Find Academic Supervisors

• Data Science
• Machine Learning
• Graph Signal Processing
• +5 more

Prof. Baltasar Beferull-Lozano is Professor, Chief Research Scientist/Research Professor and Head of the Signal and Information Processing for Intelligent Systems Department at Simula Metropolitan . His leadership role spans directing cutting-edge research at the intersection of data science, networked systems and artificial intelligence. Research Interests: His group pursues a broad agenda that includes Data science and machine learning Online optimization and streaming algorithms Graph signal processing and higher-order networks Intelligent sensing, signal processing and inference Cyber-physical systems and IoT In-network distributed and cooperative intelligence AI-driven networks and communication systems Across these themes, his work consistently develops mathematically rigorous yet computationally efficient frameworks that enable learning and inference on complex, dynamic networked data. Publication Trends: Over 2021-2025 he has produced a prolific stream of journal and conference papers. Recurrent topics include topological signal processing , graph neural networks , transfer learning for wireless radio mapping , online kernel methods , simplicial and higher-order models , and non-linear topology identification . These works collectively advance both theoretical foundations and practical algorithms for next-generation networked AI systems. Scientific Awards: No awards are listed in the provided text. Advising & Grants: While no explicit grants or student lists are supplied, the volume and senior authorship of publications suggest Prof. Beferull-Lozano leads a sizeable research group comprising PhD candidates and post-doctoral researchers working on externally funded projects. Labs & Teams: He heads the Signal and Information Processing for Intelligent Systems Department at Simula Metropolitan, a multidisciplinary unit focused on machine learning, signal processing and networked intelligence.

• Control Systems
• Dynamics
• Systems Theory
• +3 more

Martin J. Corless is a Professor in the School of Aeronautics and Astronautics at Purdue University. He holds a B.E. (First Class Honors) in Mechanical Engineering from University College Dublin and a Ph.D. from the University of California, Berkeley. His research focuses on control systems, dynamics, and uncertainty analysis, with applications in aerospace and mechanical systems. He has contributed to tools for analyzing systems with deterministic uncertainties and has published extensively on topics like eigenvalue problems, cooperative control, and robust rollover prevention. Education: B.E. (1st honors), Mechanical Engineering, University College Dublin, Ireland, 1977 Ph.D., Mechanical Engineering, University of California, Berkeley, 1984 Research Interests: Dr. Corless specializes in control systems with deterministic uncertainties, nonlinear dynamics, and applications to aerospace and automotive systems. His work emphasizes robust control methodologies, observer design, and stability analysis for complex systems. Recent efforts include rollover prevention in vehicles and adaptive control for active suspensions. Awards: C.T. Sun Research Award (2011) W.A. Gustafson Teaching Award (2003 & 2009) Advising & Grants: While no specific students are listed, his research involves collaborations with teams on automotive safety, sensor networks, and nonlinear systems. Funding sources are not explicitly mentioned, but his work aligns with grants in control theory and aerospace engineering. Labs/Teams: His research is conducted within Purdue’s School of Aeronautics and Astronautics, focusing on interdisciplinary projects involving aerospace systems, control design, and applied mathematics.

Zhaoran WangView profile

Associate Professor

• Machine Learning
• Optimization
• Reinforcement Learning
• +3 more

Zhaoran Wang is an Associate Professor at Northwestern University's McCormick School of Engineering, holding a joint appointment in Industrial Engineering and Management Sciences and Computer Science. His research focuses on developing data-driven decision-making methods that integrate machine learning, optimization, and game theory to address societal challenges. Key areas include autonomous agents, multi-agent systems, and provably efficient reinforcement learning algorithms. His work emphasizes computational and statistical efficiency, particularly in scenarios with partial observability or confounded data. Notable contributions include frameworks for mitigating overoptimization in reinforcement learning and scalable solutions for partially observed systems. He has published widely in top venues like ICML, NeurIPS, and ICLR. Research interests span machine learning theory, reinforcement learning, optimization, and their applications in control systems and multi-agent coordination. Recent publications emphasize theoretical guarantees for algorithms and practical scalability in real-world domains like autonomous driving and economic systems.

