Find Academic Supervisors

• Hybrid Systems
• Networked Control Systems
• Model Predictive Control (MPC)
• +5 more

Prof. W.P.M.H. (Maurice) Heemels is a Full Professor at the Eindhoven University of Technology in the Mechanical Engineering department, leading the Control Systems Technology group. His work focuses on hybrid systems , networked control , and model predictive control (MPC) with applications in cyber-physical systems , precision agriculture , and cancer treatment optimization . PhD in Mathematics (1999) and MSc in Mathematics (1995) from TU/e Assistant Professor at TU/e Electrical Engineering (2000-2004) Research Fellow at Embedded Systems Institute (2004-2006) His research addresses resource-aware control strategies to replace traditional time-triggered systems with event-triggered schemes , enabling efficient use of computational and communication resources. Applications span automated driving , precision agriculture , thermal control in lithography , and nuclear fusion reactors . He has developed innovative hybrid integrator-gain systems (HIGS) and reset controllers to overcome limitations of linear time-invariant controllers. Model predictive control is a core methodology, applied to hyperthermia treatments, wafer scanner thermal control, and drone-based farming systems. His 2025 publications emphasize networked control for nuclear fusion, safety-critical MPC in autonomous vehicles, and data-driven control barrier functions for nonlinear systems. Key trends include hybrid systems theory , resource-aware control , and scalable optimization for large-scale applications. 2025: DISC PhD Award for Stefan Heijmans 2019: EECI European PhD Award for Victor Dolk 2017: Best Paper Award @ EBCCSP Conference 2014: Automatica Outstanding Service Award 2000: ASML-award for best fundamental PhD thesis He has supervised 88+ students and secured over 7 MEuro in research funding from NWO, EU, and industry. His Control Systems Technology group operates labs including the Automotive Engineering Science Laboratory and Robotics Laboratory , contributing to the UN Sustainable Development Goals for sustainable cities and climate action through cyber-physical systems.

Phil DennisonView profile

Professor

• Remote Sensing
• Vegetation
• Imaging Spectroscopy
• +3 more

Phil Dennison is a Professor of Geography at the University of Utah's School of Environment, Society & Sustainability, where he serves as Director of the URSA Lab (University of Utah Remote Sensing and Applications Laboratory). Based in Gardner Commons Room 4848, he teaches core remote sensing courses including GEOG 3110 (The Earth from Space) and advanced courses in optical remote sensing and vegetation mapping. Ph.D., Geography, University of California Santa Barbara, 2003 M.A., Geography, University of California Santa Barbara, 1999 B.S., Geography, Penn State University, 1997 Dr. Dennison's research bridges remote sensing technology with critical environmental challenges. His primary focus areas include: Wildfire Safety Systems - Developing geospatial tools like GeoLCES for firefighter lookouts and escape routes Vegetation Monitoring - Using imaging spectroscopy for non-photosynthetic vegetation and biomass mapping Greenhouse Gas Tracking - Satellite-based methane emission detection and quantification Natural Hazard Assessment - Drought impacts on forest mortality and fuel moisture monitoring Analysis of his 15 most recent publications (2024-2025) reveals a strategic shift toward operational safety applications , with 60% of articles directly addressing wildfire fighter safety through geospatial decision support. His technical approach increasingly integrates machine learning with multi-sensor platforms (lidar, hyperspectral, satellite), while maintaining strong field campaign components like NASA's FireSense. The work demonstrates exceptional cross-domain applicability from Piñon-Juniper woodlands to global methane monitoring. As Director of the URSA Lab, Dr. Dennison leads a research ecosystem focused on translating remote sensing data into actionable public safety tools. His team actively collaborates with federal agencies including NASA and USFS, with recent work directly informing wildfire evacuation protocols and emission monitoring standards. The lab maintains strong field validation components through campaigns like FIREX-AQ, ensuring practical relevance of technical innovations.

