Find Academic Supervisors

Jose Almeida Da SilvaView profile

Associate Professor

• Aviation Safety
• Safety Management Systems
• Aircraft Structural Integrity
• +7 more

Associate Professor Jose Almeida Da Silva holds a position at RMIT University's School of Engineering in Melbourne, Australia. With over 20 years of academic experience, he specializes in aviation safety, aerospace materials, and structural integrity. His work bridges academia with industry through collaborations on projects involving accident investigation and safety management systems. He earned a 5-year Aeronautical Engineering degree from the University of Beira Interior (1991–1996), where he was recognized as the top graduate. Later, he received the Santander Scientific Merit Prize (2010–2013) as the best researcher in aerospace sciences. Professional credentials include a Remote Pilot Licence from CASA and memberships in the Royal Aeronautical Society and AIAA. Research focuses on aviation safety systems, composite materials, and passenger anthropometry's impact on aircraft performance. Recent work explores augmented reality in accident analysis and Bayesian networks for unmanned aircraft safety assessments. He supervises PhD/Masters students on topics like safety culture in military aviation and composite material maintenance strategies. Awards: Santander Merit Prize, Portuguese Air Force Merit Award, University of Beira Interior's Top Graduate Professional Affiliations: AIAA Senior Member, Royal Aeronautical Society Member Key Projects: Aviation Safety Programs, UAV Risk Modeling, Structural Fatigue Monitoring

• Pharmacometrics
• Mathematical Modeling
• Pharmacokinetics
• +3 more

Professeure titulaire at the Université de Montréal's Faculty of Pharmacy, Fahima Nekka holds the NSERC Industrial Research Chair in Pharmacometrics. She leads research in pharmacometrics, mathematical modeling, and quantitative systems pharmacology. Her affiliations include the CRM (Centre de recherches mathématiques), PhysNum (Laboratoire de physique numérique), GRUM (Groupe de recherche universitaire sur le médicament), and RQRM (Réseau québécois de recherche sur le médicament). Research focuses on drug fate and action, integrating pharmacokinetics, pharmacodynamics, and complex data analysis. Notable projects include modeling dopamine systems for Parkinson’s and ADHD, optimizing chemotherapy regimens, and developing probabilistic dosing strategies. She has supervised over 15 PhD and Master's students since 2000, addressing topics like vancomycin monitoring, G-CSF use in chemotherapy, and medication adherence modeling. Active in grants, she has led projects funded by NSERC, FRQNT, and industry partners like Pfizer. Her work bridges mathematics, pharmacology, and clinical applications, with a focus on translational research to improve therapeutic outcomes.

• Safe AI
• Formal Methods
• Reinforcement Learning
• +5 more

Francesco Belardinelli is a Senior Lecturer in the Department of Computing at Imperial College London, part of the Faculty of Engineering. He co-directs the UKRI Centre for Doctoral Training (CDT) in Safe and Trusted AI and leads the EPSRC-funded project 'An Abstraction-based Technique for Safe Reinforcement Learning'. His research focuses on Safe AI, formal methods, and strategic reasoning in multi-agent systems under imperfect information. He holds a PhD and undergraduate degrees from Scuola Normale Superiore, Italy. Belardinelli's work bridges formal verification techniques with AI systems, particularly in ensuring safety in reinforcement learning and multi-agent dynamics. He has secured grants including EPSRC NIA, ANR projects (e.g., AGAPE, SVeDaS), and Marie Curie Fellowships. He supervises multiple PhD and postdoctoral researchers in areas like safe RL, formal methods, and multi-agent systems. His educational contributions include courses on logical aspects of AI and scientific research methodologies. He organizes events like the FMAI Seminar series and chairs international conferences (e.g., AAMAS 2021 workshop). His outreach activities include invited talks on topics like safety-aware RL and epistemic logic.

