جستجوی استاد راهنمای دانشگاهی

• Switched and hybrid systems
• Nonlinear control theory
• Control with limited information
• +۵ مورد دیگر

Daniel M Liberzon is the Richard T. Cheng Professor in the Department of Electrical and Computer Engineering and a Professor at the Coordinated Science Laboratory (director of the Decision and Control group) at the University of Illinois Urbana-Champaign. He also holds an affiliate appointment in the Department of Mathematics. Ph.D. in Mathematics from Brandeis University (1998), advised by Roger W. Brockett (Harvard) Undergraduate studies in Mathematics at Moscow State University (1989-1993) His research interests focus on theoretical and applied aspects of nonlinear, switched, and hybrid systems with limited information. Key areas include: Stability analysis via Lyapunov functions and Lie algebras Finite-data-rate control and topological entropy Robust synchronization and observers Stochastic switched systems Supervisory control for uncertain systems Recent scientific awards include: IFAC Fellow (2016) IEEE Fellow (2013) AACC Donald P. Eckman Award (2007) IFAC Young Author Prize (2002) NSF CAREER Award (2002) He has taught graduate courses such as ECE 517 (Nonlinear and Adaptive Control), ECE 553 (Optimum Control Systems), and ECE 586 DL (Hybrid Systems and Control). Current sponsored projects include NSF grants on switching control and AFOSR MURI on hybrid dynamics.

Long Nguyenمشاهده پروفایل

استاد

• Nonparametric Bayesian Statistics
• Optimal Transport
• Machine Learning
• +۴ مورد دیگر

Long Nguyen is a Professor of Statistics at the University of Michigan, Ann Arbor, with a courtesy appointment in Electrical Engineering and Computer Science. He is affiliated with the Michigan Institute for Data Science (MIDAS) and the Vietnam Institute for Advanced Study in Mathematics (VIASM). His research focuses on Bayesian nonparametrics, optimal transport, machine learning, and spatiotemporal data analysis. Nguyen holds a PhD in Computer Science from UC Berkeley and has held postdoctoral positions at Duke University and the Statistical and Applied Mathematical Institute. Education: B.Sc. in Computer Science from Pohang University of Science and Technology; M.Sc. in Mathematics from Arizona State University; Ph.D. in Computer Science from UC Berkeley (2007). Research Interests: Bayesian nonparametric methods, optimal transport theory, statistical inference for complex models, and applications in spatiotemporal data, functional data analysis, and hierarchical modeling. He emphasizes developing scalable algorithms and geometric approaches for statistical learning. Editorial Roles : Annals of Statistics Journal of Machine Learning Research SIAM Journal on Mathematics of Data Science Bayesian Analysis Awards : IMS Fellow, ASA Fellow NSF CAREER Award IEEE Signal Processing Young Author Award L. J. Savage Dissertation Award (via student Aritra Guha) Advising & Collaborations : Guided over 20 PhD students and postdocs, many now in academia and industry. Collaborates on projects in AI ethics, music theory, and environmental data science. Active in organizing summer schools in Vietnam on Bayesian statistics and machine learning. Labs & Teams : Co-leads the Statistical Machine Learning reading group at U-M and collaborates with the VIASM on advanced mathematical research in Hanoi.

Victor Vianuمشاهده پروفایل

استاد

• Computational Logic
• Database Systems
• Database Theory
• +۴ مورد دیگر

Victor Vianu is a Professor of Computer Science and Engineering at the University of California, San Diego and holds the INRIA International Chair at INRIA-Saclay in Paris. He has maintained continuous faculty status at UC San Diego since 1983 while developing extensive international collaborations, particularly with French research institutions including INRIA, ENST-Paris, ENS-Paris, and the University of Paris. His academic credentials include a Ph.D. in Computer Science from the University of Southern California (1983) and undergraduate studies in Mathematics and Informatics at the University of Bucharest (1974-1977). Professor Vianu's research spans computational logic, database systems and theory, and automatic verification. His work uniquely bridges theoretical foundations with practical applications in XML processing, workflow systems, and data-driven applications. He has made seminal contributions to understanding the theoretical underpinnings of database query languages and their expressive power, particularly in the context of XML technologies and workflow systems. His research demonstrates a consistent trajectory from theoretical computer science to practical database systems applications. His publication record reveals significant contributions to database theory spanning over three decades, with particular emphasis on XML technologies, workflow systems, and formal methods for data-driven applications. His work shows a clear evolution from foundational theoretical work to practical applications in business processes and web technologies. INRIA International Chair (2013) Fellow of the American Association for the Advancement of Science (AAAS) (2013) ACM PODS Alberto O. Mendelzon Test-of-Time Award (2010) Fellow of the Association for Computing Machinery (ACM) (2006) Professor Vianu has held significant leadership roles including Editor-in-Chief of the prestigious Journal of the ACM, numerous program committee chair positions for major database conferences (PODS, ICDT, ASIAN), and General Chair for ACM SIGMOD conferences. He has served on the executive committees of SIGMOD (1998-2000) and PODS (1993-2004), and was a member of the ICDT Council (1997-2007), demonstrating sustained influence in the theoretical database community. His extensive invited talks at major conferences including College de France, ACM PODS, and International Conference on Database Theory highlight his international recognition.

