Find Academic Supervisors

Panos LouvierisView profile

Professor

• Cybersecurity
• Data and Information Fusion
• Defence Analytics
• +7 more

Panos Louvieris serves as Professor of Information Systems at Brunel University London within the Department of Computer Science, College of Engineering, Design and Physical Sciences. He leads the Defence & Cyber Security (DCS) research group and holds co-directorship roles at the Brunel Intelligent Digital Economy and Society (IDEAS) Research Centre and the Trusted Open Models Institute (TOMI) at the Hartree Centre. His academic profile includes committee membership in the EPSRC Digital Personhood Network, EPSRC ITaaU+ Network, and NEMODE+ Network, reflecting his influence in UK research infrastructure. His research spans cybersecurity, data fusion, and computational finance with emphasis on cyber situational awareness, explainable AI, and distributed ledger technologies. Recent work integrates agent-based simulation for pandemic modeling, Markov models for threat prediction, and social media analysis for security awareness. This interdisciplinary approach bridges computer science, operations research, and social sciences to address defense, finance, and public health challenges through advanced computational models. Analysis of his publication trajectory reveals evolving focus from foundational information fusion (2005-2010) to cyber-physical systems (2010-2015), with current emphasis on digital personhood markets (2020-present). His most recent publications demonstrate methodological innovation in blockchain applications for tax compliance and geospatial pandemic modeling, while maintaining core expertise in cyber defense analytics. Louvieris actively supervises research and collaborates with institutions including the Hartree Centre and EPSRC networks. His grant portfolio features the EPSRC-funded Digital Personhood project (2013-2016), Innovate UK's Contact Tracing project (2020-2021), and current leadership of the EPSRC Making Tax Smart initiative (2022-2023) exploring distributed ledger technology for central bank digital currency compliance. He directs the Defence & Cyber Security research group and co-leads the IDEAS Research Centre, fostering cross-disciplinary work on digital economy challenges. Current projects involve HSBC sandbox testing of Open Banking prototypes and development of futures markets for digital personhood data, positioning his team at the intersection of cybersecurity, FinTech, and societal digital transformation.

Luca SpalazziView profile

Associate Professor

• Computer Science
• Cybersecurity
• Blockchain Technology
• +5 more

Luca Spalazzi is an Associate Professor at the Department of Information Engineering , Università Politecnica delle Marche , Italy. His research spans multiple domains including cybersecurity , blockchain technology , machine learning , and telerehabilitation systems for Parkinson's disease. He applies formal methods to software verification and security analysis, with a focus on real-time systems and distributed architectures . Key research areas: Cybersecurity, Blockchain, Machine Learning, IoT, Formal Verification Recent work: Blockchain-based sustainable supply chains, Zero-Knowledge Proofs, Smartphone health monitoring His publications (2013-2025) demonstrate expertise in malware detection , smart contract verification , and AI-driven health solutions . Articles include BRAIN 2024 workshop organization and RAPIDO system for Parkinson's telerehabilitation.

• Real-Time Embedded Systems
• Real-Time Scheduling
• Schedulability Analysis for single-core, multi-core, and networked systems
• +6 more

Dr. Robert Davis is a Reader at the Real-Time Systems Research Group within the Department of Computer Science at the University of York, UK. He is renowned for his work in real-time embedded systems, particularly in scheduling theory and its applications to mixed-criticality and multi-core platforms. Current role: Reader, University of York Research group: Real-Time Systems Research Group Contact: Department of Computer Science, University of York, Deramore Lane, York YO10 5GH, UK Education DPhil in Computer Science, University of York (1995) Research Interests include real-time scheduling, mixed-criticality systems, cache-related preemption delays (CRPD), probabilistic real-time systems, and scheduling IDK classifiers. His work addresses the integration of schedulability analysis with worst-case execution time (WCET) analysis, focusing on systems with single-core, multi-core, and networked architectures. Article Trends show a focus on IDK classifiers, mixed-criticality systems, and multi-core schedulability. His recent work explores probabilistic timing, adaptive scheduling, and industry-driven real-time practices, often bridging theoretical analysis with industrial applications (e.g., automotive, avionics). Scientific Awards Outstanding paper at RTNS 2016, RTNS 2015, ECRTS 2015 Best paper at RTNS 2014, ECRTS 2011, RTCSA 2013 Influential paper award at RTNS 2017 Advising and Grants involve collaborations on European projects like HI-CLASS and MCC, and he has received funding from EPSRC and Inria International Chair. He co-founded three spin-out companies for technology transfer. Labs and Teams include the Real-Time Systems Research Group at York and collaborations with international institutions (e.g., Inria, France; NJIT, USA).

