Roberto Merlin is a Peter A. Franken Collegiate Professor of Physics and Professor of Electrical Engineering and Computer Science (EECS) at the University of Michigan. Born in Buenos Aires, Argentina, he earned an M.S. in 1973 from the University of Buenos Aires and a Ph.D. in 1978 from the University of Stuttgart under Manuel Cardona. After postdoctoral work at the University of Illinois, he joined the University of Michigan Physics faculty in 1980 and holds a joint appointment in EECS since 2000. He has held visiting positions at institutions including Max-Planck-Institut FKF, Hong Kong University of Science and Technology, and ETH Zurich. Merlin's research focuses on experimental condensed matter physics, particularly ultrafast optical techniques like spontaneous and impulsive Raman spectroscopy. His work spans coherent phonon dynamics, metamaterials for subwavelength focusing, and light-induced phase transitions in quantum materials. Recent publications address magnetophononics, phonon Bloch oscillations, and radiation-less interference in evanescent-field plates, reflecting his interdisciplinary interests in optics, quantum mechanics, and materials science. His scientific contributions have been recognized by fellowships from the American Physical Society (1996), Optical Society of America (2000), and Simons Foundation (2013), along with the Frank Isakson Prize (2006) and Ellis R. Lippincott Award (2017). He has served in leadership roles for APS committees and conference chairs, and his editorial work includes Physical Review Letters and Solid State Communications.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۷۲۱-۷۳۵ از ۸٬۴۴۸ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Hakan Türeci is Professor of Electrical and Computer Engineering at Princeton University, with joint affiliation at the Princeton Materials Institute. A theoretical physicist by training, his research explores quantum optics, quantum information science, and superconducting circuits for quantum computing applications. Education: Ph.D. from Yale University (2003) M.S. in Physics from Bilkent University, Turkey (1996) B.S. in Physics from Bilkent University, Turkey (1994) Research Interests: Focuses on non-equilibrium quantum phenomena in optical and microwave platforms, quantum simulation, quantum error correction, and the development of near-term quantum devices for computation and machine learning. Publication Trends: Recent work emphasizes quantum system modeling, quantum measurement techniques, Josephson junction physics, and applications of reservoir computing in quantum information processing. Academic Leadership: Advises a large group of graduate students and researchers in quantum engineering projects, collaborating across physics and engineering disciplines to advance quantum technologies.
Alan Kaptanoglu is a Professor leading the Plasma Physics Group at New York University's Courant Institute. His research focuses on the intersection of applied mathematics, scientific machine learning, and nuclear fusion. He develops theoretical and computational tools for plasma modeling, control, and optimization in complex dynamical systems. Key areas include stellarator design optimization, machine learning-driven MHD simulations, and reactor-scale plasma confinement solutions. His work advances fusion energy research through innovations in magnetic field shaping, coil optimization, and data-driven modeling of plasma behavior. Research interests span plasma turbulence analysis, magnetohydrodynamics, and the application of physics-informed neural networks (PINNs). He explores how magnetic geometry influences plasma stability and turbulence using machine learning techniques. His team addresses challenges in fusion reactor design, such as minimizing Lorentz forces in electromagnetic coils and optimizing permanent magnet configurations for stellarators. Recent work emphasizes data-driven methods for discovering interpretable models in fluids and plasmas, including reduced-order modeling and sparse regression approaches. He collaborates on projects like the DIII-D tokamak and ITER, advancing fusion energy solutions through interdisciplinary computational and experimental efforts. His group's contributions bridge plasma physics with advanced mathematics and machine learning to tackle grand challenges in energy and astrophysical systems. Advising and grants are not explicitly detailed in the provided texts, but his leadership role suggests active mentorship in graduate research. The NYU Plasma Physics Group serves as a hub for cutting-edge research, hosting internships, summer schools, and collaborations with industry/academic partners.
- Optimization
- Control Systems
- Multi-agent Systems
- +۷ مورد دیگر
Ashish Cherukuri is an Associate Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Optimization and Decision Systems group. His research focuses on optimization-based control, game theory, and multi-agent systems applied to energy, transportation, and robotics. He holds a Ph.D. from UC San Diego and postdoctoral experience at ETH Zurich. Education: Ph.D., University of California, San Diego (2012–2017) M.Sc., ETH Zurich (2008–2010) B.Tech, Indian Institute of Technology Delhi (2004–2008) Research Interests: Data-driven optimization, distributed algorithms, networked cyber-physical systems, and uncertainty handling in energy and transportation systems. Recent work emphasizes stochastic optimization, game-theoretic routing, and risk-aware control. Awards: Robert E. Skelton Dissertation Award (2017) Outstanding Graduate Student Award (2016) Focht-Powell Fellowship (2012–2015) Grants & Service: Editor for the IEEE Control Systems Society, organizer of Energy-Open 2019, and member of professional societies (IEEE, INFORMS, SIAM). Active in conference organization and academic leadership roles. Labs/Teams: Part of the Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen (ENTEG). Research integrates theoretical advancements with practical applications in energy networks and smart systems.
