Find Academic Supervisors

Marc BOCQUETView profile

Professor

• Data Assimilation
• Inverse Problems
• Machine Learning in Geosciences
• +3 more

Marc BOCQUET is a Professor and Senior Researcher at CEREA, a joint laboratory between École des Ponts ParisTech and Électricité de France (EdF) R&D. He serves as Deputy Director of CEREA and holds academic affiliations with Sorbonne Université, École Polytechnique, and other Paris-area institutions. His research focuses on data assimilation, inverse problems, and machine learning applications in geosciences, environmental statistics, and climate modeling. He has authored 110+ papers and co-edited books, and is a Fellow of the European Centre for Medium-Range Weather Forecasts. **Research Interests:** Development of mathematical methods for estimating atmospheric/oceanic states and improving geoscience models using observational data. Applications to atmospheric chemical transport models and dispersion modeling in urban environments. Integration of machine learning (e.g., deep learning, generative AI) with data assimilation for climate projections and emissions quantification. **Key Contributions:** Pioneered hybrid models combining machine learning and classical data assimilation for weather forecasting (e.g., IFS system). Advanced generative AI-based emulators for sea ice dynamics and wildfire spread. Developed neural network approaches for CO₂ plume inversion using satellite imagery (e.g., OCO-3 SAM data). **Awards:** Fellow of the European Centre for Medium-Range Weather Forecasts (ECMWF). **Labs/Teams:** Active in CEREA and collaborates with institutions like École Normale Supérieure and Université de Toulouse through projects like DRUIDS (PEPR Maths-Vives).

Samuel LeggeView profile

Research Fellow

• Quantum Sensing
• Atom Interferometry
• Cold Atom Physics
• +3 more

Samuel Legge is a Research Fellow at the Department of Quantum Science & Technology, Australian National University (ANU). His research focuses on advanced quantum sensing technologies, particularly atom interferometry and cold atom systems applied to gravimetry and navigation. He leads projects involving compact quantum sensors and hybrid multisensor systems. Key roles: Principal Investigator (PI) on ANU projects including MiniGrav and quantum-assured positioning systems. Research areas: Quantum gravimeters, cold atom interferometry stabilization, supercontinuum generation in photonic crystal fibers. Collaborations: Extensive work with ANU teams on deployable sensors and fusion of classical/quantum accelerometers. His work emphasizes practical applications of quantum physics in navigation, subsurface mapping, and gravitational wave detection. Notable contributions include dual open atom interferometry designs and low-noise detection strategies for cold atoms.

Xinfan LinView profile

Associate Professor

• Dynamic System Modeling
• Machine Learning
• Battery Management Systems
• +5 more

Xinfan Lin is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California Davis, part of the College of Engineering. His research focuses on dynamic system modeling, diagnostics, and control, with a particular emphasis on machine learning, data analytics, and control-integrated design optimization. His work addresses applications in intelligent battery management systems, electric vehicles, unmanned aerial systems (UAS), electric-vertical-takeoff and landing (eVTOL) aircraft, and spacecraft. He leads the Lin Research Lab, which combines multi-physical domain knowledge, control theories, and machine learning to advance transportation and aerospace technologies. Lin has received the NSF CAREER Award and was elected as an IEEE Senior Member. His research interests include battery electrochemical dynamics, system-level estimation and control, and energy systems optimization. He has contributed to advancing methodologies for battery health monitoring, energy-efficient UAV mission planning, and hybrid physics-based machine learning models. Key applications of his work include improving battery management for electric vehicles, optimizing multirotor drone efficiency, and enhancing aerospace systems through integrated system modeling. His recent publications highlight advancements in energy-optimal trajectory planning, reinforcement learning for battery diagnostics, and lightweight electrochemical modeling techniques.

