Find Academic Supervisors

David BuscherView profile

Professor

• Optical Interferometry
• Astronomical Instrumentation
• High-Resolution Imaging
• +3 more

Prof David Buscher is a Professor of Astrophysics at the University of Cambridge, affiliated with the Cavendish Laboratory. His work focuses on advancing optical/infrared interferometry through the Magdalena Ridge Observatory Interferometer (MROI) project and the ANDES spectrograph for the Extremely Large Telescope (ELT). He leads efforts in instrument design, automation, and data analysis, with a focus on high-resolution imaging of celestial objects. Education details are not explicitly stated in the text, though his affiliation with the Cavendish Laboratory implies a strong academic background in physics or astronomy. Research Interests: Optical interferometry techniques, interferometric imaging of distant objects (e.g., AGN dust tori, geosynchronous satellites), and development of advanced astronomical instruments. His work bridges theoretical frameworks with practical engineering challenges, emphasizing thermal stability, precision alignment, and real-time data processing. Articles from 2025 to 2020 reflect a progression toward achieving first fringes with MROI, optimizing fringe tracking systems, and exploring applications in space surveillance and exoplanet studies. Collaborations include the European Interferometry Initiative, emphasizing community-driven advancements and diversity in science. Scientific Awards: None explicitly mentioned in the provided texts. Advising & Grants: No explicit student names or grant details are listed. However, his role in MROI and ANDES suggests significant institutional and collaborative project funding. His work involves interdisciplinary teams across mechanical engineering, software development, and astrophysical theory. Labs/Teams: Lead investigator for MROI projects at the Cavendish Laboratory. Collaborates with international teams on ELT instrumentation and interferometry education initiatives.

Pulkit AgrawalView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Machine Learning
• +3 more

Pulkit Agrawal is an Associate Professor at the Massachusetts Institute of Technology (MIT), holding dual appointments in the departments of Artificial Intelligence and Decision-making (AI+D), Computer Science (CS), and Electrical Engineering (EE) within the School of Engineering. He is a core member of the Embodied Intelligence Community of Research, focusing on advancing intelligent systems that interact with the physical world through robotics, AI, and machine learning. His work emphasizes bridging simulation and real-world applications in robotics, human-robot interaction, and medical technology. Key research areas include reinforcement learning for robotic manipulation, simulation-to-real transfer, and embodied intelligence. Notable projects include developing methods for robust home robot training using real-to-sim-to-real approaches, tactile-driven object retrieval systems, and AI-driven glucose control models for post-surgical care. He also explores ethical AI in healthcare and assistive technologies for cognitive impairment monitoring. Dr. Agrawal’s contributions span robotics hardware design (e.g., DEXOS hand exoskeleton), neuro-inspired algorithms (e.g., grid cell-based map building), and scalable robot learning via crowdsourced environments. His work often integrates interdisciplinary methods from control theory, computer vision, and biomedical engineering. He leads the MIT CSAIL Embodied Intelligence group and collaborates on projects like OpenEQA for embodied question answering and DexHub for large-scale robot data collection. His research has been recognized through promotions within EECS and featured in outlets like MIT News for innovations in home robotics and AI-driven medical solutions. He advises on grants related to intelligent systems and maintains active collaborations across academia and industry.

Ross McAreeView profile

Professor

• Mining Automation
• Robotics
• Machinery Dynamics
• +2 more

Ross McAree is Professor and Head of the School of Mechanical and Mining Engineering at the University of Queensland, and Fellow of the Australian Academy of Technology and Engineering. His research focuses on mining automation, including pioneering work on autonomous excavators and bulldozers. Research encompasses machinery dynamics, sensor fusion for terrain mapping, and collision avoidance systems. Recent publications address point cloud processing for mining applications, predictive maintenance using machine learning, and hyperspectral ore classification techniques. Publication themes show progression from fundamental sensing/control algorithms toward integrated autonomous mining systems, with increasing emphasis on AI applications in harsh environments. Work consistently addresses industrial challenges through rigorous theoretical development and field validation. Awards & Honors: Fellow of the Australian Academy of Technology and Engineering (ATSE)