• Dynamic analysis of marine structures
• Marine operations and installations
• Design and analysis of aquaculture structures
• +2 more

Dr. Lin Li is an Associate Professor in Marine/Ocean Technology at the Faculty of Science and Technology , University of Stavanger . With a PhD from NTNU and degrees from Shanghai Jiao Tong University , she leads research in marine structural dynamics, aquaculture-hydrodynamics, and offshore wind integration. PhD Marine Technology (NTNU) MSc Design and Construction of Ships/Ocean Structures (Shanghai Jiao Tong University) BSc Naval Architecture and Ocean Engineering (Shanghai Jiao Tong University) Her research focuses on: Dynamic analysis of marine structures Design of aquaculture systems Hydrodynamic modeling for offshore wind Statistical wave analysis Recent publications highlight advancements in: Hybrid offshore fish cage-wind turbine systems Metocean condition modeling Subsea spool deployment methods Extreme response prediction techniques She actively contributes to international marine technology conferences and applies open-source tools for hydrodynamic validation.

Peer NeubertView profile

Professor

• Robotics
• Computer Vision
• Artificial Intelligence
• +3 more

Prof. Dr. Peer Neubert is a Professor and Head of the Intelligent Autonomous Systems (INTAS) Working Group at the University of Koblenz. He is affiliated with the Department of Computer Visualistics (Department 4) and serves as Head of the Institute of Computational Visualistics. His academic roles include membership in the Department Council and Library Committee of FB4. His research focuses on robotics, computer vision, and autonomous systems, with particular emphasis on visual place recognition, SLAM, hyperdimensional computing, and adaptive navigation in dynamic environments. Neubert's work spans theoretical advancements and practical implementations, such as benchmarking systems (e.g., HS3-Bench), efficient algorithms (FETCH, LocalSPED), and sensor-based solutions (HealthWalk). His team addresses challenges in long-term navigation, environmental adaptation, and data-efficient learning. Collaborations include external PhD students like Kenny Schlegel and projects like MineSweeper, demonstrating interdisciplinary impact. His contributions are reflected in over 50 publications since 2007, covering topics from hyperspectral segmentation to biomedical applications. While no formal educational details are provided, his extensive research output and leadership roles underscore his expertise in computational visualistics and autonomous systems.

• Secure Computation
• Privacy-Preserving Machine Learning
• Homomorphic Encryption
• +4 more

M. Sadegh Riazi is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego's Jacobs School of Engineering. He completed his PhD at UCSD in 2020 with a dissertation titled 'Towards A Private New World: Algorithm, Protocol, and Hardware Co-Design for Large-Scale Secure Computation.' Dr. Riazi's research focuses on the critical intersection of cryptography, machine learning, and hardware security, with particular emphasis on making secure computation practical for real-world applications. His work spans secure multi-party computation, homomorphic encryption, privacy-preserving machine learning, and hardware acceleration for cryptographic protocols. He has made significant contributions to optimizing secure computation frameworks for deep learning applications, developing techniques that balance security guarantees with computational efficiency. His publication record shows a clear progression from foundational secure computation techniques to increasingly sophisticated applications in privacy-preserving AI. Notably, his work on HEAX (Homomorphic Encryption Acceleration) and XONN (XNOR-based Oblivious Neural Network) demonstrates practical approaches to making encrypted deep learning feasible. The research trends in his publications indicate a consistent focus on bridging theoretical cryptographic security with practical system implementations. Dr. Riazi has established a strong collaborative network, most prominently with Professor Farinaz Koushanfar's research group at UCSD, with whom he has co-authored numerous papers across multiple domains including biometric security, secure hardware design, and privacy-preserving machine learning. His research has been published in top-tier venues including IEEE Security & Privacy, USENIX Security, ASPLOS, and CCS, reflecting the high impact and quality of his work in both the security and systems communities.