Andreas WaechterView profile

Professor

• Large-scale nonlinear optimization
• Mixed-integer nonlinear optimization
• Software implementation of numerical algorithms
• +4 more

Andreas Waechter is a Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. He holds a Ph.D. in Chemical Engineering from Carnegie Mellon University (2002) and an M.S. in Mathematics from the University of Cologne (1997). Before joining Northwestern in 2011, he was a Research Staff Member at IBM Research (Yorktown Heights, NY). His research focuses on developing numerical algorithms for nonlinear continuous and mixed-integer optimization, with applications in power systems, scientific computing, and sustainability. Key contributions include the open-source solver Ipopt (winner of the 2011 Wilkinson Prize and 2009 INFORMS Computing Society Prize), and the Bonmin and Couenne solvers for mixed-integer optimization. Waechter has received numerous awards, including the 2017 Charles Broyden Prize and the 2020 ARPA-E Grid Optimization Competition award. His work emphasizes both theoretical advancements and practical software implementation, with applications ranging from power grid optimization to machine learning. Education: Ph.D., Chemical Engineering, Carnegie Mellon University, 2002 M.S., Mathematics, University of Cologne, 1997 His research interests span optimization algorithms, algorithmic differentiation, and interdisciplinary applications. He has advised over a dozen graduate students and collaborates with industry and academic institutions globally. Waechter's software packages are widely used in academia and industry, reflecting his commitment to open-source computational tools.

• Stochastic Model Updating
• Active Vibration Control
• Aeroelasticity
• +3 more

John Mottershead is the Alexander Elder Professor of Applied Mechanics at the University of Liverpool, affiliated with the School of Engineering under the Faculty of Science and Engineering. His research focuses on stochastic model updating, active vibration control, aeroelasticity, and full-field vibration analysis. He has held editorial roles in journals like Mechanical Systems and Signal Processing and is a Fellow of the Institution of Mechanical Engineers. Education: BSc, PhD, DEng (degrees not specified in detail). Research Interests : Stochastic model updating with uncertainty quantification Active vibration control strategies for nonlinear systems Aeroelastic stability and flutter suppression Full-field vibration measurement techniques using digital image correlation Grants & Collaborations : Leadership in EU and EPSRC-funded projects on model updating, aeroelasticity, and structural dynamics. Collaborations with institutions like University of Kassel, Politecnico di Torino, and Airbus. Awards & Recognition : Over 20 keynote presentations at international conferences (e.g., Uncertainties 2016, ICSV 2015). Recipient of the 2021 Aerospace Best Paper Award for morphing actuator research. Labs & Teams : Active in interdisciplinary teams addressing aerospace structural dynamics and experimental modal analysis.

N Sri NamachchivayaView profile

Professor

• Multi-scale Modeling
• Stochastic Dynamical Systems
• Stability Theory
• +3 more

Dr. N. Sri Namachchivaya is Professor of Applied Mathematics at the University of Waterloo with PhD from University of Waterloo. His research develops mathematical frameworks for stochastic dynamical systems, focusing on stability analysis, bifurcation theory, and multi-scale modeling. Recipient of NSF Presidential Young Investigator Award and multiple distinguished professorships, he has secured $8M+ in research funding and published over 250 scholarly works. Research examines noise-induced phenomena in nonlinear systems using asymptotic methods, dimensional reduction, and computational techniques. Current projects investigate stochastic bifurcations in fluid-structure systems, data assimilation for chaotic systems, and filtering algorithms for multiscale dynamics. Publications demonstrate theoretical advances in stochastic stability analysis and practical algorithms for engineering systems. Recent work focuses on Hopf bifurcations in turbulent flow models, particle filtering for chaotic systems, and stability of infrastructure in turbulent conditions. Scientific Awards: NSF Presidential Young Investigator (1990) Russell Severance Springer Distinguished Professor (2011) MSRI Distinguished Professor (2007) Xerox Research Award (1989, 1993) ASME Outstanding Service Award (2007) Supervised 20 doctoral students and 21 master's students. Lectures internationally on stochastic dynamics and nonlinear systems theory. Directs the Fields-CQAM Laboratory for Inference & Prediction, developing mathematical tools for complex system analysis across physics, engineering, and environmental science domains.

Asok RayView profile

Professor

• Machine Learning
• Neural Networks
• Fatigue Damage Detection
• +6 more

Asok Ray is a Distinguished Professor of Mechanical Engineering and Mathematics at The Pennsylvania State University, affiliated with the Eberly College of Science. His research focuses on interdisciplinary applications of machine learning, data-driven modeling, and symbolic dynamics to solve complex engineering challenges in areas such as fatigue damage detection, combustion instability prediction, and structural health monitoring. He has pioneered methods combining neural networks with traditional engineering principles to enhance the accuracy and efficiency of predictive systems. Key research interests include: Neural networks and deep learning for material science and mechanical systems Symbolic time series analysis for anomaly detection Data fusion and pattern classification in dynamical systems Thermoacoustic instability mitigation in combustion systems Bayesian optimization techniques for engineering design His work emphasizes real-time monitoring and decision-making in safety-critical systems, such as robotics and nuclear reactors. Notable contributions include frameworks for early-stage fatigue crack detection using ultrasonic sensors and digital twin technology for worker safety during robotic operations.