• Stochastic Analysis
• Rough Path Theory
• Mathematical Finance
• +3 more

Dr. Christian Litterer is a Lecturer in the Department of Mathematics at the University of York. He holds a DPhil from the University of Oxford and has held postdoctoral positions at Oxford, Imperial College London, and École Polytechnique. His research bridges pure and applied mathematics, focusing on stochastic analysis, rough path theory, and their applications to mathematical finance and numerical methods. Key interests include Malliavin calculus, non-linear filtering algorithms, and pricing derivatives. He currently supervises Xintao Xie and leads a research group in Mathematical Finance and Stochastic Analysis. Dr. Litterer's work explores the intersection of geometric rough paths and probabilistic applications, with contributions to high-order numerical methods like cubature on Wiener space. He has been involved in organizing conferences (e.g., 2024 Conference on Modern Topics in Stochastic Analysis) and serves on the editorial board of Stochastic Analysis and Applications 2025 . His research projects are supported by grants such as the EPSRC-funded 'Making Cubature on Wiener Space Work.' Dr. Litterer’s academic activities include teaching roles in MSc programs like Financial Engineering and Mathematical Finance. His research collaborations span institutions globally, reflecting his interdisciplinary approach to stochastic analysis and its real-world applications.

Stephen ConnorView profile

Lecturer

• Applied Probability
• Coupling Theory
• Perfect Simulation
• +4 more

Dr. Stephen Connor is a Lecturer in the Department of Mathematics at the University of York. He obtained his undergraduate and PhD degrees from the University of Warwick and joined York in 2008. His research focuses on applied probability, coupling theory, and convergence properties of Markov processes. Research Interests: Specializes in Markov process convergence, stochastic stability, perfect simulation algorithms, and cutoff phenomena in random walks. Key applications include card shuffling, queueing systems, and interacting particle systems. Publication Trends: Recent articles demonstrate theoretical advances in stochastic processes with experimental validation. Works frequently combine mathematical rigor with computational methods to solve complex probabilistic models. Recurring themes include optimal coupling strategies, mixing time analysis, and algorithm development. Professional Activities: Editor for Applied Probability Trust journals Academic Steering Group member - Scottish Mathematical Sciences Training Centre Founder - Probability in the North East research network Research Grants Committee - London Mathematical Society Advising & Outreach: Currently supervises PhD students Georgia Sowerby and Wachiraphong Ratiphaphongthon. Leads school outreach programs on mathematical topics including card shuffling probabilities and cryptography. Served as LMS Holgate Session Leader (2017-2020).

Hans-Andrea LoeligerView profile

Professor

• Signal Processing
• Information Theory
• Communications
• +6 more

Prof. Hans-Andrea Loeliger is a Full Professor at ETH Zurich's Department of Information Technology and Electrical Engineering and serves as Deputy Head of the Signal and Information Processing Laboratory. With a career spanning over two decades at ETH Zurich since 2000, he has established himself as a leading researcher in signal processing, information theory, and related fields. His work bridges theoretical foundations with practical applications in communications, electronics, and machine learning. Loeliger's research interests encompass a broad spectrum of topics including signal processing, information theory, communications, system theory, electronics, machine learning, quantum systems, error correcting codes, and neural computation. His work on factor graphs and message passing algorithms has been particularly influential, providing a unifying framework for various signal processing techniques. His recent publications demonstrate continued innovation in areas such as NUV priors, control-bounded analog-to-digital conversion, and neural network applications. His publication record shows consistent high-impact contributions across multiple disciplines, with recent work focusing on the intersection of statistical signal processing, machine learning, and circuit design. The trend in his research demonstrates an evolution from foundational work in factor graphs and information theory toward increasingly sophisticated applications in machine learning, neural computation, and practical circuit implementations. Fellow of the IEEE Loeliger has supervised numerous PhD students and master's candidates through ETH Zurich's Signal and Information Processing Lab, though specific student names aren't listed in the provided text. His teaching responsibilities include courses such as Discrete-Time and Statistical Signal Processing, Electronic Circuits & Signals Exploration Laboratory, and Introduction to Estimation and Machine Learning. His research has been supported by various grants enabling the development of novel signal processing techniques and their implementation in practical systems. The Signal and Information Processing Laboratory under his leadership serves as a hub for interdisciplinary research connecting theoretical signal processing with applications in communications, imaging, and neural systems. The lab maintains strong connections with both academic and industrial partners, facilitating the translation of theoretical advances into practical implementations.

