Find Academic Supervisors

Richard BaileyView profile

Professor

• Environmental Systems
• Human-Environment Interactions
• Mathematical Modeling
• +7 more

Professor Richard Bailey is Professor of Environmental Systems at the University of Oxford's School of Geography and the Environment. He serves as a Tutorial Fellow and Dean of St Catherine's College, a Senior Research Fellow at the Institute for New Economic Thinking, and a member of the Oxford Biodiversity Institute. Bailey co-directs the Oxford Luminescence Dating Laboratory and leads the CoHESys-lab research group focused on complex human-environmental systems. Richard's research centers on the dynamics of natural environmental systems and coupled human-environment interactions across various timescales. His work employs mathematical modeling, numerical simulations, and empirical data analysis to understand complex systems, with a strong focus on sustainability applications, particularly in ocean systems. The CoHESys-lab develops models that integrate machine learning methods to explore implementable policies for environmental challenges. His recent publications demonstrate a strong trend toward interdisciplinary research combining environmental science, computational modeling, and sustainability. Bailey's work spans from theoretical explorations of complex systems to applied projects addressing pressing issues like plastic pollution, fisheries management, and climate change impacts on marine ecosystems. His research increasingly incorporates agent-based modeling and machine learning approaches to tackle coupled human-environment challenges. Co-Director of Oxford Martin School Programme on Sustainable Oceans Co-Director of Oxford Luminescence Dating Laboratory Leader of CoHESys-lab research group Professor Bailey currently supervises graduate students Maike Nowatzki (focusing on dunefield morphology using deep learning) and Thomas Youngman (working on macroeconomic modeling for UK climate transition). His recent advisees include Emily Neil (rewilding impacts at Knepp Estate) and Diana Bailey (LM OSL analysis techniques). His research is supported through collaborations with organizations including Ocean Conservancy, Stanford University, George Mason University, and the University of California at Santa Barbara, with computational support from Amazon Web Services. Bailey leads the CoHESys-lab (Complex Human Environmental Systems Simulation Laboratory), which develops sophisticated models for understanding coupled human-environment systems. His OSIRIS framework (Ocean Systems Interactions, Risks, Instabilities and Synergies) represents a significant contribution to analyzing multiple stressors on marine systems. The lab maintains strong collaborations with environmental organizations and academic institutions worldwide to address sustainability challenges through innovative computational approaches.

• AI Agents
• Multi-Agent AI
• Artificial Intelligence
• +14 more

Saptarashmi Bandyopadhyay is a Tenure-Track Assistant Professor of Computer Science at the City College of New York and the Graduate Center at the City University of New York (CUNY). Her research focuses on Artificial Intelligence Agents and Autonomous Decision Making, with special emphasis on Multi-Agent Reinforcement Learning, Multi-Agent Imitation Learning, and related paradigms. She has established significant collaborations with leading institutions including Google DeepMind, Carnegie Mellon University, Oxford University, and MIT. Dr. Bandyopadhyay received her PhD from the University of Maryland, College Park, where she was advised by Professor John Dickerson and Professor Tom Goldstein. Prior to that, she graduated from Penn State in 2020 with a thesis on Multimodal Computer Vision in Medical Domain advised by Prof. William Evan Higgins. Her research expertise spans multiple domains of AI including Multi-Agent Systems, Reinforcement Learning, Imitation Learning, and Multimodal Perception. She specializes in developing AI agents for applications in climate conservation, economic systems, and AI safety. Her work integrates techniques from computer vision, natural language processing, and robotics to create more robust and explainable AI systems that can operate effectively in complex, real-world scenarios. Current work includes improving explainable AI, developing Multimodal LLM/VLM/Robotic Agents, and creating libraries to speed up Multi-Agent evolutionary training with JAXMARL. Analysis of Dr. Bandyopadhyay's publication record reveals a clear progression from foundational work in medical imaging and natural language processing toward increasingly sophisticated multi-agent AI systems. Her recent publications demonstrate a strong focus on Multi-Agent Reinforcement Learning frameworks like JAXMARL, with applications spanning from supply chain orchestration to climate conservation. The interdisciplinary nature of her work is evident in publications spanning computer vision, NLP, and multi-agent systems conferences including AAAI, NeurIPS, AAMAS, EMNLP, and ACL. DoGood Fellow (2022) UMD Dean's Summer Fellow (2021) Dr. Bandyopadhyay has been actively involved in securing research funding from major agencies including NSF, NIH, DoD, and ARL. She served as the lead PhD student RA in a DoD project for Multi-Agent Explainable AI to improve AI trustworthiness. Her service to the academic community includes membership on program committees for major conferences including IJCAI 2024, KDD 2024, ACL 2024, and AAMAS 2023-2024. She has also created the AI Agents Seminar Series at UMD in 2022 with over 1,000 participants from six continents. Currently, Dr. Bandyopadhyay leads research on improving explainable AI, developing Multimodal LLM/VLM/Robotic Agents, and creating libraries to speed up Multi-Agent evolutionary training with JAXMARL. Her lab collaborates prominently with researchers from Google DeepMind, Carnegie Mellon University, Oxford University, University of Sheffield, Waymo, Meta AI, and MIT, with special focus on Dr. Jakob Foerster's and Dr. Robert Loftin's groups.

