Find Academic Supervisors

Tingting MuView profile

Senior Lecturer

• Machine Learning
• Computer Vision
• Text Mining
• +3 more

Tingting Mu is a Senior Lecturer in Machine Learning at the University of Manchester's Department of Computer Science. She holds a B.Eng. from the University of Science and Technology of China (2004) and a Ph.D. from the University of Liverpool (2008). Prior to joining Manchester in 2016, she was a lecturer at Liverpool’s Department of Electrical Engineering and Electronics. Her research focuses on advanced mathematical modeling and large-scale optimization in machine learning and data analytics. Key areas include developing algorithms for human intelligence simulation, analyzing complex data (e.g., text, images, signals), and applications in text mining and scientific data analytics. Teaching includes courses like COMP24112 Machine Learning and COMP34111 Natural Language Systems. Her work contributes to UN Sustainable Development Goals related to Digital Futures and Sustainable Futures. Notable projects include MCAIF (Centre for AI Fundamentals) as a researcher and a Knowledge Exchange Partnership with Fusion21 Ltd. Over 60 research outputs span topics like neural networks, adversarial learning, and ontology-based systems. Current projects emphasize AI fundamentals, surrogate models for engineering systems, and interdisciplinary applications. Her lab explores techniques to enhance model interpretability, scalability, and robustness across vision, NLP, and multimodal domains.

Omar RivasplataView profile

Senior Lecturer

• Machine Learning
• Deep Learning
• Reinforcement Learning
• +6 more

Omar Rivasplata is a Senior Lecturer in Machine Learning and Robotics, specializing in theoretical and applied machine learning. He is affiliated with the MCAIF: Centre for AI Fundamentals, focusing on advancing foundational aspects of artificial intelligence. His research spans reinforcement learning, Bayesian analysis, neural networks, and generalization bounds. Key research interests include the theoretical underpinnings of deep learning architectures, optimization strategies for large-scale models, and probabilistic methods in machine learning. Notable contributions include work on gradient clipping for wide/deep networks and semi-pessimistic reinforcement learning frameworks. Leading the MCAIF project (2021–2026), a multidisciplinary initiative exploring core AI principles. Active collaborations with institutions worldwide, emphasizing open-access publications (e.g., Transactions on Machine Learning Research). His articles explore topics like PAC-Bayesian theory, convergence of diffusion models, and meta-analyses of Bayesian methods. He advises a diverse cohort of postgraduate researchers in AI fundamentals.

• Machine Learning
• Quantitative Finance
• Cryptocurrency
• +7 more

Dr. Paul Bilokon is a Visiting Lecturer in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. He holds leadership roles as CEO and Founder of Thalesians Ltd, Chief Scientific Advisor to Thalesians Marine Ltd, and Head of Faculty at the Machine Learning Institute (MLI) and Quantitative Developer Certificate (QDC). Previously, he led global credit and core e-trading quant teams at Deutsche Bank and held senior roles at Morgan Stanley, Lehman Brothers, and Citigroup. His research focuses on machine learning in finance, cryptocurrency, high-frequency trading systems, and domain theory. He earned a distinction from the University of Oxford and won the Donald Davis Prize and British Computer Society SET Award for his work on stochastic processes and imaging. His book contributions include 'Machine Learning and Big Data with kdb+/q' and 'Machine Learning in Finance: From Theory to Practice.' He teaches advanced machine learning and C++ programming, emphasizing reinforcement learning and low-latency applications. Education: Christ Church, University of Oxford (MSci with distinction); Imperial College London (Donald Davis Prize-winning MSci thesis). Research interests include algorithmic trading, quantum machine learning, and drug discovery via graph neural networks. Awards: Quant of the Year 2023, multiple industry accolades. Projects span cryptocurrency arbitrage, low-latency systems, and topological approaches to number theory. Advised numerous students on topics like deep reinforcement learning and electronic market making. Labs/Teams: Thalesians Ltd (quantitative research), MLI (machine learning education), QDC (certification programs). Grants and consulting span financial institutions and tech firms. His work bridges academia and industry, driving innovation in finance and computer science.