Pelekis ChristosView profile

Assistant Professor

• Game Theory
• Probability Theory
• Combinatorics
• +3 more

Christos Pelekis is an Assistant Professor in the Department of Mathematics at Aristotle University of Thessaloniki. He holds a PhD in Mathematics from Delft University of Technology (2014), focusing on Probability and Game Theory with a thesis titled Search Games on Hypergraphs . His academic journey includes postdoctoral research at institutions like the Czech Academy of Sciences, KU Leuven, and NTUA. His research interests span Game Theory, Probability Theory, Combinatorics, Discrete Mathematics, Measure Theory, and Optimization. Education: PhD in Mathematics (Delft University of Technology, 2014), Master's in Applied Mathematical Sciences (NTUA, 2008), and a Bachelor's in Mathematics (University of Crete, 2005). Research focuses on probabilistic methods in combinatorics, game-theoretic models for resource allocation, and optimization in networked systems. Notable work includes contributions to search games, concentration inequalities, and distributed network algorithms. His publications address topics ranging from Turán-type theorems to 5G wireless power allocation using game theory. Professional experience includes roles at The American College of Greece – Deree (2022–2023) and postdoctoral positions at NTUA (2020–2023), the Czech Academy of Sciences (2017–2019), and KU Leuven (2014–2016). Key research trends in his articles include applying game theory to network optimization, analyzing probabilistic inequalities, and exploring combinatorial structures in Euclidean geometry. His work bridges theoretical foundations with practical applications in telecommunications and distributed systems.

Can LiView profile

Professor

• Physical Chemistry
• Environmental Catalysis
• Chiral Catalysis
• +4 more

Can Li is a Full Professor at the Dalian Institute of Chemical Physics, affiliated with the Chinese Academy of Sciences (elected in 2003) and holds memberships in prestigious institutions such as the Academy of Sciences for Developing World (TWAS, 2005) and Academia Europaea (2008). His academic career spans decades, including leadership roles as Director of the State Key Laboratory of Catalysis (1998–2015), President of the International Association of Catalysis Societies (2008–2012), and Chairman of the Catalysis Society of China (2005–2012). Dr. Li’s research focuses on broad areas of catalysis and physical chemistry, including Environmental Catalysis , Chiral Catalysis , Photo(electro)catalysis , and Biocatalysis . His work also involves advanced analytical techniques like In-situ Characterization of Catalytic Reactions and Time-resolved Spectroscopy . While recent Google Scholar publications appear to focus on computer science topics like AI fairness and safety, his core research remains rooted in chemical sciences. His scholarly contributions have been recognized with multiple awards, including the National Catalysis Achievement Award of China (2014) , Natural Science Award of China (2011) , and the International Catalysis Award (2004) . He has held visiting professorships at institutions in Belgium, the USA, the UK, Japan, and France. 2014 National Catalysis Achievement Award of China 2011 Natural Science Award of China 2005 Outstanding Science and Technology Achievement Prize of the CAS 2005 HoLeungHoLee Prize 2004 International Catalysis Award 1998 National Award for Outstanding Young Scientists in China 1997 Hong Kong Qiu-Shi Outstanding Young Scientist Award

• Operations Research
• Queueing Theory
• Traffic Management
• +3 more

Professor Neil Walton is a Professor and Deputy Head of the Department of Operations Management, Strategy, Innovation, Information Systems, and Entrepreneurship at the Business School. His research primarily focuses on operations research, queueing theory, stochastic processes, and their applications in transportation systems, networking, and optimization. He has contributed significantly to understanding scheduling algorithms, traffic signal control, and decentralized systems. His work bridges theoretical foundations with practical applications in urban mobility and resource allocation. Research interests include advanced scheduling techniques for quantum switches, stochastic approximation methods, and optimization frameworks for vehicular networks. He has explored topics such as regret analysis in multi-arm bandits and the convergence of stochastic processes. His studies often address real-world challenges like traffic congestion and healthcare resource management through rigorous mathematical modeling. Publications highlight a consistent focus on optimizing systems under uncertainty, with notable contributions to network stability, adaptive control mechanisms, and reinforcement learning applications. His work on platooning in connected vehicle networks and decentralized signal control systems underscores a commitment to cutting-edge solutions for modern transportation challenges. Professor Walton has advised students such as Yao Shao, contributing to the training of future researchers in operations management and systems optimization.

