Find Academic Supervisors

• Control Engineering
• Numerical Optimization
• Machine Learning
• +6 more

Sergio Lucia is a Full Professor (W3) for Process Automation Systems at the Department of Biochemical and Chemical Engineering, TU Dortmund University. His research integrates control engineering, numerical optimization, and machine learning to address challenges in chemical processes, biotechnology, and energy systems. Education: Dr.-Ing. (summa cum laude) from TU Dortmund University (2014); Diploma in Electrical Engineering from University of Zaragoza (2010) Professional Journey: Full Professor (2023–present), Professor (W2) at TU Dortmund University (2020–2023), Assistant Professor at TU Berlin (2017–2020), Postdoctoral Fellow at MIT (2016) His recent work focuses on combining machine learning with model predictive control (MPC) for robust applications in chemical recycling, bioreactors, and energy networks. Key trends include AI-driven optimization, uncertainty quantification, and real-time control for complex systems. Scientific Awards Teaching award, TU Dortmund (2023) Best student paper award (PhD student Sarah Braun) (2022) Best paper by young author award (PhD student Benjamin Karg) (2021) VAA Dissertation Award for outstanding work in process engineering (2015) Erasmus Scholarship (2010) He has advised PhD students in chemical and biotechnological process optimization and led the Laboratory of Process Automation Systems at TU Dortmund's North Campus. His service includes Vice Chair of IFAC Technical Committee on Optimal Control (2020–present) and editorial roles in leading journals.

Mauro SalazarView profile

Assistant Professor

• Optimization Models
• Systems and Control
• Cyber-Socio-Technical Systems
• +7 more

Mauro Salazar is an Assistant Professor in the Control Systems Technology section of the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e), with co-affiliations at the Eindhoven AI Systems Institute (EAISI) in both Health and Mobility domains. He leads his own research group focused on optimization models and methods for systems and control. His educational background includes: B.Sc. and M.Sc. in Mechanical Engineering from ETH Zürich (2012, 2015) Master thesis conducted at EPFL's Automatic Control Lab Ph.D. in Mechanical Engineering from ETH Zürich (2019) in collaboration with Ferrari Formula 1 Postdoctoral Scholar at Stanford University's Autonomous Systems Lab (2019-2020) Salazar's research centers on optimization models for multi-scale cyber-socio-technical systems with applications spanning mobility systems, pandemic response, and material design. His work bridges theoretical optimization with practical implementation, particularly in sustainable mobility solutions where he develops methods for optimal design and operation of transportation systems from single vehicles to entire networks. He investigates mesoscopic user behavior modeling and designs incentive schemes to align individual and system-level objectives in transportation networks. His recent publications (2025) demonstrate a strong focus on sustainable mobility systems, with recurring themes in electric vehicle fleet management, battery degradation modeling, ride-pooling algorithms, and energy management for transportation electrification. His work spans multiple disciplines including automotive engineering, control systems, energy management, and public health, characterized by rigorous mathematical optimization approaches applied to real-world challenges. His scientific recognitions include: Two ETH Medals (for Master's and PhD theses) Best Student Paper awards at ITS 2018 and ECC 2022 Best Teacher Award (2021) Nomination for TU/e Young Researcher Award (2022) Salazar actively advises research projects and secured funding through the Dutch Research Council's NEON project (Crossover research program, Grant 17628). His teaching portfolio includes Optimal Control and Reinforcement Learning, Engineering Optimization, and Advanced Full-Electric and Hybrid Powertrain Design. He leads the 'Group Salazar' within the Control Systems Technology section and contributes to EAISI Health and Mobility initiatives.

Tobias Kasper Skovborg RitschelView profile

Assistant Professor

• Control theory
• Model predictive control
• Stochastic adaptive control
• +9 more

Tobias Kasper Skovborg Ritschel serves as Assistant Professor (Tenure Track) in the Department of Applied Mathematics and Computer Science (DTU Compute) at the Technical University of Denmark. His research bridges theoretical advances in control systems with practical implementation across energy systems, bioreactors, epidemiology, and medical applications. Ritschel directs a productive research program focusing on computational methods for complex dynamical systems with rigorous thermodynamic foundations. His educational background demonstrates deep technical training: PhD in Applied Mathematics (2015-2018), Technical University of Denmark MSc in Mathematical Modeling and Computation (2013-2015), Technical University of Denmark BSc in Mathematics and Technology (2010-2013), Technical University of Denmark Ritschel's research expertise spans multiple domains of control theory and mathematical modeling. His core competencies include stochastic adaptive control, model predictive control, and optimal control frameworks applied to nonlinear dynamical systems. He specializes in numerical methods for differential equations (stochastic, partial, delay, and differential-algebraic) with computational implementations in MATLAB, C/C++, and Python. His application areas demonstrate remarkable breadth: from oil reservoir management and nuclear power systems to bioreactor operations, epidemiological modeling, and diabetes management technologies. Analysis of his publication trajectory reveals a strategic evolution from foundational work on thermodynamically rigorous reservoir simulation toward increasingly diverse applications. Early publications (2017-2019) focused on oil and gas applications with rigorous phase equilibrium modeling, while recent work (2020-2024) addresses pressing societal challenges including pandemic response strategies, power grid flexibility for renewable integration, and biomedical control systems. This demonstrates his ability to transfer core methodological expertise across disparate application domains while maintaining mathematical rigor. Dr. Ritschel actively supervises numerous students across all academic levels, with recent BSc projects focusing on molten salt reactors and demand-side flexibility in power systems. His teaching portfolio includes advanced graduate courses in stochastic adaptive control, dynamical systems, and time series analysis. He maintains strong industry connections through EU-funded projects including COCOP (Horizon 2020) and OPTION (Innovation Fund Denmark).