Herschel RabitzView profile

Professor

• Quantum control
• Molecular dynamics
• Systems biology
• +3 more

Herschel Rabitz is the Charles Phelps Smyth Professor of Chemistry at Princeton University, leading research in quantum control and systems biology. His group combines theoretical and experimental approaches to manipulate molecular quantum dynamics and biological networks. Research focuses include quantum optimal control, molecular spectroscopy, and algorithms for bio-network engineering. Key research areas: laser-driven molecular dynamics, quantum computing applications, and metabolic pathway optimization. Recent publications (2022-2025) demonstrate advances in quantum algorithms, molecular control techniques, and interdisciplinary methods integrating machine learning with quantum physics. Awards and Honors: Alexander von Humboldt Award (2014, 2000) Willis E. Lamb Medal for Quantum Optics (2003) Fellow of AAAS (2004) Genetic and Evolutionary Computation Gold Award (2004) Advising includes 4 doctoral students and collaboration with Princeton’s Andlinger Center for Energy and the Environment. Lab resources feature femtosecond laser systems for high-throughput experiments.

Jing WenView profile

Assistant Professor

• Construction education
• Workforce recruitment and retention
• Human-technology interaction
• +3 more

Jing Wen is an Assistant Professor in the School of Building Construction at Georgia Institute of Technology (Georgia Tech), part of the College of Design. She holds a Ph.D. from the M.E. Rinker, Sr. School of Construction Management at the University of Florida. Her research focuses on human-technology interactions in construction management, emphasizing emerging technologies to enhance diversity, education, and workforce development in the construction industry. Specific interests include attracting students to construction, improving learning through educational technology, and leveraging reality capture and visualization technologies. Dr. Wen’s expertise spans construction education, workforce recruitment strategies, human-technology interaction frameworks, and advanced visualization techniques. She teaches courses on Building Information Modeling (BIM) and Digital Twins for the Built Environment, integrating cutting-edge research into her pedagogy to prepare students for future industry challenges. Her multidisciplinary approach combines educational technology and collaboration with practitioners in construction and computer science. Her recent publications explore operator learning, inverse problems, and neural network scaling laws, reflecting her computational and theoretical contributions to machine learning and data-driven methodologies. Though no specific awards are listed, her work underscores innovation in applying advanced technologies to solve construction sector challenges. Advising and grant activities are not explicitly detailed, but her research emphasizes collaboration with industry and academic partners. She leads efforts to bridge educational and technological gaps in construction through her research and teaching initiatives.

Patrick DohertyView profile

Professor

• Artificial Intelligence
• Autonomous Systems
• Knowledge Representation
• +6 more

Patrick Doherty is a Professor and Head of the Artificial Intelligence Laboratory (AILAB) at Linköping University's Department of Computer and Information Science (IDA). His research focuses on knowledge representation, automated planning, and intelligent autonomous systems, with applications in UAV-based emergency rescue and disaster informatics. He leads the AIICS division, emphasizing collaborative robotics and multi-agent systems. He holds a EURAI (ECCAI) fellowship and serves as Editor-in-Chief of the Artificial Intelligence Journal. His work includes developing UAV systems for alpine rescue, real-time mission planning frameworks, and robust robotic collaboration protocols. Notable contributions include the SHERPA project and the RGS RDF graph synchronization system. Doherty's research also addresses human-robot collaboration, safety-critical decision-making, and resilient autonomous systems within the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research group has won international competitions in UAV and automated planning domains, demonstrating advanced UAV deployment in emergency scenarios. Key areas include geolocated object detection, distributed situation awareness, and efficient wireless mesh network configurations for disaster response. Doherty frequently delivers keynote speeches at conferences such as IJCAI, KR, and ECAI, emphasizing practical applications of AI in safety-critical systems. Key Projects: SHERPA (human-robot collaboration in alpine rescue), WASP public safety arena, UAV navigation systems Awards/Fellowships: EURAI Fellow, Editor-in-Chief of Artificial Intelligence Journal Labs/Teams: AILAB, Unmanned Aircraft Systems Technology Laboratory (UASTECH) Publications span foundational AI theory (e.g., forgetting policies, rough set reasoning) to applied robotics (e.g., UAV navigation, real-time search algorithms). His work bridges high-level mission planning with low-level control, ensuring robustness and autonomy in dynamic environments.

