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Steve UlrichView profile

Associate Professor

• Spacecraft Guidance, Navigation and Control (GN&C)
• Spacecraft Proximity Operations
• Attitude Dynamics and Control
• +3 more

Steve Ulrich is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Carleton University and Director of the Spacecraft Robotics and Control Laboratory. He holds a B.Eng. and M.A.Sc. in Electrical Engineering from Université de Sherbrooke and a Ph.D. in Aerospace Engineering from Carleton University. His research focuses on spacecraft guidance, navigation, and control (GN&C), computer vision-based navigation systems, and space robotics. He has contributed to ESA’s PROBA-2 spacecraft systems and MIT’s space systems laboratory experiments. Education: B.Eng, Electrical Engineering, Université de Sherbrooke (2004) M.A.Sc, Electrical Engineering, Université de Sherbrooke (2006) Ph.D, Aerospace Engineering, Carleton University (2012) Research Interests: Spacecraft GN&C algorithms/software Proximity operations and formation flying Attitude dynamics and control Robotics and computer vision Solar drag and perturbation management His recent work emphasizes deep learning for autonomous capture systems, real-time vision-based navigation, and adaptive control strategies for space robotics. Articles highlight advancements in obstacle avoidance, optimal guidance, and experimental validation of control algorithms. The Spacecraft Robotics and Control Laboratory develops technologies for on-orbit servicing and debris removal. Advising focuses on graduate students in aerospace robotics and control systems. His lab collaborates with industry on projects like tether-based debris capture and CubeSat navigation systems.

Alberto MilazzoView profile

Professor

• Aerospace Structures
• Composite Materials
• Computational Mechanics
• +1 more

Alberto Milazzo is Full Professor of Aerospace Structures at University of Palermo's Engineering department. His research develops computational methods for aerospace structures, composite materials, and fracture mechanics. Teaching responsibilities include Aerospace Construction, Aerospace Structures, and Materials courses. Research advances numerical methods for structural analysis including discontinuous Galerkin formulations, boundary element techniques, and homogenization approaches for complex materials. Published work demonstrates applications in UAV design, morphing structures, and hydrogen storage systems for sustainable aviation. Recent projects involve European collaborations on multiscale analysis of aircraft structures.

• Numerical Analysis
• Optimal Control
• Finite Element Methods
• +9 more

Enrico Bertolazzi is an Associate Professor in Numerical Analysis at the University of Trento's Department of Industrial Engineering. He specializes in numerical methods for optimal control, finite element methods, sparse matrix computations, and symbolic computation. His work integrates advanced programming skills in C/C++, MATLAB, and other languages to develop computational tools for engineering and mathematical challenges. Bertolazzi's research addresses complex systems such as vehicle dynamics, trajectory optimization, and real-time simulations, with applications in automotive engineering, renewable energy, and robotics. Research Interests: Numerical Optimal Control and Trajectory Planning Finite Element/Finite Volume Methods for PDEs Sparse Matrix Algorithms Clothoid Curve Modeling Symbolic-Numeric Hybrid Techniques Vehicle Dynamics and Autonomous Systems Publications: Recent work focuses on real-time tire models, DAE index reduction, and autonomous vehicle path planning. His contributions span algorithm development, computational toolboxes (e.g., TrussMe-Fem), and interdisciplinary applications combining mathematics with engineering. Grants & Advising: Advises on optimal control, robotics, and simulation projects. Active in developing educational software like the virtual racing car championship platform. Labs/Teams: Involved in research groups focused on intelligent vehicles, computational mechanics, and numerical methods for industrial applications.