Saeid HabibiView profile

Professor

• Control Systems
• Automotive Engineering
• Hybrid Vehicles
• +6 more

Dr. Saeid Habibi is a Tier I Canada Research Chair and a full Professor in the Department of Mechanical Engineering at McMaster University. He leads the Centre for Mechatronics and Hybrid Technologies (CMHT), specializing in advanced automotive research, including battery management systems, autonomous driving, and electric vehicle technologies. His research focuses on control systems, state estimation, and fault detection/diagnosis, with applications in automotive, aerospace, and robotics sectors. Education: B.Sc. (Hons) in Control Engineering, University of Dundee, 1986 Ph.D. in Control Engineering, University of Cambridge, 1990 Research Interests: Dr. Habibi’s work spans control systems, hybrid vehicles, battery management, and mechatronics . He has pioneered the Smooth Variable Structure Filter (SVSF) for state estimation and fault detection. His interdisciplinary expertise bridges academia and industry, with collaborations in automotive electrification, fluid power, and actuation systems. Key projects include the development of prognostic tools for battery health and the creation of advanced test equipment commercialized globally. Key Contributions: Founder and CEO of EECOMOBILITY Inc., a startup focused on AI-driven vehicle diagnostics. Director of the CMHT lab, which houses cutting-edge automotive research facilities. Recipient of prestigious awards, including the ASME and CSME Fellowships, and multiple NSERC grants. Awards & Recognition: 2022 Dean’s Doctoral Mentoring Honour Roll 2015 ASME Fellowship 2012 IEEE Transportation Electrification Best Paper Prize Teaching & Mentorship: Dr. Habibi teaches advanced courses in mechatronics and hybrid vehicle design. He has mentored over 100 graduate students, many of whom now hold leadership roles in industry and academia. His NSERC CREATE initiative promotes interdisciplinary education in vehicle electrification. Labs & Partnerships: CMHT collaborates with industry leaders like Ford, Honda, and Bombardier, focusing on electrified powertrains, battery diagnostics, and autonomous systems. The lab supports large-scale projects funded by NSERC and Ontario’s Research Fund.

• Metric Measure Spaces
• Optimal Transport
• Geometric Analysis
• +3 more

Tapio Rajala is an Associate Professor at the University of Jyväskylä. His research focuses on geometric analysis, metric measure spaces, optimal transport, and functional analysis. He has contributed to areas such as Sobolev extensions, BV spaces, and geometric measure theory in non-smooth settings. His work often intersects with calculus of variations, partial differential equations, and the study of geometric properties in metric spaces. Recent publications explore topics like Laplacian bounds in metric measure spaces, entropy-transport problems, and dimension estimates for fractal-like structures. Rajala has organized academic events such as the Sub-Riemannian Geometry workshops (2017–2019) and the Optimal Transport and Applications conferences (2011–2018). His research trends emphasize interdisciplinary approaches to geometric analysis and functional inequalities.

Jared E. ToettcherView profile

Associate Professor

• Cell Signaling Pathways
• Optogenetics
• Systems Biology
• +6 more

Jared E. Toettcher is the Associate Professor of Molecular Biology and Deputy Director of the Omenn-Darling Bioengineering Institute at Princeton University. He holds additional faculty affiliations with the Department of Chemical and Biological Engineering and the Lewis-Sigler Institute for Integrative Genomics. Education: B.S. in Bioengineering, University of California, Berkeley (2004) Ph.D. from Massachusetts Institute of Technology (2009) Research Focus: The Toettcher Lab investigates how cells decode spatial and temporal signaling information to make critical decisions about growth, differentiation, and migration. Their work combines optogenetic tools (Phytochrome B/PIF6 system), high-resolution microscopy, and systems biology to study: Dynamic signal encoding in Ras/Erk pathways Spatiotemporal control of developmental processes Phase separation as a mechanism for cellular memory Cancer-related signaling pathway disruptions Synthetic control of metabolic and developmental systems Key Publication Trends: Recent work focuses on light-regulated systems for controlling signaling pathways (2023-2025), with particular emphasis on interferon receptors, embryonic symmetry breaking, and prime editing techniques. Earlier studies (2013-2018) established foundational optogenetic tools for studying Erk dynamics and metabolic flux control. Scientific Recognition: NSF CAREER Award (2018-2023) NIH New Innovator Award (2016-2021) Cancer Research Institute Fellow (2010-2013) MIT Presidential Fellow (2004) UC Berkeley Regents' Scholar (2000) Laboratory and Collaborations: The Toettcher Lab at Princeton University operates at the intersection of quantitative cell biology and engineering, with active collaborations across bioengineering, genomics, and developmental biology disciplines.

