Find Academic Supervisors

• Hydropower Turbines
• Cavitation Modeling
• Computational Fluid Dynamics (CFD)
• +7 more

Håkan Nilsson is a Full Professor in Fluid Dynamics at Chalmers University of Technology. His research focuses on computational fluid dynamics (CFD) with applications in hydropower systems, particularly turbine flow analysis, cavitation modeling, and generator cooling air dynamics. He utilizes OpenFOAM for numerical simulations and develops advanced algorithms for mesh deformation and flow control. Academic Rank: Full Professor Institution: Chalmers University of Technology Primary Research: Hydropower Turbines, Cavitation, CFD, Machine Learning, Multiphase Flow His recent work integrates machine learning with CFD for optimizing turbine operations and predictive maintenance in hydropower plants. Key projects include ALPHEUS and investigations into contra-rotating pump-turbine systems for low-head energy storage. Collaborations span experimental validation with experts in PIV, laser Doppler velocimetry, and industrial partners in renewable energy. Research trends highlight 15+ years of publications on turbulence modeling, vortex dynamics, and fluid-structure interaction in hydraulic systems. Sub-fields include Francis turbine transients, Kaplan turbine rotor-stator interactions, and applications in biomedical fluid dynamics and welding processes. Scientific awards are not explicitly mentioned.

• Inverse Problems
• Machine Learning for Imaging
• Numerical Analysis
• +5 more

Ozan Öktem is a Professor at KTH Royal Institute of Technology , specializing in applied mathematics with a focus on inverse problems, machine learning, and numerical analysis. He works in the Division of Numerical Analysis, Optimization and Systems Theory and develops theory and algorithms for solving inverse problems, particularly in medical imaging and cryogenic electron microscopy (Cryo-EM). His research integrates mathematical analysis, machine learning, and numerical methods to address challenges in recovering hidden model parameters from indirect observations. He emphasizes regularization techniques to stabilize ill-posed problems and computational feasibility for large-scale applications. Key areas include tomographic reconstruction, deep learning-based methods, and applications in biomedical imaging. Recent publications highlight his work on learned primal-dual architectures for CT, Riemannian geometry in protein dynamics analysis, and regularization strategies for Cryo-EM. Collaborations span computational biology, medical imaging, and optimization. He serves as course responsible for advanced courses in differential equations, inverse problems, and scientific computing.

• Algorithms
• Combinatorial Optimization
• Geometric Optimization
• +5 more

Dr. Joachim Spoerhase is a Lecturer at the Department of Computer Science , University of Liverpool , specializing in algorithm design and combinatorial optimization. His research focuses on approximation algorithms for clustering, network design, and geometric optimization problems. PhD in Computer Science (2010) and Habilitation (2017) from the University of Würzburg Former Research Associate at the Max Planck Institute for Informatics Research positions at Aalto University and University of Wroclaw His recent work explores high-dimensional data structures, polyline bundle simplification, and robust clustering frameworks. He also investigates hardness of approximation and theoretical limits in algorithm design. The University of Liverpool serves as his primary affiliation, with contributions to both computational geometry and machine learning. Notable research trends include interdisciplinary applications in network design, geometric optimization, and interpretable AI frameworks. Dr. Spoerhase teaches modules like Computer Networks (COMP211) and contributes to algorithmic theory development.