Cameron J. TurnerView profile

Associate Professor

• Computational Design
• Engineering Design
• Design Optimization
• +7 more

Dr. Cameron J. Turner serves as Associate Professor in Clemson University's College of Engineering, Computing and Applied Sciences since 2016, teaching engineering design methods, optimization, mechanical systems, and CAD/CAM. His research bridges computational capabilities with engineering design processes across multiple domains. His academic credentials include: Ph.D. in Mechanical Engineering from The University of Texas at Austin (2005) MSE from The University of Texas at Austin (2000) BSME from the University of Wyoming (1997) Turner's research centers on Computational Design Methods with emphasis on design analogy identification , early-stage problem modeling , surrogate modeling for complex systems , and additive manufacturing automation . His work integrates digital twin technology, tradespace exploration, and function-based design to solve engineering challenges in nonlinear and uncertain environments. Current investigations focus on immersive virtual environments for design reviews and intelligent robotic systems. Recent publications reveal accelerating research in digital twin applications for vehicle design, tradespace exploration methodologies, and data-driven decision support systems. These works span mechanical engineering, computer science, and systems engineering with strong emphasis on practical implementation in manufacturing and robotics. His professional recognition includes: CSM Design Program Director’s Award for service to capstone design program (2015) Turner actively shapes engineering education through leadership roles as Program Chair for ASME's CIE Division Executive Committee and member of ASME's International Design Simulation Competition Committee. His service extends to ASEE design communities and the Design Society, demonstrating commitment to advancing design pedagogy and practice. Current projects indicate expanding work in ground vehicle digital agents and Stewart platform calibration techniques. While specific laboratory details weren't provided, his research trajectory suggests active collaboration with computational design groups focusing on digital manufacturing and autonomous systems integration.

Nathan DahlinView profile

Assistant Professor

• Machine Learning
• Stochastic Control
• Optimization
• +2 more

Nathan Dahlin is an Assistant Professor in the Department of Electrical and Computer Engineering at the University at Albany's College of Nanotechnology, Science, and Engineering. He holds a BS, MS, and PhD in Electrical Engineering and an MA in Applied Mathematics from the University of Southern California. Prior to joining UAlbany, he was a Postdoctoral Research Associate at the University of Illinois Urbana-Champaign and a senior audio DSP research engineer at Audyssey Laboratories. Dr. Dahlin's research focuses on fundamental problems in machine learning, stochastic control, optimization, and microeconomics, with applications in developing computationally efficient decision-making approaches for smart energy systems. His work emphasizes reliability in uncertain environments and risk management. His recent publications demonstrate strong focus on machine learning applications in control systems, energy management, and algorithm design. Articles frequently address topics like imitation learning, economic dispatch optimization, neural network transformation, and kernel-based learning methods, often with practical implementations in energy systems and smart grids. Dr. Dahlin is active in professional organizations including the Institute of Electrical and Electronics Engineers (IEEE) and the Association for the Advancement of Artificial Intelligence (AAAI). He serves as a reviewer for leading conferences and journals including AAAI Conference on Artificial Intelligence, IEEE Transactions on Control of Network Systems, IEEE Transactions on Power Systems, and IEEE Transactions on Smart Grid.

Se Young YoonView profile

Associate Professor

• Control Systems
• Active Magnetic Bearings
• Multi-Agent Systems
• +5 more

Se Young Yoon is an Associate Professor in the Department of Electrical and Computer Engineering at the University of New Hampshire's College of Engineering and Physical Sciences. He earned his B.S. and M.S. in System Science and Mathematics from Washington University in St. Louis (2005) and a Ph.D. in Electrical Engineering from the University of Virginia (2011). His research focuses on control systems, active magnetic bearings, multi-agent systems, and nonlinear dynamics, with applications in robotics, energy systems, and mechatronics. He is a member of IEEE, IEEE-CSS, Tau Beta Pi, and Kappa Mu Epsilon. Education: Ph.D., Engineering, University of Virginia (2011) M.S., Systems Science and Theory, Washington University (2005) B.S., Systems Science and Theory, Washington University (2005) Research Interests: Robust control of systems with delays and uncertainties Cooperative control of multi-agent systems Active magnetic bearing (AMB) systems for turbomachinery Surge control in compressors Data-driven and model-based control algorithms His recent work emphasizes stabilization of dynamic systems with uncertain equilibria and distributed control under communication constraints. He has collaborated on experimental platforms like rotor-AMB test rigs for energy storage flywheel emulation and compressor surge suppression. Labs/Teams: Previously affiliated with the Rotating Machinery and Controls (ROMAC) Lab at University of Virginia.