• Sleep and Circadian Rhythms
• Data Science
• Mathematical Biology
• +4 more

Anne Skeldon is a Professor of Mathematics at the University of Surrey , actively contributing to the Mathematics of Life and Social Sciences research group and co-leading the Centre for Mathematical and Computational Biology . Her work bridges mathematical theory with applications in sleep science, circadian biology, and complex systems analysis. Primary Affiliation: School of Mathematics and Physics, University of Surrey Key Collaborations: Surrey Sleep Research Centre, Department of Psychology, Centre for Mathematical and Computational Biology Research Interests focus on: Mathematical modeling of sleep-wake regulation and circadian rhythms Impact of light environment on sleep physiology Data science applications for fatigue risk management Nonlinear dynamics in biological systems Personalized interventions for sleep disorders Recent Publications highlight: Integrating circadian and sleep-wake driven gene expression models Designing digital tools for circadian monitoring in dementia Mathematical analysis of light sensitivity in circadian pacemaker assessment Model-based approaches for circadian entrainment in modern environments Supervision includes: Current PhD students in sleep modeling and circadian data science Completed students with projects spanning mathematical ecology to neuronal dynamics

Kevin LewisView profile

Professor

• Planetary Geology
• Planetary Geophysics
• Mars Climate History
• +3 more

Kevin Lewis is the Morton K. Blaustein Vice Chair and Professor in the Department of Earth and Planetary Sciences at The Johns Hopkins University. His research focuses on planetary geophysics, with a particular emphasis on Mars' geological and climatic history. Lewis investigates sedimentary rocks and their records of past climate and habitability, as well as planetary lithospheres using magnetic, gravity, and topography data sets. Education: PhD in Earth & Planetary Sciences from the California Institute of Technology (Caltech). Research interests span Martian sedimentology, aeolian processes, planetary surface evolution, and the integration of rover and orbital data to understand Martian geology. His work includes analyzing Gale Crater's layered deposits, assessing methane dynamics in Mars' atmosphere, and developing drilling technologies for lunar ice mining. Lewis has contributed to missions like OSIRIS-REx and VIPER, focusing on sample return and in-situ resource utilization. His recent articles highlight studies on Mars' stratigraphic unconformities, methane cycling mechanisms, and rover-based observations of dust and sediment transport. He also explores challenges in extraterrestrial terrain navigation for robotic missions. Lewis' interdisciplinary approach combines field geology, remote sensing, and computational modeling to advance understanding of planetary environments. No scientific awards or grants are explicitly listed in the provided texts, though his extensive publication record indicates sustained research engagement. He collaborates on projects involving NASA missions and planetary exploration technology development.

Abdulmotaleb El SaddikView profile

Professor

• Multimedia Computing
• Haptics
• Ambient Intelligence
• +7 more

Abdulmotaleb El Saddik is a Distinguished Professor and University Research Chair in Ambient Interactive Media and Applications at the University of Ottawa's School of Electrical Engineering and Computer Science. He directs the Multimedia Communications Research Laboratory (MCRLab) and the Distributed & Collaborative Virtual Environments Research Laboratory (DISCOVER). His research focuses on haptic systems, multimedia security, and ambient intelligence. He has authored over 350 publications, secured $17M in research grants, and supervised over 100 researchers. El Saddik holds IEEE Fellow status (2009) and multiple prestigious awards, including the Friedrich Wilhelm Bessel Award (2007) and the George S. Glinski Award (2012). He has served as editor for ACM and IEEE journals, chaired over 30 international conferences, and pioneered work in haptics, service-oriented architectures, and collaborative environments. His labs develop cutting-edge solutions for immersive VR, tele-surveillance, and AI-driven systems. Notable contributions include advancements in tactile communication, cybersecurity frameworks, and the integration of digital twins in healthcare. His work bridges academia and industry, with applications in consumer electronics, robotics, and smart cities.

Juan YeView profile

Academic

• Adaptive Pervasive Systems
• Sensor-Based Human Activity Recognition
• Sensor Fusion
• +6 more

Dr Juan Ye is a Reader in the School of Computer Science at the University of St Andrews. He holds a PhD from University College Dublin and BSc/MSc degrees from Wuhan University. His research focuses on adaptive pervasive systems, sensor-based human activity recognition, and overcoming challenges in real-world sensor data analytics. He teaches modules on component technologies and machine learning, and supervises PhD students and research internships. His research addresses the limitations of classical machine learning by developing continual-learning frameworks for long-term, real-world applications. Key interests include sensor fusion, context awareness, ontologies, and uncertainty reasoning. He has contributed to projects on gaze tracking, animal density estimation, and robust gaze interaction methods. Dr Ye collaborates with interdisciplinary teams and has published extensively on topics like continual learning, gaze estimation, and ecological monitoring. His work aligns with UN Sustainable Development Goals through applications in environmental and assistive technologies.