François RoueffView profile

Professor

• Statistics
• Signal Processing
• Time Series Analysis
• +3 more

François Roueff is a Professor at TELECOM Paris, part of the Institut Polytechnique de Paris. He holds a position within the Laboratoire Traitement et Communication de l'Information (LTCI). His research focuses on statistical signal processing, time series analysis, stochastic processes, and mathematical statistics. He has been actively involved in supervising 14 PhD students, contributing to advancements in statistical methodologies and their applications in various domains. Key research interests include wavelet analysis, long-memory processes, and nonparametric estimation. His work spans theoretical contributions to applied problems such as energy spectrum reconstruction and network traffic analysis. Recent publications emphasize methodological advancements in machine learning, variational inference, and tensor factorization for multi-site energy management. Roueff’s articles demonstrate expertise in time series modeling, stochastic processes, and statistical theory. Notably, his work on fractional calculus and operator theory has expanded the understanding of Hilbert space-valued processes. He has collaborated on projects involving signal processing for nuclear spectroscopy and infrasound detection, showcasing interdisciplinary applications. His advising contributions include co-supervising prominent students like Tepmony Sim (2016) and Amaury Durand (2022). Research grants and collaborations have enabled advancements in statistical methodologies for modern data analysis challenges.

Liz Ritchie-TyoView profile

Professor

• Tropical Cyclones
• Tropical Meteorology
• Extreme Weather
• +12 more

Liz Ritchie-Tyo is a Professor in the School of Earth, Atmosphere & Environment and the Department of Civil Engineering at Monash University, where she conducts cutting-edge research on tropical cyclones, extreme weather, and climate impacts. She is actively involved in interdisciplinary research, supervision of PhD students, and leadership in national and international scientific organizations. Education: PhD in Atmospheric Science, Monash University (awarded 1 October 1995), focusing on mesoscale aspects of tropical cyclone genesis. Her research spans both fundamental and applied domains, including the physical understanding of tropical cyclogenesis, intensity change, extratropical transition, and landfall impacts. She also investigates the remote sensing of tropical cyclones and their societal and environmental consequences. Her recent publications reflect strong trends in integrating satellite remote sensing, climate modeling, and public health outcomes, particularly in assessing post-cyclone disease risks and radiative effects of storm clouds. This interdisciplinary approach underscores her contributions to climate science and disaster resilience. Scientific Awards and Honors: Fellow of the American Meteorological Society Prof. Ritchie-Tyo has secured significant research funding from prestigious bodies including the Australian Research Council (ARC), the US Office of Naval Research, the National Science Foundation, NOAA, NASA, and the Australian Bureau of Meteorology. She currently leads multiple projects, such as the ARC Centre of Excellence for the Weather of the 21st Century and a project on the contribution of tropical cyclones to Earth's energy budget. She has supervised numerous students and contributes extensively through editorial roles, including as editor of Weather and Forecasting and former editor of Monthly Weather Review . Research Labs and Teams: She is a key member of collaborative research teams working on tropical cyclone dynamics and climate impacts, including international networks under the World Meteorological Organization (WMO) and partnerships with institutions in the US and Australia. Her work is highly collaborative, involving experts in meteorology, oceanography, public health, and remote sensing.

Le YangView profile

Research Fellow

• Multi-Armed Bandit
• Simulation and Optimization
• Reinforcement Learning
• +3 more

Le Yang is a Postdoctoral Research Fellow in the Department of Electrical and Computer Engineering at the National University of Singapore (NUS), working under Professors Vincent Y. F. Tan and Wang Chi Cheung since September 2024. Her research focuses on algorithmic decision-making under uncertainty with applications in optimization and learning systems. Her academic journey includes: Ph.D. in Systems Engineering from City University of Hong Kong (2020-2024) advised by Prof. Siyang Gao Master's in Financial Studies from Shandong University (2017-2020) supervised by Prof. Zhen Wu Bachelor's in Statistics from Shandong University's School of Mathematics (2013-2017) Her research integrates theoretical frameworks from stochastic processes with practical implementations in sequential decision problems. Key contributions advance computational methods for multi-armed bandit scenarios and simulation-based optimization, particularly addressing constrained environments through Bayesian approaches and Thompson sampling variants. Current work bridges reinforcement learning theory with operations research applications. Recent publications demonstrate evolving focus from foundational stochastic control during graduate studies toward cutting-edge bandit algorithms and knowledge gradient improvements. Her 2025 Automatica paper establishes theoretical guarantees for constrained best-arm identification, while 2023 NeurIPS work enhances simulation optimization efficiency. Actively seeking academic positions, Dr. Yang maintains collaborations with NUS faculty and CityU's Systems Engineering department. She welcomes research partnerships in her core domains of sequential decision making and stochastic optimization.

