جست‌وجوی استاد و پژوهشگر

Xiaodong Chengمشاهده پروفایل

استادیار

• Control Systems
• Optimization
• Machine Learning
• +۵ مورد دیگر

Xiaodong Cheng is an Assistant Professor at the Mathematical and Statistical Methods (Biometris) group in the Department of Plant Science at Wageningen University & Research. His research focuses on control systems, optimization, and machine learning, with applications in agricultural and energy systems. He holds a Ph.D. (cum laude) from the University of Groningen, under Prof. Jacquelien Scherpen, and prior roles include Research Associate at the University of Cambridge and Postdoctoral Researcher at Eindhoven University of Technology. Education: B.S. and M.E. from Northwestern Polytechnical University, China (2011, 2014) Ph.D. (cum laude) in Engineering from the University of Groningen, Netherlands (2018) Research Interests: Data-driven modeling, dimensionality reduction, learning-based control, system identification, and applications in agriculture and energy. He emphasizes practical implementations through tools like SYSDYNET and Bayesian neural ODEs for greenhouse systems. Key Contributions: His work spans model reduction for network systems, fault-tolerant control, and stochastic MPC for greenhouse production. Recent trends in his publications highlight advancements in resilient microgrid control, precision agriculture via drone-based sensing, and Bayesian methods for dynamic systems. Awards: Automatica Paper Prize Award (2017–2019) IEEE Transactions on Control Systems Technology Outstanding Paper Award (2020) Labs/Teams: Leads the Biometris group's efforts in integrating control theory and machine learning for sustainable systems. His work often collaborates with agricultural and energy sector stakeholders for real-world impact.

• Differential Privacy
• Markov Chain Monte Carlo Methods
• Generative Models
• +۳ مورد دیگر

Andrea Bertazzi is a Researcher (POST-DOCTORANT) in the Centre de Mathématiques Appliquées (CMAP) at École Polytechnique. His work focuses on stochastic processes, differential privacy in algorithms, and computational statistics, with a particular emphasis on Markov chain Monte Carlo (MCMC) methods and piecewise deterministic models. His recent research explores the theoretical guarantees of privacy-preserving algorithms and the development of adaptive sampling techniques. Research interests include differential privacy, stochastic modeling, and numerical analysis of MCMC algorithms. He has contributed to advancing piecewise deterministic Monte Carlo methods, including their convergence properties and applications in generative models. His work bridges theoretical foundations with practical computational tools for statistical inference and machine learning. Publications highlight trends in algorithmic design for privacy, stochastic process analysis, and numerical methods. While no awards or grants are explicitly listed, his active publication record reflects ongoing engagement in cutting-edge research areas at the intersection of mathematics and computer science.

• Partial Differential Equations
• Computational Micromagnetism
• Numerical Analysis
• +۵ مورد دیگر

Michael Feischl is a Professor for Computational PDEs at TU Wien (since 2022) and holds an ERC Consolidator Grant for his project "New Frontiers in Optimal Adaptivity" (2024–2029). His research focuses on partial differential equations with random coefficients, computational micromagnetism (Landau-Lifshitz-Gilbert equation), and optimal adaptive mesh refinement techniques. He leads the Computational PDEs research group within the Institute of Analysis and Scientific Computing. Education and career highlights include roles as Associate Professor at TU Wien (2019–2022), W2 Professor at University of Bonn (2017–2018), and Junior Research Group Leader at KIT (2015–2017). His work bridges numerical analysis, computational physics, and machine learning, with a strong emphasis on rigorous mathematical foundations and algorithmic efficiency. Research interests include: Adaptive finite element and boundary element methods Stochastic modeling and uncertainty quantification Computational methods for micromagnetic simulations Machine learning applications in numerical analysis His recent work explores optimal adaptivity for time-dependent PDEs, neural network-based solvers, and efficient discretization strategies for complex physical systems. Key contributions include advancements in a posteriori error estimation and hierarchical training of neural networks.