• Spatial Statistics
• Spatiotemporal Modeling
• Environmetrics
• +۳ مورد دیگر

Philipp Otto is a Professor of Statistics and Data Science at the University of Glasgow. Previously, he was a Reader in Statistics and Data Analytics (2023–2024) and held a Junior Professorship in Big Geospatial Data at Leibniz University Hannover (2018–2023). He earned his PhD in Statistics (summa cum laude) from European University Viadrina in 2016 and a B.Sc. in International Economics, with study visits to Saint Petersburg State University. His research focuses on spatial and spatiotemporal statistics, environmetrics, network modeling, and machine learning applications. Education: PhD in Statistics (2016), European University Viadrina, Frankfurt (Oder) B.Sc. in International Economics (with study visits to Saint Petersburg) Research Interests: Philipp’s work centers on spatial statistics, spatiotemporal volatility modeling, environmental data analysis, and network processes. He develops statistical methods for geo-referenced and network data, with applications in climatology, finance, and environmental risk assessment. His contributions include advancements in GARCH models, spatiotemporal clustering detection, and statistical process monitoring for AI systems. Grants & Projects: He has secured €1,038,847 in research grants, leading projects on historical map time series analysis, agricultural air quality impacts, and high-dimensional spatial dependence structures. Industry collaborations include survival analysis for building information models. Awards: 2017 Fellowship to attend the Lindau Nobel Laureate Meeting (Economic Sciences) 2017 Best Presentation Award (Data Science, Statistics, and Visualisation) Teaching: He teaches statistics and data science across disciplines, including economics, engineering, and mathematics, at both undergraduate and postgraduate levels. Professional Activities: Editorial Boards: Environmetrics (2021), AStA Advances in Statistical Analysis (2020) Member of German Statistical Society (Treasurer, 2013)

Yanhua Liمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Spatial-Temporal Data Science
• Smart Cities
• +۵ مورد دیگر

Yanhua Li is an Associate Professor in the Computer Science Department and Data Science Program at Worcester Polytechnic Institute (WPI), where he has served since 2021 (previously as Assistant Professor from 2015-2021). He is also an affiliated researcher at UMass Transportation Center (UMTC). His educational background includes dual PhDs: Computer Science from University of Minnesota, Twin Cities (2013) and Electrical Engineering from Beijing University of Posts and Telecommunications (2009), along with an MS (2006) and BS (2003) in Electrical Engineering from Sichuan University. Dr. Li's research focuses on artificial intelligence and spatial-temporal data science with applications in smart cities and urban intelligence. His work particularly emphasizes imitation learning and meta learning in AI for understanding and influencing urban human agents' decision-making strategies, such as taxi drivers' passenger-seeking behaviors and urban travelers' transit choices. His laboratory develops advanced computational methods for urban transportation systems, traffic prediction, and spatial-temporal data analytics. His publication record shows a strong trajectory in top AI and data science venues, with recent work bridging foundation models with urban computing, enhancing robustness in spatial-temporal representation learning, and applying generative models to urban traffic estimation. His research spans computer vision, reinforcement learning, generative modeling, and spatio-temporal data analysis with applications in transportation, environmental monitoring, and urban planning. Best Applied Data Science Paper Award at SDM 2019 NSF CAREER Award (2020) Runner-up for the 10-Year Impact Award for SIGSPATIAL Conference (2024, for 2014 paper) Dr. Li has secured significant research funding including an NSF CAREER award ($529k), multiple NSF grants totaling over $2 million, and industry collaborations with DiDi Chuxing Research. He has advised numerous PhD students who have gone on to faculty positions at institutions like San Diego State University and SUNY Binghamton University. His research group maintains active collaborations with industry partners including DiDi Chuxing, Pitney Bowes Inc., and NVIDIA. He leads several research initiatives including the CityLines project for urban transportation systems and has contributed to foundational work in spatial-temporal imitation learning. His laboratory continues to expand into new areas including applying large language models to urban dynamics prediction and developing advanced methods for environmental monitoring.

