جست‌وجوی استاد و پژوهشگر

Christian Bargetzمشاهده پروفایل

استاد

• Nonlinear Functional Analysis
• Banach Spaces
• Locally Convex Spaces
• +۴ مورد دیگر

Christian Bargetz is a Professor of Functional Analysis at the University of Innsbruck, Austria, affiliated with the Faculty of Mathematics, Computer Science, and Physics (MIP). His primary research focuses on nonlinear functional analysis, Banach space theory, and distribution theory. He teaches advanced courses such as Optimization, Distribution Theory, and Functional Analysis, demonstrating his expertise in both theoretical and applied aspects of his field. Education: Completed his PhD in 2012 at the University of Innsbruck under the supervision of Norbert Ortner. His diploma thesis (2008) explored differential behaviors with Ulrich Oberst. Bargetz has held continuous academic positions since 2008, including roles as a lecturer and researcher. Research Interests: Specializes in iterative projection methods, generic properties of nonexpansive mappings, Fréchet spaces, and vector-valued distributions. His work bridges functional analysis with geometric measure theory and optimization, with applications in metric geometry and topological tensor products. Publications: Over 30 peer-reviewed articles in prestigious journals such as Canadian Journal of Mathematics , Journal of Mathematical Analysis and Applications , and Proceedings of the American Mathematical Society . Recent work includes studies on extremal nonexpansive mappings and Lipschitz function spaces. Grants & Projects: Principal investigator in FWF-funded projects on nonexpansive mappings and Banach spaces. Collaborates internationally, including with institutions in Israel, Poland, and Serbia. Teaching: Leads advanced courses in functional analysis, optimization, and distribution theory. Supervises bachelor's theses and master's projects on topics like extension operators for Lipschitz functions. Affiliations: Active member of the Functional Analysis working group and regularly participates in international conferences such as the Banach Afternoon, Winter School in Abstract Analysis, and DMV-ÖMG Annual Conferences.

Xiaoming Huمشاهده پروفایل

استاد

• Multi-agent systems
• Nonlinear control systems
• Systems theory
• +۴ مورد دیگر

Xiaoming Hu is a Professor at the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH Royal Institute of Technology (Kungliga Tekniska Högskolan) in Stockholm, Sweden. Born in Chengdu, China, he received his B.S. degree from University of Science and Technology of China in 1983, followed by M.S. and Ph.D. degrees from Arizona State University in 1986 and 1989 respectively. After serving as a research assistant at the Institute of Automation, Chinese Academy of Sciences (1983-1984), he was a Gustafsson Postdoctoral Fellow at KTH (1989-1990) before becoming a faculty member. His educational background includes: B.S. in Engineering, University of Science and Technology of China, 1983 M.S. in Engineering, Arizona State University, 1986 Ph.D. in Engineering, Arizona State University, 1989 Xiaoming Hu's research primarily focuses on multi-agent systems, nonlinear feedback stabilization, nonlinear observer design, and sensing and active perception. His work bridges theoretical control theory with practical applications in robotics and autonomous systems. He has made significant contributions to geometric control theory, mathematical systems theory, and nonlinear systems analysis and control. His research often involves developing theoretical frameworks for distributed control, formation control, and cooperative behavior in multi-robot systems. Professor Hu's publication record shows a consistent research trajectory with numerous high-impact publications in top-tier journals like Automatica, IEEE Transactions on Automatic Control, and Systems & Control Letters. His research has evolved from fundamental control theory to more applied problems in robotics and multi-agent systems, while maintaining strong mathematical foundations. Recent work shows increasing focus on safety-critical control, inverse problems in estimation, and networked systems. His scientific contributions include: Development of theoretical frameworks for multi-agent coordination and formation control Advances in nonlinear observer design for robotic systems Contributions to geometric control theory and systems theory Research on distributed estimation and control algorithms Applications of control theory to robotics and autonomous systems Professor Hu teaches several advanced courses including Mathematical Systems Theory, Geometric Control Theory, and Nonlinear Systems: Analysis and Control. He has supervised numerous degree projects at both undergraduate and graduate levels in mathematics, optimization, systems theory, and scientific computing. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications of control theory.

