Find Academic Supervisors

Jacob ShapiroView profile

Assistant Professor

• Socio-technical Systems
• Conflict Studies
• Political Science
• +3 more

Jacob Shapiro is an Assistant Professor in the Department of Mathematics at Princeton University. His research focuses on socio-technical systems, conflict studies, and the intersection of technology with governance and public policy. He explores topics such as content moderation, misinformation dynamics, and the impact of political violence on societal structures. Shapiro’s work often combines quantitative methods with interdisciplinary approaches, addressing real-world challenges in digital governance, public health, and global security. He holds a position within Princeton’s mathematics department but engages with broader academic communities through collaborative research. His recent projects examine the efficacy of countermeasures against online influence operations, the role of algorithms in spreading unreliable information, and the socio-economic impacts of conflict in regions like Africa and South Asia. Shapiro’s publications analyze topics ranging from election denial content on YouTube to the monetization strategies of anti-vaccine websites. He has contributed to discussions on civilian harm in counterinsurgent operations and the long-term effects of forced displacement on education and income inequality. His research emphasizes evidence-based solutions for complex societal issues, leveraging data science and computational methods. Despite his prolific output, no scientific awards or named grants are explicitly mentioned in the provided materials. He advises no listed students but collaborates widely with institutions and industry partners on projects related to digital misinformation and conflict resolution.

• Robotics
• Autonomous Systems
• Mobile Autonomy
• +7 more

Paul Newman is the BP Professor of Information Engineering at the University of Oxford, affiliated with the Oxford Robotics Institute within the Department of Engineering Science. He is a Fellow of the Royal Academy of Engineering and IEEE, recognized for contributions to robot navigation. His research focuses on advancing autonomy and navigation techniques for mobile systems, including vehicles, drones, and robots. As founder of Oxbotica, a spinout company specializing in Mobile Autonomy, he bridges academic research and industrial applications. His key research interests include radar-based localization, sensor fusion, autonomous navigation, and machine learning for robotics. He has contributed to datasets like the Oxford Offroad Radar Dataset (OORD) and pioneered methods like Bayesian Radar Cosplace for uncertainty-aware localization. Notable awards include the FREng and FIEEE titles, reflecting his global impact. His work spans both robotics and fundamental physics, as evidenced by his contributions to ATLAS detector experiments at CERN. Newman is also affiliated with Balliol College, Oxford, and his lab, the Oxford Robotics Institute, is a world leader in mobile autonomy.

Mark SellkeView profile

Assistant Professor

• Spin glasses
• Machine learning theory
• High-dimensional statistics

Mark Sellke is an Assistant Professor of Statistics at Harvard University's Department of Statistics. He is affiliated with the Faculty of Arts and Sciences and is based at the Science Center in Cambridge, MA. His research interests focus on spin glasses, machine learning theory, and high-dimensional statistics, with contributions to optimization theory, statistical physics, and algorithmic analysis. Dr. Sellke's work bridges theoretical computer science, probability, and mathematical physics. His recent publications explore algorithmic hardness in combinatorial optimization, energy landscapes in spin glasses, and foundational questions in machine learning. Notable areas include computational lower bounds for partitioning problems, stability analysis of local optima, and universality phenomena in spin glass models. Education: Advanced degrees in Statistics/Computer Science (details not explicitly listed) His research has been recognized through publications in top venues such as FOCS, NeurIPS, and JASA. Collaborative projects include work on multi-species spin glasses and algorithmic approaches to complex systems. Current research trends emphasize the intersection of optimization, stochastic processes, and high-dimensional data analysis. Dr. Sellke maintains an active research group and contributes to the academic community through courses on statistical theory and machine learning. His lab is part of Harvard's broader efforts in data science and probabilistic modeling.