Burcu BeykalView profile

Assistant Professor

• Process Systems Engineering
• Machine Learning/AI
• Data-driven Optimization
• +5 more

Dr. Burcu Beykal is an Assistant Professor in the Department of Chemical & Biomolecular Engineering at the University of Connecticut (UConn), part of the School of Engineering. She holds a Ph.D. in Chemical Engineering from Texas A&M University (2020), an M.S. from Carnegie Mellon University (2014), and a B.S. from Koç University (2013). Her research focuses on Process Systems Engineering , leveraging Machine Learning and Data-Driven Optimization to address challenges in energy systems, sustainability, and environmental engineering. Key areas include lithium recovery from geothermal brines, zero-liquid-discharge desalination systems, and endocrine disruptor identification using single-cell data. Her work bridges computational methods with real-world applications, such as optimizing supply chains, designing sustainable processes, and advancing techno-economic analysis . Awards include the 2023 ACS Doctoral New Investigator Award and the 2020 AIChE CAST Directors’ Award. She advises multiple graduate students who have received accolades like the UConn Research Excellence Award and Teaching Fellowships. Education: Ph.D. in Chemical Engineering, Texas A&M University (2020) M.S. in Chemical Engineering, Carnegie Mellon University (2014) B.S. in Chemical & Biological Engineering, Koç University (2013) Research Themes: Data-driven optimization of energy systems Sustainable process design and lifecycle assessment Machine learning for endocrine disruptor identification Process systems integration and scheduling Recent Trends in Publications: Focus on sustainable lithium recovery and energy systems Development of physics-informed machine learning algorithms Integration of bi-level optimization frameworks Awards: 2023 ACS Petroleum Research Fund Doctoral New Investigator Award 2020 AIChE CAST Directors’ Award Multiple student awards for teaching and research excellence Lab/Team: Leads the Process Systems and Sustainability Lab at UConn Collaborates with industry partners on desalination and resource recovery projects

Sam AstillView profile

Senior Lecturer

• Time Series Econometrics
• Financial Econometrics
• Theoretical Econometrics

Dr. Sam Astill is a Senior Lecturer in Finance at the Essex Business School, University of Essex. He holds a PhD from the University of Nottingham and specializes in econometric methodologies applied to financial time series analysis. PhD (University of Nottingham) His research focuses on: Time Series Econometrics Financial Econometrics Theoretical Econometrics Recent research includes developing Bonferroni-type tests for return predictability, CUSUM-based monitoring systems for explosive financial episodes, and robust methodologies for bubble detection. His work addresses challenges in time-varying volatility, trending predictors, and nonlinear deterministic components. Current teaching responsibilities include: Quantitative Methods and Finance (BE303) Financial Time Series: Methods and Applications (BE372) Research Methods in Financial Econometrics (BE990) Current PhD students: Boubacar Drame Yanfeng Xu Previous PhD supervisees: Seth Richard James Sidney Harrison (awarded 2025) Nhat Minh Vuong Chu (awarded 2023) Ioannis Korkos (awarded 2020) Contact: sastill@essex.ac.uk | Office: EBS.3.8, Colchester Campus

Sabur H BaidyaView profile

Assistant Professor

• Autonomous Systems
• Cyber-Physical Systems
• Internet-of-Things (IoT)
• +4 more

Sabur H Baidya is an Assistant Professor in the Department of Computer Science and Engineering at the University of Louisville's J.B. Speed School of Engineering. He leads the Autonomous Intelligent Mobile Systems Lab (AIMS Lab) and is affiliated with the Louisville Automation & Robotics Research Institute (LARRI). His research focuses on autonomous systems, cyber-physical systems, IoT, edge computing, and distributed intelligence. Education: B.S. in Communication Engineering, West Bengal University, 2007 M.S. in Computer Science, University of Texas at Dallas, 2013 Ph.D. in Computer Science, University of California, Irvine, 2019 Research Interests: His work integrates sensing, communication, and computing systems to develop intelligent distributed systems. Key areas include IoT security, optimization of edge computing architectures, and autonomous drone/robotics applications. He explores machine learning techniques for resource-constrained environments and cybersecurity in containerization platforms. Awards & Recognition: No specific awards listed, though his research has been published in top-tier venues across autonomous systems, IoT, and cybersecurity domains. Advising & Grants: While no student names or grant details are provided, his lab actively engages in collaborative projects with industry partners like Nokia Bell Labs and Huawei, and academic collaborations at Rutgers WINLAB and UC San Diego. Labs & Teams: Directs the AIMS Lab, collaborating with the Louisville Automation & Robotics Research Institute (LARRI) to advance robotics and automation technologies. His work also intersects with the Jacobs School of Electrical and Computer Engineering (UCSD) and WINLAB (Rutgers).