Stefan Wrobelمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Artificial Intelligence
• +۳ مورد دیگر

Prof. Stefan Wrobel is a Professor of Computer Science at the University of Bonn and Director of the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS). He holds leadership roles, including Co-Director of the Lamarr Institute for Machine Learning and Artificial Intelligence and Managing Director of the Bonn-Aachen International Center for Information Technology (b-it). His research focuses on AI, machine learning, and big data applications in industry and society. He earned his PhD from the University of Dortmund and has held academic positions at Magdeburg University and Berlin Technical University. Active in national/international AI initiatives, he chairs the Fraunhofer Strategic Research Field on Artificial Intelligence and co-leads the Machine Learning Rhine-Ruhr (ML2R) Competence Center. Education: Master's (Georgia Tech), PhD (University of Dortmund). Research emphasizes intelligent algorithms, data analysis, and AI ethics. Awarded GI-Fellow (2022) and honored by the German Computer Science Society for contributions to AI history. Key roles include Editorial Board member of Machine Learning journals and advisory roles in AI ethics and certification. Scientific contributions span over 100 publications in machine learning, data mining, and visual analytics. Advised numerous PhD students on topics like graph mining and trustworthy AI. Leadership in institutions like Fraunhofer Technology Hub for Machine Learning and the German Computer Science Society's Special Interest Group on Knowledge Discovery.

Ivan Selesnickمشاهده پروفایل

استاد

• Signal Processing
• Sparse Signal Models
• Biomedical Signal Processing
• +۳ مورد دیگر

Ivan Selesnick is a Professor of Electrical and Computer Engineering at the NYU Tandon School of Engineering, with joint appointments in Biomedical Engineering and Radiology. He holds affiliations with the Center for Advanced Technology in Telecommunications (CATT) and leads the Selesnick Lab. His research focuses on signal and image processing, sparse signal models, wavelet analysis, and biomedical applications. He received his degrees from Rice University (BS, MEE, PhD in EE) and has been recognized with prestigious awards including the Alexander von Humboldt Fellowship (1997), NSF Career Award (1999), and IEEE Fellow (2016). Education: BS, MEE, and PhD in Electrical Engineering from Rice University (1990, 1991, 1996). He joined NYU Tandon in 1997 and served as a visiting professor at the University of Erlangen-Nuremberg in 1997. Research Interests: Signal Processing, Sparse Signal Models, Wavelet Analysis, Biomedical Signal Processing, and Optimization Techniques. His work emphasizes applications in medicine, imaging, and engineering systems. Awards: In addition to his fellowships, he received the Jacobs Excellence in Education Award (2003) and the Budd Award for Best Engineering Thesis (1996). He has held editorial roles at IEEE Transactions on Image Processing, Signal Processing Letters, and Computational Imaging. Teaching: Courses include Signals, Systems, and Transforms (EE 3054), Digital Signal Processing I/II (EL 6113/EL 7133), Wavelets and Filter Banks (EL 7163), and Biomedical Signal Processing (EL 9133). Labs and Affiliations: Director of the Selesnick Lab, involved in NYU Tandon Future Labs (business incubators) and CATT (telecommunications research). His research spans biomedical sensing, radar signal processing, and algorithm development for medical diagnostics.

Laura Marusterمشاهده پروفایل

استادیار

• Process Mining
• Process Modelling
• Healthcare Networks
• +۵ مورد دیگر

Laura Maruster is an Assistant Professor at the Faculty of Economics and Business, University of Groningen, Netherlands. She holds an MSc from the West University of Timisoara, Romania, and a PhD in Technology Management from Eindhoven University of Technology, Netherlands. Her research focuses on process mining, process modelling, and healthcare networks, with applications in operations research and data science. She collaborates extensively with industry and public sector partners on projects like the Casimir initiative (RuG and Gasunie). Teaching areas include business processes, data mining, and research methodology. Recent work includes studies on healthcare logistics optimization and process mining beyond traditional workflows. Her publications appear in leading journals such as BMJ Open , IEEE Transactions on Engineering Management , and Computers in Industry . Research interests span healthcare networks, emergency medical services analytics, and enterprise process optimization. Notable recent contributions include analyzing interhospital patient transfers and redesigning engineering design processes using data-driven methods.