Martim BrandãoView profile

Lecturer

• Responsible AI
• Robotics
• Artificial Intelligence
• +5 more

Martim Brandão is a Lecturer (Assistant Professor) in Robotics and Autonomous Systems at King's College London, leading the Responsible Robotics and AI (RRAI) Lab and co-directing the UKRI Centre for Doctoral Training in Safe and Trusted AI. His work focuses on mitigating risks in AI/robotics, ethical algorithm design, and socially just technologies. He holds a PhD in Engineering from Waseda University, with postdoctoral experience at King's College London, Oxford Robotics Institute, and Waseda's Takanishi Laboratory. Education: PhD in Engineering (Waseda University, Japan), Postdoc (King's College London, Oxford Robotics Institute, Waseda) Key Research Interests: Ethical AI, Explainable Planning, Fairness in Robotics, Adversarial Attacks on Motion Planners, Human-Robot Interaction His recent publications address critical issues such as bias in reinforcement learning, ethical dilemmas for delivery robots, and fair drone delivery path optimization. He co-leads initiatives like the Sustainability Hub and Trusted Autonomous Systems Hub, emphasizing responsible innovation. His work bridges technical rigor with societal impact, advocating for transparency and equity in autonomous systems. Awards: None explicitly listed, though his roles and collaborations reflect institutional recognition. Grants: Secured £400k from the AI Security Institute for human-AI collaboration research (2025). Labs/Teams: RRAI Lab (King's), CDT in Safe and Trusted AI (co-director), and collaborations with interdisciplinary centers like the Centre for Data Futures.

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +6 more

Juan Carlos Gonzalez Victores is an Associate Professor at the Department of Systems Engineering and Automation, Universidad Carlos III de Madrid (UC3M), actively leading research in the Robotics Lab. His work spans multiple disciplines including Computer Science, Medicine, and Robotics and Industrial Informatics. Research interests focus on: Human-Robot Interaction with applications in neurorehabilitation and accessibility Autonomous Systems for smart cities and underground infrastructure inspection Deep Learning and Reinforcement Learning for robotic manipulation Medical Robotics with emphasis on surgical and diagnostic applications Recent publications highlight trends in: Garment-agnostic manipulation techniques Neurorehabilitation systems using synthetic data Human-like sketching via Deep Q-Learning Safety analysis of AI-human organizational interfaces Contact: juancarlos.gonzalez@uc3m.es | jcgvicto@ing.uc3m.es

• Inverse dynamics
• Structural health monitoring
• Linear and nonlinear vibrations
• +5 more

Dr. Hamed Kalhori is a Lecturer in the School of Mechanical and Mechatronic Engineering at the University of Technology Sydney (UTS), Faculty of Engineering & Information Technology. As a Casual Academic, he contributes to teaching and research in mechanical engineering with a focus on dynamics, vibrations, and structural health monitoring. His work bridges theoretical and practical applications in civil infrastructure, aerospace systems, and composite materials. Dr. Kalhori earned his PhD in Mechanical Engineering from the University of Sydney between March 2013 and July 2017. Dr. Kalhori's research spans multiple domains within mechanical and structural engineering. His primary expertise lies in inverse dynamics problems , particularly impact force identification, where he develops advanced methodologies combining model-based approaches with machine learning techniques. He extensively investigates linear and nonlinear vibrations in various structures, from micro-plates to large-scale bridges. His work in structural health monitoring focuses on innovative approaches for bridge inspection, including drive-by assessment techniques and sensor network optimization. Additionally, he explores smart materials and structures , particularly carbon nanotube-reinforced composites and their mechanical behavior under dynamic loading conditions. Dr. Kalhori's publication record demonstrates a strong trajectory in mechanical engineering research with increasing focus on interdisciplinary approaches. His recent work shows growing integration of machine learning techniques with traditional mechanical engineering methods, particularly for solving inverse problems in structural dynamics. There's a clear progression from fundamental vibration analysis toward practical applications in infrastructure monitoring and composite material characterization. His research maintains strong connections between theoretical modeling and experimental validation across diverse structural systems. Dr. Kalhori has received notable recognition for his academic contributions: 2019 Dean's Award for Excellence in Teaching and Tutoring Multiple research grants in vibration-related fields Dr. Kalhori has secured research funding for projects related to vibration analysis and structural monitoring, including industry collaborations such as the Alstom DMU traincar crash test project (2025). He has led international research collaborations and conducted numerous workshops and short courses both locally and internationally. While the specific details of his advising relationships aren't provided in the available information, his extensive publication record and research leadership suggest active engagement with graduate students and early-career researchers. His research involves collaboration with the Centre for Autonomous Systems at UTS and Data61-CSIRO, where he previously worked as a research associate and research assistant respectively. These collaborations focus on structural health monitoring projects across New South Wales, leveraging advanced sensing technologies and computational methods for infrastructure assessment.