• Smart Grids
• Energy Communities
• Data Mining Applications in Power Systems
• +7 more

Sérgio Filipe Carvalho Ramos serves as Coordinator Professor at the School of Engineering (ISEP) of the Polytechnic Institute of Porto (IPP), where he has held academic positions since 2001. Since 2021, he directs the Bachelor Program in Electrical Engineering - Electrical Power Systems and previously served as deputy director of the Electrical Engineering Department (2018-2020). He maintains active research collaborations with international institutions including Universidade de Vigo and Universidade da Coruña in Spain, and UNESP in Brazil. His educational background includes a PhD (2015) and MSc (2006) in Electrical and Computer Engineering from the University of Lisbon - IST. His research focuses on smart grids, energy communities, data mining applications in power systems, and renewable energy optimization . He has pioneered work in smart buildings optimization, shared PV generation, and flexible power contracts through significant R&D projects. The analysis of his recent publications reveals strong trends in energy community modeling , with 8 of the 15 most recent papers addressing peer-to-peer energy sharing, community optimization, and local market design. His work increasingly integrates machine learning for forecasting (particularly for EV integration and PV generation) and employs advanced optimization techniques including game theory and MBNLP methods. The research consistently targets practical implementation in smart grid contexts with emphasis on real-world applicability. Premio REN 2008 Scientific Publication Merit from IPP (2011, 2015, 2023) Ramos has supervised 2 PhD students (ongoing), 29 MSc, 6 ERASMUS, and 116 BSc students. His grant portfolio demonstrates exceptional success in securing competitive funding, including FCT projects BENEFICE and SPET as PI/Co-PI, the EU-funded SATCOMM project coordinating Atlantic energy pilots, and multiple Honda Research Institute consultancies. His research group has successfully coordinated over 20 national and international R&D projects with total funding exceeding €5M. As a core researcher in the GECAD research group, he leads initiatives in smart buildings and energy communities. He serves as General Secretary for IPP at ATHENA European University and participates in the CARPE Network's Sustainable and Smart Cities working group, demonstrating institutional leadership beyond his departmental responsibilities.

• Machine Learning
• Large-Scale Data Analysis
• Bioinformatics
• +3 more

Inderjit Dhillon , Professor at the University of Texas at Austin , is a leading researcher in machine learning and big data analytics. As Director of the Center for Big Data Analytics, his work focuses on scalable algorithms for high-dimensional data, social network analysis, and gene-disease association prediction. ACM, IEEE, SIAM, and AAAS Fellow Gottesman Family Centennial Professor (2014) ICES Distinguished Research Award (2013) Research Interests: His contributions span machine learning , bioinformatics , and scientific computing , with recent work emphasizing deep learning for time series, adversarial robustness, and distributed optimization. Publications in top venues like NeurIPS and ICML highlight his expertise in inductive matrix completion and non-convex optimization . Scientific Awards: 2016: AAAS Fellow 2014: ACM, SIAM, IEEE Fellow 2013: ICES Distinguished Research Award 2011: SIAM Outstanding Paper Prize His students and collaborators frequently publish in areas like extreme classification , kernel methods , and multi-scale spectral decomposition . Labs and teams under his leadership have advanced nomadic computing and asynchronous optimization for massive datasets.

Desh RanjanView profile

Professor

• Bioinformatics
• Computational Biology
• High Performance Computing
• +5 more

Desh Ranjan is a Professor at the College of Sciences, Old Dominion University , with a focus on Bioinformatics , High Performance Computing , and Algorithm Design . His work bridges Computational Biology and Parallel Computing . Ph.D., Cornell University (1992) M.S., Cornell University (1990) Other, Indian Institute of Technology Kanpur (1987) His research interests revolve around efficient algorithms for bioinformatics and computational complexity , with applications in protein structure prediction , GPU optimization , and particle accelerator simulations . He has secured over $2 million in federal grants , including a major 2015-2018 $2M award for Hispanic-Serving Institutions . Recent work emphasizes machine learning and real-time simulations in high-fidelity physics and genomic data analysis . 2011: Sage Graduate Fellowship, Cornell University 2011: Outstanding Faculty Member, Iowa State University 2009/2008: NMSU Millionaire Researcher 2006: University Research Council Distinguished Career Award, NMSU 1995: Morrison Award for Best Technical Presentation, Regional ACM Ranjan's publications span 25+ years , with recent trends in GPU-accelerated algorithms , structural biology , and parallel computing . His grants highlight collaborations in bioinformatics , physics simulations , and STEM education projects.