Olugbenga Moses AnubiView profile

Assistant Professor

• Resilient Control Systems
• Robust Control
• Adaptive Control Systems
• +6 more

Olugbenga Moses Anubi is an Assistant Professor in the Department of Electrical & Computer Engineering at the FAMU-FSU College of Engineering, Florida State University. He directs the Resilient and Autonomous Systems Lab (RASLab) and contributes to the Center for Advanced Power Systems (CAPS) and the Center for Intelligent Systems, Control, and Robotics (CISCOR). Ph.D. from the University of Florida His research focuses on Resilient Control Systems for cyber-physical infrastructures, including Adaptive Control , Vehicle Dynamics , and Autonomous Systems . He also explores Real-time Optimization and Collaborative Learning in distributed environments. Recent publications highlight advancements in Cyber-Physical Systems , with emphasis on Resilient State Estimation , Energy Storage Dispatch , and Autonomous Vehicle Control . Key areas include mitigating Adversarial Attacks , Battery Degradation Modeling , and Decentralized Power Systems . He teaches courses such as Signals and Systems , Advanced Control Methods , and Model Predictive Control , alternating specialized electives annually. Labs and teams: RASLab develops self-driving platforms ( MicroNole , AutoNole ), focusing on vision-based lane detection , robust motion control , and collaborative learning in distributed systems.

Alessandro Di GiorgioView profile

Associate Professor

• Smart grids and micro-grids control
• Model Predictive Control for energy systems
• Integration of renewable generation and storage
• +3 more

Alessandro Di Giorgio is Associate Professor of Automatic Control at the Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG) , Sapienza University of Rome, where he teaches courses on Automation, Network Control and Handling, and Modeling & Simulation. He coordinates the Smart Energy Research Group of the Consorzio per la Ricerca nell’Automatica e nelle Telecomunicazioni and acts as scientific referent of the university start-up Applied Research to Technologies . Education Ph.D. in Systems Engineering, Sapienza University of Rome, 2010 M.Sc. in Physics (110/110 cum laude), Sapienza University of Rome, 2005 Research interests revolve around advanced control architectures for future power systems, with particular emphasis on smart-grid planning and real-time operation , model-predictive control of micro-grids , large-scale integration of renewable generation and storage , stochastic optimisation of electric-vehicle charging infrastructures , and cyber-physical security against malicious attacks . His work combines systems theory with practical implementations validated within several European and national projects. Recent publications (2023-24) demonstrate a clear trend toward data-driven and stochastic model-predictive strategies that coordinate millions of grid-edge resources—electric vehicles, batteries, renewable plants—while guaranteeing economic efficiency, reliability and resilience of distribution networks. Projects & Technology Transfer Scientific responsible in 10 EU-funded research projects on smart grids and electromobility Principal Investigator of Italian academic and Industria 2015 programmes Co-founder & scientific reference, start-up Applied Research to Technologies (technology transfer on EV-charging optimisation) He is member of the Networked Systems Cybersecurity initiative and serves as reviewer and organiser for major IEEE conferences on power systems and control.

• Control Engineering
• Automation
• Marine Icing
• +5 more

Sushmit Dhar serves as an Associate Professor in the Department of Automation and Process Technology at UiT The Arctic University of Norway, focusing on control engineering and automation solutions for Arctic maritime challenges. His work bridges theoretical control methods with practical applications in extreme environments. His research spans: Classical and modern control methods (PID, LQR, MPC) State estimation and Kalman filtering Physics-Informed Neural Networks and Reinforcement Learning for decision support IoT-based automation for maritime operations Marine icing phenomena and sea spray measurement Recent publications demonstrate cutting-edge work in marine icing prediction using LiDAR and capacitive sensors, Arctic road safety systems, and optimization of maritime operations. His research integrates advanced sensing technologies with data-driven modeling to address safety challenges in cold climates, particularly through sea spray measurement innovations and route planning algorithms for fishing vessels and search-and-rescue operations. Dr. Dhar actively contributes to collaborative initiatives: Member of the Sustainable Technology and Safety (STS) research group Member of the IR, Spectroscopy, and Numerical Modelling Research Group Key participant in the SPRICE project (Multidisciplinary approach for spray icing modelling and decision support) His teaching portfolio includes foundational courses in Linear Systems (AUT-2604) and Control Engineering (AUT-2801) for Automation Engineering undergraduates, preparing students for technical challenges in Arctic engineering contexts.

• Control Theory
• Model Predictive Control
• Data-Driven Control
• +5 more

Frank Allgöwer is a Professor and Head of the Institute for Systems Theory and Control Engineering at the University of Stuttgart's Faculty of Engineering. With an extensive publication record through 2025, he leads a prominent research group specializing in advanced control theory methodologies. His research interests span multiple domains of modern control theory, with particular emphasis on Model Predictive Control (MPC), data-driven control approaches, nonlinear systems analysis, and event-triggered control strategies. His work bridges theoretical foundations with practical implementations, focusing on stability guarantees, performance optimization, and computational efficiency. Recent research shows a strong focus on Koopman operator theory applications to control systems, distributed multi-agent coordination, and the integration of machine learning techniques with traditional control frameworks. Analysis of his 15 most recent publications reveals a consistent research trajectory centered around developing theoretically sound control methodologies with practical applicability. His work demonstrates increasing integration of data-driven approaches with traditional model-based control, particularly in the areas of nonlinear system control and distributed multi-agent systems. The publications span top-tier control journals including IEEE Transactions on Automatic Control, Automatica, and IEEE Control Systems Letters. Professor Allgöwer actively mentors numerous junior researchers, with frequent collaborations suggesting a strong supervisory role for PhD students and postdoctoral researchers. His group maintains productive international collaborations while being firmly rooted at the University of Stuttgart.