Find Academic Supervisors

Edward LamView profile

Research Fellow

• Combinatorial Optimization
• Neuro-Symbolic AI
• Operations Research
• +3 more

Edward Lam is a Research Fellow in the Department of Data Science and Artificial Intelligence at Monash University. His research focuses on the intersection of combinatorial optimization and neuro-symbolic AI, developing algorithms for routing, scheduling, control, and analytics, primarily applied to automated decision-making in transportation and logistics. He holds a PhD in Computer Science (2019) and a BSc(Hons) in Mathematics and Statistics (2010), both from the University of Melbourne. Lam is affiliated with CSIRO Data61 and actively supervises PhD students in optimization and machine learning. **Education**: PhD in Computer Science, University of Melbourne (2019) BSc(Hons) in Mathematics and Statistics, University of Melbourne (2010) **Research Interests**: Combinatorial optimization, neuro-symbolic AI, multi-agent systems, and applications in autonomous logistics. His work addresses challenges in routing, scheduling, and decision-making under constraints. **Article Trends**: Recent publications emphasize branch-and-cut-and-price algorithms for multi-agent pathfinding, electric vehicle routing, and hybrid optimization methods. His work bridges theoretical algorithm design with real-world applications in transportation and warehousing. **Awards**: Received the Doctoral Research Award (2019) for outstanding contributions to optimization algorithms. **Advising & Grants**: Currently accepting PhD students focused on combinatorial optimization and machine learning, with available scholarships. Collaborates on projects funded by industry and governmental entities like CSIRO. **Affiliations**: Maintains an external position at CSIRO Data61 and has contributed to patents in optimization algorithms (2018).

Pierre Le BodicView profile

Senior Lecturer

• Discrete Optimization
• Combinatorial Optimization
• Approximation Complexity
• +4 more

Pierre Le Bodic is a Senior Lecturer in the Department of Data Science & AI at Monash University's Faculty of IT. His research focuses on Discrete Optimization, Combinatorial Optimization, and Approximation Complexity, with applications in Multi-Agent Path Finding, Energy Systems, and Constraint Reasoning. He holds a PhD in Computer Science from Université Paris-Sud (2012) and has held postdoctoral positions at Georgia Institute of Technology. His work contributes to UN Sustainable Development Goals related to clean energy and sustainable development. Le Bodic has led or co-led multiple research projects, including the 'AI for Clean Energy and Sustainability' initiative (2023–2027) and 'Improved Constraint Reasoning for Robust Multi-agent Path Planning' (2020–2024). He has published over 40 peer-reviewed articles, emphasizing algorithm design, optimization frameworks, and applications in robotics and sustainability. His recent publications explore abstract branching models, multi-agent path planning, and interpretable machine learning techniques. He actively collaborates internationally, with projects spanning Australia, the US, and Europe.

• Combinatorial Optimization
• Machine Learning
• Operations Research
• +3 more

Marc Huber is a PreDoc Researcher (academic rank: Researcher) at the Institute of Logic and Computation within the Faculty of Informatics at TU Wien. Currently on leave, he conducts research at the intersection of combinatorial optimization and machine learning, focusing on algorithmic innovations for complex scheduling and sequence problems. His core research interests include: Combinatorial Optimization Machine Learning for Optimization Beam Search and Metaheuristics Staff and Flow Shop Scheduling Sequence Problems (Longest/Shortest Common Subsequence) Algorithm-ML Integration Huber's publication trajectory (2021-2024) reveals a consistent methodological evolution: progressively integrating reinforcement learning and value function approximation into beam search frameworks. This approach has yielded significant advances in staff rostering, multi-agent path finding, and sequence alignment, demonstrating how learned heuristics outperform traditional optimization techniques in solution quality and computational efficiency. Scientific Awards: No awards, fellowships, or medals were documented in available sources. As a thesis supervisor, Huber has guided four master's students at TU Wien through research on graph neural networks for clique problems, reinforcement learning for flow shop scheduling, e-mobility fleet scheduling, and staff resortering algorithms. His advising emphasizes practical implementation of learning-based optimization techniques. He operates within the Algorithms and Complexity research group (E192-01), collaborating closely with Prof. Raidl on algorithm engineering projects funded through TU Wien's research infrastructure. Current work focuses on extending learning beam search to multi-objective combinatorial problems.