Andrea CarronView profile

Senior Lecturer

• Model Predictive Control
• Learning-based Control
• Coverage Control
• +7 more

Dr. Andrea Carron is a Senior Lecturer at ETH Zürich, affiliated with the Intelligent Control Systems group under Professor M. Zeilinger at the Department of Mechanical and Process Engineering. He holds a PhD in Information Technology from the University of Padova (2016) and was a Postdoc at ETH Zurich from 2016 to 2020. Education: B.S. and M.Sc. in Control Engineering (University of Padova, 2010 and 2012) Professional Roles: Senior Lecturer (ETH Zurich, 2022–present), Postdoc Fellow (ETH Zurich, 2016–2020) Research Interests: Andrea Carron's work focuses on Model Predictive Control (MPC) and Learning-based Control with safety guarantees. His research addresses challenges in Distributed Safe Learning , Coverage Control , and Autonomous Racing , utilizing Gaussian Processes and Stochastic Control frameworks. He has developed safety filters for racing vehicles, scalable MPC for mobility-on-demand systems, and Kalman-filter-enhanced GP regression techniques. Article Trends: Recent publications emphasize Autonomous Racing (ForzaETH Race Stack), Safe Learning for distributed systems, Gaussian Process applications in control, and Robust MPC under uncertainty. His work bridges machine learning and classical control theory, with applications in robotics and real-time systems. Teaching Activities: He has taught courses such as Signals and Systems and Advanced Model Predictive Control at ETH Zurich and Ashesi University since 2017. Course content includes discrete-time signal processing, system identification, and control algorithms.

• Probabilistic Machine Learning
• AI in Drug Discovery
• Bayesian Methods
• +3 more

Arslan Masood is a Doctoral Researcher at the Department of Computer Science, School of Science, Aalto University. His research focuses on Probabilistic Machine Learning and its applications in Drug Discovery . University: Aalto University Department: Department of Computer Science Academic Rank: Researcher His work emphasizes data-efficient approaches using BERT-based models and Bayesian active learning , particularly for molecular property prediction and toxicity modeling. Recent publications explore imbalanced data handling in drug safety prediction and deep Bayesian optimization for experimental design.

• Machine Learning
• Bayesian Deep Learning
• Gaussian Process Models
• +7 more

Rui Li is a Doctoral Researcher in the Department of Computer Science at Aalto University, Finland. Their research focuses on Bayesian deep learning, Gaussian processes, and robust machine learning methods. They collaborate closely with Professor Arno Solin and colleagues on problems involving uncertainty quantification, model generalization, and sensor fusion applications in robotics. Recent work highlights advancements in few-shot classification generalization, scalable Bayesian deep learning predictions, and hyperparameter optimization for Gaussian process models. Publications span conferences like WACV, ICLR, ICML, and ICCVW, addressing theoretical and applied challenges in machine learning and computer vision. Rui Li contributes to interdisciplinary research groups tackling problems in semantic segmentation robustness and visual-inertial SLAM optimization. Their collaborative efforts include participation in the Robust Semantic Segmentation UNCV2023 Challenge with a multi-institutional team.