Harish Ravichandarمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Human-Robot Interaction
• +۲ مورد دیگر

Harish Ravichandar is an Assistant Professor at the School of Interactive Computing , Georgia Institute of Technology, and a core faculty member of the Institute for Robotics and Intelligent Machines (IRIM) . He leads the Structured Techniques for Algorithmic Robotics (STAR) Lab , focusing on structured computational frameworks and learning algorithms with inductive biases to enhance robot efficiency, reliability, and self-sufficiency in human-robot collaboration and complex applications like dexterous manipulation and multi-agent coordination. His research bridges robot learning , human-robot interaction , and multi-agent systems , emphasizing stable, frugal, and safe skill acquisition from human demonstrations. Key themes include intention inference , trajectory optimization , and heterogeneous team coordination , often leveraging Koopman operators , hypernetworks , and graph-based methods . Scientific recognition includes the NSF CAREER Award , IEEE MRS Best Paper Award , and Georgia Tech’s College of Computing Outstanding Post-Doctoral Research Award . His work also received the ASME DSCC Best Student Paper Award and P&W Institute Graduate Fellowship . Harish’s educational background includes a Ph.D. in Electrical and Computer Engineering from the University of Connecticut (2018) , an M.S. from the University of Florida (2014) , and a B.E. in Instrumentation and Control Engineering from Anna University (2012) . He previously held postdoctoral and research scientist roles at Georgia Tech before his current position.

• Control Theory
• Machine Learning
• Nonlinear Estimation
• +۶ مورد دیگر

Prashant Mehta is a Professor of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign , affiliated with the Coordinated Science Laboratory . His research focuses on controlled interacting particle systems and machine learning applications , particularly in human activity recognition using motion sensors. Education: Ph.D. in Mathematics, Cornell University (2004) M.S. in Electrical & Computer Engineering, University of Massachusetts Amherst (1996) B.E. in Electrical & Electronics Engineering, Birla Institute of Technology & Sciences (1993) Mehta's work has pioneered the feedback particle filter (FPF) algorithm for nonlinear estimation, applied in robotic systems and gesture recognition. His research spans control of combustion instabilities in jet engines, mean-field games , and dynamical systems in aerospace engineering. His publications emphasize nonlinear control theory and stochastic filtering , with recent trends in sensor data pattern recognition and cyber-physical systems . He has received multiple scientific awards , including the MURI award for the Cyberoctopus project and Excellence in Undergraduate Advising Awards . Scientific Honors: MURI Award (2019) for Cyberoctopus Excellence in Undergraduate Advising (2010, 2008) Outstanding Teaching Assistant Award (1994) Senior Member, IEEE Control Systems Society Member, ASME Energy Systems Subcommittee Member, SIAM Dynamical Systems Group Mehta has supervised students like Jin Kim (IEEE CDC Best Student Paper, 2019) and co-founded the startup Rithmio , acquired by Bosch Sensortec . His laboratory develops gesture-detection filters for applications in soft robotics and human-machine interfaces .