Joris PascalView profile

Lecturer

• Medical Sensor Systems
• Biomedical Engineering
• Magnetic Field Sensing
• +3 more

Prof. Joris Pascal is a Lecturer in Life Science Technologies at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW), affiliated with the School of Life Sciences and the Institute for Medical Engineering and Medical Informatics in Muttenz, Switzerland. He holds a Ph.D. in Microelectronics from the University of Strasbourg (2008), an M.S. in Electrical Engineering from Supélec (2005), and a B.S. in Electrical Engineering from the University of Strasbourg (2003). His professional experience includes roles as Senior Scientist and Scientist at ABB Switzerland Ltd. (2009–2015), and Research Assistant at the iCube laboratory (2005–2009). His research focuses on advanced biosignal processing, medical sensor systems, magnetic field sensing, and miniaturized diagnostic/therapeutic tools. Key projects include electromagnetic tracking systems for neurosurgery, magnetic safety assessment for MRI workers, and wearable sensor integration. He has 9 patents (5 granted, 4 pending) and extensive IEEE review involvement. Recent publications emphasize magnetic field cameras, MRI safety protocols, and nanoscale sensor technologies. His work bridges electrical engineering and biomedical applications, with contributions to medical robotics, portable NMR devices, and cardiac implant safety. Active in PhD committee roles and industry-academia collaborations, he leads projects at the intersection of sensor innovation and clinical needs.

Alfred SteinView profile

Professor

• Spatial Statistics
• Image Analysis
• Remote Sensing
• +7 more

Prof. Alfred Stein is a Full Professor in Spatial Statistics and Image Analysis at the Department of Earth Observation Science, Faculty ITC, University of Twente. He earned his MSc in Mathematics and Information Science from Eindhoven University of Technology and a PhD in Spatial Statistics from Wageningen University. His career spans roles at Wageningen University (1988–2002), ITC (2002–present), including leadership positions as department head, vice-rector research, and portfolio holder for education. Education: MSc (Eindhoven University of Technology), PhD (Wageningen University) Leadership: Department Head (Earth Observation Science), Vice-Rector Research (2008–2012), Portfolio Holder Education (2012–) His research focuses on Spatial and Spatio-Temporal Statistics , emphasizing Bayesian inference , data quality , image analysis , and fuzzy techniques . Key application domains include agriculture, health, urban land use, coastal systems, hazards, and wildlife. He has mentored over 30 PhD students since 1998, with 11 currently under supervision. Recent research trends highlight AI-driven remote sensing for glacier mapping, urban livability, and disease modeling. Publications span Deep Learning for SAR tomography, Bayesian hierarchical models for health data, and multitemporal SAR analysis for environmental monitoring. Awards include the Best Paper Award (2019) and ISARA Founder's Award (2020) . Scientific Awards Best Paper Award (2019) ISARA Founder's Award (2020) As Editor-in-Chief of Spatial Statistics and associate editor for multiple journals, he leads academic discourse. Collaborations include the University of Cape Town and University of Pretoria as Honorary Professor. His work contributes to UN Sustainable Development Goals, particularly in climate action and sustainable cities.