• Machine Learning
• Computer Vision
• Robotics
• +5 more

Qiang Qiu is an Associate Professor in the Department of Electrical and Computer Engineering at Purdue University's College of Engineering. He actively contributes to the Vertically Integrated Projects (VIP) program, fostering interdisciplinary collaboration between faculty, students, and projects. His research spans machine learning, computer vision, robotics, and generative AI, with particular emphasis on diffusion models, transformer fine-tuning, and graph neural networks. Key themes include improving model generalizability through orthogonal low-rank embeddings, developing efficient parameter-tuning strategies, and advancing visuotactile manipulation in robotics. Recent publications reveal trends in attention control for text-to-image alignment, posterior sampling for diverse image generation, and federated learning security. His work addresses gradient conflicts in machine unlearning and explores multimodal sensing for robotic manipulation.

Kemal UçakView profile

Associate Professor

• Control Engineering
• Nonlinear Systems
• Machine Learning
• +3 more

Kemal Uçak serves as an Associate Professor in the Department of Control and Automation Engineering at Istanbul Technical University, specializing in advanced control systems and machine learning integration. His research bridges theoretical control engineering with practical applications in nonlinear dynamical systems. Research Interests: Development of adaptive controllers for nonlinear systems using machine learning techniques Integration of support vector regression with classical control frameworks Fuzzy logic applications in MIMO control systems Novel observer designs for system state estimation Hybrid optimization approaches combining evolutionary algorithms and gradient-based methods His publication trend demonstrates consistent innovation in control theory, with recent works focusing on Runge-Kutta based adaptive controllers, LSSVR-integrated optimal control, and fuzzy PID systems. These contributions primarily advance nonlinear control methodologies applicable to industrial automation and robotics. Current Research Activity: Principal Investigator for the active project "Runge-Kutta and Machine Learning Based Adaptive Controller Architecture for Nonlinear Systems" (2024-2026) Collaborations spanning computational intelligence and control engineering domains Development of model-free control strategies reducing dependency on system identification

Andreas AristidouView profile

Associate Professor

• Computer Graphics
• Character Animation
• Motion Capture
• +5 more

Dr. Andreas Aristidou is an Associate Professor at the Department of Computer Science, University of Cyprus, and a Senior Research Fellow at CYENS Centre of Excellence. He leads the Graphics & Extended Reality Lab and specializes in character animation, motion capture, and digital heritage. His research integrates machine learning, generative AI, and VR/AR technologies to preserve cultural heritage and advance interactive virtual environments. Educations: PhD in Signal Processing and Communications (University of Cambridge, 2007–2010) MSc in Mobile and Personal Communications (King's College London, with honors) BSc in Informatics and Telecommunications (National and Kapodistrian University of Athens) Research Interests: Focus on character animation analysis/synthesis, motion capture techniques, cultural heritage digitization, and applications of Conformal Geometric Algebra. His work bridges computer graphics with tangible/intangible cultural preservation. Key Projects: Lead Principal Investigator for Horizon Europe-funded HAMLET (2024–2027) to democratize generative AI for cultural industries. Principal Investigator for PREMIERE (2022–2025), enhancing performing arts with AI/XR. Developed the Virtual Dance Museum and 3D Reptiles Database for cultural and ecological documentation. Awards & Grants: Received ΔΙΔΑΚΤΩΡ Fellowship (2012–2014) and NVIDIA GPU Grant (2017). Secured over €8M in funding from Horizon Europe, ERASMUS+, and Cyprus Seeds. Best Paper Award at Eurographics Workshop on Graphics and Cultural Heritage (2014). Editorial & Community Roles: Editorial board member of The Visual Computer and Heritage journals; active in SIGGRAPH, Eurographics, and ACM-SCA program committees. Served in Cyprus’s Parallel Parliament for research policy (2020–2021).