Wouter Kouwمشاهده پروفایل

استادیار

• Probabilistic Machine Learning
• Variational Bayesian Inference
• Active Inference
• +۶ مورد دیگر

Wouter Kouw is an Assistant Professor at the Electrical Engineering department of Eindhoven University of Technology (TU/e) , leading the Bayesian Intelligent Autonomous Systems lab. With a dual PhD in Computer Science (2018, TU Delft) and MSc in Neuroscience (2013, Maastricht University) , he bridges neurobiology and artificial intelligence through variational Bayesian inference and active inference frameworks. Research Focus : Probabilistic machine learning systems using message passing algorithms on factor graphs , applied to mobile robotics and adaptive control Key Projects : CONTACT-AI (contact-rich robot navigation), FEP-walker (active inference-based locomotion), and BayesBrain (hybrid neuro-in-silico computing) His work spans nonlinear system identification , uncertainty quantification , and sensor modeling , with recent publications in IEEE Transactions , Entropy , and Communications in Computer and Information Science . Awards include the Niels Stensen Fellowship (2017) and TU/e Team Science Award nomination (2023) . Collaborations extend to institutions in Germany, USA, and Denmark, with teaching responsibilities in Bayesian Machine Learning and Neuro Computation .

Amirreza Razmjoo Fardمشاهده پروفایل

پژوهشگر

• Robotics
• Artificial Intelligence
• Control Theory
• +۴ مورد دیگر

Amirreza Razmjoo Fard is a PhD student and Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering (STI) and the Robot Learning and Interaction (RLI) Group at the Idiap Research Institute. Supervised by Dr. Sylvain Calinon, he focuses on developing adaptive, efficient, and intelligent robotic control methods for contact-rich environments and constrained scenarios. Research Areas: Generative AI (diffusion models, flow matching), Model composition (product of experts), System dynamics, Control theory, Physics-based simulation (Isaac Sim) Key Goals: Bridging theory and real-world applications, enhancing robot autonomy, interaction, and physical intelligence His work spans publications at top robotics conferences like IROS, CoRL, RSS, and ICRA, with a Best Paper Finalist recognition at RSS 2024. Notable methods include CCDP for diffusion policy composition, CDF for differentiable robot geometry, and D-LGP for hybrid planning. He has also collaborated with Honda Research Institute Europe during a six-month internship.

Anastasios Tsiamisمشاهده پروفایل

مدرس

• Statistical Learning and Control
• Data-Driven Control
• Online Learning
• +۳ مورد دیگر

Dr. Anastasios Tsiamis is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich, working in the Automatic Control Laboratory (Professur Control and Computation). His research focuses on the intersection of control theory and machine learning, specifically investigating how system theoretic properties affect the statistical difficulty of learning in system identification, online estimation, and control. Dr. Tsiamis received his Diploma (MEng, five-year degree) in Electrical and Computer Engineering from the National Technical University of Athens (NTUA). He completed his Ph.D. in Electrical and Systems Engineering at the University of Pennsylvania under Professor George Pappas, following graduate research with Professor Petros Maragos and undergraduate work with Professor Kostas J. Kyriakopoulos at NTUA. His primary research areas include Statistical Learning and Control, Data-Driven Control, Online Learning, Risk-Aware Control, and Security and Privacy in Networked Control Systems. Dr. Tsiamis has made significant contributions to understanding the fundamental statistical limits of learning in control systems, particularly focusing on sample complexity. His work on risk-aware optimization develops algorithms that safeguard against catastrophic events while maintaining good average performance, and his security research addresses eavesdropping attacks in remote estimation and motion planning. Analysis of Dr. Tsiamis's recent publications reveals a strong focus on data-driven approaches to control theory with emphasis on distributionally robust methods, risk-aware optimization, and finite sample guarantees. His work bridges theoretical foundations with practical applications across system identification, online learning, and adaptive control, providing rigorous non-asymptotic guarantees for learning-based control algorithms. Dr. Tsiamis has received several notable research recognitions: Best student paper award at IEEE 61th Conference on Decision and Control (2022) Spotlight Presentation at 41st International Conference on Machine Learning (2024) Finalist for best student paper award at American Control Conference (2019) Finalist for young author prize at IFAC World Congress (2017) Oral presentation at 2nd L4DC Conference (2020) Dr. Tsiamis teaches Linear System Theory (227-0225-00L) at ETH Zürich and collaborates extensively with Professor John Lygeros, Professor Manfred Morari, and researchers from the University of Pennsylvania. His publication record demonstrates strong collaborative research across multiple institutions while advancing theoretical foundations of learning-based control. As an active member of the Automatic Control Laboratory at ETH Zürich, Dr. Tsiamis contributes to advancing control systems science through rigorous mathematical analysis and innovative algorithmic development, with applications spanning robotics, energy systems, and networked control.