Allahyar Montazeriمشاهده پروفایل
مدرس ارشد
Dr. Allahyar Montazeri is a Senior Lecturer in Control and Electronics Engineering at Lancaster University's School of Engineering, specializing in advanced control systems and signal processing. His research focuses on adaptive signal processing, robust control, system identification, and applications in robotics, active noise/vibration control, and wave energy conversion. He has over 110 publications and serves on editorial boards such as Frontiers in Robotics and AI, and IFAC Technical Committees. Montazeri holds a Humboldt Research Fellowship (2011) and ERCIM Fellowship (2010). His industrial collaborations include Bosch for automotive noise control and Fraunhofer Institute. He has supervised PhD students in acoustic signal processing and leads projects on autonomous robotics and environmental monitoring. Notable awards include 'Outstanding Associate Editor' (2023) and 'Fellow of The Higher Education Academy.' He actively participates in conferences like IEEE CDC and chairs sessions on mechatronics systems. His work bridges theoretical control advancements with practical applications in extreme environments, including nuclear robotics and underwater systems.
Dr. Martin Zuidhof is a Professor in the Department of Agricultural, Food & Nutritional Science at the University of Alberta. His primary research focus is on Poultry Systems Modeling and Precision Feeding, particularly in optimizing broiler breeder management and energy partitioning. He holds a PhD in Animal Science from the University of Alberta and has pioneered transformative precision feeding systems that achieve unprecedented flock uniformity ( Education: PhD, Animal Science, University of Alberta Research Interests: Dr. Zuidhof’s work centers on advancing precision livestock systems through mathematical modeling (e.g., multiphasic growth models), optimizing pullet body weight for reproductive efficiency, and addressing societal concerns about animal welfare in poultry production. His innovations in precision feeding systems reduce nutrient waste while enabling precise metabolic studies. Publications: Over 50 peer-reviewed articles since 2004, spanning topics from energy partitioning to smart farming technologies. Recent work emphasizes low-cost sensor applications and Big Data integration in poultry systems. Teaching: Instructs courses like Applied Poultry Science (AFNS 571/AN SC 471) and Principles of Animal Agriculture , emphasizing experiential learning and critical scientific thinking. Grants/Advising: No explicit grants listed, but his research has received institutional support. No advisee names found in provided texts.
Liz Ratnamمشاهده پروفایل
دانشیار
Associate Professor Liz Ratnam is a leading academic in the Department of Electrical and Computer Systems Engineering at Monash University, serving as Deputy Postgraduate Director of Education ECSE. Her expertise spans power systems control, energy optimization, and resilient grid design. She holds a BEng (Hons I) and PhD in Electrical Engineering from the University of Newcastle (2006 and 2016), with postdoctoral research at UC San Diego and Berkeley. She previously served as Senior Lecturer and Sub-Dean for Educational Programs at ANU's College of Engineering & Computer Science, supported by a Future Engineering Research Leader (FERL) Fellowship. Her current research focuses on advancing transactive energy markets, EV coordination in distribution networks, and secure grid operations. She leads major grants including the National Facility for Electricity Grid Security (ARC LIEF 2023) and projects on EV fast-charging infrastructure. Education Background: - BEng (Hons I) in Electrical Engineering, University of Newcastle (2006) - PhD in Electrical Engineering, University of Newcastle (2016) Research Interests: Resilient, carbon-neutral power grid design Control and optimization of energy systems Synchrophasor-based estimation for grid stability Transactive multi-agent systems for energy markets Electric vehicle integration and coordination in unbalanced grids Grants & Projects: ARC Linkage Infrastructure Grant LE23010058 (2023): National Facility for Electricity Grid Security ARC Discovery Project DP22010135 (2022): Neural Architecture Search for Deep Learning ARC Linkage Project LP21200473 (2022): Building Australia's EV Fast-Charging Infrastructure Awards & Memberships: Fellow of Engineers Australia Senior Member of IEEE FERL Fellowship (ANU)
Professor Yahya Fathi specializes in optimization and operations research at North Carolina State University. His research includes mathematical programming, production systems, and quality engineering, with applications in manufacturing and data analytics. Awarded multiple teaching excellence honors.