Mohammad Rostamiمشاهده پروفایل

دانشگاهی

• Machine Learning
• Transfer Learning
• Domain Adaptation
• +۵ مورد دیگر

Mohammad Rostami is a Research Assistant Professor at the University of Southern California (USC) in the Department of Computer Science and Electrical and Computer Engineering, with a joint appointment at the USC Information Sciences Institute (ISI). He holds a PhD in Electrical and Systems Engineering from the University of Pennsylvania and additional degrees in Robotics, Philosophy, Electrical Engineering, and Pure Mathematics from prestigious institutions including the University of Waterloo and Sharif University of Technology. His research focuses on machine learning in data-scarce environments, particularly transfer learning, domain adaptation, low-shot learning, and improving learning efficiency through continual and collective learning. He incorporates symbolic logic and neuro-symbolic approaches to address challenges in catastrophic forgetting and knowledge retention. Applications span medical imaging, computer vision, and explainable AI. Rostami has received several accolades including the UPenn Best PhD Dissertation Award, IJCAI Distinguished Student Paper Award, and University of Waterloo Outstanding Achievement Award. His work bridges theoretical advancements with practical implementations, emphasizing real-world applications in healthcare and autonomous systems. He teaches graduate courses in applied natural language processing and knowledge graph construction. Rostami advises students at all academic levels and collaborates with remote researchers, emphasizing motivated, long-term project commitments.

• Solid Mechanics
• Micromechanics
• Dislocation Plasticity
• +۳ مورد دیگر

Professor Dan Balint is the Head of the Mechanics of Materials Division in the Department of Mechanical Engineering at Imperial College London. He holds a Ph.D. in Engineering Sciences from Harvard University (2003), an S.M. in Applied Mathematics from Harvard (2001), and a B.S. in Engineering Mechanics from Michigan State University (1998). Prior to joining Imperial in 2006, he was a Research Associate at the Cambridge Centre for Micromechanics. His research spans theoretical and computational solid mechanics, with focus areas including: Micromechanics of crystalline materials (metals/ceramics) Dislocation-defect interactions and failure mechanisms Discrete dislocation plasticity methods Nuclear cladding materials and zirconium hydrides Thin film failure and metal forming processes Fracture mechanics and material size effects Recent publications (2022-2025) predominantly explore dislocation dynamics, zirconium alloy behavior under nuclear conditions, computational modeling of microstructural stresses, and machine learning applications in materials science. Common themes include thermomechanical degradation, crack initiation mechanisms, and multi-scale modeling approaches. Professor Balint serves as Associate Editor of the European Journal of Mechanics - A/Solids and consults for industrial partners including Rolls Royce, BP, and the US Air Force.

Eivind Rudjord Hillesundمشاهده پروفایل

دانشیار

• Mathematics Education
• Engineering Education
• Educational Resources
• +۳ مورد دیگر

Eivind Rudjord Hillesund is an Associate Professor in the Department of Mathematical Sciences at the University of Agder. He holds qualifications in teaching mathematics and physics from the University of Oslo's lektorprogrammet and defended his doctoral thesis in January 2021 on engineering students' use of learning resources in mathematics courses. His teaching focuses on statistics courses within GLU programs and assignments in EVU courses at UiA since 2019. Research Interests: Resource use and decision-making in undergraduate mathematics education Educational strategies for engineering students Development of tools for tracking student resource utilization Publications include studies on resource systems analysis, didactical purposes of resources, and data collection methodologies. His work emphasizes improving understanding of how students interact with learning materials in STEM fields.

Saidul Islamمشاهده پروفایل

مدرس

• Prebiotic Chemistry
• Organic Chemistry
• Chemical Origin of Life
• +۳ مورد دیگر

Dr Saidul Islam is a Lecturer in Chemistry at King's College London, part of the Department of Chemistry within the Faculty of Natural, Mathematical & Engineering Sciences. He specializes in prebiotic chemistry, focusing on understanding the chemical origins of life and systems chemistry. His research explores pathways for nucleic acids, peptides, and organic cofactors under prebiotic conditions. Joined King's College London in 2022 as part of the Department's expansion program. Previously held postdoctoral positions at Queen Mary University of London and UCL. Teaching roles include leading 'Organic Chemistry 2' and guiding postgraduate research. Research interests center on prebiotic systems chemistry, including catalytic peptide ligation, chemoselective synthesis, and the role of cyanide in early metabolism. The Islam Group investigates chemical pathways that could have led to life's emergence on early Earth, employing synthetic organic chemistry and advanced analytical techniques like NMR spectroscopy. Key contributions include studies on pantetheine synthesis, peptide ligation mechanisms, and the enrichment of prebiotic molecules via heated gas bubbles. His work bridges organic chemistry with the origins of biological systems, addressing fundamental questions about life's chemical beginnings.