Sriram SankaranarayananView profile

Professor

• Cyber-physical systems
• Hybrid dynamical systems
• Formal methods
• +8 more

Sriram Sankaranarayanan is a Professor in the Department of Computer Science at the University of Colorado Boulder and also serves as Associate Dean for Digital Education in the College of Engineering and Applied Science. Since joining the faculty in 2009, he has built an internationally recognized research program that blends programming languages, formal methods, and control theory to reason about cyber-physical systems. Education: Ph.D. in Computer Science, Stanford University, 2005 (advisers Zohar Manna & Henny Sipma) B.Tech., Indian Institute of Technology Kharagpur (President’s Gold Medal, 2000) Research Interests: Prof. Sankaranarayanan’s work centers on hybrid dynamical systems —models that capture discrete software interacting with continuous physical environments—and on developing formal-methods techniques for their verification, control, and synthesis. Specific themes include control-barrier & Lyapunov function synthesis, neural-network verification, stochastic-game models for human-autonomy interaction, and physics-informed machine learning. Application domains range from autonomous robotics and surgical-task planning to safety-critical medical devices such as the artificial pancreas. Recent Publication Trends (2024-2025): His latest papers advance safe control synthesis (successive control barrier functions, piecewise-affine Lyapunov functions) and trustworthy AI (Taylor-model enhanced physics-informed neural networks), while also exploring game-theoretic anticipation for robotic systems interacting with uncertain human operators. Honors & Awards: NSF CAREER Award (2009) Siebel Scholar (2005) President’s Gold Medal, IIT Kharagpur (2000) CU Boulder Dean’s Award for Outstanding Junior Faculty (2012) CU Boulder Outstanding Teaching Award (2014) CU Boulder Provost’s Faculty Achievement Award (2014) Coursera Outstanding Innovation Award (2022) Student Advising & Grants: He has mentored numerous PhD students; recent graduates include Dr. Emily Jensen, Dr. Monal Narasimhamurthy, and Dr. Kandai Watanabe (2024). His group regularly publishes at top venues such as HSCC, POPL, PLDI, CAV, and WAFR, supported by NSF, NIH, and industry grants. Group & Teaching: Prof. Sankaranarayanan leads activities within the Programming Languages & Verification group and teaches graduate and undergraduate courses on programming languages, algorithms, optimization, and formal methods. He is active in conference organization (e.g., PC Chair VMCAI 2025) and maintains open-source courseware and research notebooks on GitHub.

• Artificial Intelligence
• Optimization
• Logistics
• +9 more

Eduardo Anibal Lalla is an Associate Professor at the Digital Society Institute and Industrial Engineering & Business Information Systems. His research focuses on applying artificial intelligence and optimization techniques to logistics and transportation challenges, particularly in maritime and urban logistics. He has published extensively on topics such as vehicle routing, metaheuristics, and mathematical programming, contributing to advancements in intelligent transport systems and container logistics. Research Trends : Over the past decade, his work has emphasized optimization algorithms for multi-depot vehicle routing collaborative berth allocation models matheuristics for dynamic scheduling AI-driven decoupling strategies in inter-terminal transport robot stations integration in logistics networks Scientific Awards : Best algorithm in Generalized Continuous Optimization Contest (2016) EURO Excellence in Practice Finalist/Runner-up (2021) Extraordinary doctoral distinction (2017)

Fabian AkkermanView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Autonomous Agent Systems
• +7 more

Fabian Akkerman is a researcher at the Digital Society Institute within the Industrial Engineering & Business Information Systems department at the University of Twente . His work bridges theoretical advancements in machine learning with practical applications in logistics, energy sustainability, and transportation systems. Primary Affiliation : University of Twente, Industrial Engineering & Business Information Systems Research Focus : Artificial Intelligence, Reinforcement Learning, Autonomous Systems, and Sustainable Logistics Fabian's research specializes in sequential decision-making problems, particularly in dynamic and stochastic environments. A key area of his contributions lies in applying reinforcement learning and optimization techniques to address challenges in: Vehicle routing with uncertain demand Inventory management and warehouse operations Time slot pricing for delivery services Smart transportation and freight logistics Stochastic modeling for supply chain resilience Industry 4.0 adoption in production systems His work demonstrates a strong emphasis on developing algorithmic frameworks (e.g., DynaPlex) that combine statistical modeling with real-world implementation. Recent publications highlight applications in autonomous vehicles, intelligent transport systems, and circular economy strategies. Scientific Awards : 2024 Transportation Science Meritorious Service Award 2025 2nd Place in ISIR Research Challenge for Production-Inventory Planning at ASML

• Database Systems
• In-Database Machine Learning
• GPU Acceleration for Databases
• +3 more