• Theory of Machines and Mechanisms
• Mechanical Vibrations
• Kinematic and Dynamic Analysis of Mechanisms
• +5 more

Lluïsa Jordi Nebot is a Lecturer in the Department of Mechanical Engineering at the Polytechnic University of Catalonia (UPC) , attached to the Barcelona School of Industrial Engineering (ETSEIB) . She teaches core subjects such as Theory of Machines and Mechanisms and Mechanical Vibrations across a variety of undergraduate and master’s programmes and has supervised over 80 student theses and projects since 2006. Education and Programmes. The curricula she contributes to include: Master’s Degree in Statistics and Operations Research (2006) Master’s Degree in Industrial Engineering (2014) Degree in Engineering Physics (2011) Degree in Chemical Engineering (2010) Erasmus Mundus Master’s in Mechanical Engineering (2006 & 2009) Master’s Degree in Teacher Training for Secondary and High-School Education (2009) Degree in Industrial Technologies and Economic Analysis (2018) Research Focus. Her research spans kinematics & dynamics of mechanisms , mechanical vibrations , biomechanical analysis using smartphones , and educational technology —particularly augmented reality and AI-enhanced learning in mechanical engineering. She actively integrates sustainable development goals into engineering curricula and participates in the CDEI_DM research group on industrial equipment design and machine dynamics. Recent Work Trends. Over the past three years her scholarly output has concentrated on: AR/VR tools for teaching mechanism theory ChatGPT and AI applications in engineering classrooms Low-cost biomechanical analysis with mobile devices Sustainable engineering education initiatives These themes are reflected in both peer-reviewed articles and innovative teaching materials she has developed. Scientific Awards & Recognition. No specific prizes, fellowships, or medals are explicitly listed in the provided text. Supervision & Grants. She has directed 40 master’s theses and 15 bachelor’s theses to date, working with multidisciplinary teams of students on projects ranging from climbing-robot mechanisms to AI-based secondary-school teaching tools. Funding sources are not detailed, but her affiliation with CDEI_DM implies participation in competitive research and educational innovation grants. Laboratories & Teams. She collaborates with the Centre de Disseny d’Equips Industrials – Dinàmica de Màquines (CDEI_DM) research group and maintains close ties with colleagues across ETSEIB, the Barcelona School of Informatics (FIB), and the Manresa School of Engineering (EPSEM).

• Robot Learning
• Reward Inference
• Imitation Learning
• +5 more

Daniel Brown is an Assistant Professor at the University of Utah's Robotics Center and School of Computing . Previously, he completed a postdoc at UC Berkeley (2020–2022) and earned his PhD in Computer Science at UT Austin in 2020 under Scott Niekum . He leads the Aligned, Robust and Interactive Autonomy (ARIA) Lab , focusing on human-robot interaction and AI safety. Research interests include: Robot learning under uncertainty Reward inference from human input Robust policy optimization Bayesian reward inference High-confidence performance bounds Assistive AI systems Awards : NIH Trailblazer Award (2024) RSS Pioneer (2021) Teaching : CS 5955/6955 Advanced Artificial Intelligence (Spring 2025) CS 6300 Artificial Intelligence (Spring 2024) CS 5960/6960 Human-AI Alignment (Fall 2023)

• Machine Learning
• Control Systems
• Reinforcement Learning
• +3 more

Benjamin Recht is a Professor at the California Institute of Technology , affiliated with the Center for the Mathematics of Information . His work spans Machine Learning , Control Systems , Reinforcement Learning , and Optimization , with a focus on theoretical guarantees, adaptive algorithms, and real-world applications. His research includes: Control Systems : Certainty equivalence, adaptive control, LQR, and robustness in dynamic environments. Machine Learning : Generalization bounds, interpolation in classifiers, test set overuse, and ethical frameworks for systemic harm detection. Neural Rendering : K-Planes for explicit radiance fields in space-time-appearance modeling. Recent publications (2025-2018) highlight trends in automating adaptive control , ethical machine learning , distributed computing , and 3D reconstruction . No student lists, awards, or lab details are explicitly mentioned.

