Find Academic Supervisors

• Energy System Decarbonization
• Environmental Engineering
• Systems Engineering
• +5 more

Catherine Lindsay Anderson is Professor and Chair of the Department of Biological & Environmental Engineering at Cornell University's College of Agriculture and Life Sciences. She serves as interim Director of the Cornell Energy Systems Institute and was previously the Kathy Dwyer Marble and Curt Marble Faculty Director for Energy with the Cornell Atkinson Center for Sustainability. Dr. Anderson also holds the position of House Professor Dean at William Keeton House. Her educational background includes a B.Sc.(Engineering) and M.S. in Environmental Engineering from University of Guelph (Canada) and a Ph.D. in Applied Mathematics from the University of Western Ontario (Canada). Dr. Anderson's research focuses on energy system decarbonization at the interface of environmental and systems engineering, electric power systems, applied optimization and decision science. Her work investigates operational methods for more effective integration of renewable energy technologies, addressing the complex interactions among energy, food and water systems. The lab takes an interdisciplinary approach, combining engineering, economics and mathematics to understand the interactive forces of systems and markets. Current research areas include analysis of grid transitions, climate impacts on power system reliability, and scalable optimization for decision making under uncertainty. Her recent publications demonstrate a strong focus on energy system vulnerabilities in zero-emission grids, identification of pressure points in modern power systems, and rethinking electricity access metrics with a focus on energy justice, particularly in Latin America. Her work spans technical power systems analysis, climate impacts on grid reliability, and socio-technical aspects of energy transitions. 2021 Energy Systems Integration Group (ESIG) Award of Excellence for Contributions to Energy Systems Optimization IEEE Senior Member Grade (since 2019) 2016 College of Engineering Award for Research Excellence, Cornell University 2015 NSF CAREER Award 2012 Norman R. Scott Sesquicentennial Faculty Fellowship in Energy Systems Engineering 2010 College of Engineering Excellence in Teaching Award Dr. Anderson has advised numerous graduate students who have gone on to prestigious positions, including Assistant Professors at University of Connecticut, Pitzer College, and Texas A&M University, as well as research scientists at the National Renewable Energy Laboratory. Her lab team is committed to impactful research while fostering an inclusive and equitable environment. Current research includes USDA-funded work on agricultural-to-energy transitions in New York State, examining the nexus of energy, water, and climate.

Stephen BatesView profile

Assistant Professor

• Statistical Inference with AI Models
• Decision-making with Data
• Uncertainty Quantification
• +3 more

Stephen Bates is an Assistant Professor of AI and Decision-making in the MIT EECS department, specializing in statistical inference for AI systems, foundations of decision-making with data, and approaches to distribution shift. He directs the Quantum Nanostructures and Nanofabrication Group focusing on nanoscale device physics. His research develops statistical tools for: Uncertainty quantification in deep learning systems Inference with strategic agents and game-theoretic settings Non-IID data environments with feedback loops with applications in life sciences and sustainability. Education includes: PhD in Statistics from Stanford University Postdoctoral research at UC Berkeley BS in Electrical Engineering/Physics from Missouri S&T Awards: Theodore W. Anderson Dissertation Award. Currently teaching Modern Mathematical Statistics and organizing ICLR/NeurIPS workshops on reliable AI.

Marcelo H Ang JrView profile

Professor

• Robotics
• Mechatronics
• Intelligent Systems
• +4 more

Marcelo H. Ang Jr. is a Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS), Faculty of Engineering. He serves as Director of the Advanced Robotics Centre at NUS and has been actively contributing to robotics research since joining NUS in 1989. Education: Ph.D. in Electrical Engineering, University of Rochester, NY (1988) M.Sc. in Electrical Engineering, University of Rochester, NY (1986) M.Sc. in Mechanical Engineering, University of Hawaii (1985) B.Sc. in Mechanical Engineering and Industrial Management Engineering, De La Salle University, Philippines (1981) Professor Ang's research spans robotics, mechatronics, and intelligent systems with particular expertise in mobile robotics, robot control systems, and intelligent control methodologies. His work consistently bridges theoretical foundations with practical industrial applications, especially in manufacturing automation. His research interests evolved from fundamental control theory to complex integrated systems involving multiple robots and sensor networks. His publication record shows a strong emphasis on solving practical robotics challenges, with increasing focus on vision-based systems, mobile manipulation, and multi-robot coordination in recent years. The research demonstrates progression from single robot systems to complex networked robotic systems. Scientific Awards: Awards for Excellence 2000, for Most Outstanding Paper in 1999 Volume (for "A Walk-Through Programmed Robot for Welding in Shipyards") Professor Ang has supervised numerous graduate students including PhD candidates like Zheng Liu who worked on multi-robot surveillance systems. His research has been supported by significant grants including "Integration of Solid Modeling Systems and Robot Controller Architectures Towards Intelligent Robotic Systems" ($267,150), "Management of Manufacturing Technologies" ($33,000), and "Research and Development of a Ship-Welding Robot" ($1,651,200 plus $60,000). He leads the robotics group at NUS involved in the Robust Mobile Manipulation project with SIMTech, focusing on developing advanced robotic systems for unstructured environments. Professor Ang is also the founding chairman of the Singapore Robotic Games, demonstrating his commitment to robotics education and outreach.