• Numerical Analysis
• Quasi-Monte Carlo Methods
• Lattice Rules
• +۳ مورد دیگر

Ronald Cools is a Professor in the Department of Computer Science within the Science & Technology Group at KU Leuven (Katholieke Universiteit Leuven) in Belgium. His research spans numerical analysis, approximation theory, and computational mathematics, with a particular focus on lattice rules and quasi-Monte Carlo methods for high-dimensional problems. His work has significant applications in scientific computing, financial mathematics, and solving partial differential equations. Professor Cools' research interests center on developing efficient algorithms for high-dimensional integration and approximation. His work on lattice rules, component-by-component construction methods, and tent-transformed lattices has advanced the field of numerical analysis. He has made significant contributions to understanding the trigonometric degree of exactness, worst-case error analysis in various function spaces, and the development of practical algorithms for multivariate problems. His research bridges theoretical mathematical analysis with practical computational methods that address the curse of dimensionality in scientific computing. The analysis of his recent publications reveals a consistent focus on lattice-based algorithms for approximation and integration in high dimensions. His work demonstrates increasing sophistication in handling general weight parameters, extending methods to non-periodic settings, and developing faster construction algorithms. The research trajectory shows a progression from theoretical foundations to practical implementations with applications in PDEs, financial mathematics, and scientific computing. The publications exhibit strong international collaboration, particularly with researchers like Frances Kuo, Dirk Nuyens, and Ian Sloan. Professor Cools has supervised numerous PhD students, including Weiwen Mo, Laurence Wilkes, Yuya Suzuki, T. Nguyen, and Gowri Suryanarayana. His mentorship has produced significant contributions to the field of numerical analysis. While specific grant information isn't detailed in the provided text, his extensive publication record spanning multiple decades suggests sustained research funding supporting his work in computational mathematics. His research group at KU Leuven appears to be a hub for advanced computational mathematics, focusing on quasi-Monte Carlo methods, lattice rules, and high-dimensional approximation techniques. The collaborative nature of his publications indicates an active research team working on both theoretical aspects of numerical methods and their practical implementations.

Nikolay Malkinمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Generative Modeling
• Computational Biology
• +۵ مورد دیگر

Nikolay Malkin is a researcher at the University of Edinburgh specializing in machine learning, generative modeling, and computational biology. His work focuses on simulation-free methods for stochastic dynamics and optimal transport, particularly in applications like single-cell dynamics and protein structure prediction. Research Interests: Flow-based generative models, Schrödinger bridges, diffusion models, and their applications in high-dimensional biological data analysis. Key Contributions: Development of [SF] 2 M for cell dynamics modeling, generalized conditional flow matching (CFM) for improved generative tasks, and subtrajectory balance (SubTB) for GFlowNet stability. Collaborations: Active collaboration with Yoshua Bengio, Alexander Tong, and other researchers in advancing flow matching and generative modeling techniques. Publishing Trends: Recent papers emphasize protein backbone generation, tree-structured Schrödinger bridges, and velocity-growth dynamics in single-cell populations.

Mathieu Blondelمشاهده پروفایل

پژوهشگر

• Differentiable Programming
• Machine Learning
• Deep Learning
• +۹ مورد دیگر

Mathieu Blondel is a Researcher at Google DeepMind in Paris, France. He obtained his PhD in Machine Learning from Kobe University in 2013. From 2013 to 2019, he worked at NTT's Communication Science Laboratories in Kyoto, Japan. Research Interests: Differentiable programming Language models and deep learning Loss functions for structured prediction Optimization algorithms Optimal transport theory Polynomial networks Machine learning software Scientific Awards: Open-science honorable mention at ECML PKDD 2013 Software Contributions: Mathieu is a core contributor to scikit-learn , a foundational Python library for machine learning. He also maintains open-source projects on differentiable sorting, optimal transport, and Fenchel-Young losses.