Bhimsen ShivamoggiView profile

Professor

• Fluid Dynamics
• Solar Wind Modeling
• Magnetohydrodynamics
• +3 more

Bhimsen Shivamoggi is a Professor in the Department of Mathematics at the University of Central Florida's College of Sciences. His primary research focuses on theoretical and applied fluid dynamics, with significant contributions to solar wind modeling, magnetohydrodynamics, and turbulence theory. He maintains an active research program in astrophysical fluid dynamics and plasma physics. His research interests span both fundamental theory and practical applications in space physics, including solar wind dynamics, plasma stability analyses, and turbulent flow characterization. The work demonstrates strong interdisciplinary connections between applied mathematics, astrophysics, and plasma physics. Analysis of recent publications reveals a consistent focus on Parker's solar wind model extensions, magnetohydrodynamic stability criteria, and advanced turbulence modeling techniques. Significant theoretical work addresses nonlinear regularization of critical points in astrophysical flows and topological characterization of MHD systems. No awards or student advising information is documented in available materials. Research activities appear concentrated in theoretical development and computational modeling without explicit mention of laboratory facilities or research teams.

Jezabel CurbeloView profile

Researcher

• Applied Mathematics
• Geophysical Fluid Dynamics
• Fluid Dynamics
• +4 more

Jezabel Curbelo is a Ramón y Cajal Researcher at the Polytechnic University of Catalonia (UPC), affiliated with the Department of Mathematics, IMTech Institute, and Centre de Recerca Matemàtica. She holds a PhD from the Autonomous University of Madrid (UAM) and has held postdoctoral positions at institutions including UCLA and the University of Lyon. Her research focuses on applied mathematics in geophysical fluids, dynamical systems theory, and planetary mantle convection. She is an editor for Nonlinear Processes in Geophysics , EMS Magazine , and Physica D , and actively contributes to academic leadership roles, including the UPC’s Department of Mathematics Chair Office. Education: Bachelor’s in Mathematics, University of La Laguna (2009) Master’s in Mathematics and Applications, UAM (2010) PhD in Mathematics, UAM (2014) Research Interests: She investigates fluid dynamics in geophysical settings, including atmospheric and oceanic transport, Lagrangian coherent structures, and planetary mantle convection. Her work combines analytical and numerical methods to model complex fluid behaviors, with applications to climate science and pollution tracking. Recent projects include analyzing the 2019/2020 Australian wildfire smoke plume and stratospheric mixing patterns. Awards & Grants: 2022 BBVA Foundation Leonardo Fellowship 2020 L'Oréal-Unesco For Women in Science Award (Spain) 2015 Donald L. Turcotte Award (AGU) 2015 Vicent Caselles Award (RSME-BBVA) Current funding: 'Research Consolidation' grant (State Research Agency) Teaching & Outreach: She teaches advanced mathematics courses at UPC and actively engages in science communication, including media interviews and public lectures. Notable outreach includes features in El País and La Vanguardia , and her inclusion in the Canary Islands’ 'Scientific Women' calendar. Leadership & Conferences: Co-organizes the UB-UPC Dynamical Systems Seminar and chairs sessions at the European Geosciences Union. She co-organized the 2025 conference 4th Nonlinear Processes in Oceanic and Atmospheric Flows in Barcelona.

James BruseyView profile

Professor

• Machine Learning
• Reinforcement Learning
• Wireless Sensor Networks
• +6 more

James Brusey is a Professor of Computer Science at Coventry University , leading AI for Cyberphysical Systems within the Centre for Data Science . With a PhD from RMIT University (2003) and over 15 years of industry experience, he specializes in Machine Learning , Reinforcement Learning , and wireless networked sensing for real-world applications. Research Interests span Reinforcement Learning for Cyberphysical Systems Thermal Comfort Optimization in Vehicles and Buildings Wireless Sensing for Safety-Critical Applications Sim2Real Challenges in Autonomous Systems Research Trends in his recent work include Advancing RL for Human-Centric Systems Multi-Objective Optimization in Energy Applications Wireless Sensor Networks for Refugee Camps Machine Learning in Anaerobic Digestion and HVAC Systems Expertise includes EU H2020 DOMUS project (2018-2022) for electric vehicle thermal comfort £35 million in grants across 25 projects Mentorship for 20+ PhD students Collaborations extend to Jaguar Land Rover TUV-NEL Rolls-Royce British Council International Projects