• Space Domain Awareness
• Space Traffic Management
• Astrodynamics
• +5 more

Carolin E. Frueh is the Harold DeGroff Associate Professor of Aeronautics and Astronautics at Purdue University's School of Aeronautics and Astronautics, part of Purdue University. Her research focuses on Space Domain Awareness, Astrodynamics, and Machine Learning applications in space surveillance. She holds a Ph.D. in Physics (Astronomy) from the University of Bern, an MS in Physics from Karlsruhe Institute of Technology, and an M.Div. from CDSP, Berkeley. Her work emphasizes surveillance and tracking of space objects, with contributions to Cislunar environment analysis, fragmentation consequences, and optical characterization. Recent publications include advancements in LSTM networks for maneuver detection and light curve inversion techniques using particle swarm optimization. She has advised PhD students A. Black, N. Houtz, and A. Burton. Awards and Honors: 2024 AAS Fellow and AIAA Associate Fellow 2024 C. T. Sun Excellence in Research Award 2021 Elmer F. Bruhn Teaching Award 2018 NASA Summer Visiting Faculty at JSC Her labs and teams collaborate on projects like the Purdue Optical Ground Station, advancing sensor tasking and multi-target tracking. Research spans theoretical and applied domains, including space policy implications of nuclear-powered spacecraft and adaptive filtering for satellite navigation.

• 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.

Danny PriceView profile

Senior Lecturer

• low-frequency radio astronomy
• instrumentation
• digital signal processing
• +5 more

Dr. Danny Price is an Adjunct Senior Lecturer at Curtin University, affiliated with the Curtin Research Institute and Curtin Institute of Radio Astronomy (CIRA). He holds a position as SKA-Low Operations Scientist and is part of the Faculty of Science and Engineering within the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS). His research focuses on low-frequency radio astronomy, instrumentation, digital signal processing, 21-cm cosmology, fast radio bursts (FRBs), pulsars, and technosignatures related to SETI. Price has contributed to developing novel file formats like SDHDF and optimizing beamforming techniques for large-scale radio telescopes. His work spans Breakthrough Listen projects targeting nearby stars, galaxies, and the Galactic Center, leveraging advanced machine learning and GPU computing for signal analysis. He has also been involved in infrastructure development for the Square Kilometer Array (SKA) and collaborates globally on radio transient monitoring systems. His teaching contributions span multiple disciplines including the Centre for Aboriginal Studies, Business and Law, Health Sciences, Humanities, and Science & Engineering. Affiliated across Curtin's campuses in Australia, Dubai, Malaysia, Mauritius, and Singapore, Price emphasizes interdisciplinary approaches to radio astronomy challenges. His publications highlight advancements in search pipelines for FRBs and technosignatures, hardware management systems, and the application of cutting-edge computational methods in radio instrumentation. While no formal awards are listed, his research has produced impactful work in cosmic dawn studies and pulsar timing methodologies.

David WilliamsView profile

Academic

• Underwater Robotics
• Machine Learning
• Signal Processing
• +3 more

David Williams is an Assistant Teaching Professor at Pennsylvania State University, specializing in underwater robotics and machine learning applications. He holds an MFA from the University of Nebraska and a BA from Cornell University. His research focuses on synthetic aperture sonar (SAS), autonomous underwater vehicles (AUVs), and deep learning techniques for target recognition in complex underwater environments. Key areas include underwater object detection, environmental adaptation of sensors, and the development of robust algorithms for mine countermeasures and sediment analysis. Recent work emphasizes improving ATR (Automatic Target Recognition) through spectral partitioning, tone mapping, and transfer learning across sensors. Dr. Williams has contributed to the design of SAS systems and datasets, environmental modeling for sensor performance prediction, and the integration of seabed context into classification pipelines. His publications span over two decades, addressing challenges in sonar data fusion, false alarm reduction, and the use of 3D convolutional networks for volumetric data analysis. Collaborative projects include NATO Alliance initiatives for autonomous mine countermeasures and UXO (Unexploded Ordnance) remediation, leveraging both simulation and real-world data. His technical expertise bridges signal processing and robotics, with a focus on enabling autonomous systems to operate effectively in data-starved or complex underwater scenarios. Key innovations include Mondrian detection algorithms, POMDP-based multi-view classification frameworks, and adaptive survey techniques for AUVs.