Torstein Kastberg NilssenView profile

Associate Professor

• Nonlinear stochastic partial differential equations
• Rough paths
• Malliavin calculus
• +2 more

Torstein Kastberg Nilssen is an Associate Professor in the Department of Mathematical Sciences at the University of Agder, Norway (since 2019). He has held postdoctoral positions at Technische Universität Berlin (2017-2019), University of Oslo (2014-2017), and guest researcher roles at University of Southern California (2015-2016), University of Manchester (2014), and Humboldt University Berlin (2013). Current affiliation: University of Agder Prior institutions: TU Berlin, University of Oslo, USC, University of Manchester, Humboldt University Berlin Research Focus: Nilssen's work revolves around nonlinear stochastic partial differential equations, rough path analysis, Malliavin calculus, and regularization effects of noise in systems with irregular coefficients. His research explores pathwise solutions for rough PDEs, stochastic transport equations, and robust methods in fluid dynamics under rough perturbations. Publication Trends: Recent articles (2019-2025) emphasize rough path applications to fluid mechanics, geometric rough paths in infinite dimensions, McKean-Vlasov dynamics for Kalman filters, and regularization of SDEs/PDEs with singular drift. Key keywords include stochastic differential equations, fractional Brownian motion, variational principles, and nonlocal diffusions.

Gustaf HendebyView profile

Associate Professor

• Statistical Sensor Fusion
• Model-Based Sensor Fusion
• Target Tracking
• +4 more

Gustaf Hendeby is an Associate Professor and Docent in Automatic Control at Linköping University's Department of Electrical Engineering (ISY). His career spans academia and defense research, including a part-time role at the university and prior positions at the German Research Institute for Artificial Intelligence (DFKI) and the Swedish Defence Research Agency (FOI). Dr. Hendeby’s research focuses on statistical and model-based sensor fusion , particularly in target tracking, SLAM, positioning, and nonlinear estimation. He has contributed to Kalman filter approximations (EKF, UKF) and particle filter methodologies, aiming to enhance sensor data utilization and algorithm accessibility for non-experts. His recent publications address magnetometer-IMU calibration, magnetic-field SLAM, DVB-T signal localization, and adaptive basis function selection for efficient predictions. Collaborations include researchers like Isaac Skog and Chuan Huang, with applications in autonomous systems and sensor networks. Teaching : Lectures on Sensor Fusion (TSRT14) and supervises Master’s theses. Projects : Technical coordinator for EU’s COGNITO project; software integration for Trivisio GmbH’s Colibri IMUs.

• Computational Intelligence
• Intelligent Control
• Machine Learning
• +7 more

Rui Araújo is an Associate Professor at the University of Coimbra (UC) since 2024, with a Habilitation in Electrical Engineering and Intelligent Systems. He is a Senior Member of IEEE and founding member of the Portuguese Institute for Systems and Robotics (ISR-Coimbra) . His career spans over 25 years in academia and research. BSc (1991), MSc (1994), PhD (2000), and Habilitation (2024) - all from UC Research interests focus on computational intelligence , intelligent control , and machine learning , with applications in robotics, automation, energy systems, and industrial processes. Key subtopics include fuzzy systems, neural networks, optimization algorithms, and real-time control architectures. Recent publications demonstrate expertise in deep learning for additive manufacturing , fuzzy control of photobioreactors , and machine learning for wastewater treatment . His work integrates edge computing and evolving systems for industrial environments. Scientific recognition includes: 2005: Fumio Harashima Best Paper Award (ETFA) 2018: Best Paper Award (ICELTICS) 2021: Best Paper Award (INDIN) 2023: Honorable Mention (SIMPEP) Active in research grants (49 total), including EVAI Charge (electric vehicle AI), RELIABLE (robotic safety systems), and InGestAlgae (microalgae production). Supervises research teams at ISR-Coimbra and collaborates with European institutions.

Seokhyun ChungView profile

Assistant Professor

• Internet of Things (IoT)
• Data-Driven Analytics
• Federated Learning
• +4 more

Seokhyun Chung is an Assistant Professor at the University of Virginia, located in Lab 257 of Olsson Hall. His research focuses on IoT-enabled systems, leveraging data-driven methods such as federated learning, multi-task learning, and Bayesian probabilistic modeling to address challenges in statistical heterogeneity, scalability, and personalization in connected systems. Applications span smart healthcare and manufacturing systems, emphasizing reliability and efficiency. Research Interests: Chung's work integrates IoT, machine learning, and systems engineering to develop collaborative analytics frameworks. He explores federated learning architectures for distributed data fusion, probabilistic modeling for uncertainty-aware predictions, and optimization techniques for resource allocation in edge computing environments. His healthcare focus includes gait analysis and load estimation for wearable devices, while manufacturing applications involve job scheduling and additive manufacturing quality control. Key Contributions: Recent work highlights include federated multi-output Gaussian processes for heterogeneous systems, real-time adaptation of time-series predictions, and fairness-aware machine learning for load carriage tasks. His interdisciplinary projects address both technical and operational challenges, such as emergency department layout optimization and toxic gas monitoring networks. Labs & Infrastructure: Chung's research is conducted in Olsson Hall's advanced labs, leveraging IoT sensor networks, edge computing setups, and collaborative simulation environments to prototype innovative smart systems solutions.

