جست‌وجوی استاد و پژوهشگر

Esen Yelمشاهده پروفایل

استادیار

• Autonomous Systems
• Robotics
• Safety-Critical Systems
• +۳ مورد دیگر

Esen Yel is an Assistant Professor in the Electrical, Computer, and Systems Engineering (ECSE) department at Rensselaer Polytechnic Institute (RPI) since January 2024. She leads the Reliable Intelligent Systems Lab (RISL), focusing on enhancing safety in autonomous systems through planning, uncertainty-aware decision-making, and runtime monitoring. Her work integrates reachability analysis, machine learning, and adaptive control to ensure safe operations in unpredictable environments. Educational background: Ph.D. in Systems Engineering, University of Virginia (2021) M.S. and B.S. in Electrical and Electronics Engineering, Bogazici University, Turkey (2016 and 2014) Postdoctoral Scholar in Aeronautics and Astronautics at Stanford University (2021–2023), contributing to the Stanford Intelligent Systems Lab (SISL) and Stanford Center for AI Safety Research interests emphasize safety-critical autonomous systems , including: Uncertainty-aware planning and decision-making Runtime monitoring and recovery mechanisms Machine learning for adaptive control Formal verification of neural networks Robotics and UAV operations under degraded conditions Her publications (2017–2024) explore themes like spatiotemporal prediction, reachability analysis, meta-learning for UAVs, and safety validation in perception systems. She directs the RISL lab, advancing interdisciplinary research in reliable AI and robotics.

Nanwei Wangمشاهده پروفایل

استادیار

• Statistical Learning
• Graphical Models
• Bayesian Inference
• +۱ مورد دیگر

Nanwei Wang is an Assistant Professor in the Department of Mathematics and Statistics at the University of New Brunswick. He obtained his Ph.D. in Statistics from York University in 2017 following a Bachelor's degree in Mathematics from Xi'an Jiaotong University. His research develops statistical learning methods for high-dimensional data in genetics and machine learning. His core research interests include: Graphical models for dependency modeling Composite likelihood estimation methods Bayesian model selection techniques Linear mixed models in genetic association studies Wang's publication record demonstrates consistent focus on computational statistics, with recent emphasis on scalable algorithms for genomic data integration and Bayesian methods for high-dimensional inference. Methodological innovations in MCMC and penalized estimation feature prominently. Awards: OICR-CANSSI Biostatistics Postdoctoral Fellowship Award He leads research projects in statistical methodology development and welcomes graduate students interested in mathematical statistics, Bayesian analysis, and machine learning applications.

Marcin Witkowskiمشاهده پروفایل

استادیار

• Signal Processing
• Speech Dereverberation
• Speaker Verification
• +۳ مورد دیگر

Dr. Marcin Witkowski is an Assistant Professor at the AGH University of Science and Technology in Cracow, Poland, specializing in the Department of Electronics and Telecommunications . His work focuses on signal processing with particular emphasis on speaker verification, speech dereverberation, and multichannel audio analysis. He holds a Ph.D. (2022) and master's degree (2012) in Electronics and Telecommunication from AGH UST, alongside a B.E.E. in Acoustics Engineering (2013). Research Interests: Far-field speaker verification, speech dereverberation techniques, robust audio processing, and blind source separation. His projects include developing anti-spoofing systems and enhancing distant speech recognition in reverberant environments. Recent work explores neural network hallucinations in Whisper ASR and VR-based voice training tools. Labs/Teams : Core member of the Signal Processing Group at AGH UST. Active in EU-funded projects on multichannel signal processing and speaker recognition systems.

WonJoon Chungمشاهده پروفایل

دانشیار

• Co-Design
• Design Pedagogy
• Design Thinking
• +۳ مورد دیگر

Dr. WonJoon Chung is an Associate Professor at Carleton University's School of Industrial Design. He holds a B.A. from Konkuk University, an M.A. from Ohio State University, and a Ph.D. from Illinois Institute of Technology. His professional experience includes design roles at ENEX, Battelle Memorial Institute, and LPK. His research focuses on individual and collective creativity, design pedagogy, and design thinking processes. He investigates practical applications of abductive reasoning, reflective practices, and iterative prototyping to solve complex design problems. His work has led to government-funded international collaborations with industry and academic partners in Korea. Recent research projects include designing creativity-promoting learning spaces and developing convergence design frameworks for SMEs. His publications demonstrate consistent focus on creativity methodologies, emotional design, and interdisciplinary collaboration in industrial design contexts.