Michael ManganView profile

Senior Lecturer

• Machine Learning
• Robotics
• Biorobotics
• +5 more

Dr. Michael Mangan is a Senior Lecturer in Machine Learning and Robotics at the University of Sheffield's School of Computer Science, and Operations Director of Sheffield Robotics. He holds an MEng in Avionics from the University of Glasgow (2004), an MSc in Neuroinformatics from the University of Edinburgh (2006), and a PhD in Biorobotics (2011). His research focuses on understanding insect navigation and applying these insights to robotics, emphasizing bio-inspired systems for autonomous navigation and smart agriculture. Research interests include modeling insect vision, navigation strategies, and neural mechanisms, with applications to robotics and pest control. Mangan leads the ManganLab, exploring how simple nervous systems enable complex behaviors through computational and robotic models. His work integrates biology, computer science, and engineering to develop robust autonomous systems. Key contributions include tools like CompoundRay for simulating compound eyes and I2Bot for multi-modal insect navigation simulations. He has secured grants such as ActiveAI (£953k) and SkyEye (£25k), focusing on AI transparency, neuromorphic control, and vision-based localization. Professional memberships include the Machine Learning research group and involvement in Living Machines conferences. Current projects explore fruit detection in agriculture, neuromorphic sequence learning, and animal tracking in natural environments.

Xiaoyu ZhengView profile

Research Fellow

• AI in Healthcare
• Medical Imaging
• Biomedical Data Analysis
• +3 more

Xiaoyu Zheng is a Research Fellow at Queen Mary University of London, affiliated with the Digital Environment Research Institute (DERI). They hold a Postdoctoral Research Associate position focused on AI for Musculoskeletal (MSK) Conditions, working on the NIHR Barts Biomedical Research Centre (BRC) Grant in the MSK theme. Their research involves analyzing multi-omic data from a cohort of 1000 rheumatoid arthritis (RA) patients, integrating longitudinal biopsy data, blood samples, and patient-reported outcomes (PROMs). Key contributions include developing an MSK Digital Twin platform to enable personalized treatment plans, disease trajectory prediction, and therapeutic response forecasting. Research interests center on AI-driven healthcare solutions, particularly in musculoskeletal conditions, multi-omic data integration, and real-time physiological monitoring systems. Their work bridges machine learning advancements with clinical applications, emphasizing wearable sensor technologies and signal processing for continuous health assessment. Recent publications highlight innovations in medical image classification, opto-physiological monitoring systems, and noise reduction techniques for physiological signals during physical activity. Advising and grants include the NIHR Barts BRC Grant, focusing on translating multi-omic insights into clinical tools through digital twin frameworks. Collaborations are embedded within interdisciplinary teams at DERI, combining computational methods with biomedical expertise to address unmet clinical needs in musculoskeletal disease management. Labs and teams: Active contributor to the Digital Environment Research Institute (DERI), part of Queen Mary University of London’s research ecosystem. Projects emphasize AI integration with clinical workflows to enhance precision medicine and patient-centric care models.