Keith LeGrandمشاهده پروفایل

استادیار

• Multi-sensor Multi-object Tracking
• Spacecraft Navigation
• Space Domain Awareness
• +۵ مورد دیگر

Keith LeGrand is an Assistant Professor in the School of Aeronautics and Astronautics at Purdue University, part of the Cislunar Space Initiative. He holds a Ph.D. in Aerospace Engineering from Cornell University (2022), an M.S. (2015), and a B.S. (2014) in Aerospace Engineering from Missouri University of Science and Technology. His research focuses on multi-object tracking, spacecraft navigation, space domain awareness, and intelligent sensor control. Key projects include developing probabilistic filters for cislunar space object tracking and information-driven autonomy systems. LeGrand leads the Sensing, Controls, and Probabilistic Estimation (SCOPE) Group, which advances space surveillance and autonomous systems. Notable awards include the 2025 AFOSR and ISIF Young Investigator Awards, 2023 AMOS Best Paper Award, and the E.F. Bruhn Teaching Award for excellence in undergraduate instruction. LeGrand advises a diverse group of graduate and undergraduate students in astrodynamics and space applications. His work is supported by grants from AFOSR, ISIF, and partnerships with institutions like Sandia National Laboratories and Draper Labs. The SCOPE lab collaborates on projects such as lunar landing navigation, satellite proximity operations, and multi-sensor fusion for space surveillance.

Rekha R. Thomasمشاهده پروفایل

استاد

• Optimization
• Applied Algebraic Geometry
• Computer Vision
• +۳ مورد دیگر

Rekha R. Thomas is a Professor of Mathematics and Undergraduate Program Director at the University of Washington. She holds a Ph.D. in Operations Research from Cornell University (1994), with postdoctoral experience at Yale University and the Konrad-Zuse-Zentrum in Berlin. Her research focuses on optimization, applied algebraic geometry, and computer vision, with contributions to semidefinite programming, graphical designs, and geometric algorithms. She has held distinguished positions such as the Robert R. and Elaine F. Phelps Professorship (2008–2012) and the Robert B. Warfield Jr. Faculty Fellowship (2017–2020). Her work bridges theory and application, addressing challenges in computer vision, combinatorial optimization, and algebraic geometry. Notable contributions include advancements in multiview geometry, kernel learning, and the geometric analysis of rank-deficient matrices. She actively collaborates across disciplines, publishing extensively and supervising numerous graduate students and postdocs. Rekha also engages in academic leadership, mentoring students, and participating in international conferences. Her research has been recognized through invited talks at major events like the International Congress of Mathematicians (2018) and SIAM Annual Meetings. She continues to explore the intersections of algebraic geometry, optimization, and computational methods.

• Signal Processing
• Wireless Communications
• Underwater Acoustic Communications
• +۷ مورد دیگر

Professor Yue Rong is a Full Professor at Curtin University's Department of Electrical and Computer Engineering, within the School of Electrical Engineering, Computing and Mathematical Sciences. He holds editorial roles at IEEE Transactions on Signal Processing and IEEE Wireless Communications Letters. His research focuses on signal processing for communications, underwater acoustic systems, wireless networks, and healthcare IoT. Rong has authored over 140 journal and conference papers and received multiple awards, including the 2010 Young Researcher of the Year Award. Education: B.E. (Electrical Engineering), Shanghai Jiao Tong University (1999) M.Sc. (Electrical Engineering), University of Duisburg-Essen (2002) Ph.D. (Electrical Engineering), Darmstadt University of Technology (2005) Research Interests: Rong's work spans cooperative MIMO communications, underwater acoustic systems, OFDM modulation, radar-based healthcare monitoring, and secure wireless protocols. His innovations include adaptive modulation schemes for underwater environments and radar-based vital signs detection. Recent trends in his publications emphasize AI-driven signal processing for healthcare IoT and underwater optical communication systems. Awards: Best Paper Awards (WCSP 2011, APCOMM 2010) Chinese Government Award (2004) DAAD/ABB Fellowship (2001-2002) Grants & Labs: His research is supported by grants focusing on UAV-enabled data collection and underwater network optimization. He leads projects in the Distributed Data Fusion and Emerging Technologies (DDFE) lab, advancing radar-cardiography and wearable health monitoring systems.