Christiana Mavroyiakoumouمشاهده پروفایل

استادیار

• Fluid Dynamics
• Fluid-Structure Interactions
• Vortex Dynamics
• +۶ مورد دیگر

Christiana Mavroyiakoumou is a Courant Instructor/Assistant Professor at the Courant Institute of Mathematical Sciences, New York University. She specializes in fluid dynamics and fluid-structure interactions, with a focus on vortex dynamics, membrane flutter, and bio-inspired systems. Her research integrates modeling, numerical simulations, and experimental insights to study phenomena such as bird flock formations and fish swimming hydrodynamics. Mavroyiakoumou holds a Ph.D. from the University of Michigan (2022), an M.Sc. from the University of Oxford (2017), and a B.Sc. from Imperial College London (2016). Education: PhD in Applied & Interdisciplinary Mathematics, University of Michigan (2017–2022) MSc in Mathematical Modeling and Scientific Computing, University of Oxford (2016–2017) BSc in Mathematics, Imperial College London (2013–2016) Her research interests span fluid-structure interactions, vortex dynamics, and collective locomotion. She investigates how fluid flows mediate interactions between bodies, such as the aerodynamics of bird formations and the hydrodynamics of flapping foils. Her work bridges theoretical models with experimental observations, contributing to both fundamental science and bio-inspired engineering. Mavroyiakoumou has received prestigious awards including the Joseph B. Keller Fellowship (NYU), Peter Smereka Award (U-M), and ProQuest Distinguished Dissertation (U-M). She actively engages in academic service, organizing conferences and mentoring students. Her teaching experience includes courses on mathematical modeling, differential equations, and algebra at NYU and the University of Michigan. Key Research Themes: Flow-mediated collective behavior and instability mechanisms Vortex wake interactions and their role in locomotion Membrane dynamics in inviscid and viscous flows She collaborates with experimentalists like Leif Ristroph and Jun Zhang at NYU's Applied Math Lab, focusing on experimental validation of theoretical models. Her recent work explores self-amplifying waves in bird formations and the aerodynamic origins of flight coordination.

Giancarlo Ferrari Trecateمشاهده پروفایل

استاد مدعو

• Automatic control
• State estimation
• System identification
• +۷ مورد دیگر

Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.

Bryon Aragamمشاهده پروفایل

دانشیار

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +۶ مورد دیگر

Bryon Aragam is an Associate Professor of Econometrics and Statistics and Robert H. Topel Faculty Scholar at the University of Chicago Booth School of Business. His research focuses on the intersection of causality, statistical machine learning, and probabilistic modeling, with particular emphasis on applications to artificial intelligence systems including large language models like ChatGPT and generative models like DALL-E. Dr. Aragam completed his PhD in Statistics and a Masters in Applied Mathematics at UCLA, where he was an NSF graduate research fellow. Prior to joining the University of Chicago, he was a project scientist and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. Research Focus: Causal structure learning in probabilistic generative models Key Areas: Causal machine learning, deep generative models, latent variable models, statistical learning theory Applications: AI interpretability, ethics, and fairness in artificial intelligence systems Teaching: Business Statistics, Econometrics and Statistics Colloquium His recent publications demonstrate a strong theoretical foundation combined with practical applications, particularly in understanding and improving AI systems. His work spans causal discovery, graphical models, deep learning, and latent variable modeling, with particular attention to the theoretical properties of these methods and their applications to real-world AI challenges. The research shows a progression toward increasingly complex problems in causal representation learning and AI interpretability. Scientific Awards: Robert H. Topel Faculty Scholar NSF Graduate Research Fellow Dr. Aragam's work has been published in top statistics and machine learning venues including the Annals of Statistics, Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), and the Journal of Machine Learning Research (JMLR). His research group publishes broadly across both statistical and machine learning communities, demonstrating the interdisciplinary nature of his work at the intersection of statistics, machine learning, and causal inference. As a data science consultant for technology and marketing firms, Dr. Aragam has applied his expertise to problems in survey design, customer retention, logistics, and ranking, bridging the gap between theoretical research and practical applications.

James E. Westمشاهده پروفایل

استاد

• Topology
• Geometric Topology
• Infinite-Dimensional Topology
• +۳ مورد دیگر

James E. West is a Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. He holds a Ph.D. from Louisiana State University (1967). His research focuses on geometric topology, infinite-dimensional topology, and the symmetries of manifolds, particularly exploring Hilbert cube manifolds, function spaces, and equivariant homeomorphisms. He has contributed to understanding stabilization processes in topology and the interplay between finite and infinite-dimensional structures. His teaching includes advanced courses such as MATH 4500 (Matrix Groups), MATH 4900 (Supervised Research), and MATH 4901 (Supervised Reading), alongside foundational courses like MATH 2220 (Multivariable Calculus). His work bridges pure mathematics with applications in transformation groups and representation theory. West has published extensively on topics including fixed point sets, compact group actions, and fibration theory. His research emphasizes the control of homeomorphism theories and the classification of topological spaces with complex symmetries.