Do Young Eun is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University (NC State), with affiliations in Computer Science and Operations Research. He holds a Ph.D. from Purdue University and M.S./B.S. degrees from KAIST, Korea. His research focuses on distributed optimization for machine learning, network modeling, and algorithms for social/wireless networks, with applications in epidemic analysis and graph analytics. Education: Ph.D. in Electrical and Computer Engineering, Purdue University (2003) M.S. in Electrical Engineering, KAIST (1997) B.S. in Electrical Engineering, KAIST (1995) Research Interests: Distributed optimization and machine learning Network modeling and performance analysis Epidemic modeling and control Graph analytics and social network analysis Stochastic processes and algorithms Highlighted Awards: NSF CAREER Award (2006) Outstanding Paper Award, ICML 2023 Best Paper Awards at IEEE ICCCN (2005), IPCCC (2006), NetSciCom (2015) Best Student Paper Award, ACM MobiCom 2007 Advising & Grants: Current advisees include Jie Hu, Yi-Ting Ma, and Feiya Xiang NSF Grant (2024–2027): 'Toward Maximally Efficient Sampling and Optimization for Decentralized Learning' Supervised over 10 Ph.D. students, many in academic or industry leadership roles Labs/Teams: His research group focuses on interdisciplinary projects at the intersection of networking, machine learning, and stochastic systems, with collaborations in academia and industry.
Yang Kuang is Professor of Mathematics at Arizona State University's School of Mathematical and Statistical Sciences, with joint affiliations with the CRESMET and Mathematical Computational Modeling Sciences Center. His research bridges mathematical biology, oncology, population dynamics, and delay differential equations. His cancer modeling investigates glioblastoma growth mechanisms, tumor-immune interactions, and prostate cancer treatment optimization. Ecological work pioneers stoichiometric modeling of producer-grazer systems and virus-host-environment interactions. He directs NSF-funded projects including 'Mathematical Classification of Complexity in Population Dynamics' and 'Predictive Modeling of Pattern Formation'. Publications demonstrate recurring themes: reaction-diffusion tumor models, wastewater-based epidemiology during pandemics, and ecological stoichiometry. His textbook Introduction to Mathematical Oncology establishes core frameworks for cancer modeling.
A. Douglas Stone is the Carl A. Morse Professor of Applied Physics and Physics at Yale University and Deputy Director of the Yale Quantum Institute. His research focuses on theoretical condensed matter physics and optics, with contributions to quantum transport, laser physics, and mesoscopic systems. He holds a PhD from MIT (1983) and has been recognized with awards like the Willis Lamb Medal (2015) and Phi Beta Kappa Science Book Award (2014). Roles: Deputy Director, Yale Quantum Institute; Carl A. Morse Professor of Applied Physics and Physics Affiliations: Yale University; multiple collaborations with experimental groups in optics and photonics Research Interests: Quantum chaos, wave chaos in microcavities, coherent perfect absorption, steady-state ab initio laser theory (SALT), and mesoscopic physics. His work bridges theoretical advancements with experimental applications in lasers, quantum computing, and nanophotonics. Key Projects: Development of SALT for microlaser design, exploration of topological photonics, and studies on quantum measurement and error correction in quantum systems. Grants & Awards: Over 20 grants and fellowships, including NSF support and major prizes in laser science and condensed matter physics. Labs/Teams: Leads the Stone Research Group at Yale, collaborating on projects like random lasers and quantum coherence.
Guanrui Liمشاهده پروفایل
استادیار
- Aerial Robotics
- Control Systems
- Robot Perception
- +۳ مورد دیگر
Guanrui Li is an Assistant Professor at Worcester Polytechnic Institute's Robotics Engineering Department and the director of the Aerial-robot Control and Perception Lab (ACP Lab). He holds a Ph.D. in Electrical and Computer Engineering from NYU (2024), an M.S. in Robotics from the University of Pennsylvania (2018), and a B.E. in Theoretical and Applied Mechanics from Sun Yat-sen University (2016). His research focuses on aerial robotics, including control methodologies for collaborative transportation, human-robot interaction, and perception-aware systems. Key contributions include Hybrid Perception-Aware MPC frameworks, cooperative manipulation algorithms, and simulation tools like RotorTM. Education: Ph.D., Electrical and Computer Engineering, NYU (2024) M.S., Robotics, University of Pennsylvania (2018) B.E., Theoretical and Applied Mechanics, Sun Yat-sen University (2016) Research Interests: Control and perception of aerial robots, human-robot collaboration, cooperative manipulation, and autonomous systems. Notable work addresses challenges in payload transportation, sensor fusion, and safety-critical navigation. Awards: NSF CPS Rising Stars (2023) Outstanding Deployed System Paper Finalist (IEEE ICRA 2022) NYU Dante Youla Award (2022) NYU Outstanding Dissertation Award (2024) Lab & Team: ACP Lab focuses on advancing aerial robotics through interdisciplinary research. Current projects include mixed reality interfaces for human-robot interaction and fault-tolerant control systems. Recent collaborations include workshops on Rust for Robotics (ICRA 2025) and embodied-AI for aerial systems (ICUAS 2025).