Can Deha KarıksızView profile

Assistant Professor

• Functional Analysis
• Dynamics of Linear Operators
• Mean-Field Games
• +3 more

Can Deha Karıksız is an Assistant Professor of Mathematics at Özyeğin University's Faculty of Engineering. He holds a PhD in Mathematics from Sabancı University (2014), with prior degrees from Sabancı University (2007, MSc) and Middle East Technical University (2005, BSc). His academic journey includes a postdoctoral fellowship at Adam Mickiewicz University (2014-2015) supported by a TÜBİTAK grant and an instructorship at the University of Zurich (2019-2020). Assistant Professor, Özyeğin University (current) Postdoctoral Researcher, Adam Mickiewicz University Instructor, University of Zurich His research spans three primary domains: Applications of functional analysis to complex analysis Dynamics of linear operators Mean-field games and stochastic control Combining mathematical rigor with computational approaches, his work bridges theoretical analysis with practical implementation in multi-agent systems and reinforcement learning frameworks. Recent publications focus on mean-field games (2019-2024), particularly through algorithmic approaches like Q-learning, value iteration, and inverse reinforcement learning. Earlier work (2018) examines linear operator dynamics on real analytic function spaces. Key trends show increasing specialization in stochastic control and game theory applications to machine learning and engineering. Scientific achievements include: TÜBİTAK Postdoctoral Research Fellowship He has taught diverse courses across institutions and levels since 2005, including Linear Algebra, Calculus sequences for engineering/social sciences, Differential Equations, and graduate-level Numerical Linear Algebra and Analysis II. His teaching portfolio demonstrates expertise in both foundational mathematics and advanced computational methods.

• Approximate computing
• Bayesian networks
• Cybersecurity
• +7 more

Alessandro Savino is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) of Politecnico di TORINO. He serves as an academic advisor for Bachelor’s and Master’s degree programs in Computer Engineering (Ingegneria Informatica) and contributes to PhD programs in Artificial Intelligence and Computer Engineering. Research Interests: Approximate computing, Cybersecurity (including automotive systems), Dependability, Parallel computing, Reliability analysis, and Neuromorphic architectures. Key Projects: Leads RESCHIP4EU (2024-2028), NEUROPULS (2023-2027), and commercial contracts focused on real-time OS validation and avionics design. Publications: Recent work spans hardware security (e.g., VeriSide for leakage assessment), spiking neural networks (SpikeExplorer, SpikingJET), and automotive cybersecurity (CARACAS, CAN-MM). Teaching: Instructs courses on Parallel and Distributed Computing, Hardware & Wireless Security, and System Programming across Politecnico di TORINO and Scuola IMT Alti Studi - LUCCA. Research Group: Leads the SMILIES group, focusing on resilient computer architectures and life sciences.

• Autonomous Vehicles
• Reachability Analysis
• Temporal Logic
• +3 more

Florian Lercher is a Researcher at the Department of Cyber Physical Systems at the Technical University of Munich (TUM). He joined the group led by Prof. Matthias Althoff in April 2023 as a PhD student and research assistant. His work focuses on motion planning for autonomous vehicles, particularly integrating reachability analysis and temporal logic for specification-compliant driving corridors. Education: B.Sc. in Informatics (TUM, 2020), M.Sc. in Software Engineering (TUM, LMU Munich, and University of Augsburg, 2022) Research: Traffic rule-compliant motion planning, hybrid systems verification, and formal methods in autonomous driving Teaching: Lectures and practical courses on AI fundamentals and formal methods for cyber-physical systems His recent publications emphasize efficient driving corridor extraction, temporal logic simplification, and model checking for hybrid systems. He actively supervises student theses on topics like SAT-based refinement guidance and inverse reinforcement learning for autonomous vehicles.