Bedřich Sousedíkمشاهده پروفایل

دانشیار

• Applied and Computational Mathematics
• Numerical Analysis
• Scientific Computing
• +۵ مورد دیگر

Bedřich Sousedík is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). He joined UMBC in 2014, transitioning from roles as a Research Associate at the University of Southern California and University of Maryland, College Park. His academic journey includes a Ph.D. in Applied Mathematics (2010, University of Colorado Denver) and a Ph.D. in Civil Engineering (2008, Czech Technical University in Prague). Education: Ph.D. in Applied Mathematics, University of Colorado Denver (2010) Ph.D. in Civil Engineering, Czech Technical University (2008) M.Eng. in Mechanical Engineering, Czech Technical University (2001) Research Interests: Focuses on applied and computational mathematics, including numerical analysis, scientific computing, uncertainty quantification, stochastic finite element methods, and domain decomposition techniques. His work emphasizes scalable algorithms for complex systems with parametric uncertainty. Professional Contributions: Author of ~43 refereed publications (24 journals, 12 conferences) with an h-index of 15 (Google Scholar) Principal Investigator on NSF grants totaling $219,999 for multilevel methods research Recipient of prestigious awards including the 2010 Prof. Babuška Prize and 2020 Early Career Excellence Award Advising & Teaching: Supervised 8 Ph.D. and Master’s students since 2014 Mentored undergraduate research projects in computational fluid dynamics and epidemiological modeling

Paul Mannsمشاهده پروفایل

استادیار

• Mathematical Optimization
• PDE-Constrained Optimization
• Integer Programming
• +۱ مورد دیگر

Paul Manns is an Assistant Professor of Optimization at TU Dortmund University's Department of Mathematics, appointed in 2021. His research specializes in mathematical optimization involving partial differential equations and integer constraints, with emphasis on regularization techniques and trust-region algorithms. Education includes: Ph.D. in Mathematics, TU Braunschweig (2019) Computational Engineering studies, TU Darmstadt Prior research experience includes positions at Heidelberg University, TU Braunschweig, and Argonne National Laboratory (USA), including a James H Wilkinson Fellowship. Recent publications develop novel methods for mixed-integer control problems, domain decomposition, and convergence analysis in non-convex optimization spaces.

Zhuoran Wangمشاهده پروفایل

استادیار مهمان

• Scientific Computing
• Partial Differential Equations
• Numerical Analysis
• +۳ مورد دیگر

Zhuoran Wang is a Visiting Assistant Professor in the Department of Mathematics at the University of Kansas, affiliated with the College of Liberal Arts & Sciences. His research focuses on scientific computing, numerical analysis, and partial differential equations, with a particular emphasis on weak Galerkin finite element methods applied to fluid dynamics, poroelasticity, and linear elasticity. He contributes to computational mechanics through innovative solver designs and software tools like DarcyLite and implementations within the deal.II library. His work spans theoretical analysis of numerical methods, including convergence properties and robustness of iterative solvers, as well as practical applications in quadrilateral and hybrid mesh discretizations. Key themes include penalty-free FEMs, locking-free solvers, and efficient preconditioning techniques for complex systems such as Stokes flow and poroelastic problems. No scientific awards are listed. Advising and grants information is unavailable. Wang’s research also involves developing computational frameworks for simulating subsurface flow and fluid-structure interactions through open-source tools like DarcyLite, reflecting a commitment to both theoretical and applied computational mathematics.

Derya Aksarayمشاهده پروفایل

استادیار

• Robotics
• Control Theory
• Formal Methods
• +۴ مورد دیگر

Derya Aksaray is an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University, where she directs the Dependable Autonomy Lab (DAL). She holds a PhD in Aerospace Engineering from Georgia Tech (2014) and has prior roles as an Assistant Professor at the University of Minnesota (2018-2022), and postdoctoral positions at MIT CSAIL (2016-2017) and Boston University (2014-2016). Her research focuses on control theory, formal methods, and machine learning for robotics and autonomous systems, particularly in uncertain and dynamic environments. Education: PhD, Aerospace Engineering, Georgia Institute of Technology (2014) MS, Aerospace Engineering, Georgia Institute of Technology (2011) BS, Aerospace Engineering, Middle East Technical University (2008) Research Interests: Formal methods for autonomous systems Reinforcement learning with temporal logic constraints Multi-agent coordination and distributed planning Resilient control for robotics and aerospace systems Energy-aware planning for UAVs and heterogeneous systems Lab Activities: The Dependable Autonomy Lab develops theory and algorithms for achieving reliable autonomous robot behaviors, with experimental validation on ground and aerial robots. Current projects include resilient planning under uncertainty, distributed multi-agent systems, and reinforcement learning with formal guarantees. Advising: Prof. Aksaray supervises graduate students in PhD and MS programs, focusing on control theory, formal methods, and robotics applications. Former advisees include Ali Tevfik Buyukkocak (PhD, 2024) and Ryan Peterson (MS, 2020). Professional Affiliations: Member of the American Institute of Aeronautics and Astronautics (AIAA) and Institute of Electrical and Electronics Engineers (IEEE).