Andrej Risteskiمشاهده پروفایل

دانشیار

• Machine Learning
• Statistics
• Theoretical Computer Science
• +۵ مورد دیگر

Andrej Risteski is an Associate Professor at the Machine Learning Department of Carnegie Mellon University (CMU) since 2025. He previously held the Norbert Wiener Research Fellow position jointly between the Applied Math Department and IDSS at MIT (2017–2019) after completing his PhD in Computer Science at Princeton University (2012–2017) under Sanjeev Arora . His research focuses on the intersection of machine learning, statistics, and theoretical computer science , emphasizing generative models, representation learning, and out-of-distribution generalization with applications to natural language processing and scientific domains . His recent publications explore edge embeddings in Graph Neural Networks (GNNs) , score matching efficiency , and theoretical foundations of diffusion models . Key contributions include analyzing computational bottlene.com/activities/statistical-and-computational-challenges-in-probabilistic-scientific-machine-learning-sciml/">NSF CAREER Award , DOE Computational Science Graduate Fellowship for Stephen Huan, and co-organizing the COLT workshop on Theory of AI for Scientific Computing . He advises PhD students across Machine Learning, Computer Science, and Mathematics , including Bingbin Liu (Kempner Institute Fellow) and Elan Rosenfeld (Google Research Scientist). Teaching includes Probabilistic Graphical Models and Advanced Deep Learning at CMU, plus Applied Mathematics at MIT. His work is supported by NSF, DoD , and OpenAI Superalignment grants. Education : PhD in Computer Science (Princeton), BSc in Computer Science (Princeton) Current Positions : Associate Professor, CMU Machine Learning Department Former Positions : Norbert Wiener Fellow, MIT IDSS & Applied Mathematics Research Areas : Generative Models (GANs, Diffusion Models) Representation Learning Out-of-Distribution Generalization Neural Language Models AI for Scientific Applications Sampling and Optimization Algorithms Scientific Awards : NSF CAREER Award (2023) Google Research Award (2024) Amazon Research Award (2022) OpenAI Superalignment Grant (2023) Recent Talks (2023–2025): "Architectural Nuances and Benchmark Gaps in Scientific ML" (UC Berkeley, 2025) "The Statistical Cost of Score-Based Losses" (Simons Institute Boot Camp, 2024) "Neural Networks for PDEs" (ETH Zurich, 2024) "Discernible Patterns in Transformers" (Theory of Interpretable AI, 2024) He leads a research group producing work at the interface of computational complexity and graph learning , with empirical validation on topological bottlenecks and hub node dynamics . Current projects include ICML 2025 paper on edge embeddings in GNNs and COLT 2025 workshop on AI for Scientific Computing co-organized with MIT, Duke, and ETH Zurich collaborators.

Robert L. Givanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Reinforcement Learning
• Knowledge Representation
• +۴ مورد دیگر

Robert L. Givan is an Associate Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. His academic affiliations include Purdue’s Department of Electrical and Computer Engineering and the Max W and Maileen Brown Family Hall. He holds a B.S. in Mathematics and Biology from Stanford University (1987), an M.S. in Computer Science from Stanford (1987), and a Ph.D. in Computer Science from MIT (1996). His research focuses on artificial intelligence, reinforcement learning, automated reasoning, knowledge representation, and stochastic modeling. Notable projects include applying automated reasoning to AI planning domains and exploring reinforcement learning techniques for stochastic control problems. He has contributed to areas such as probabilistic planning, heuristic search, and formal verification. His recent publications span topics like stochastic enforced hill-climbing, automated feature induction, and formal verification methods. His work often bridges theoretical foundations with applied challenges in AI and robotics. Givan’s research has been supported by NSF grants in AI and computer engineering. Labs and collaborations include involvement with Purdue’s AI and robotics initiatives, though no specific lab names are mentioned. He emphasizes student collaboration through directed research and thesis opportunities, particularly in AI and related fields.

Jorge A. Sefairمشاهده پروفایل

دانشیار

• Network optimization
• Combinatorial optimization
• Integer programming
• +۵ مورد دیگر

Jorge A. Sefair is an Associate Professor and Associate Director at the Center for Applied Optimization in the Department of Industrial & Systems Engineering at the University of Florida. He also serves as Masters Program Co-Coordinator. His expertise spans operations research, mathematical programming, and optimization in environmental/urban planning, public policy, and service systems. Dr. Sefair holds a PhD in Industrial and Systems Engineering from the University of Florida (2015), with prior degrees including an MSc and BSc in Industrial Engineering, and a BA in Economics from Universidad de los Andes, Colombia. His research focuses on network optimization, multistage optimization, and integer programming, with interdisciplinary applications in civil engineering, public health, ecology, and economics. His work has been published in high-impact journals like INFORMS Journal on Computing and European Journal of Operational Research. Research funding comes from the National Science Foundation, Office of Naval Research, and Department of Homeland Security. He received an NSF CAREER award in 2021 for Operations Engineering. Key research trends include optimization models for hydropower planning, human trafficking interdiction, biosurveillance, and conservation planning. Collaborations span diverse fields such as ecology, biology, and economics. Current efforts emphasize scalable solutions for complex, real-world systems.