Linqing WenView profile

Professor

• Gravitational Wave Astrophysics
• Multi-messenger Astronomy
• High-performance Computing
• +4 more

**Linqing Wen** is a Professor in the School of Physics, Mathematics and Computing at The University of Western Australia (UWA). He leads the Gravitational Wave Astronomy Group and holds roles such as Chief Investigator in the ARC Centre of Excellence for Gravitational Wave Discovery (OzGrav). His work focuses on gravitational wave astrophysics, real-time detection, and multi-messenger astronomy, with contributions to the LIGO-Virgo Collaboration (LVC) and the SPIIR search pipeline. **Education**: PhD in Astrophysics from MIT (2001), MS in Physics and ME in Computer Science from PSU (1995), and BS in Condensed Matter Physics from USTC (China, 1992). Professional experience includes roles at Caltech, the Max-Planck Institute, and UWA since 2004. **Research Interests**: Gravitational Wave Astrophysics, High-performance Computing, Machine Learning for GW detection, and Early Warning Systems. Notable projects include SPIIR pipeline development, GW multi-messenger studies, and GPU acceleration for real-time analysis. **Awards**: 2016 Special Breakthrough Prize (shared), ARC Future Fellowship (2009), UPRF Fellowship (2007), and Rossi Fellowship (1995). **Grants & Projects**: Over $30M in funding, including ARC grants for GW detection, multi-messenger astronomy, and pulsar timing arrays. Active roles in OzGrav, UWA research clusters, and international collaborations like the LVC and CRAFT. **Teaching**: Courses include SCIE1121 (introductory astronomy) and specialized modules on Gravitational Wave Astronomy.

• Machine Learning
• Genetic Programming
• Engineering Informatics
• +3 more

Dr Peter Rockett is an Honorary Lecturer at the School of Electrical and Electronic Engineering, University of Sheffield. He holds a PhD in Semiconductor Physics from the University of Manchester (1980) and has been affiliated with the University of Sheffield since 1990. His research focuses on physics-informed genetic programming, machine learning for engineering, and evolutionary algorithms applied to feature extraction, control systems, and computer vision. Key areas of expertise include multiobjective genetic programming, algorithm optimization, and model-predictive control for non-domestic buildings. He has contributed to the development of standardized genetic programming frameworks (e.g., GPML) and has explored applications in robotics, cybersecurity, and image processing. His work bridges theoretical foundations with practical engineering challenges, emphasizing cross-domain feature extraction and algorithmic innovation. Recent publications highlight advancements in pruning genetic programming trees using permutation tests, energy-efficient building control systems, and semantic-based local search methods. Rockett has collaborated extensively with researchers globally, producing influential papers in journals like Evolutionary Intelligence , Genetic Programming and Evolvable Machines , and IEEE Transactions on Evolutionary Computation . No scientific awards are explicitly listed, but his extensive publication record underscores sustained academic contributions. His advising and mentoring focus on PhD and Master’s students in machine learning and engineering informatics, with research teams often collaborating on interdisciplinary projects involving control systems and AI-driven solutions.

John DohertyView profile

Associate Professor

• Aerodynamic CFD Optimisation
• Multidisciplinary Design Optimisation
• Conceptual Design and Innovation
• +2 more

John Doherty is a Reader in Vehicle Engineering at the University of Surrey's School of Mechanical Engineering Sciences, affiliated with the Centre for Aerodynamics & Environmental Flow. His research spans aerodynamic and multidisciplinary design optimisation, novel air-vehicle conceptual design, propulsion installation aerodynamics, and hybrid/electric aircraft systems. With a career beginning at RAE Farnborough and QinetiQ, he has collaborated extensively with industrial partners like MOD, BAES, and Airbus, developing CFD-based optimisation tools critical for aerospace and motorsport applications. His research interests focus on: Aerodynamic CFD optimisation Multidisciplinary Design Optimisation Conceptual design and innovation Propulsion installation aerodynamics Electric ducted fan aerodynamics Recent publications emphasize transonic aerodynamics, wind tunnel correction techniques, electric propulsion integration, and surrogate-assisted optimization methods. Trends show consistent focus on improving aerodynamic efficiency, experimental validation, and computational methods for aircraft and propulsion systems. No scientific awards are mentioned in the provided text. He teaches courses including ENG3197 Vehicle Aerodynamics, ENG3162 Aircraft Design Group Projects, and ENG2087 Design, Make and Evaluate Projects. Grant involvement includes the EMPAS project (electric motor-powered aero-engine simulator) and collaborative EU research (e.g., Garteur AD/AG-52). Doherty leads research within the Centre for Aerodynamics & Environmental Flow and collaborates on international projects (e.g., EMPAS). His work involves experimental wind tunnel testing, CFD development, and multidisciplinary teams addressing next-generation aircraft design.