Hans-Joachim Wunderlichمشاهده پروفایل

استاد

• Computer Architecture
• Hardware Reliability
• Fault Tolerance
• +۷ مورد دیگر

Prof. Hans-Joachim Wunderlich is a Professor at the University of Stuttgart's Institute of Computer Architecture and Computer Engineering, within the Faculty of Computer Science, Electrical Engineering, and Information Technology. His research focuses on hardware reliability, fault tolerance, and testing methodologies for VLSI circuits and embedded systems. He specializes in areas such as delay fault testing under PVT variability, approximate communication, and aging-aware design. Key research interests include robust testing techniques for small delay faults, error-tolerant communication protocols (e.g., RAPPER and Gray code-based approaches), and GPU-accelerated simulation of faults. His work addresses challenges in functional safety, interconnect reliability, and securing reconfigurable architectures against security violations. He explores energy-efficient iterative solvers for approximate computing environments and develops methods for predicting device aging and early life failures. Prof. Wunderlich’s contributions span academic and industrial applications, with a focus on real-time systems and dependable multi-processor systems-on-chip (MPSoCs). His research integrates formal verification, machine learning for defect detection, and hybrid protection schemes for reconfigurable scan networks. Recent work emphasizes stress-aware testing strategies and optimization of sensor data streaming in resource-constrained environments.

Hoomaan Maskanمشاهده پروفایل

پژوهشگر

• Signal Processing
• Statistical Learning
• Compressive Sensing
• +۵ مورد دیگر

Hoomaan Maskan is a doctoral student at the Department of Mathematics and Mathematical Statistics, Umeå University. His research focuses on Signal Processing , Statistical Learning , and Compressive Sensing with applications in sparsity and spatio-temporal data analysis. His recent publications highlight advancements in high-resolution ODEs for accelerated optimization, nonconvex programming, and super-resolution DOA estimation. Key themes include mathematical modeling, sparse signal recovery, and algorithm design for spatio-temporal data. Research projects include " Compressive Sensing and Statistical Learning with Sparsity " (2019-2024). He is a member of the Mathematical Programming and Statistical Learning and Inference for Spatio-Temporal Data research groups.

Eugene A. Feinbergمشاهده پروفایل

استاد

• Operations Research
• Markov Decision Processes
• Stochastic Optimization
• +۳ مورد دیگر

Eugene A. Feinberg is a Distinguished Professor in the Department of Applied Mathematics and Statistics at Stony Brook University. He received his Ph.D. in Mathematics (Probability and Statistics) from Vilnius University, Lithuania in 1979. Prior to joining Stony Brook University, Dr. Feinberg held research and faculty positions at Moscow State University of Railway Transportation (1976-1988) and a visiting faculty position at Yale University (1988-89). He has also spent two one-semester sabbatical leaves at MIT. Dr. Feinberg's primary research focuses on stochastic methods of operations research, with particular expertise in Markov decision processes and their industrial applications. His work has significantly contributed to the theory and practice of operations research in telecommunication, manufacturing, transportation, service systems, and electric energy applications. Since 1999, he has been actively working on electric energy applications, including optimizing electric energy transmission and forecasting energy demand. His extensive publication record includes over 150 papers and the edited Handbook on Markov Decision Processes. His research has evolved to encompass increasingly sophisticated mathematical frameworks for Markov decision processes while maintaining strong connections to practical applications in energy systems, telecommunications, and manufacturing. Recent work demonstrates a continued focus on theoretical foundations of MDPs with emerging applications in healthcare and inventory management. Honorary Doctorate from the Institute of Applied System Analysis, National Technical University of Ukraine Fellow of INFORMS (2011) for fundamental contributions to Markov decision processes and dynamic programming IEEE Charles Hirsh Award (2012) for developing electric load forecasting methods and smart grid technologies IBM Faculty Award (2012) Dr. Feinberg's research has been consistently supported by major funding agencies including the National Science Foundation (NSF), Department of Energy (DOE), Office of Naval Research (ONR), New York Office of Science, Technology and Academic Research (NYSTAR), and New York State Energy Research and Development Authority (NYSERDA), as well as industry partners. He serves on editorial boards for prestigious journals including Mathematics of Operations Research, Operations Research Letters, and Applied Mathematics Letters, reflecting his standing in the academic community.