• Nonlinear Optics
• Whispering Gallery Resonators
• Optical Parametric Oscillation
• +6 more

Prof. Dr. Karsten Buse is a Professor at the Department of Microsystems Engineering (IMTEK) at the University of Freiburg and Director of the Fraunhofer Institute for Physical Measurement Techniques (IPM). He holds academic degrees including Habilitation (1997), Dr. rer. nat. (1993), and Diploma (1991) from the University of Osnabrück. His research focuses on nonlinear optics, whispering gallery resonators, and photonic engineering. Key projects include optical parametric oscillation, frequency comb generation, and applications in terahertz spectroscopy. Awards include OSA Fellow (2019) for work on lithium niobate and the Karl-Heinz-Beckurts-Award (2001) for optical wavelength filter development. He advises startups like Ondax Inc. and serves on advisory boards for solar physics and photonic engineering centers. His lab develops advanced optical systems with emphasis on high-Q resonators, nonlinear effects, and industrial applications. Research spans materials science, sensor technology, and holography.

Reza KashaniView profile

Professor

• Control of Dynamic Systems
• Noise and Vibration Control
• Acoustic Instability Control
• +7 more

Reza Kashani is a full-time Professor in the Department of Mechanical and Aerospace Engineering at the School of Engineering, University of Dayton. He specializes in control of dynamic systems with a focus on sound and vibration control, as well as manufacturing process control. His research is applied in aerospace, automotive, and industrial contexts. Educational Background: Ph.D., University of Wisconsin-Madison, 1989 M.S., University of Wisconsin-Madison, 1986 M.S., University of Wisconsin-Madison, 1979 B.S., Sharif University of Technology, Tehran, 1977 His research interests include active and passive noise and vibration control, thermoacoustic instability mitigation, electrohydrodynamic heat transfer enhancement, and feedback control in manufacturing processes. He applies control theory to mechanical systems for improved performance and reliability. The recent articles demonstrate a strong trend in tuned mass dampers, active acoustic filtering, and adaptive vibration control systems, spanning applications from structural engineering to automotive and aerospace systems. His work combines theoretical modeling, numerical simulation, and experimental validation. Scientific Awards: SAE Ralph A. Teetor Educational Award (1995) ASME Dynamic Systems and Control Division Certificate of Appreciation (1998) Dr. Kashani has secured research funding as Principal Investigator from the Air Force Wright Laboratories and DaimlerChrysler Corp., with projects focused on acoustic instability control and active boom noise damping. He has taught core courses such as Dynamic Systems and Controls, Noise and Vibration Control, and Automatic Control Theory. He has also conducted professional training for Caterpillar, John Deere, and SAE, and provided consulting services in noise and vibration control for industry. He has organized symposia on Noise and Vibration Control at ASME IMECE. He leads research in active and adaptive vibration control systems, with applications in aerospace propulsion, automotive NVH, and precision manufacturing. His lab work involves experimental modeling of acoustic enclosures, development of smart dampers, and control of micro heat transfer systems.