Maximilian Schüle serves as Assistant Professor in the Department of Data Engineering at the University of Bamberg's Faculty of Information Systems and Applied Computer Sciences since October 2022. Previously, he held research positions at Technical University of Munich (2017-2022). His research bridges database systems and machine learning through compiler-based approaches. His research focuses on in-database machine learning , GPU-accelerated query processing , and recursive SQL extensions . Key contributions include: Developing MLIR-based compilers for automatic differentiation in SQL (DuoLingo-AutoDiff) Creating GPU code generators for database kernels using NVRTC Designing higher-order lambda functions for expressive query languages Implementing end-to-end neural network training within database engines His recent publications (2023-2025) demonstrate consistent output in top venues including ICDE, VLDB workshops, and BTW conferences, with growing emphasis on hardware-aware optimization and compiler techniques for analytical workloads. He currently leads a DFG-funded project on elastic memory hierarchies for memory-intensive applications (2025-2028), supporting multiple PhD researchers. His supervision emphasizes open-source contributions to database systems like Umbra and practical implementation skills alongside theoretical foundations. As an active member of the database community, he serves as workshop chair for BTW 2025 and regularly reviews for ACM TODS, VLDB Journal, and Information Systems. His work on public transport analytics demonstrates real-world impact through collaborations with urban mobility initiatives in Bamberg.

Felipe Gohring de MagalhaesView profile

Assistant Professor

• Integrated circuits
• Microelectronics
• Computer systems organization
• +7 more

Felipe Gohring de Magalhaes is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he has been working since July 2018 and assumed his current position in October 2024. He holds a dual doctorate from PUC-RS (Brazil) and Polytechnique Montréal (2016), with degrees in both computer science and computer engineering. His research spans embedded system architectures, real-time systems, avionics, and cybersecurity for emerging technologies. He is affiliated with the Microelectronics and Microsystems Research Group and the Multidisciplinary Institute for Cybersecurity and Cyber Resilience, reflecting his focus on integrated circuits, microelectronics, and system security. Professor Gohring de Magalhaes has published over 40 articles in international journals and conferences, with recent work focusing on photonic integrated circuits security, optical neural networks, and post-quantum cryptography for avionic systems. His publication trend shows consistent output with increasing focus on security aspects of emerging computing technologies. He has supervised at least one PhD student to completion and teaches courses including Introduction to Programming and Operating System Kernel. His research interests align with NSERC topics in integrated circuits, microelectronics, computer systems organization, and VLSI systems.

Stefano MarkidisView profile

Professor

• High-Performance Computing
• Heterogeneous Systems
• Quantum Computing
• +3 more

Stefano Markidis is a Professor of Computer Science specializing in high-performance computing systems at KTH Royal Institute of Technology in Sweden. He works in the Division of Computational Science and Technology, focusing on supercomputers, quantum computers, and computational methods for scientific simulations. His research spans multiple domains including plasma physics, computational fluid dynamics, and quantum computing. Markidis holds an MS degree from Politecnico di Torino and a PhD in Nuclear Engineering from the University of Illinois at Urbana-Champaign. Prior to joining KTH, he was a graduate research assistant at Los Alamos National Laboratory and Lawrence Berkeley National Laboratory, followed by a postdoc at KU Leuven. His academic journey reflects a strong foundation in both engineering and computational science. His primary research interests include High-Performance Computing , Heterogeneous Systems , and Quantum Computing . Markidis develops computational methods for plasma physics, particle-in-cell simulations, and fluid dynamics. His work bridges theoretical physics and practical computing, with applications in space physics, fusion energy, and materials science. He is particularly known for contributions to parallel computing, GPU acceleration, and the development of scalable simulation frameworks like Neko for computational fluid dynamics. His research increasingly integrates machine learning techniques with traditional numerical methods. Analysis of Markidis' recent publications reveals a strong focus on quantum-classical hybrid computing, advanced particle-in-cell methods, and high-fidelity computational fluid dynamics. His work demonstrates expertise in programming models for heterogeneous architectures including GPUs and quantum processors, with growing emphasis on AI-enhanced scientific computing. R&D100 award (2005) for the CartaBlanca project R&D100 award (2017) for the SHIELDS project Markidis teaches multiple courses at KTH including Applied GPU Programming, Quantum Computing for Computer Scientists, and High-performance Computing for Computational Scientists. He has supervised numerous degree projects across various specializations in computer science and electrical engineering. His research has been supported by various grants related to high-performance computing and quantum technologies, with applications spanning from space physics to medical treatments. Markidis leads research in computational science with a focus on developing frameworks like Neko for extreme-scale computational fluid dynamics. His team works on integrating traditional HPC methods with emerging technologies including quantum computing and AI, contributing to advancements in scientific simulation across multiple disciplines.