• Control Systems
• Nonlinear Control Theory
• Systems Theory
• +3 more

Dr. Kanghong Shi is affiliated with The University of Sydney, contributing to advanced research in control systems and nonlinear dynamics. His work focuses on negative imaginary systems, hybrid integrator-gain systems, and their applications in MEMS nanopositioning, power systems, and networked control. He has authored numerous peer-reviewed articles in top journals like IEEE Transactions on Automatic Control and conferences such as the IFAC Symposium on Nonlinear Control Systems. His research emphasizes theoretical foundations and practical implementations, addressing challenges in system stability, feedback equivalence, and robust control. Research Interests: Control theory, nonlinear systems, negative imaginary systems, hybrid systems, MEMS technology, power systems stability, and consensus protocols in networked systems. His methodologies often integrate passivity theory and advanced control design principles. Publications: Over 15 articles since 2020, with recent works exploring discrete-time HIGS-based control, neural ODEs for mechanical systems, and unified voltage/frequency/angle control in electrical grids. These contributions highlight advancements in both theoretical frameworks and real-world applications. Labs/Teams: While not explicitly stated, his research aligns with control engineering and robotics groups at The University of Sydney, likely collaborating on cutting-edge automation and systems projects.

Daoyi DongView profile

Professor

• Quantum control
• Machine learning
• System identification
• +3 more

Prof. Daoyi Dong is a Professor and ARC Future Fellow at the School of Engineering, The Australian National University (ANU), and an IEEE Fellow. He holds a B.E. (2001) and Ph.D. (2006) from the University of Science and Technology of China. His research focuses on quantum control, machine learning, system identification, and renewable energy systems. He has received prestigious awards including the Humboldt Research Fellowship and the ACA Temasek Young Educator Award. Education: B.E., University of Science and Technology of China (2001) Ph.D., University of Science and Technology of China (2006) Research Interests: Quantum control and stabilization Machine learning applications in quantum systems System identification and optimization Renewable energy systems and microgrids Key Contributions: Developed quantum tomography methods using neural networks Advanced reinforcement learning for quantum gate design Optimized energy management strategies for community microgrids Awards: ARC Future Fellowship (Australia) Humboldt Research Fellowship (Germany) ACA Temasek Young Educator Award (Asia) Grants & Editorial Roles: Recipient of Australian Research Council grants (Discovery, International) Associate Editor: IEEE Transactions on Neural Networks, Cybernetics, and Mechatronics Labs & Collaborations: Active in quantum control and energy systems research, collaborating with global institutions including the Alexander von Humboldt Foundation and the Asian Control Association.

Alexander BlattView profile

Researcher

• Speech Recognition
• Natural Language Processing & Understanding
• Privacy
• +2 more

Alexander Blatt is a researcher at Saarland University, specializing in speech recognition and multi-modal learning within aviation contexts. His work integrates natural language processing with air traffic control systems, focusing on privacy preservation and error detection. He is based in Building C7.1, Room 0.12, Saarbrücken. Email: ablatt@lsv.uni-saarland.de Contact: +49 681 302 58 XXX Research Focus Blatt’s research bridges speech recognition , privacy , and air traffic control , with emphasis on: Robust speech processing in noisy aviation environments Contextual information boosting in ASR systems Multi-modal data integration for call-sign recognition Stress detection in anonymized speech data Ethical challenges in air traffic communication recording Publications Trends His recent work (2020-2023) shows a consistent focus on improving air traffic control communication systems through: Deep learning techniques (e.g., Wav2vec 2.0 applications) Hybrid approaches combining acoustic and surveillance data Privacy-preserving methods for sensitive aviation speech data Error detection frameworks for out-of-airspace communications