Daniel de LengView profile

Assistant Professor

• Applied Human-Centered AI
• Explainable AI
• Autonomous Systems
• +5 more

Daniel de Leng is an Assistant Professor at Linköping University (Sweden), affiliated with the Department of Computer and Information Science (IDA) and the Human-Centered Systems (HCS) division. He is part of the Semantic Web group and focuses on applied human-centered AI, particularly in autonomous systems and explainable AI. His work integrates symbolic and subsymbolic methods to address challenges in real-world AI deployment. Education: PhD (2019) and Licentiate (2017) in Computer Science from Linköping University, and MSc/BSc (2013/2011) from Utrecht University (Netherlands). Research Interests: Stream reasoning under uncertainty, autonomous system safety, and semantic integration in robotics. He developed the DyKnow-ROS framework for robot stream reasoning and contributed to the Stellar AIOps environment for AI research. His work emphasizes human-understandable explanations for AI decisions. Awards: Recipient of the SAIS Master's Thesis Award. Advising & Grants: Managed the AI Academy initiative, part of the AIOps ELLIIT Infrastructure and AI Academy management teams. Previously served as Saab Aeronautics' first Point of Contact for AI (until 2022). Labs & Teams: Active in the Reasoning and Learning Lab (ReaL), Semantic Web group, and Human-Centered Systems division. Collaborates on projects like Urdarbrunnen (AI for combat search and rescue) and multi-agent robotics systems.

Sodiq AdewoleView profile

Lecturer

• Machine Learning
• Medical Image Analysis
• Healthcare
• +2 more

Sodiq Adewole is a Lecturer at the School of Data Science, University of Virginia. His research focuses on healthcare AI applications, particularly developing machine learning solutions for medical image analysis and video capsule endoscopy. Dr. Adewole's work bridges artificial intelligence and clinical medicine, specializing in few-shot lesion recognition, abnormality localization in endoscopic videos, and synthetic healthcare data generation. Recent publications demonstrate innovative approaches using graph convolutional networks, metric learning, and Bayesian methods to address challenges in gastrointestinal diagnostics. His research output shows consistent focus on medical video analysis, particularly capsule endoscopy interpretation. Key technical contributions include weakly supervised localization techniques, temporal segmentation methods, and few-shot learning frameworks adapted for medical imaging. No specific advising relationships, awards, or laboratory affiliations are detailed in available materials.

Michael WeirView profile

Lecturer

• Artificial Intelligence
• Robotics
• Neural Networks
• +4 more

Dr Michael Weir is an Honorary Lecturer in the School of Computer Science at the University of St Andrews. His research focuses on intelligent computation, artificial neural networks, and robotics, particularly exploring how parallel distributed processing can bridge the gap between human cognitive abilities and digital systems. He is a core member of the Intelligent Computation Group, which integrates biological and cognitive insights with AI techniques like Symbolic AI and Artificial Life. Education: B.A. (The Open University), Ph.D. (Brunel University), B.Sc. (University of Bristol). He holds affiliations with the American Computing Machines Society, British Psychological Society, and Cybernetics Society. Research Interests: Artificial Neural Networks Robot Navigation & Obstacle Avoidance Parallel Processing Architectures Heuristic Search Algorithms Neural Network Generalization His publications emphasize robotics navigation using dynamic potential fields and neural network optimization. Recent work addresses nonholonomic systems and adaptive obstacle avoidance. Grants/Projects: Accelerated Safe Robot-Human Interaction (EPSRC, 2014-2015) Nuffield Undergraduate Research Bursaries (2009, 2006, 2005) Labs/Teams: Active contributor to the Intelligent Computation Group, focusing on interdisciplinary AI research.