Esra Ece Bayatمشاهده پروفایل

دانشیار

• Earthquake Engineering
• Soil Mechanics
• Geotechnics
• +۳ مورد دیگر

Assoc. Prof. Dr. Esra Ece Bayat is an Associate Professor in the Civil Engineering Department at Istanbul Technical University (ITU), specializing in Earthquake Engineering, Soil Mechanics, and Geotechnics. She holds a PhD from Northeastern University (USA) and has been affiliated with ITU since 2010, progressing from Lecturer to her current rank. Her research focuses on liquefaction mitigation, soil-structure interaction, and dynamic soil behavior under seismic loads. She has led multiple projects funded by TUBITAK and BAP, including studies on induced partial saturation (IPS) techniques and p-y model development for earthquake-resistant designs. Her education includes a BSc from Middle East Technical University (1997–2002) and a PhD from Northeastern University (2004–2009). She has served in administrative roles such as Erasmus Coordinator and Education Coordinator, emphasizing international collaboration and academic development. Her work contributes to UN Sustainable Development Goals related to resilient infrastructure and disaster risk reduction. Dr. Bayat has published extensively on topics like dynamic soil behavior, liquefaction assessment, and foundation systems. Her research combines experimental methods (e.g., laminar soil containers, shaking table tests) with numerical modeling (e.g., FLAC 3D, SASSI). Recent work addresses the 2023 Kahramanmaraş earthquake’s impact on foundation systems in Iskenderun. She has advised over 20 graduate students and holds the ASCE Thomas Middlebrooks Award (2014).

Tyler H. Summersمشاهده پروفایل

استادیار

• Feedback Control
• Optimization in Complex Dynamical Networks
• Electric Power Networks
• +۴ مورد دیگر

Dr. Tyler H. Summers is an Assistant Professor of Mechanical Engineering at the University of Texas at Dallas (UTD), with an affiliate appointment in Electrical Engineering. He holds a PhD in Aerospace Engineering from the University of Texas at Austin (2010) and completed a postdoctoral fellowship at ETH Zurich (2011–2015). His research focuses on feedback control and optimization in complex dynamical networks, including electric power grids and distributed robotics. Key contributions include stochastic optimal power flow methods, distributed formation control algorithms, and robust control design under uncertainty. Education: PhD in Aerospace Engineering (University of Texas at Austin, 2010) M.S. in Aerospace Engineering (University of Texas at Austin, 2007) B.S. in Mechanical Engineering (Texas Christian University, 2004) His research interests emphasize theoretical and computational tools for cyber-physical systems, including power networks and robotic teams. Notable achievements include a NSF CAREER Award ($500K) and an Army YIP grant ($350K). He leads the Control, Optimization, and Networks (COIN) Lab, which develops algorithms for robust control and distributed optimization. Recent work addresses challenges in integrating renewable energy into power systems and enabling safe autonomous robotics in uncertain environments. Grants and projects involve collaborations with institutions like the University of Melbourne and the Australian National University. Grants & Awards: NSF CAREER Award (2021) Army Research Office YIP (2017) Air Force Office of Scientific Research (2019) Lab & Team: The COIN Lab focuses on interdisciplinary projects involving students and postdocs in control theory, robotics, and optimization.

• Fluid Dynamics
• Renewable Energy Technologies
• Mechanical Engineering Education
• +۳ مورد دیگر

Buyung Kosasih is a Professor in the School of Mechanical, Materials, Mechatronic and Biomedical Engineering at the University of Wollongong. He has held this position since 2000 and focuses on teaching and research in mechanical engineering, including Machine Dynamics, Finite Element Methods, and Renewable Energy Technology. His research spans fluid dynamics in industrial processes, renewable energy systems, and aqueous lubrication. Key projects include 3D-printed surfboard fin optimization and steel coating dynamics. Research interests emphasize experimental and computational fluid dynamics, particularly in renewable energy turbines and tribological systems. Notable awards include the 2013 Outstanding Contribution to Teaching and Learning Award. He has supervised numerous students and led over 20 funded projects, including ARC grants for steel innovation and renewable energy. Collaborative work includes the Steel Research Hub and HVAC/cool roof efficiency studies.