• Power Quality
• Renewable Energy Systems
• Wind Energy
• +6 more

Ambrish Chandra is a **Professor** at the École de technologie supérieure (ÉTS) , part of the Université du Québec network, in the Department of Electrical Engineering. He holds degrees including a B.Eng. from Roorkee (India), M.Tech. from New Delhi (India), and a Ph.D. from the University of Calgary. His research focuses on power quality, renewable energy integration, and smart grid technologies, with affiliations to the GREPCI – Power Electronics and Industrial Control Research Group and the CIRODD – Interdisciplinary Research Centre on Sustainability Operationalization . Research interests include power quality enhancement (e.g., unified power quality conditioners, active filters), renewable energy systems (wind, solar, hybrid microgrids), and advanced control strategies for grid stability and energy management. He has supervised numerous graduate students in projects involving renewable energy integration, power electronics, and grid resilience. Key scientific achievements include awards such as the 2024 IEEE Montreal Section Gold Medal and the 2024 Bimal Bose Award. His work has been published in over 200 peer-reviewed articles, focusing on topics like STATCOM control, MPPT optimization, and fault-tolerant systems. Collaborations span academia and industry, addressing challenges in distributed energy resources and grid modernization. Notable contributions include experimental implementations of hybrid microgrids, real-time controllers for wind-PV systems, and innovative solutions for electric vehicle charging infrastructure. His research bridges theoretical advancements with practical applications, ensuring sustainable energy solutions for modern power systems.

• Feedback Control Theory
• Actuator Nonlinearities
• Zames-Falb Multipliers
• +4 more

William Heath is Chair of Feedback and Control in the Department of Electrical and Electronic Engineering at the University of Manchester. He holds a BA in Mathematics from Cambridge University (1987), an MSc in Systems and Control, and a PhD from UMIST (1992). His research balances classical control theory with digital implementation for nonlinear control problems, specializing in actuator nonlinearities (saturation, hysteresis) and multiplier theory. Recent breakthroughs include generalizations of Zames-Falb multipliers and discoveries regarding discrete-time Kalman conjecture counter-examples. Applications span nanopositioning, engine controls, robotics, and process automation. Professor Heath has supervised numerous PhD students to completion who now hold academic positions globally. Current projects involve applying control techniques to industrial robotics and energy systems.

• Data-driven intelligence
• Control systems
• Machine learning
• +5 more

Dr. Long Zhang is a Senior Lecturer in the Department of Electrical and Electronic Engineering at the University of Manchester. His research focuses on data-driven intelligence and control systems, including machine learning, neural networks, and their applications in renewable energy, robotics, and transportation. He leads the first industrial-scale wind turbine pitch bearing and blade laboratory at the university and directs the MSc program in Advanced Control and System Engineering. His work is supported by over £2.5m in research funding from EPSRC, Innovate UK, and industry partners. Research interests include data-driven modeling, system identification, wind turbine condition monitoring, and smart device design. His group has supervised 12 PhD students, 50 MSc students, and 50 undergraduate projects. Key contributions include fault diagnosis methodologies for wind turbines and robotics, and predictive maintenance frameworks using advanced signal processing and machine learning techniques. Recent publications highlight advancements in autonomous system fault detection, energy grid flexibility mechanisms, and wave energy converter monitoring. Dr. Zhang collaborates with interdisciplinary teams across robotics, aerospace, and digital futures, contributing to UN Sustainable Development Goals related to affordable energy and climate action.