• Spatio-temporal Data Mining
• Graph Neural Networks
• Causal Discovery
• +3 more

Guojun Liang is a Postdoctoral Researcher at Halmstad University's School of Information Technology, actively contributing to the Cardiovascular health care project at the CAISR Health Center. He earned his Ph.D. in Artificial Intelligence from Macau University of Science and Technology in 2023, achieving the highest GPA in the Faculty of Computer Science and Engineering. His educational background includes doctoral studies at Macau University of Science and Technology (2023), where he received the Herry Fox Scholarship (2021) for academic excellence. His research career began with participation in China's National Artificial Intelligence and Industrial Robot Competition, where he secured a silver medal. Dr. Liang's research specializes in spatio-temporal data mining, with core expertise in Graph Neural Networks, causal discovery frameworks, diffusion modeling techniques, and time series analysis including prediction and imputation. His work bridges theoretical machine learning with practical applications in healthcare monitoring, transportation systems, and remote sensing. Recent publications demonstrate his focus on physics-informed architectures and causal inference mechanisms for spatio-temporal modeling. Analysis of his eight most recent publications (2021-2025) reveals a strong trend toward integrating physical constraints with deep learning architectures, particularly using graph-based approaches for spatio-temporal forecasting and imputation. His work increasingly incorporates causal discovery techniques and diffusion models to enhance model interpretability and probabilistic reasoning in time-series applications across healthcare and transportation domains. Herry Fox Scholarship (2021) Silver medal in National Artificial Intelligence and Industrial Robot Competition (China) As an educator, Dr. Liang has taught Analog/Digital Electronic Technology and Microcontroller Systems in China, and currently serves as Teaching Assistant for Artificial Intelligence, Applied Machine Learning, and Big Data Processing courses at Halmstad University. He maintains active research collaboration with the CAISR Health Center, focusing on cardiovascular health applications through advanced spatio-temporal data analysis.

• Process Mining
• Process Modelling and Simulation
• Distributed Computing
• +5 more

Claudio di Ciccio is an Associate Professor at the Department of Information and Computing Science within the Faculty of Science at Utrecht University, Netherlands. Previously, he worked with the Department of Computer Science of Sapienza University of Rome (Italy) and the Institute for Information Business of the Vienna University of Economics and Business (WU Vienna), Austria. He received his PhD in Computer Science and Engineering in 2013 from Sapienza University. His research interests span across Process Mining, Formal Methods, Automated Reasoning, and Blockchain & Distributed Ledger Technologies. He has developed expertise in Process Modelling and Simulation, Distributed Computing, Logic, and Artificial Intelligence. His work bridges theoretical formal methods with practical applications in business process management and decentralized systems. His recent publications focus on advancing Process Mining techniques while addressing critical challenges in data privacy and security, particularly in decentralized environments. His research explores the intersection of visual analytics and process mining, developing innovative approaches for multi-faceted process information analysis through time and space. He has made significant contributions to formal methods for process specification verification and constraint satisfaction. Member of the Steering Committee of the IEEE Task Force on Process Mining General Chair of the Conference on Process Mining (ICPM) in 2023 PC chair of ICPM in 2021 PC chair of the Conference on Business Process Management (BPM) in 2022 PC chair of the Blockchain Forum at BPM in 2019 and 2024 Di Ciccio has been actively involved in numerous significant research projects related to process mining, blockchain applications, and formal methods. His work demonstrates a consistent focus on developing practical tools and frameworks that address real-world challenges in business process management while maintaining strong theoretical foundations. His research group appears to focus on the intersection of process science with emerging technologies, particularly in secure and decentralized environments.

Adrian StaubView profile

Professor

• Psycholinguistics
• Eye movement research in reading
• Language comprehension
• +4 more

Adrian Staub is a Professor in the Department of Psychological and Brain Sciences at the University of Massachusetts Amherst. He directs the UMass Eyetracking Laboratory and focuses on psycholinguistics, particularly the cognitive processes underlying language comprehension and production. His work frequently employs eye-tracking methodologies to investigate syntactic parsing, word recognition, and predictability effects in reading. PhD in Psychological and Brain Sciences (2008), University of Massachusetts Amherst Research Interests Staub's research spans psycholinguistic theory, eye movement analysis during reading, and methodological issues in psychological science. He studies how readers process syntactic structures, recognize words, and integrate predictability information. His recent work includes investigations of function word errors in reading, statistical power in language research, and the theoretical foundations of the replication crisis in psychology. Recent Publications His publications examine phenomena such as the 'Sentence Superiority Effect,' the effects of predictability on lexical processing, and methodological challenges in eye-tracking research. These studies span diverse topics including cross-linguistic comparisons (e.g., Italian, French, Chinese), computational modeling of reading processes, and theoretical debates in psycholinguistics. Academic Roles Since January 2023, Staub has served as Editor-in-Chief of the Journal of Memory and Language . He has also acted as guest editor for a special issue on eye movements in reading at 50. His teaching includes graduate seminars on the replication crisis and undergraduate courses on the psychology of reading. International Collaborations Staub has held visiting appointments at institutions including the Labex EFL Project in Paris, the University of Salzburg, and the University of Milan-Bicocca. These roles reflect his engagement in international research networks focused on reading cognition and eye-tracking methodologies.