Pierre Raphaëlمشاهده پروفایل

استاد

• Nonlinear Waves
• Singularity Formation
• Mathematical Physics
• +۳ مورد دیگر

Pierre Raphaël is the Herchel Smith Professor of Pure Mathematics at the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, where he joined in 2019. His academic career spans prestigious institutions including École Polytechnique, University of Cergy-Pontoise, Princeton University, and various research positions in France before his appointment at Cambridge. Professor Raphaël's research lies at the border between physics and pure mathematics, focusing on understanding energy concentration mechanisms and singularity formation during the propagation of non-linear waves. His work is deeply connected to fundamental nonlinear structures occurring in electromagnetism, astrophysics and turbulent fluid flows. He has made significant contributions to the mathematical analysis of nonlinear partial differential equations, particularly in understanding blow-up phenomena and singularity formation. His publications reveal a consistent research trajectory focused on nonlinear wave phenomena, with particular emphasis on energy concentration mechanisms and singularity formation across various mathematical models. His work spans multiple areas including nonlinear Schrödinger equations, heat equations, Korteweg-de Vries equations, and harmonic heat flows, demonstrating his expertise in analyzing critical and supercritical regimes where singularities may form. Grand Prix Alexandre Joannides 2014 from the French Academy of Sciences Royal Society Wolfson Fellowship 2019 Invited Speaker at International Congress of Mathematicians 2014 ERC Advance Grant recipient Professor Raphaël has secured significant research funding including European Research Council grants, demonstrating his leadership in the field. His research group at Cambridge focuses on singularity formation for nonlinear PDEs, as evidenced by the conference he organized at St Catharine's College in September 2024. His work bridges pure mathematics with physical applications, particularly in understanding extreme regimes of nonlinear wave propagation.

Giulia Giordanoمشاهده پروفایل

استاد

• Automatic control
• Complex networks
• Control systems
• +۱۲ مورد دیگر

Giulia Giordano is a Full Professor in the Department of Industrial Engineering at the University of Trento, Italy, where she leads the Dynamical Networks and Systems Biology research group. She also holds a dual appointment as Visiting Professor and Delft Technology Fellow at the Delft Center for Systems and Control, Delft University of Technology, The Netherlands. Her career includes previous positions as Assistant Professor at Delft University of Technology (2017-2019), Postdoctoral Research Fellow at Lund University, Sweden (2016-2017), and Research Fellow at the University of Udine, Italy (2016). Giulia earned her Ph.D. in Industrial and Information Engineering: Automation (Excellent) from the University of Udine with a thesis titled "Structural Analysis and Control of Dynamical Networks." She completed her M.Sc. and B.Sc. in Electrical Engineering (both Summa cum laude) at the same institution. She also undertook research visits at Caltech (2012) as a SURF Fellow and at the University of Stuttgart (2015) as a DAAD Research Scholar. Her primary research focuses on the analysis and control of dynamical networks with applications in systems biology, mathematical ecology, and mathematical epidemiology. She develops mathematical frameworks that bridge control theory, network theory, and dynamical systems to address complex problems in biological systems. Her recent work spans epidemic modeling, opinion dynamics, biochemical networks, and neurological disorders, with a particular emphasis on structural analysis of networked systems. She employs both theoretical and computational approaches to understand system behavior under uncertainty. Giulia's publications reveal a strong interdisciplinary focus, spanning from theoretical control systems to practical applications in epidemiology and biology. Her recent work shows increasing emphasis on epidemic modeling (particularly related to mpox and SARS-CoV-2), network synchronization, and the application of control theory to biological phenomena like fibromyalgia pathogenesis and opinion formation. Many of her papers appear in top-tier control journals including Automatica and IEEE Transactions on Automatic Control. 2024: Outstanding Service as Associate Editor of IEEE Control Systems Letters 2021: SIAM Activity Group on Control and Systems Theory Prize 2020: Outstanding Reviewer, Annals of Internal Medicine 2017: NAHS Best Paper Prize and EECI PhD Award 2016: Outstanding TAC Reviewer, IEEE Transactions on Automatic Control Giulia actively mentors students and postdoctoral researchers, currently supervising five postdoctoral researchers and two Ph.D. students at the University of Trento. She has advised numerous M.Sc. and B.Sc. students on topics ranging from bio-inspired modeling to optimal control of epidemic systems. Her research is supported by competitive grants including the ERC Starting Grant INSPIRE (Integrated Structural and Probabilistic Approaches for Biological and Epidemiological Systems). She serves as Associate Editor for IEEE Control Systems Letters and Automatica, and is a Senior Member of IEEE and the Control Systems Society. Giulia leads the Dynamical Networks and Systems Biology research group at the University of Trento, which maintains strong international collaborations across Europe and North America. The group's work combines theoretical advances in control theory with practical applications to pressing problems in public health and biological systems, demonstrating the power of mathematical approaches to understanding complex phenomena in the life sciences.