• Cybersecurity
• Privacy
• Threat Modeling
• +5 more

Wouter Joosen is a Professor at KU Leuven (Catholic University of Leuven) in Belgium, with an extensive research career spanning from 1988 to the present. His primary research focuses on cybersecurity, privacy, and secure software engineering, with significant contributions to threat modeling frameworks, particularly LINDDUN. Dr. Joosen's research interests center around privacy-enhancing technologies , security threat modeling , and machine learning applications in cybersecurity . His work bridges theoretical security concepts with practical implementations, focusing on real-world security challenges in cloud computing, authentication systems, and data protection. He has pioneered approaches to automate threat modeling processes and develop robust security mechanisms for modern distributed systems. His publication record shows a clear evolution from traditional software security in the 1990s-2000s to contemporary research on privacy-preserving machine learning, secure infrastructure as code, and advanced authentication systems. A notable trend in his recent work (2021-2025) is the integration of machine learning techniques to enhance security mechanisms while maintaining privacy constraints, particularly in access control systems and threat modeling frameworks. Dr. Joosen has collaborated extensively with researchers across Europe, particularly with Dimitri Van Landuyt, Davy Preuveneers, Danny Hughes, and Bert Lagaisse, indicating strong research group leadership and international collaboration. His research has significant practical applications in GDPR compliance, secure authentication, privacy-preserving technologies, and robust security frameworks for cloud-native applications. His work on OAuth 2.0 security, biometric authentication, and infrastructure security directly addresses current industry challenges in securing modern web applications and services.

• Control Theory
• Robust Control
• Nonlinear Control Systems
• +3 more

Richard Seeber is a Researcher at the Institute of Control and Automation (Institut für Regelungstechnik und Automation) at TU Graz. His academic titles include Privatdozent (Priv.-Doz.), Diplom-Ingenieur (Dipl.-Ing.), Doctor of Engineering (Dr.techn.), and Bachelor of Science (BSc). His work focuses on advanced control theory, nonlinear systems, and observer design. He holds a teaching authorization in system dynamics and control theory, reflecting his dual role in research and education. Research interests include robust exact differentiators, sliding mode control, observer design for linear and nonlinear systems, and applications in thermal management and energy systems. He has contributed to methodologies addressing actuator saturation, discretization challenges, and real-time control implementation. His recent work emphasizes predefined-time convergence algorithms and stability analysis under bounded noise or disturbances. Selected projects involve heater modeling for fuel cell stacks, frequency converter nonlinearity compensation, and control strategies for biomass furnaces. His contributions are often applied to industrial systems requiring precise control under uncertainty, such as gas engine power plants and transient operating conditions. He collaborates within TU Graz's Institute of Control and Automation, contributing to both theoretical advancements and practical tool development. His research portal (https://irt.tugraz.at/) hosts publications and tools related to robust control methodologies.

• Microbial Bioprocess Control
• Biotechnology
• Control Engineering
• +5 more

Prof. Nadav Bar is a Professor at the Department of Chemical Engineering, NTNU, leading the Microbial Feedback Control (MFC) laboratory. His research focuses on integrating biotechnology with control theory to develop feedback systems for microbiological processes. He coordinates major EU projects including iCulture (7.8M€, 2023-2028) and AILEEN (1.5M€, 2023-2028), focusing on AI-driven microbial control and food safety. His lab specializes in real-time bioprocess control using advanced methods like Model Predictive Control (MPC) and Reinforcement Learning, alongside Digital Twin technologies for bioreactor monitoring. Key projects include optimizing food preservation via high-pressure processing (SafeFood, 2016-2019) and COPD diagnosis through systems biology (ERA SysMed-COPD, 2018-2022). His research spans microbial fermentation optimization, gene regulatory network analysis, and systems biology. Current PhD students include Fabienne Roessler and Yiwen Li. The lab actively recruits students in control engineering, biotechnology, and analytical chemistry. Notable contributions include developing noise reduction algorithms for gene expression data and advancing sensor fusion techniques in bioprocess monitoring. Research Themes: Feedback control of microorganisms, hybrid modeling with machine learning, optimal control of bio-processes, microbial sensor systems, and COPD systems biology. Lab infrastructure includes state-of-the-art bioreactors, autosamplers, and software tools like MATLAB-CASADI for real-time control. Key Publications: Recent works focus on robust bioprocess optimization using spent sulfite liquor (2024), advanced state estimation in bioreactors (2023), and high-pressure processing effects on Listeria monocytogenes transcriptomes (2021). Over 50 peer-reviewed articles demonstrate cross-disciplinary impact across biotechnology and control engineering.