Yong ZhuView profile

Professor

• Nanomaterials
• Flexible Electronics
• Wearable Sensors
• +5 more

Yong Zhu is a Professor of Materials Science and Engineering at North Carolina State University, affiliated with the College of Engineering. His research focuses on nanomaterials, flexible electronics, and wearable sensors, with an emphasis on sustainability and biomedical applications. He leads projects on recyclable nanowire networks, biodegradable electronics, and functional textile integration. His work often bridges material science with engineering innovations for healthcare, agriculture, and environmental monitoring. Dr. Zhu maintains an active laboratory and collaborates on multidisciplinary projects, as evidenced by his extensive publication record and industry partnerships. Education: Details not explicitly provided in the text. Research Interests: Dr. Zhu's work explores cutting-edge domains such as: Design of stretchable and recyclable nanowire-based devices Integration of sensors into wearable and textile platforms Development of eco-friendly materials for flexible electronics Advanced ultrasound and bioelectronic sensing systems Mechanical behavior of nanomaterials under diverse conditions His research underscores a commitment to both technological innovation and environmental responsibility. Article Trends: Recent publications highlight advancements in biodegradable electronics, ultrasonic transducers with tunable focusing, and novel sensor designs for real-time health monitoring. He consistently addresses challenges in materials durability, scalability, and sustainability. Emerging themes include wearable systems for plant health and non-invasive medical diagnostics. Awards/Grants: No specific awards are mentioned in the provided text, though his extensive publication record suggests recognition in materials and biomedical engineering circles. Active grants are likely tied to his research projects but details are not provided here. Advising & Labs: While specific advisees are not listed, Dr. Zhu's laboratory focuses on nanomaterials and soft electronics, likely training graduate students in these areas. His work frequently involves collaborations with industry and other academic institutions. Future Work: Priorities include scaling sustainable nanowire technologies, advancing wearable systems for clinical and agricultural applications, and improving the recyclability of electronic components. His lab continues to explore the mechanical and electrical limits of nanomaterials in dynamic environments.

• Orbital Dynamics
• Uncertainty Quantification
• Autonomous Navigation
• +6 more

Dr. Jinglang Feng is a Senior Lecturer in the Mechanical and Aerospace Engineering department at the University of Strathclyde , within the Faculty of Engineering. Her research focuses on advanced aerospace systems, including orbital dynamics, uncertainty quantification, autonomous navigation, and space robotics. She holds a PhD in Aerospace Engineering from Delft University of Technology and has conducted postdoctoral research at Nanjing University and the Purple Mountain Observatory. Her work addresses challenges in space sustainability, such as asteroid deflection, active debris removal (ADR), and in-orbit servicing (ISAM). Education: Ph.D. in Aerospace Engineering, Delft University of Technology (Focus: Nonlinear Dynamics Modelling) Postdoctoral Researcher, Nanjing University and Purple Mountain Observatory Research Interests: Irregular Gravity Modelling Resonance Dynamics and Chaos Estimation AI-Based Sensor Fusion and Autonomous Navigation Space Robotics and Robotic Arm Control Key awards include the 2023 Robust Trajectory Design Award (Hera Mission) and 2022 Excellent Reviewer for the Journal of Guidance, Control, and Dynamics. She actively contributes to professional organizations like the International Astronautical Federation (IAF) Astrodynamics Committee. Her projects include leading initiatives on AI-driven space operations (A14S3), solar power satellite analysis (SOLERO), and sensor fusion for asteroid navigation. Current research emphasizes sustainable space technologies, such as robotic arm control for debris removal and AI-enhanced navigation systems.

• Robotics
• Biomedical Engineering
• Control Systems
• +5 more

Jason J. Gu , PhD, PEng., FEIC, FCAE is a full Professor in the Department of Electrical and Computer Engineering at Dalhousie University within the School of Biomedical Engineering . He has served as IEEE Canada President-elect (2018–2019) and IEEE Canada President (2020–2021) , and is a Fellow of both the Engineering Institute of Canada and the Canadian Academy of Engineering . Research Interests: Robotics : mobile, surgical, rehabilitation, and humanoid robots Biomedical Engineering : medical robotic devices, artificial eye implant control, rehabilitation assistive devices Control Systems : real-time intelligent and adaptive control, industrial and medical applications Alternative Energy : green plug filters, V2G/V2H systems His laboratory focuses on developing new technologies for robotic systems, integrating multi-sensor data fusion, embedded systems, and advanced control methodologies. His work spans theoretical innovation and practical implementation, with applications in healthcare, aerospace, and autonomous systems. Recent Publications: His 2024–2025 publications demonstrate a strong focus on computer vision , robotics , and AI-driven control systems . Topics include SLAM, YOLO-based object detection, UAV systems, human-robot interaction, and biomedical imaging, reflecting a multidisciplinary approach to solving real-world problems. Awards & Honors: IEEE Canada President-elect (2018–2019) IEEE Canada President (2020–2021) Fellow of the Engineering Institute of Canada (FEIC) Fellow of the Canadian Academy of Engineering (FCAE) Laboratory & Projects: Dr. Gu leads a research lab that develops technologies for robotic systems, including the PA10 Portable General-Purpose Intelligent Arm and the B21r Mobile Robotic System. His lab is actively involved in projects related to real-time control, sensor fusion, and biomedical applications.