Kapil R. DandekarView profile

Professor

• Wireless Communications
• Antennas
• Software Defined Radio
• +7 more

Kapil R. Dandekar serves as the Interim Dean of Drexel Engineering and holds the E. Warren Colehower Chair Professorship in the Department of Electrical and Computer Engineering at Drexel University's College of Engineering. He has been a faculty member at Drexel since 2001, contributing significantly to education and research in wireless communications and engineering innovation. His educational background includes a PhD and MS in Electrical and Computer Engineering from the University of Texas at Austin (2001 and 1998, respectively) and a BS in Electrical Engineering from the University of Virginia (1997). PhD, Electrical and Computer Engineering, University of Texas at Austin, 2001 MS, Electrical and Computer Engineering, University of Texas at Austin, 1998 BS, Electrical Engineering, University of Virginia, 1997 Dandekar's research spans wireless communications, antennas, and engineering education with current emphases on reconfigurable intelligent surfaces (RIS), software-defined radio (SDR), functional fabrics, cybersecurity, and IoT applications. His work bridges theoretical innovation and practical implementation, particularly in healthcare-focused wireless sensing and wearable technologies. As an educator, he pioneered hands-on SDR laboratory classes with "Radio Wars" projects and co-founded the EPICS-in-IEEE international service-learning program. Analysis of his 15 most recent publications (2023-2025) reveals strong trends toward AI/ML integration in wireless systems, next-generation RIS applications for signal control, and healthcare-focused wireless sensing. His team increasingly combines millimeter-wave communications with machine learning for interference mitigation while advancing physical layer security and 6G modulation techniques through FPGA implementations. His scientific contributions have been recognized with prestigious awards: 2023: Fellow of the American Institute of Medical and Biological Engineering 2019: College of Engineering Outstanding Innovation Award 2016: Provost’s Award for Outstanding Mid-Career Scholarly Activity 2015: Drexel University College Outstanding Research Award 2013: President’s Award for Civic Engagement 2012: IEEE Meritorious Service Award 2007: Department Research Award Dandekar directs the Drexel Wireless Systems Lab (DWSL), securing major funding from NSF, Army CERDEC, NSA, ONR, FAA, DARPA, and industry partners. His educational leadership includes co-developing SDR curricula and leading Team Dragon Radio (with Dr. Geoffrey Mainland) to 8th place in the DARPA SC2 competition. DWSL's research has yielded commercialized intellectual property, particularly in RFID-based healthcare monitoring and reconfigurable antenna systems. The Drexel Wireless Systems Lab serves as the primary research hub, fostering cross-disciplinary collaborations with the College of Computing and Informatics. Recent projects include wearable DVT prevention devices, RF jamming mitigation systems using machine learning, and functional fabric-based wireless sensors, often developed through student teams competing in national challenges.

Fernando Mancilla-DavidView profile

Professor

• Power System Planning
• Renewable Energy
• Smartgrids
• +6 more

Fernando Mancilla-David is a full Professor of Electrical Engineering at the University of Colorado Denver, affiliated with the College of Engineering, Design and Computing. He specializes in power systems, renewable energy integration, and smart grid technologies. He holds a BS from Universidad Técnica Federico Santa María (Chile), and M.S. and Ph.D. from the University of Wisconsin-Madison. His research focuses on sustainable energy systems, converter dynamics, and grid modernization. Dr. Mancilla-David has held visiting professorships at institutions worldwide, including L’Ecole Supérieure d’Electricité (France), Berlin Institute of Technology (Germany), and the Technical University of Catalonia (Spain). During 2016-2017, he was on sabbatical at PUC-Rio University in Brazil. His honors include the IEEE Industrial Electronics Society Student Best Paper Award (2019), Fulbright Scholar (2016), and the Eduardo Neale-Silva Memorial Scholarship (2000-2001). His work spans energy efficiency, photovoltaic system modeling, and advanced control strategies for power converters. Recent research emphasizes data-driven optimization frameworks, machine learning applications in grid management, and affine policy models for active distribution networks. He has authored over 150 publications in peer-reviewed journals and conferences.