nash equilibria, and commodity futures pricing with regime switching. Research Interests: Stochastic Control, Insurance Mathematics, Mathematical Finance, Mean-Field Games, System Identification. Editorial Duties: Editor-in-Chief of Scandinavian Actuarial Journal , Associate Editor of Finance and Stochastics , and roles in multiple other journals. Grants & Collaborations: Collaborations include work on disability insurance modeling, credit scoring, and energy market dynamics via mean-field-type games. Labs/Teams: Involved in cross-disciplinary initiatives like the Digital Futures research center, focusing on digital technologies and societal challenges.

• Water Resources Engineering
• Environmental Engineering
• Biogeochemistry
• +۵ مورد دیگر

Lazaro J. Perez is an Assistant Professor in the School of Civil and Construction Engineering at Oregon State University, part of the College of Engineering. His research focuses on hydrological and biogeochemical processes in subsurface porous media, particularly quantifying incomplete mixing effects on chemical reactions and contaminant transport. He integrates experimental and numerical methods to address groundwater remediation, aquifer management, and bioremediation challenges. Education: Ph.D., Civil and Environmental Engineering, Polytechnic University of Catalonia (2019) M.S., Engineering Geology and Geologic Resources, University of Oviedo (2015) B.S., Geology, University of Oviedo (2013) Research interests include bacterial transport dynamics, biofilm impacts on fluid flow, and multiscale modeling of reactive transport. His work spans pore-to-field scales, emphasizing practical applications like arsenic removal using metal-organic frameworks and climate change effects on water systems. Key contributions include advancements in solute transport prediction via machine learning, upscaling bacterial migration in porous media, and understanding biofilm-induced flow heterogeneities. Current projects involve climate change impacts on mountain hydrology and innovative remediation strategies for contaminated waters.

• Fluid Mechanics
• Hydrodynamics
• Micro-swimmers
• +۵ مورد دیگر

Tommaso Redaelli is a researcher at ETH Zürich, affiliated with the Professorship for Groundwater and Hydromechanics. His work integrates fluid mechanics with biological and active-matter systems, covering phenomena from biofilm deformation to plankton sensing and micro-swimmer propulsion. Research Interests Low-Reynolds-number hydrodynamics and micro-swimmers Biofilm rheology and mechanical response to flow Hydrodynamic signalling in planktonic organisms Stochastic models of turbulence and scalar transport Across his recent publications one observes a trajectory from theoretical turbulence studies toward biologically inspired fluid problems, combining analytical modeling with experimental relevance, notably in understanding how small organisms exploit fluid-borne signals. Contact Email: tredaelli@ethz.ch Postal: ETH Zürich, HIF D 22.2, Laura-Hezner-Weg 7, 8093 Zürich, Switzerland

Samuel Isaacsonمشاهده پروفایل

استاد

• Numerical Analysis
• Mathematical Biology
• Mathematical Physics
• +۴ مورد دیگر

Samuel Isaacson is a Professor at Boston University's Department of Mathematics and Statistics, specializing in numerical analysis, mathematical biology, and mathematical physics. His research focuses on developing and analyzing numerical methods for stochastic reaction-diffusion models in cellular biology, with applications to cell signaling, T cell activation, and antibody-antigen interactions. He emphasizes rigorous coarse-grained modeling, unstructured mesh methods, and parameter inference from experimental data. Recent work includes advancements in reactive Langevin dynamics models, mean-field limits of particle systems, and molecular mechanisms underlying antibody efficacy. His interdisciplinary approach combines computational modeling, experimental collaboration, and mathematical theory to address biophysical questions. Notable contributions include the Catalyst software for reaction network modeling and studies on spatial effects in genetic circuits and chromatin structure. Isaacson's grants and collaborations span computational methods for stochastic systems, parameter estimation in biochemical networks, and the influence of cellular geometry on signaling. His lab focuses on bridging microscopic particle-level models with macroscopic biological observations, with applications in immunology and synthetic biology. Current projects explore the role of molecular 'reach' in antibody-virus interactions and the development of efficient simulation tools for complex biological systems.