Guilherme Afonso MazantiView profile

Adjunct Professor

• Control Theory
• Time-Delay Systems
• Mean Field Games
• +4 more

Guilherme Afonso Mazanti is an Adjunct Professor at the Mathematics Department of CentraleSupélec and holds a Starting Faculty Position at Inria Saclay–Île-de-France since 2020. He is affiliated with the Laboratory of Signals and Systems (L2S) UMR 8506. His research focuses on control theory, time-delay systems, mean field games, hyperbolic PDEs, and switched systems. Mazanti completed his Ph.D. in Applied Mathematics at École Polytechnique (2016) under Yacine Chitour and Mario Sigalotti, with prior degrees from the University of São Paulo and École Polytechnique. Education: Ph.D. in Applied Mathematics, École Polytechnique (2013–2016) M.Sc. in Applied Mathematics, École Polytechnique (2012–2013) Engineering Degree, École Polytechnique (2009–2012) His research interests emphasize stability analysis, controllability criteria, and optimal control in delay systems and hyperbolic PDEs. Recent work includes decay rate assignment in delay systems, stability of linear hyperbolic systems, and mean field games. He has developed Python toolboxes like YALTAPy for control system analysis and contributed to software such as P3δ for delay dynamics. Collaborations involve institutions like Inria, L2S, and international conferences (IEEE CDC, IFAC TDS). His work bridges theoretical developments and practical applications, addressing challenges in control engineering and mathematical systems theory.

• Machine Learning
• Artificial Intelligence
• Blockchain
• +1 more

Dr. Ismail Alarab serves as a Lecturer in Computer Science at Bournemouth University, teaching core subjects including Artificial Intelligence, Databases, Big Data, and Programming. His research focuses on applied artificial intelligence with specialized expertise in blockchain technology and real-world machine learning applications, culminating in a December 2022 PhD from Bournemouth University. PhD in Novel Machine Learning Models Based on Uncertainty Estimation and Sequential Predictions for Blockchain Networks, Bournemouth University, 2022 MSc in Signal and Image Processing: Applications and Methods, Université Grenoble Alpes, France Diploma in Telecommunication Engineering (equivalent to MEng), Lebanese University, Lebanon Alarab's research centers on machine learning for blockchain systems, particularly uncertainty estimation in neural networks and graph-based models for cryptocurrency transaction analysis. His work bridges theoretical AI frameworks with practical financial security applications, emphasizing adversarial robustness and sequential prediction capabilities in distributed ledgers. Key methodologies include graph convolutional networks and LSTM architectures adapted for anti-money laundering systems. Analysis of his 14 publications (2020-2024) reveals consistent focus on machine learning applications in blockchain environments, with evolving sophistication from foundational transaction analysis (2020) to advanced uncertainty-aware sequential prediction models (2024). Dominant themes include adversarial attack methodologies for graph neural networks, thermal comfort optimization extensions, and specialized techniques for imbalanced financial datasets. No major scientific awards are documented, though professional development includes Leadership and Management Training (AshorneHill, 2023), Deploying Machine Learning Models in Production certification (DeepLearning.AI, 2023), and Microsoft Azure Fundamentals (AZ-900, 2023). Current advising activities and research grants are not specified in available materials. Dr. Alarab serves as reviewer for IEEE Transactions on Knowledge and Data Engineering and Elsevier's Neural Networks journal, indicating active peer engagement within academic communities. His research operates within Bournemouth University's Department of Computer Science, primarily collaborating with Dr. Prakoonwit and Dr. Nacer on blockchain-AI integration projects. While no dedicated lab is mentioned, his work demonstrates strong cross-institutional partnerships in financial technology security research.