• Machine Learning
• Non-parametric Methods
• Computational Methods
• +6 more

Yifan Chen is an Assistant Professor in the Department of Computer Science and affiliate faculty in the Department of Mathematics at Hong Kong Baptist University's Faculty of Science. He joined HKBU in Fall 2023 after completing his PhD in Statistics from the University of Illinois Urbana-Champaign in 2023 under the guidance of Prof. Yun Yang. His educational background includes: B.S. in Statistics from Fudan University (2018), advised by Prof. Juan Shen and Prof. Chenghong Zhang Ph.D. in Statistics from University of Illinois Urbana-Champaign (2023), advised by Prof. Yun Yang Dr. Chen's research focuses on developing efficient algorithms for machine learning, with particular emphasis on non-parametric models and neural networks featuring intensive matrix operations. His work bridges statistical theory with practical computational challenges in modern machine learning systems, especially those involving Transformers (language models) and Graph Neural Networks (GNNs). He approaches machine learning from both theoretical and applied perspectives, seeking to understand statistical structures while addressing real-world computational constraints. His publication record shows consistent output in top-tier venues including ICML, NeurIPS, KDD, and EMNLP, with recent work spanning graph coarsening, optimal transport, efficient language model fine-tuning, and causal inference. His research demonstrates strong mathematical foundations combined with practical applications in AI systems. Among his notable achievements: NSFC Young Scientists Fund (2025) GDSTC General Program funding (2024) RGC Early Career Scheme proposal grant (2024) ICML 2023 Grant Award ($1,500) Dr. Chen actively mentors students through his research group, supervising PhD students and visiting research assistants. He has successfully guided students who have gone on to PhD programs at institutions including Institute of Science Tokyo, HKU, Fudan, and NUS. His teaching includes COMP 7070 Advanced Topics in Artificial Intelligence and Machine Learning, which covers core machine learning concepts for AI application research, and COMP 2027 Applied Linear Algebra for Computing. His research group focuses on efficient machine learning algorithms, with current projects spanning graph neural networks, optimal transport, language model efficiency, and causal inference. He collaborates with researchers from institutions including UIUC, Fudan University, and industry labs like Amazon Alexa AI.

• Evolutionary Computation
• Multi-Objective Optimization
• Artificial Intelligence
• +7 more

Dr. Ciprian Zavoianu is an academic researcher at Robert Gordon University (RGU) in the School of Computing, Engineering & Technology. He leads the Net Zero Operations research programme at the National Subsea Centre and is affiliated with the Complex Optimisation Research Group. His work focuses on applying artificial intelligence, particularly evolutionary computation algorithms, to solve complex real-world optimization problems with practical engineering applications. Dr. Zavoianu earned his academic qualifications from West University of Timisoara, Romania (BSc and MSc in Computer Science) and Johannes Kepler University Linz, Austria (PhD in Computer Science, 2015). His doctoral research focused on enhancing multi-objective evolutionary algorithms for computationally-intensive optimization problems. His primary research interests include: Evolutionary Computation Multi-Objective Optimization Data Mining & Machine Learning (particularly for surrogate modeling) Timetabling and Rostering Parallel/Distributed Computing Dr. Zavoianu's recent publications (2021-2025) demonstrate a strong focus on applying optimization techniques to transportation systems, electrical machine design, and sustainable energy solutions. His work consistently addresses the challenge of computationally expensive optimization through innovative surrogate modeling approaches, enabling practical applications of evolutionary algorithms to real-world engineering problems. He has secured multiple research grants including 'Data For Net Zero' and 'Ferry Passenger and Freight Modelling for Shetland,' demonstrating the practical relevance and industry applicability of his research. Dr. Zavoianu actively supervises five PhD/EngD students across diverse topics including predictive analytics for subsea installations, optimization of electrical machines, and operations optimization for harbor operations. His supervision approach emphasizes bridging theoretical algorithm development with practical implementation in engineering contexts. His laboratory work is centered around the National Subsea Centre, where he leads the Net Zero Operations research programme, focusing on sustainable solutions for the energy transition through advanced computational methods.