Jack UmenbergerView profile

Research Fellow

• Control Theory
• Optimization
• Machine Learning
• +3 more

Jack Umenberger is a Senior Research Fellow at the University of Oxford’s Department of Engineering Science and the ZERO Institute for zero-carbon energy research. He joined Oxford in 2023 as a Departmental Lecturer within the Control Group. Previously, he held postdoctoral positions at MIT (2019-2022) and Uppsala University, Sweden (2017-2019), and earned his PhD from the University of Sydney in 2018 for work on data-driven modeling of dynamical systems. His research focuses on the intersection of control theory, optimization, and machine learning, with applications to energy systems (e.g., battery management with Eatron Technologies) and robotics (e.g., motion planning with Dexai Robotics). Key areas of interest include data-driven system identification, model predictive control, reinforcement learning, and stability guarantees in dynamical systems. Notable projects include work on shortest paths in graphs of convex sets for robotic motion planning, convergence analysis of direct policy search in reinforcement learning, and system identification with stability guarantees. His contributions emphasize blending discrete and continuous decision-making frameworks in optimization and control problems. Umenberger collaborates with industry partners like Eatron Technologies and Dexai Robotics, advancing applications in energy and robotics. His work often bridges theoretical advancements with practical implementations, reflecting a commitment to impactful engineering solutions.

• Dynamical Systems Theory
• Complex Systems Theory
• Transportation Modeling
• +4 more

Thomas Stemler is an Associate Professor at the University of Western Australia (UWA), affiliated with the Department of Mathematics and Statistics within the School of Physics, Maths and Computing. He holds a PhD in Physics from Technische Universität Darmstadt (2006) and has been at UWA since 2007, initially as a postdoctoral researcher and since 2009 in a teaching and research role. His research focuses on dynamical systems theory and complex systems applications, including transport modeling, climate analysis, and ocean dynamics. He leads the Complex Systems Group and collaborates with industry and government on projects funded by ARC, offshore industries, and the Department of Infrastructure. Education: PhD in Physics (Technische Universität Darmstadt, 2006); MSc in Physics (Technische Universität Darmstadt, 2001). Research interests include traffic system modeling, nonlinear time series analysis, and uncertainty quantification. He has contributed to projects like the ARC Research Hub for Transforming Energy Infrastructure and the PATREC transport research center. His work addresses real-world challenges in transport infrastructure, environmental systems, and data-driven decision-making. Grants include funding from ARC, Main Roads WA, and iMove CRC for projects on roundabout modeling, automated vehicle impacts, and traffic incident detection. He is actively involved in PhD supervision and has published extensively on topics ranging from stochastic processes to swarm intelligence. Labs/Teams: Complex Systems Group (UWA), PATREC Transport Research Collaborative.

• Process Systems Engineering
• Mathematical Optimisation
• Sustainable Energy Systems
• +10 more

Dr Michael Short is an Associate Professor of Process Systems Engineering at the University of Surrey , affiliated with the School of Chemistry and Chemical Engineering and the Surrey Institute for Sustainability . His research focuses on mathematical optimisation tools for sustainable process design, bioenergy systems, renewable energy integration, and pandemic risk modelling. Current projects include AI-driven biogas production optimisation and carbon-negative chemical synthesis. EPSRC-funded £1.7M project on AI for biogas production Co-I in £5M Supergen Bioenergy Impact Hub Developed open-source DECO2 software for ASEAN decarbonisation His team applies mixed-integer nonlinear programming (MINLP) and machine learning to industrial challenges in pharmaceuticals, aquaculture, and microbreweries. Collaborations span Eli Lilly, Pfizer, and universities in Japan, Malaysia, and Brazil. Supervised projects address grid-scale energy storage, CO2 utilisation, and rural electrification. Scientific Awards : 2021 EPSRC Impact Acceleration Account Commercialisation Fellow Best Speaker Award at 2020 Sustainable Process Integration Lab Conference Editorial Board Member of Journal of Water Process Engineering Recent publications examine direct air capture integration, waste-to-energy brewing processes, and whole-system energy models. Supervises students in distributed energy systems, catalytic processes, and sustainable design.