Parviz Moinمشاهده پروفایل

استاد

• Computational Physics
• Turbulent Boundary Layers
• Hypersonic Flows
• +۴ مورد دیگر

Parviz Moin holds the Franklin P. and Caroline M. Johnson Professorship in Stanford University's School of Engineering. As founding director of the Center for Turbulence Research (CTR)—a NASA-Stanford consortium established in 1987—he has pioneered computational methods for turbulence physics, including direct numerical simulation and Large Eddy Simulation (LES) techniques. CTR serves as an international hub for turbulence studies across engineering, mathematics, and physics disciplines. Moin's research encompasses computational physics of turbulent flows, with emphasis on boundary layer control, hypersonic aerodynamics, propulsion systems, and aircraft icing. His recent work advances high-fidelity simulations for aerospace applications, particularly developing wall models for LES that accurately capture separation phenomena under complex pressure gradients and Reynolds number effects. Recent publications demonstrate extensive applications of LES to aircraft design challenges, including transonic buffet prediction, high-lift configuration analysis, and icing aerodynamics. Investigations consistently address fundamental turbulence physics while developing practical computational tools for aerospace engineering, with particular focus on hypersonic boundary layers, flow separation mechanisms, and conjugate heat transfer in iced environments.

Daniel Boltمشاهده پروفایل

استاد

• Psychometrics
• Quantitative Methods
• Educational Data Science
• +۷ مورد دیگر

Daniel Bolt is the Nancy C. Hoefs Bascom Professor of Educational Psychology at the University of Wisconsin-Madison’s School of Education. His research focuses on psychometric methodologies in educational, social, and health sciences, including latent variable models, computational methods, and assessment of individual differences. He also collaborates on biostatistics projects at the Waisman Center. Education: PhD in Educational Psychology, University of Illinois at Urbana-Champaign (1999) MS in Statistics, University of Illinois at Urbana-Champaign (1995) BA in Psychology/Mathematics, Calvin College (1992) Research Interests: Bolt’s work bridges psychometrics and educational data science, addressing topics like response style modeling, computer-based testing, and measurement validation. His recent projects explore the intersection of IRT models with modern assessment challenges, including rating scale confusion and item complexity effects. Awards: Kellett Mid-Career Award (2019) Vilas Associates Award (2015, 2017) Chancellor’s Distinguished Teaching Award (2009) Outstanding Reviewer Awards (Journal of Educational and Behavioral Statistics, 2011/2020) Teaching & Leadership: Bolt teaches advanced courses in test theory and hierarchical linear modeling. He served as President of the Psychometric Society (2019–2021) and is a Teaching Academy Fellow at UW-Madison.