Umesh Vaidyaمشاهده پروفایل

استاد

• Control Theory
• Dynamical Systems
• Data-Driven Methods
• +۴ مورد دیگر

Umesh Vaidya is a Professor in the Department of Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research integrates control theory, dynamical systems, and data-driven methodologies to address challenges in power systems, robotics, and building automation. He leads the DYCO AI Lab and maintains active collaborations with industry and government agencies. Education: Ph.D. in Mechanical Engineering (Dynamical Systems and Control Theory), University of California, Santa Barbara B.E. in Electrical Engineering, Victoria Jubilee Technological Institute, Mumbai, India (1997) Research Focus: Vaidya pioneers operator-theoretic approaches for data-driven dynamical analysis, specializing in Koopman and Perron-Frobenius operators. His work spans robust optimization for cyber-physical systems, stability analysis of power grids, and safety-critical control for robotics. Recent innovations include density functions for safe navigation and transfer operator frameworks for building environment monitoring. Publication Trends: Analysis of 2019-2025 publications reveals escalating integration of Koopman operator theory with machine learning for control systems. Key trajectories include: (1) Safety-critical autonomy using density functions (35% of recent work), (2) Power grid stability via data-driven spectral methods (25%), (3) Optimization of networked systems (20%), and (4) Robotics control under uncertainty (20%). The shift toward real-world validation in autonomous vehicles and power systems is pronounced post-2021. Scientific Recognition: NSF CAREER Award (2012) for foundational work in dynamical systems Best Paper Award at American Control Conference (2018) for building environment monitoring Keynote invitations at Set-Oriented Numerics workshop (2016) and IPAM/UCLA (2019) Litton Industries Professorship (2010-2011) for engineering excellence Research Leadership: Vaidya directs the DYCO AI Lab, securing major grants including NSF CAREER and collaborative power grid analytics projects. His team develops convex approaches for data-driven control with safety guarantees, bridging theoretical advances with applications in autonomous vehicles and renewable energy integration. Current projects focus on digital twins for robust autonomy and Koopman-based stability assessment in high-penetration renewable grids. Technical Infrastructure: The DYCO AI Lab employs high-performance computing for operator-theoretic methods, with experimental validation platforms for off-road autonomous vehicles and building energy systems. Partnerships include national labs (NREL, ORNL) and industry leaders in power systems (Siemens, Duke Energy) and robotics (Boston Dynamics).

Fei Xueمشاهده پروفایل

دانشیار

• Scientific computing
• Numerical linear algebra

Fei Xue is Associate Professor in the Department of Mathematical Sciences at Clemson University. His research specializes in numerical linear algebra and scientific computing, particularly developing efficient algorithms for large-scale eigenvalue problems and linear systems. Research focuses on preconditioned iterative methods, Krylov subspace techniques, and numerical solutions to partial differential equations. Recent work has developed novel algorithms for eigenvalue computation, including the Block Preconditioned Harmonic Projection method for nonlinear eigenproblems and a Chebyshev-based Locally Optimal Block Preconditioned Conjugate Gradient method. Applications span quantum physics, materials science, and computational finance. Dr. Xue has received sustained NSF funding since 2011 for his work on eigenvalue solvers and matrix computations. Teaching responsibilities include graduate courses in numerical linear algebra and scientific computing.

• Computer Systems Performance Analysis
• Autonomic Computing
• Cloud Computing
• +۵ مورد دیگر

Daniel Brazier is a part-time Research Affiliate at George Mason University, focusing on advanced computing systems research. His work intersects autonomic computing, cybersecurity, and distributed systems optimization. He specializes in performance modeling for cloud/fog environments, dynamic reconfiguration strategies, and resilience engineering. Key research areas include: Autonomic resource management in edge and cloud systems Stochastic optimization algorithms Security mechanisms like moving target defense Performance-security tradeoff analysis Decentralized runtime system modeling His recent work emphasizes practical frameworks for distributed system trustworthiness and adaptive elasticity control under variable workloads. Outputs include novel meta-heuristic algorithms for virtual network optimization and analytic models for parallel server architectures. Publications highlight contributions to fog/cloud computing, manufacturing process optimization, and security-aware system design. Current efforts focus on applying decision analytics to smart manufacturing and autonomic emergency department frameworks.