- Control Theory
- Nonlinear Systems
- Robust Control
- +۱۲ مورد دیگر
Ariadne Justi Bertolin is a Lecturer in the Department of Mechanical Engineering at the University of Bath, affiliated with the Centre for Sustainable Energy Systems (SES) and The Foundry: Centre for Digital, Manufacturing & Design. She is actively involved in research and is currently accepting doctoral students. Her research focuses on control theory, particularly in the areas of nonlinear systems, robust control, and stability analysis. Key topics include Lur'e systems, output feedback, Zames-Falb multipliers, and LMI-based design methods. She applies these theories to autonomous systems, energy systems, transportation, and industrial automation. The recent publications show a strong trend in developing LMI-based algorithms for stability and control synthesis of both continuous-time and discrete-time nonlinear systems. Her work emphasizes robustness, performance guarantees (e.g., ℒ2-gain), and practical implementation using finite impulse response and noncausal multipliers. She has been the Principal Investigator on a project titled 'Improving stability and control synthesis conditions for nonlinear systems,' indicating leadership in her research domain. While no formal awards are listed, her publications in IEEE and IFAC journals reflect high-quality contributions to control engineering. Dr. Bertolin advises doctoral students and is engaged in collaborative research, particularly with scholars like G. Valmorbida, R. C. L. F. Oliveira, and P. L. D. Peres. Her work is supported by ongoing research projects and contributes to the theoretical foundations of modern control systems. She is affiliated with key research centers at Bath, including the Centre for Sustainable Energy Systems and The Foundry, which focus on digital manufacturing, sustainable energy, and design innovation.
Gokhan Pekcan is a Professor at the University of Nevada, Reno, serving as Graduate Program Director and Associate Chair for Graduate Affairs. His research focuses on earthquake engineering, structural dynamics, seismic hazard mitigation, and structural health monitoring. Key research areas include: Seismic response of bridges and buildings under torsional ground motions Development of active and adaptive control systems for seismic resilience Integration of machine learning and compressive sensing for structural health monitoring Experimental validation of seismic retrofit techniques Analysis of structural and nonstructural systems' vulnerability Design of energy dissipation systems for civil infrastructure Recent publications highlight his work on torsional ground motion effects, hybrid simulation methodologies, and smart material applications in bridge systems. He employs deep learning for damage detection and data compression in monitoring frameworks.
- Game Theory
- Control Theory
- Optimization
- +۷ مورد دیگر
Lacra Pavel is a Professor in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, Faculty of Applied Science and Engineering. She joined the department in August 2002 after industry experience at Nortel Networks and Solinet Systems, and remains active in the System Control Group and Photonics Group. Her educational background includes: Diploma of Engineering (with distinction) in Automatic Control, Technical University Gh. Asachi of Iasi, Romania (1989) PhD in Electrical and Computer Engineering, Queen's University at Kingston (1996) Research focuses on integrating game theory, control theory, and optimization within networked systems. She pioneered applications in noncooperative/evolutionary game theory for network control, nonlinear/robust control frameworks, and energy-efficient optical/transportation networks. Current work develops mathematical foundations for learning in games via control-theoretic approaches to enable autonomous multi-agent network optimization. Recent publications (2019-2013) reveal dominant trends: distributed Nash equilibrium seeking using passivity-based and operator-splitting methods, stability analysis for optical network power control with time-delays, and extensions to transportation systems like railway timetabling. Key subfields include graphical games, ADMM algorithms, and Lyapunov-based boundary control for distributed parameter systems. Scientific recognition includes: Fellow of the IEEE (2025) for contributions to game theory, control, and optimization for network systems Connaught New Staff Award, University of Toronto (2003) New Opportunities Infrastructure Award, CFI/OIT (2003) Award for Innovation, Solinet Systems (2001, 2002) Inventor Recognition Award, Nortel Networks (2000) She has advised over 20 graduate students including current PhD candidates and former students now at MIT, Princeton, Amazon, and Ciena. Major grants include the CFI/OIT New Opportunities Infrastructure Award (2003). Her research bridges theoretical game control with practical implementations in optical and transportation networks. As co-director of the System Control Group and member of the Photonics Group, she leads teams developing algorithms for autonomous network optimization. Current projects focus on stochastic approximation methods for multi-agent learning and energy-efficient network design.