• Global Optimization
• Operations Research
• Computational Mathematics
• +3 more

Marco Locatelli is a Full Professor in the Department of Computer Engineering at the University of Parma, Italy. His research focuses on Global Optimization , Operations Research , and Computational Mathematics , with applications in Robotics and Optimization Algorithms . Education: Ph.D. in Computational Mathematics and Operations Research (University of Milano/Napoli, 1997); Laurea in Computer Science (University of Milano, 1992). Positions: Post-Doc (University of Trier, Firenze); Assistant/Associate Professor (University of Torino); Full Professor (University of Parma since 2010). His research spans Global Optimization over intervals, multistart algorithms, concave optimization, packing problems, and convex underestimators. Recent work includes Multi-Agent Pathfinding and Speed Planning with energy/time optimization, leveraging Machine Learning and Dynamic Programming . He has received distinguished accolades, including the Europt Fellow (2018), Best Paper in the Journal of Global Optimization (2016), and co-authored a SIAM-published book on Global Optimization (2013). He serves on editorial boards of top-tier journals like Computational Optimization and Applications and Operations Research Letters . Scientific Awards: Al Zimmermann's Programming Contest Winner (Circle Packing) AIRO Award for Daniele Depetrini's Master Thesis (2007) Chairman's Recognition of Outstanding Paper (2009 IEEE Congress) Europt Fellow (2018) His teaching includes Operations Research and Algorithms for Decision Support across multiple Italian universities (Torino, Parma, Firenze). He has contributed to Research Projects like MoSPRAS, MOST, and COSO, addressing real-world optimization challenges in healthcare, robotics, and transportation.

Henrik EbelView profile

Assistant Professor

• Robotics
• Cooperative Robotics
• Model Predictive Control
• +6 more

Henrik Ebel is an Assistant Professor (Tenure Track) in Mechanical Engineering at Lappeenranta-Lahti University of Technology (LUT), specifically within the LUT School of Energy Systems. He joined LUT in 2024 after previously working at the University of Stuttgart's Institute of Engineering and Computational Mechanics from 2016 to 2024. Dr. Ebel's research focuses on the intersection of artificial intelligence, machine learning, and mechanical engineering, with particular emphasis on: Distributed cooperative robotics Model predictive control systems Non-holonomic robot control Data-driven approaches to mechanical engineering problems Automation systems development His research aims to bring cooperative robotics from laboratory settings to real-world applications in Finland, focusing on using multiple simple and cost-efficient robots that collaborate to solve problems rather than relying on single complex machines. He believes automation promotes sustainability through better energy conservation, generation, and storage compared to manual processes. His recent publications demonstrate consistent advancement in cooperative robotics, control systems, and machine learning applications in mechanical engineering. As an educator, Dr. Ebel supervises doctoral students and finds fulfillment in seeing them grow and succeed in their research. He values the human aspect of academia and enjoys teaching as a complement to his research work, noting that honing teaching skills helps him explain research better and pass on important future-oriented knowledge.

• Robotics
• Artificial Intelligence
• Computational Neuroscience
• +7 more

Robin Dietrich is a Researcher at the Department of Informatics 6 - Chair of Robotics, Artificial Intelligence and Real-time Systems at the Technical University of Munich . His work bridges computational neuroscience and robotics, focusing on translating neural mechanisms from mammalian brains into algorithms for mobile robot navigation. B.Sc. and M.Sc. in Computer Science Research on hippocampal temporal dynamics for neuromorphic SLAM Specializes in spiking neural networks for navigation and radar processing Research Interests : Robin's research explores the intersection of robotics, artificial intelligence, and computational neuroscience . His work specifically investigates spiking neural networks, FMCW radar data processing, and neuromorphic algorithms for autonomous systems. Recent projects focus on uncertainty quantification, evolutionary optimization, and multi-robot exploration using biologically inspired models. Publication Trends : Robin's publications (2019-2025) demonstrate a consistent focus on neuromorphic computing for robotic perception , with increasing specialization in spiking neural networks for radar processing and biologically inspired navigation algorithms . Key collaborations include contributions to multi-robot exploration metrics and hardware acceleration frameworks. Teaching Contributions : Robin has taught Digital Signal Processing and Real-Time Systems lectures since 2019, co-led seminars on Bio-inspired Data Processing , and supervised practical courses on Intelligent Mobile Robots using ROS.