Abhishek CauligiView profile

Assistant Professor

• Spacecraft Autonomy
• Robotics
• Machine Learning
• +3 more

Abhishek Cauligi is an incoming Assistant Professor of Mechanical Engineering, starting in Summer 2025. Prior to this role, he worked as a Robotics Technologist at NASA Jet Propulsion Laboratory and earned his PhD in Aeronautics & Astronautics from Stanford University. His doctoral research focused on accelerating trajectory optimization using machine learning, with experiments conducted on the International Space Station. His research interests center on spacecraft and robotic autonomy, leveraging nonlinear optimization, machine learning, and control theory to advance autonomous space exploration. Key areas of focus include trajectory optimization, autonomous systems, and machine learning-driven solutions for space robotics. Recent work highlights include studies on transformer-based constraint prediction for powered descent guidance, diffusion policies for spacecraft trajectory generation, and stochastic optimization for asteroid reconnaissance. He has also contributed to lunar navigation systems (ShadowNav) and multi-agent autonomy for planetary exploration, such as the Cadre Lunar Technology Demonstration. Notable collaborations include the CoSTAR team’s victory in Phase II of the DARPA Subterranean Challenge, showcasing advanced robotic autonomy in challenging environments. His innovations span from gecko-inspired adhesive testing aboard the ISS to recurrent neural network approaches for mixed-integer optimal control problems.

Anna Lichtwarck-AschoffView profile

Professor

• Clinical Psychology
• Developmental Psychopathology
• Complex Dynamic Systems Theory
• +4 more

Anna Lichtwarck-Aschoff is a Professor and Chair of the Child and Family Welfare unit at the University of Groningen's Faculty of Behavioural and Social Sciences. She holds a PhD in Developmental Psychology (University of Groningen, 2008) and previously worked as an assistant professor at Radboud University (2008–2020). Her research focuses on complex dynamic systems theory applied to clinical change mechanisms, early warning signals in psychotherapy, and personalized mental health interventions. She leads the Small Data Institute (iamYu) and collaborates with clinical teams like Autisme Team Noord-Nederland. Key achievements include a Vidi Grant (2020) for studying treatment trajectory tipping points, the Medische Inpiratorprijs (2019) for client-scientist collaboration, and an investment grant for iamYu. Her work bridges clinical practice with cutting-edge methodologies like idiographic network analysis and real-time process monitoring. Research interests span psychotherapy process dynamics, mental health game efficacy, and transdisciplinary education. She advises on clinical interventions and policy, emphasizing individualized treatment through system modeling.

• Fluid Mechanics
• Turbulence Modeling
• Computational Fluid Dynamics (CFD)
• +3 more

Stefan Wallin is a Researcher at the FLOW MECHANICS department of KTH Royal Institute of Technology in Stockholm, Sweden. His work focuses on turbulence modeling, computational fluid dynamics (CFD), and aerodynamic flow control. He teaches courses such as Applied Fluid Mechanics Calculations and Turbulence , and leads research in the FLOW Turbulence Lab. His research emphasizes Reynolds stress models, large eddy simulation (LES), and hybrid RANS-LES approaches for complex flows. Key research areas include wind energy systems, atmospheric boundary layer dynamics, and flow separation control. He has contributed to improving turbulence closure models for high-Reynolds number flows and wind turbine wake interactions. His work bridges experimental validation with CFD, addressing challenges in scale effects, variable resolution simulations, and machine learning-enhanced turbulence modeling. Wallin collaborates on projects involving morphing high-lift devices, climate modeling impacts of turbulence parameterization, and vortex generator jet flow control. His research has applications in renewable energy, aircraft design, and environmental fluid dynamics. He maintains an active publication record in journals like Physical Review Fluids and Boundary-layer Meteorology , with ongoing exploration of anisotropy-resolving subgrid models and hybrid simulation techniques.