Brett BuShaView profile

Professor

• Biomedical Engineering
• Physiological Modeling
• Medical Devices
• +2 more

Brett BuSha is a Professor of Biomedical Engineering and Chair of the Department of Biomedical Engineering at The College of New Jersey (TCNJ). His work focuses on physiological modeling, medical device development, and respiratory mechanics, with a strong emphasis on integrating stochastic processes into biological systems analysis. PhD in Biomedical Engineering from Rutgers University and UMDNJ (1998) BSE in Biomedical Engineering from Boston University (1994) Dr. BuSha’s research interests span: Exoskeleton and assistive device design for hand rehabilitation Cardiorespiratory variability and fractal scaling analysis Gender-specific physiological modeling Wavelet-based signal processing in biomedical contexts His publications highlight collaborations with students and researchers on projects like hand exoskeletons, neonatal incubators, and epilepsy monitoring systems. Dr. BuSha has received recognition including the TCNJ Support of Scholarly Activities (SOSA) Award and IEEE Senior Member status. Certificate of Outstanding Contribution in Reviewing, Biomedical Signal Processing and Control Certificates of Appreciation from IEEE and ASME He has served on numerous TCNJ committees including the Institutional Review Board and Curriculum Committee, with ongoing roles in academic governance and safety protocols.

Michael RudermanView profile

Professor

• motion control
• robotics and mechatronics
• nonlinear systems with memory
• +3 more

Michael Ruderman is a Professor in the Department of Engineering Sciences at the University of Agder, Norway, where he has been working since June 2020 (previously as Associate Professor from October 2015 to June 2020). He is currently on sabbatical from January 2025. Prior to his position at UiA, he held academic appointments at Nagaoka University of Technology, Nagoya Institute of Technology, and Technical University Dortmund. His research focuses on: Motion control and robotics Mechatronics systems Nonlinear systems with memory Nonlinear, hybrid, and robust control methodologies Ruderman's work centers on the analysis and compensation of kinetic friction in robotic and mechatronic control systems, culminating in his 2023 book 'Analysis and Compensation of Kinetic Friction in Robotic and Mechatronic Control Systems'. His research bridges theoretical control concepts with practical engineering applications, particularly in hydraulic systems, robotic actuators, and systems with hysteresis and friction effects. His recent publications (2024-2025) demonstrate a strong emphasis on advanced control techniques for complex mechanical systems, with particular focus on sliding mode control, nonlinear damping, hysteresis compensation, and oscillation control. Many of his papers address real-world challenges in hydraulic actuators, robotic systems, and mechanical interfaces with friction. Professionally, Ruderman serves on the editorial boards of IEEE Transactions on Control Systems Technology, IFAC Control Engineering Practice, IFAC Mechatronics, and IEEE/ASME Transactions on Mechatronics. He previously chaired the IEEE-IES Technical Committee on Motion Control (2018-2021) and served on the Management Committee of IEEE/ASME Transactions on Mechatronics (2020-2023). Ruderman has been actively involved in numerous research projects including DAAD mobility grants, H2020-MSCA-RISE projects on robust control, and several Research Council of Norway (RCN) projects focusing on fractional-order systems, nonlinear control methods, and compensators for non-minimum phase systems. He teaches courses including Control Theory (MSc), Advanced Control and Robotics (PhD), Electromagnetic Modeling (PhD), and Feedback Control Systems 1 (BSc), demonstrating his commitment to educating the next generation of control engineers and robotics specialists.

Naveed Ur RehmanView profile

Associate Professor

• Signal Processing
• Machine Learning
• Biomedical Signal Processing
• +3 more

Naveed Ur Rehman is an Associate Professor at Aarhus University's Department of Electrical and Computer Engineering, specializing in Signal Processing and Machine Learning. His research bridges mathematical methodologies with real-world applications in biomedical systems and environmental forecasting. His primary interests include: Multivariate signal analysis using Mahalanobis distance and detrended fluctuation analysis Graph signal processing for spatial-temporal data modeling Biomedical signal processing for clinical applications (e.g., IRRAflow® system evaluation) Machine learning integration with signal decomposition techniques Environmental monitoring systems for air quality forecasting Recent publications demonstrate his focus on developing adaptive signal processing frameworks that address both theoretical challenges and practical implementations. This includes: Time-varying graph processing methodologies Hybrid machine learning/signal decomposition techniques Statistical validation approaches for denoising algorithms Biomedical device performance analysis Multi-scale functional connectivity modeling His work spans interdisciplinary collaborations, particularly with medical researchers for intracranial pressure management systems and environmental scientists for air quality modeling.