Alex LewView profile

Assistant Professor

• Probabilistic Reasoning
• Programming Languages
• Machine Learning
• +4 more

Alex Lew is an Assistant Professor of Computer Science at Yale University, affiliated with the Yale School of Engineering & Applied Science. He is a core member of the GenLM consortium, a multi-university initiative focused on controlling and understanding large language models through probabilistic programming and Bayesian inference. His research emphasizes automating and scaling principled probabilistic reasoning, combining techniques from programming languages, machine learning, Bayesian statistics, and cognitive science. Alex holds a Ph.D. and S.M. from the Massachusetts Institute of Technology (MIT) and a B.S. from Yale University. His work bridges theoretical foundations and practical applications, particularly in probabilistic programming languages and their integration with modern machine learning systems. Key contributions include advances in programmable variational inference, denotational semantics for probabilistic programs, and scalable Bayesian data cleaning. His research has been recognized with prestigious awards, including the ACM SIGPLAN Distinguished Paper Award (POPL 2023), ACM SIGLOG Distinguished Paper Award (LICS 2023), and the Probability and Programming Research Award from Meta (2023). His publications span top venues like ICLR, PLDI, and POPL, focusing on topics such as sequential Monte Carlo, stochastic inference algorithms, and formal semantics. Alex collaborates with industry and academia through the GenLM consortium and contributes to open-source probabilistic programming systems like Gen. His lab focuses on advancing the theoretical and practical tools for probabilistic reasoning in complex systems.

Lucie CharlesView profile

Lecturer

• Cognitive Neuroscience
• Metacognition
• Decision-making
• +5 more

Dr. Lucie Charles is a Lecturer in Psychology at Queen Mary University of London, affiliated with the Centre for Brain and Behaviour within the School of Biological and Behavioural Sciences. She focuses on cognitive processes related to metacognition, introspection of decisions, and action awareness. Her work integrates psychophysics, computational modelling, and neuroimaging to study how humans evaluate their decisions and actions. Dr. Charles completed her PhD at Neurospin, France, under Stanislas Dehaene, investigating metacognition and consciousness. She held roles at UCL (Senior Research Fellow and Lecturer) and Oxford University (British Academy Fellow), collaborating with Patrick Haggard and Nick Yeung on topics like volition and error detection. She currently leads the Association for the Scientific Study of Consciousness as Executive Director since 2019. Her research explores three core areas: 1) choice awareness and freedom of choice, 2) confidence biases in decisions, and 3) motor awareness. She examines how people introspect decision processes, detect external influences, and perceive motor execution accuracy. Her grants include an ESRC-funded project on freedom of choice and introspection (2019). Key contributions include studies on paradoxical evidence weighting in confidence judgments, Bayesian models of metacognition, and neural correlates of action awareness. She supervises postdoctoral researchers like Keiji Ota and offers PhD opportunities in her research areas. Her work is disseminated through Google Scholar, ResearchGate, and @LucieCharlesCog on Twitter.

• Computer Vision
• Machine Learning
• 3D Shape Estimation
• +5 more

Dr. Anthony Dick is an Associate Professor in the School of Computer and Mathematical Sciences at the University of Adelaide. He is affiliated with the Australian Institute for Machine Learning and the Australian Centre for Visual Technologies. His research focuses on computer vision, particularly visual tracking and 3D shape estimation. He explores applications in sports analytics (e.g., AFL player tracking), medical imaging, and visual question answering leveraging external knowledge sources. He is eligible to supervise PhD and Masters students in these areas. Research interests include: 3D shape analysis, visual tracking algorithms, machine learning integration for imagery, and multimodal reasoning (combining vision and text). His work often addresses challenges in large-scale surveillance networks and automated image interpretation. Publications span topics like neural network-based tracking, deep learning for set prediction, and benchmarking generative models. He has contributed to datasets such as Tenniset for event recognition in sports videos. Affiliated with interdisciplinary teams like the Australian Centre for Visual Technologies, his research bridges academic and applied domains, with potential industrial applications in automated video analysis and medical diagnostics.