Evan Gawlikمشاهده پروفایل

دانشیار

• Numerical Analysis
• Finite Element Methods
• Computational Fluid Dynamics
• +۳ مورد دیگر

Evan Gawlik is an Associate Professor in the Department of Mathematics and Computer Science at Santa Clara University (2024–present), previously serving as Associate Professor (2023–2024) and Assistant Professor (2018–2023) at the University of Hawaii. He holds a Ph.D. in Computational and Mathematical Engineering from Stanford University (2015) and a B.S. in Applied and Computational Mathematics from the California Institute of Technology (2010). Education : Ph.D., Stanford University, 2010–2015 B.S., California Institute of Technology, 2006–2010 Research Interests : Focused on numerical analysis, finite element methods, and structure-preserving algorithms for partial differential equations. His work emphasizes geometric numerical integration, computational fluid dynamics, and applications in magnetohydrodynamics (MHD) and general relativity. Key areas include finite element discretization of differential forms, error analysis on manifolds, and thermodynamically consistent numerical schemes. Grants & Funding : NSF Grant DMS-1703719 (2017–2020) ANR Grant GEOMFLUID (ANR-14-CE23-0002-01) Labs & Collaborations : Active in computational mathematics research groups, including contributions to open-source finite element software and collaborations on geometric numerical methods. His GitHub repositories (e.g., egawlik ) showcase code implementations of his research, such as variable density Euler equations solvers.

Robin Koytcheffمشاهده پروفایل

دانشیار

• Algebraic Topology
• Geometric Topology
• Knot Theory
• +۵ مورد دیگر

Robin Koytcheff is an Associate Professor in the Department of Mathematics at the University of Louisiana at Lafayette. He previously held postdoctoral positions at Brown University, the University of Victoria, and the University of Massachusetts Amherst. Ph.D., 2010, Stanford University B.S., 2005, Columbia University (Applied Math) His research lies at the intersection of algebraic and geometric topology, focusing on spaces of knots and links, configuration spaces, operads, and Vassiliev invariants. His work combines topological methods with algebraic structures to study embedding spaces and link homotopy. Recent publications analyze the homotopy types of knot spaces in solid tori (2021), cohomology classes via diagram complexes (2020), and Bott-Taubes integrals (2009–2020). His work often involves collaborations with mathematicians like Rafal Komendarczyk and Ismar Volić. Scientific awards include an NSF Postdoctoral Fellowship (2010–2013) and a PIMS Postdoctoral Fellowship (2013–2015). He currently holds NSF grant DMS-2405370 to support his research.

Wei Chenمشاهده پروفایل

استاد

• Statistical Mechanics
• Spin Glasses
• Mathematical Physics
• +۳ مورد دیگر

Wei Chen is a Professor in the Department of Radiology and Biomedical Engineering at the University of Minnesota, affiliated with the Center for Magnetic Resonance Research. His research primarily focuses on statistical mechanics, spin glasses, and mathematical physics, with a strong emphasis on rigorous theoretical analysis. Key areas include the study of replica symmetry, disorder chaos, TAP equations, and universality in spin glass models. His work bridges probability theory and condensed matter physics, addressing fundamental questions in disordered systems such as the Sherrington-Kirkpatrick model, mixed p-spin models, and Lévy spin glasses. Recent studies explore Gaussian convexity, free energy calculations, and phase transitions in diluted and high-dimensional systems. Chen has contributed to advancing methodologies like the cavity method and Bayesian regression in the context of spin glasses. His publications reflect a deep engagement with both foundational theoretical problems and their implications for broader statistical mechanics frameworks.

Yoon Jeon Kimمشاهده پروفایل

استادیار

• Innovative Assessment Design
• Game-Based Learning
• Playful Assessment Tools
• +۴ مورد دیگر

Yoon Jeon (YJ) Kim is an Assistant Professor of Design, Creative, and Informal Education at the University of Wisconsin–Madison's Department of Curriculum and Instruction within the School of Education. Previously, she founded and directed MIT Playful Journey Lab, leading interdisciplinary teams to develop playful assessment tools. Her research focuses on innovative assessment strategies, game-based learning, and empowering educators through creative evaluation methods. Her work bridges theory and practice through embedded assessment tools for maker classrooms, digital learning games, and evidence-centered design frameworks. Key collaborations involve game designers, developers, and practitioners to create impactful learning metrics. Publications highlight her exploration of collaborative play effects in geometry education, design-based research approaches, and the evolution of game-based assessment methodologies. She advocates for playful assessment tools that align with authentic learning experiences. Labs and teams include the MIT Playful Journey Lab, emphasizing interdisciplinary innovation. While no specific grants or awards are noted, her research demonstrates significant contributions to reimagining educational assessment through technology and creativity.