Raviv RaichView profile

Professor

• Signal Processing
• Machine Learning
• Optimization
• +4 more

Raviv Raich is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University. He holds a B.Sc. and M.Sc. from Tel-Aviv University (1994, 1998) and a Ph.D. from Georgia Institute of Technology (2004). His research focuses on signal processing, machine learning, optimization, and probabilistic modeling with applications in adaptive sensing, manifold learning, and sparse signal reconstruction. He has advised numerous graduate students and contributed to impactful work in bioacoustics, hyperspectral imaging, and medical diagnostics. Notable awards include the NSF CAREER Award (2013) and multiple best paper awards. His academic leadership includes editorial roles at IEEE Transactions on Signal Processing and contributions to the IEEE Signal Processing Society. Education: B.Sc. Electrical Engineering, Tel-Aviv University (1994) M.Sc. Electrical Engineering, Tel-Aviv University (1998) Ph.D. Electrical Engineering, Georgia Tech (2004) Research Interests: Statistical signal processing and machine learning frameworks Adaptive sensing strategies for optimization in imaging and tracking Manifold learning for high-dimensional data analysis Sparse representations in signal reconstruction and inverse problems Publications: Over 100 peer-reviewed articles in top venues like IEEE Transactions on Signal Processing, IEEE Sensors Journal, and Pattern Recognition Letters, emphasizing theoretical guarantees and practical applications in bioacoustics, medical imaging, and environmental monitoring. Grants: Active funding from NSF, DARPA, and industry partnerships supporting research in optimization, multi-instance learning, and hyperspectral sensing.

Mahmoud SalamatiView profile

Research Fellow

• Formal Methods in Control
• Learning in Control Systems
• Cyber-Physical Systems
• +2 more

Dr. Mahmoud Salamati is a Research Fellow at the Max Planck Institute for Software Systems, working in the Rigorous Software Engineering group under Professor Rupak Majumdar. His research bridges formal methods, control theory, and machine learning with applications in cyber-physical systems, energy grids, and autonomous vehicles. Salamati's work develops formal verification techniques for stochastic systems and data-driven control synthesis methods. Recent publications focus on regret-free reinforcement learning for temporal logic specifications, metastable failure analysis in distributed systems, and abstraction-based controller synthesis. His research addresses fundamental questions about decidability and scalability in formal control synthesis while maintaining practical applications in energy management and autonomous systems. Key contributions include novel approaches to time-bounded reachability problems in continuous-time Markov chains, symbolic control methods for multi-agent systems, and hybrid optimization techniques for power plant control. Salamati's work demonstrates consistent integration of theoretical computer science foundations with practical engineering challenges in critical infrastructure systems.

Xueyue ZhangView profile

Assistant Professor

• Quantum Science and Technology
• Superconducting Quantum Circuits
• Solid-State Spin Systems
• +4 more

Xueyue Zhang is an Assistant Professor at Columbia University 's Department of Applied Physics and Applied Mathematics. Previously, she was a Miller Postdoctoral Fellow at UC Berkeley. Her research focuses on quantum technology development using superconducting circuits and silicon-based spin-photon interfaces. Education : Ph.D. in Applied Physics (Caltech, 2023), B.Eng. in Electrical Engineering (Tsinghua University, 2017) Research Interests center around: Quantum hardware engineering for scalable processors Spin-photon interactions in silicon platforms Topological protection in photonic quantum systems Quantum many-body simulation architectures Non-local quantum connectivity solutions Recent Publications demonstrate expertise in superconducting quantum simulation, topological waveguide QED, and silicon defect engineering for quantum networking. Key trends include photonic bandgap metamaterial applications and telecom-compatible quantum emitter development. Awards include: 2023 Rising Star in Physics 2023 Boeing Quantum Creator Prize Academic Leadership involves mentoring a diverse team of graduate and undergraduate researchers at the Zhang Lab in Schapiro Center for Engineering and Physical Science Research (CEPSR). She collaborates with global experts like Alp Sipahigil and Oskar Painter.

Mohammad Jafari JozaniView profile

Professor

• Statistical Methodology
• Biostatistics
• Machine Learning
• +6 more

Mohammad Jafari Jozani is a Professor of Statistics at the University of Manitoba's Faculty of Science. His research focuses on developing statistical methodologies and computational tools for solving complex data problems in diverse fields including healthcare, environmental science, and engineering. He holds leadership roles in the Manitoba Statistical and Health Sciences (MB-SAHS) Collaborating Centre and serves as an Associate Director of the Canadian Statistical Sciences Institute (CANSSI). His work emphasizes applications in osteoporosis research, medical imaging, wind energy modeling, and biostatistical methods. Key collaborations include Project neuroArm (University of Calgary), St. Boniface Hospital, Manitoba Bone Density Program, and Manitoba Hydro. His research integrates machine learning, ranked set sampling, and Bayesian techniques to address challenges in personalized medicine, predictive analytics, and sustainable energy systems. Dr. Jozani’s methodological contributions span small area estimation, robust regression, and finite mixture modeling. His recent studies explore automated medical image analysis for cardiovascular risk prediction, transcranial stimulation efficacy in dementia treatment, and energy storage optimization for wind power grids. He leads the Complex Analytics Research Lab (CARL) and has published extensively in top-tier statistical and interdisciplinary journals.