• Battery Storage Systems
• Power Systems Engineering
• Smart Grids
• +3 more

Dr. Ali Pourmousavi Kani is a Senior Lecturer at the University of Adelaide's School of Electrical and Mechanical Engineering, specializing in power systems and energy storage. His research focuses on optimizing battery storage systems, smart grid technologies, and renewable energy integration. He holds a PhD in Power Systems Engineering with first-class honors and is an IEEE Senior Member. Dr. Kani leads major projects like Mine Operational Vehicles Electrification (MOVE) and Flexible Aggregator Simulation Platform (FRESNO) , collaborating with industry partners such as BHP Nickel West and Watts (Denmark). His work addresses energy efficiency, grid reliability, and decarbonization in mining and smart grid contexts. He has been actively engaged in media discussions on energy transitions, including interviews with ABC, Nikkei Magazine, and Australian Mining Magazine. His research spans topics from battery management systems to demand response mechanisms, leveraging AI and advanced optimization techniques. His publications emphasize practical frameworks for energy storage applications, probabilistic forecasting for electricity markets, and innovative solutions for data-driven grid management. He is eligible to supervise postgraduate research and hosts international scholars through collaborative frameworks.

Yingzhen YangView profile

Assistant Professor

• statistical machine learning
• deep learning
• optimization for machine learning
• +9 more

Yingzhen Yang is an Assistant Professor at Arizona State University's School of Computing and Augmented Intelligence. His research focuses on statistical machine learning, deep learning, optimization, and theoretical computer science. He has held industrial internships at Microsoft Research and Hewlett-Packard Labs, and has contributed to projects in computer vision and graphics. Ph.D. from the University of Illinois Research interests include theory of deep learning, subspace learning, sparse representation, and optimization. His work bridges machine learning theory and applications, with notable contributions to L0-Sparse Subspace Clustering and neural architecture search. Recent publications highlight advancements in graph neural networks, transformer optimization, and medical image classification. His work on L0-SSC earned a Best Paper Finalist award at ECCV 2016. Awards: ECCV 2016 Best Paper Finalist, 2010 Carnegie Dean's Tuition Fellowship Advises students in the Statistical Deep Learning Lab, focusing on topics like model compression and AutoML. Collaborates on synthetic data generation and robust neural architecture search. Labs/Teams: Leads the Statistical Deep Learning (SDL) Lab, fostering research in theory-driven machine learning applications.

Eberhard VoitView profile

Clinical Professor

• Systems Biology
• Metabolic Pathway Analysis
• Biochemical Systems Theory
• +4 more

Eberhard Voit is a Clinical Professor in the Biological Sciences department at the University of Texas at Dallas, affiliated with the School of Natural Sciences and Mathematics. His work focuses on systems biology, particularly metabolic pathway analysis and the Biochemical Systems Theory framework. He holds a PhD in Developmental and Theoretical Biology from the University of Cologne (1981), an M.S. in Mathematics (1978), and an M.S. in Biology (1976). Voit’s research integrates mathematical and computational methods to study biomedical systems, including metabolic networks, signaling pathways, and personalized medicine. His lab develops dynamic, probabilistic, and bioinformatics models, addressing topics like microbial communities, environmental health, and predictive healthcare. He emphasizes education, authoring influential textbooks translated into multiple languages and mentoring students across disciplines. Notable projects include a multiscale model assessing dioxin’s health impacts using template-and-anchor modeling frameworks. His awards span academic recognition (e.g., Sigma Xi’s Lifetime Achievement Award) and teaching excellence (e.g., Georgia Tech’s Thank-a-Teacher Award).