Uday VenkatadriView profile

Professor

• Supply Chain Management
• Facilities Planning and Design
• Production Planning and Control
• +5 more

Dr. Uday Venkatadri is a Full Professor and Department Head in the Department of Industrial Engineering at Dalhousie University's Faculty of Engineering, where he has taught since 2001. His industrial experience includes roles as Lead Architect at Baan and Research Associate at Université Laval. His research bridges facilities planning, supply chain optimization, and Industry 5.0 technologies, utilizing Mixed Integer Linear Programming as a core methodology. Research interests span: Hyperconnected logistics systems Production planning under uncertainty Healthcare supply chain resilience Sustainable operations AI/ML applications in industrial systems His publication trends show strong focus on: Optimization under uncertainty (35% of recent works) Sustainable supply chains (25%) Healthcare operations (20%) Maintenance systems (15%) Industry 5.0 applications (5%) Notable scientific awards: 2021 IISE Teaching Award (Logistics Division) Dalhousie Teaching Excellence Awards (2016, 2004) Purdue University Fellowships (1991-1993, 1987-1990) IIE Master's Thesis Award (1988) Advising and grants: Supervises graduate students in supply chain optimization and production systems Research funded through industry partnerships and institutional grants Developed 15+ graduate courses including Advanced Facilities Planning Laboratory involvement includes leading computational research groups focused on logistics optimization and sustainable operations, though no dedicated physical lab is specified.

• stochastic approximation
• reinforcement learning
• sampling
• +4 more

Sergey Vladimirovich Samsonov is an Associate Professor at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he also serves as Head of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis within the Institute of Artificial Intelligence and Digital Sciences. He is affiliated with the Department of Big Data and Information Retrieval and the Joint Department of the A.A. Kharkevich Institute for Information Transmission Problems of the Russian Academy of Sciences. Samsonov began his tenure at HSE in 2018 and has accumulated 7 years of scientific and teaching experience. His educational background includes a PhD from HSE (2024) and a Bachelor's degree in Applied Mathematics and Computer Science from Lomonosov Moscow State University (2017). Samsonov's research focuses on stochastic approximation, reinforcement learning, sampling techniques, Markov chain Monte Carlo (MCMC) methods, and multivariate statistics . His work bridges theoretical mathematics with practical machine learning applications, particularly in developing algorithms with strong theoretical guarantees. He has made significant contributions to understanding convergence properties of stochastic algorithms and developing variance reduction techniques. Analysis of his recent publications reveals a consistent focus on the mathematical foundations of machine learning, particularly in stochastic approximation methods, reinforcement learning theory, and generative modeling. His work often combines rigorous theoretical analysis with practical applications, demonstrating expertise in both pure mathematics and applied machine learning. The publication venues (including top conferences like NeurIPS, ICLR, and AISTATS) reflect the high impact and quality of his research in the machine learning community. Young Scientist Badge (December 2024) Letter of gratitude from the First Vice-Rector of HSE (March 2023) Letter of gratitude from the Faculty of Computer Science at HSE (September 2021) Rector's personal allowance (2022-2023) Numerous bonuses for high-impact publications (2021-2027) Best Teacher award (2024-2025, 2022, 2020) Segalovich Scientific Prize (2022) National Prize 'Leaders in AI - 2024' Samsonov teaches advanced courses including Markov Chains, Sampling and Generative Modeling, and Matrix Computations. His laboratory work focuses on developing stochastic algorithms for machine learning applications. He has been involved in HSE's collaboration with Sber, which has resulted in 19 successfully implemented AI projects since 2021. His research has gained significant recognition, with multiple papers accepted at top-tier conferences including 12 papers presented at NeurIPS in recent years.