• Planetary Dynamos
• Fluid Dynamics
• Exoplanetary Systems
• +4 more

Yufeng Lin is an Associate Professor in the Department of Earth and Space Sciences at Southern University of Science and Technology's School of Science. He holds a B.S. in Geophysics from Wuhan University (2007), M.S. from Peking University (2010), and Ph.D. from ETH Zurich (2015), followed by postdoctoral research at the University of Cambridge. His research spans planetary dynamos , fluid dynamics of planetary interiors , and tidal interactions in exoplanetary systems . Key focus areas include magnetic field generation mechanisms, convection in rotating celestial bodies, and tidal dissipation processes. His work integrates theoretical modeling, numerical simulations, and experimental approaches to address fundamental questions about Earth and planetary evolution. Recent publications reveal strong trends in geomagnetic field analysis (using dynamic mode decomposition), Jovian system dynamics , and core-mantle interactions . His research consistently bridges geophysics and astrophysics, with increasing emphasis on computational methods for modeling complex fluid systems. IUGG SEDI Doornbos Memorial Prize (2022) National Special Expert (Youth) (2020) Pearl River Talents Plan Young Top Talent (2019) Swiss NSF Postdoctoral Fellowships (2014, 2016) Dr. Lin advises multiple graduate students (including J. Li and J.Y. Xu) and leads significant research initiatives including an NSFC Original Exploration Project (2023-2027) and a key sub-project in China's National Key R&D Program on Martian processes. His laboratory focuses on computational geodynamics and planetary fluid mechanics, with ongoing collaborations across international institutions.

Olena TymoshenkoView profile

Research Fellow

• Stochastic Differential Equations
• Stochastic Analysis
• Mathematical Finance
• +6 more

Olena Tymoshenko is a Research Fellow at the University of Oslo, Department of Mathematics, specializing in stochastic analysis and its applications. She is affiliated with the Risk and Stochastics research group and contributes to projects on robustness, asymptotic behavior, and stability of stochastic differential equation (SDE) models. Current Position: Research Fellow, Department of Mathematics, University of Oslo Previous Role: Associate Professor at National Technical University of Ukraine (2019–2023) Research Interests : Stochastic differential equations, asymptotic behavior of solutions, mathematical finance, limit theorems, pseudoregularly varying functions, and stochastic dynamics in biological systems. Recent Article Trends : Focus on partial differential equations driven by Lévy noise, McKean-Vlasov systems, and Brownian sheets. Applications span mathematical finance, epidemiology (monkeypox transmission), and control theory (Kalman filter algorithms). Teaching : Courses include Introduction to Mathematical Finance and Investment Theory (STK-MAT3700, Fall 2022) and Statistical Methods and Applications (STK4900, Spring 2023). Other Affiliation : Participant in the International Summer School as a student.

• Multi-agent systems
• Distributed artificial intelligence
• Autonomous unmanned systems
• +3 more

Zhiyun Lin is Dean and Tenured Professor at the School of Automation and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech). He holds affiliations as Foreign Academician of the Russian Academy of Engineering, Fellow of IEEE, IET, and AAIA. Previously, he served at Zhejiang University (2007-2017) and Hangzhou Dianzi University (2017-2021), with visiting positions at Yale University, ANU, and University of Tokyo. Education: PhD in Electrical and Computer Engineering, University of Toronto (2005) MSc in Control Theory, Zhejiang University (2001) BSc in Industrial Automation, Yanshan University (1998) Research Focus: Develops theoretical frameworks for multi-agent coordination, distributed AI algorithms, and cyber-physical systems. His work enables robust autonomous swarm behaviors in robotics and sensor networks through novel control strategies and optimization techniques. Publications: 230+ papers predominantly in IEEE Transactions journals demonstrate sustained contributions to formation control, distributed localization, and networked systems. Recent works (2021) advance resilient beamforming and finite-time stabilization for robotic formations. Awards: Natural Science Second Prize, Zhejiang Province (2020, 2013) Science and Technology Innovation First Prize (2020) Industry-University-Research Cooperation Innovation Award (2021) Prestigious fellowships: IEEE/IET/AAIA Leadership: Directs SUSTech's School of Automation and Intelligent Manufacturing. Formerly established the Artificial Intelligence Institute at Hangzhou Dianzi University and led research centers at Zhejiang University. Serves as Associate Editor for IEEE Systems Journal and IEEE Control Systems Letters.