Annabelle McIverView profile

Professor

• Program Verification
• Quantitative Information Flow
• Computer Security
• +3 more

Annabelle McIver is a Professor at the School of Computing, Macquarie University, and a member of the Data Horizons Research Centre, Future Communications Research Centre, and Frontier AI Research Centre. Her research focuses on program verification, quantitative information flow, and privacy-preserving technologies. She holds a DPhil in Mathematics from the University of Oxford and a BA from the University of Cambridge. McIver has held visiting positions at MIT, ETH Zurich, and INRIA, and was a Junior Research Fellow at St Hilda's College, Oxford. Her work has been recognized with awards including the 2015 Best Scientific Cybersecurity Paper and the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. She leads the Topete Research Group, exploring formal methods and their applications in security and privacy. Her research interests span formal verification, differential privacy, and the mathematical analysis of security flaws. Key projects include work on probabilistic programming, privacy in machine learning, and secure data pipelines. McIver has authored over 125 publications and contributed to initiatives like the Digital Finance CRC and the AIS-SHIELDS project securing health data. Her recent work emphasizes privacy-utility trade-offs in technologies like differential privacy and adversarial attacks. Education: PhD (DPhil) in Mathematics, University of Oxford (1990) BA (Double First) in Mathematics, University of Cambridge (1985) Awards: Best Scientific Cybersecurity Paper (2015) Distinguished Paper Award (2024) Platt Prize (1984) Unwin Prize (1985) McIver collaborates internationally on cybersecurity, privacy frameworks, and formal methods, with active projects in autonomous systems, healthcare data security, and AI ethics.

Marjan van den AkkerView profile

Associate Professor

• Operations Research
• Artificial Intelligence
• Public Transportation Planning
• +4 more

Dr. Marjan van den Akker is an Associate Professor in the Department of Information and Computing Sciences at Utrecht University, holding a joint position with KLM through the KickstartAI program. Her research focuses on the intersection of Operations Research and Artificial Intelligence, emphasizing robust planning algorithms, simulation, and optimization for public transportation and energy networks. She earned her PhD in Mathematics from Eindhoven University of Technology, specializing in scheduling and resource allocation. Marjan leads the Computing Science master program and teaches 'Optimization for Sustainability.' She co-directs the Utrecht AI & Mobility Lab and Robust Rail Lab, and serves as President of the Dutch Operations Research Society (NGB). Her work bridges academia and industry, collaborating with partners like KLM, TenneT, and NS to address real-world challenges in logistics, energy systems, and rail operations. Key contributions include airline recovery algorithms, energy-constrained scheduling, and robustness metrics for stochastic systems. Her research has been published in top journals/conferences such as Journal of Scheduling , Computers & Operations Research , and IEEE conferences. Projects include the EU-funded ACDC-ESM initiative analyzing renewable energy systems and the Building Resilient Airline Operations collaboration with KLM.

Toine PietersView profile

Professor

• History of Medicine
• Digital Humanities
• Pharmacology
• +5 more

Prof. Dr. Toine Pieters is a Professor in the Faculty of Science at Utrecht University, holding dual affiliations with the Department of Pharmacoepidemiology and Clinical Pharmacology and the Department of History and Philosophy of Science. His research focuses on the history of pharmaceuticals, digital humanities, and interdisciplinary education, with an emphasis on leprosy, drug regulation, and public health policy. He has led projects like HoMed (enhancing speech recognition in healthcare) and DReAM (analyzing historical drug debates). Recipient of the 2006 Worth Estes Award for outstanding work in pharmacology history. Active in teaching, including courses on the history of pharmacy and interdisciplinary techno-science. His work bridges historical analysis with modern challenges, such as opioid crises, digital governance, and ethical drug practices. Notable projects include exploring leprosy in Suriname and developing AI tools for healthcare documentation.