• Mathematical Optimization
• Conic Optimization
• Smart Grids
• +7 more

Miguel Anjos is Professor and Chair of Operational Research at the School of Mathematics, University of Edinburgh. He serves as Deputy Head of School and leads the Data and Decisions research theme. His professional roles extend to Chair of the Mathematical Optimization Society (2023–2025) and Vice-President (International Activities) of INFORMS. He holds Fellowships from the Humboldt Foundation, EUROPT, and the Canadian Academy of Engineering. Research Focus: Mathematical optimization for large-scale nonlinear problems in engineering applications, particularly conic optimization in facility layout and power grid optimization. Collaborations: National Grid ESO, Hydro-Québec, Schneider Electric, Rio Tinto, EDF, and Yeji Data Lab. Datasets: Maintains QAPLIB, FLPLIB, and Jones Benchmark for optimization research. His work addresses smart grid challenges: maximizing grid efficiency, integrating renewables, optimizing energy storage, and enabling customer participation. Former students have secured positions at universities (University of Tennessee, Western Ontario) and tech companies (Amazon, Shopify). Scientific Awards: Fellow of the Humboldt Foundation Fellow of EUROPT Fellow of the Canadian Academy of Engineering He has supervised PhD researchers including Elspeth Adams (2014 MOPTA runner-up), Christian Bingane (2019 CORS Student Paper Competition winner), and Mathieu Tanneau (2018–2021 FRQNT Merit Scholar). His postdocs have contributed to bilevel attacks in AI, energy storage scheduling, and electric vehicle infrastructure planning.

Giovanni PetriView profile

Professor

• Topology and Predictability of Complex Systems
• Computational Neuroscience
• Higher-order Networks
• +7 more

Giovanni Petri is a Professor in the Network Science Institute at Northeastern University London, with additional affiliations as Principal Researcher at CENTAI and Guest Scholar at Networks Units IMT Lucca. His research spans topological analysis of complex systems, neuroimaging data, and AI architectures, with applications to cognitive neuroscience and socio-technical systems. His educational background includes a PhD in Complex Networks from Imperial College London (2012), an MSc in Theoretical Physics from the University of Pisa (2008), and a BSc in Physics from the University of Pisa (2005). Professor Petri's research focuses on the theoretical and empirical analysis of complex systems, with emphasis on structural and temporal properties of networks with higher-order interactions. His work bridges statistical physics, algebraic topology, and data analysis to investigate whole-brain activation patterns, cognitive representations in neural architectures, social contagion dynamics, and team interactions. His lab employs topological data analysis to uncover multi-scale patterns in neuroscience data that traditional methods might overlook. His recent publications reveal a strong emphasis on higher-order network structures, with significant contributions to understanding how topological features influence brain function, information processing in neural networks, and collective behavior in social systems. His work demonstrates how higher-order interactions fundamentally change our understanding of complex systems dynamics. European Research Council Consolidator Grant (RUNES: Reconstruction and unification of neural and ecological systems) Professor Petri advises numerous PhD students and postdoctoral researchers across multiple institutions. His lab (NPLab) investigates the role of topology and geometry in collective dynamics of complex systems, with funding from prestigious sources including the ERC. His research spans fundamental network theory, network science of AI, and applications to neuroscience and social systems. The NPLab, which Professor Petri leads, focuses on topological neuroscience, cognitive neuroscience, higher-order networks, and Project CETI (Cetacean Translation Initiative). The lab's work on sperm whale communication through Project CETI has gained significant attention, analyzing over 9,000 recordings to identify 156 distinct codas and develop a sperm whale phonetic language using AI techniques.