Dmitry VetrovView profile

Professor

• Bayesian Deep Learning
• Diffusion Models
• Loss Landscape Analysis
• +5 more

Dmitry Vetrov serves as a Professor of Computer Science at Constructor University in Bremen, where he founded and leads the Bayesian Methods Research Group. His academic foundation includes graduation from Moscow State University in 2003 and completion of his PhD in 2006, establishing a career centered on advancing probabilistic machine learning methodologies. His educational trajectory features: Undergraduate studies at Moscow State University (2003) Doctoral degree (PhD, 2006) Vetrov's research program critically bridges Bayesian statistics with deep learning architectures, with his group pioneering efficient diffusion model algorithms, loss landscape characterization in neural networks, scalable stochastic optimization tools, tensor decomposition applications for large-scale ML systems, and enhanced conditional text generation frameworks. This work manifests practical implementations across generative AI domains while maintaining theoretical rigor in probabilistic modeling. Analysis of his 2024-2025 publications reveals a concentrated research thrust toward diffusion model innovation, spanning text generation (token embedding smoothing, language model encoding properties), image synthesis (hair transfer, gesture generation), and scientific applications (protein modeling, genetic fine-mapping). Key thematic threads include sampler acceleration, theoretical property analysis of diffusion processes, and robust evaluation frameworks for generative systems. No scientific awards were documented in the source materials. Mentorship outcomes demonstrate significant impact, with three recent PhD students securing research positions at DeepMind. While specific grant details remain undisclosed, the group's prolific output across NeurIPS, ICML, and CVPR indicates sustained research funding. The Bayesian Methods Research Group operates as an integrated innovation hub within Constructor University's academic ecosystem. The research collective he directs maintains active development of Bayesian-deep learning fusion techniques, with current projects emphasizing diffusion model efficiency, theoretical foundations of optimization landscapes, and cross-domain applications in computational biology and multimodal generation.

Kevin J LiangView profile

Researcher

• Deep Learning
• Computer Vision
• 3D Computer Vision
• +5 more

Dr. Kevin J Liang is a Research Scientist at Meta Platforms, Inc. , specializing in Deep Learning , Computer Vision , and 3D Reconstruction . He earned his PhD in Electrical & Computer Engineering from Duke University in 2020, with a dissertation on Deep Automatic Threat Recognition for Airport X-Ray Baggage Screening . His research focuses include: 3D Computer Vision (ICON, Fast3R) Few-Shot Learning (Sylph, HyperMix) Federated Learning (WAFFLe) Object Detection (EgoTracks, Self-Supervised Methods) Recent publications demonstrate his leadership in Egocentric Vision (Ego-Exo4D) and Transformer Applications (GliTr). He has received numerous awards including the E Bayard Halsted Fellowship (2017) and Summa cum laude (2015), and serves on program committees for major conferences like NeurIPS and CVPR . As an educator, he developed and taught tutorials for Duke University's Machine Learning School and Coursera courses, covering TensorFlow, PyTorch, and foundational ML concepts for over 600 students.

Gwen SalaünView profile

Professor

• Formal Methods
• Specification Languages
• Automated Verification
• +10 more

Gwen Salaün is a Professor of Computer Science at Université Grenoble Alpes, affiliated with the CONVECS and LIG research groups, and Inria Grenoble. She holds a PhD (2003) and HDR in Computer Science. Her research spans formal methods, specification languages, automated verification, concurrent systems, and component/service-based architectures. She contributes to industrial applications, particularly in BPMN processes, IEC 61499, and IoT systems. Recent publications focus on probabilistic model checking, runtime enforcement, resource isolation testing, and automated refactoring of business processes, reflecting collaborations across Europe and funded by Région Auvergne-Rhône-Alpes. She supervises PhD students Ahang Zuo, Philippe Ledent, and Irman Faqrizal, and participates in committees like SAC'25 and FormaliSE'25. Her work emphasizes rigorous modeling, verification, and tool development for complex systems.