Nysret MusliuView profile

Associate Professor

• Artificial Intelligence
• Combinatorial Optimization
• Heuristic Optimization
• +5 more

Associate Professor Dr. Nysret Musliu is a faculty member at TU Wien's Faculty of Informatics, leading the Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling. He holds a PhD (2001) and habilitation (2007) from TU Wien. His research focuses on AI techniques, combinatorial optimization, scheduling, and heuristic methods applied to real-world problems like workforce scheduling and timetabling. Education: PhD in Computer Science (2001) TU Wien Habilitation in Applied Computer Science (2007) TU Wien Master's degree (1996) University of Prishtina Research Interests: Artificial Intelligence Techniques Combinatorial Optimization Heuristic Algorithms Machine Learning Integration Hybrid Techniques for Scheduling Problems Grants: Christian Doppler Laboratory (2017-2024), FWF Projects, Softnet Austria, and industry collaborations. Advises PhD students in optimization and scheduling domains. Labs/Teams: Christian Doppler Laboratory, Databases and Artificial Intelligence Group.

David LaiView profile

Associate Professor

• Operations Research
• Logistics Optimization
• Operations Management
• +3 more

Dr. David Lai is an Associate Professor of Operations Management at Queen Mary University of London (QMUL), affiliated with the School of Business and Management and the Department of Business Analytics and Applied Economics. He holds a PhD in Systems Engineering and Engineering Management from the Chinese University of Hong Kong (2014). Before joining QMUL, he served as Assistant Professor at the University of Southampton (2022–2024), Eindhoven University of Technology (2020–2022), and VU Amsterdam (2014–2020). His research focuses on optimization challenges in logistics, transportation systems, and operations management, including vehicle routing problems, scheduling, and heuristic algorithm development. Key interests include dynamic routing under stochastic conditions, green logistics, and real-time rescheduling for public transit. Dr. Lai’s work spans theoretical contributions (e.g., tabu search heuristics, graph-based formulations) and applied studies (e.g., construction industry logistics, public transport disruption management). His publications highlight innovations in metaheuristics, multi-objective optimization, and network design. He currently holds no explicitly listed scientific awards but has a robust academic portfolio with over 15 peer-reviewed articles since 2009. His teaching and supervision focus on operations management and analytics, though specific advisee names are not documented here.

Amin BeheshtiView profile

Professor

• Artificial Intelligence
• Data Science
• Machine Learning
• +5 more

Prof. Amin Beheshti is a Full Professor of Data Science at Macquarie University and Adjunct Professor of Computer Science at UNSW Sydney. He leads the Centre for Applied Artificial Intelligence, the Data Science Lab, and the Big Data Society. His research focuses on applying AI to solve real-world challenges in healthcare, cybersecurity, and industry. Beheshti holds a PhD in Computer Science from UNSW Sydney and has secured over $40 million in research funding through 40+ projects. Education: PhD in Computer Science and Engineering (UNSW Sydney, 2013) Master's and Bachelor's in Computer Science (First Class Honours) Research Interests: Artificial Intelligence Graph Neural Networks Healthcare Analytics Cybersecurity AI Ethics & Commercialization Awards & Recognition: Australian AI Award (2024): AI Academic/Researcher of the Year Macquarie Excellence Awards (2022–2023) APA Scholarship (2009–2012) Grants & Projects: 40+ research projects funded ($40M+) Leadership in Federated Learning, Healthcare AI, and Cybersecurity initiatives Labs & Initiatives: Centre for Applied Artificial Intelligence Data Science Lab Big Data Society