Albert P.H.J. SchenningView profile

Professor

• Stimuli-responsive Materials
• Liquid Crystals
• Polymer Science
• +5 more

Albert P.H.J. Schenning is a Full Professor of Stimuli-responsive Functional Materials and Devices at Eindhoven University of Technology (TU/e), affiliated with the Institute for Complex Molecular Systems (ICMS). His work focuses on developing smart materials for sustainable energy, healthcare, and environmental applications. He holds roles in the TU/e’s Eindhoven Polymer Laboratories (EPL) and the Advanced Research Center Chemical Building Blocks Consortium (ARC CBBC). Education: M.Sc. (1992) and Ph.D. (1996) in Chemistry from Radboud University Nijmegen. Postdoctoral research at TU/e (1996) and ETH Zurich (1997). Became a KNAW fellow (1998–2003) before advancing through academic ranks at TU/e: Assistant Professor (2003), Associate Professor (2005), and Full Professor (2014). Research interests include stimuli-responsive polymers, self-healing materials, and liquid crystal actuators. Notable projects involve smart materials for food packaging, greenhouses, and photonic coatings. Awards include the Alfred Saupe Prize (2024) and Golden Medal from the Royal Dutch Chemical Society (2005). Teaching: Courses include Molecules and Materials, Experimental Soft Matter, and Nanomaterials. Supervised over 128 students. Collaborations span industries like DSM and Merck. Labs/Teams: Leads the Stimuli-responsive Functional Materials & Devices (SFD) group, engaged in multidisciplinary projects from molecule-to-device development.

• Catalysis
• Computational Chemistry
• Heterogeneous Catalysis
• +3 more

Dr. Guanna Li is an Assistant Professor in the Biobased Chemistry and Technology group at Wageningen University & Research. Her research focuses on catalyst design and reaction mechanism studies in heterogeneous catalysis, leveraging advanced computational methodologies such as DFT, ab initio molecular dynamics (AIMD), and machine learning. She investigates dynamic catalyst behavior under reaction conditions and aims to establish structure-activity relationships for rational catalyst design. Key areas include biomass conversion, CO2 hydrogenation, and plastic upcycling. Dr. Li holds a dual PhD from Eindhoven University of Technology (TU/e) and Dalian Institute of Chemical Physics, with postdoctoral experience at TU/e and Delft University of Technology, supported by a VENI Talent Programme grant (NWO, 2016). She collaborates closely with experimental groups to bridge theory and practice in catalytic processes. Education: PhD in Heterogeneous Catalysis (TU/e), PhD in Raman Optical Activity (DICP), postdoctoral research at TU/e and Delft University of Technology. Research Interests: Multiscale modeling of catalytic reactions, transition metal carbide catalysts, CO2 hydrogenation to methanol, plastic upcycling via mechano-catalysis, and surface reaction dynamics. Her methodologies include DFT, AIMD, and machine learning for mechanistic insights. Projects: Transition metal carbide catalyst design, CO2 hydrogenation collaborations with DICP, and plastic upcycling initiatives. These projects emphasize synergy between computational and experimental approaches. Awards: VENI Talent Programme Grant (2016). Grants/Advising: Supervises MSc/PhD students and postdocs on catalysis and computational chemistry projects. Open to collaborations and applications for research positions. Labs/Teams: Leads a research team in Wageningen’s Biobased Chemistry and Technology group, emphasizing computational catalysis and sustainable chemical processes.

• Satellite Networks
• 5G/6G Non-Terrestrial Networks
• Artificial Intelligence in Space
• +6 more

Peng Hu is an Adjunct Professor at the University of Waterloo, focusing on cutting-edge research in satellite networks, 5G/6G non-terrestrial networks, and AI-driven solutions for space sustainability. His work emphasizes autonomous network management, edge computing in space, and IoT applications for industrial and healthcare systems. Research interests span satellite mega-constellations, space object detection via deep learning, and optimizing free-space optical (FSO) communication. He explores challenges in latency management, energy efficiency, and fault tolerance across heterogeneous networks, including UAV-assisted systems and industrial IoT. Key contributions include the SatAIOps framework for autonomous satellite operations and the SatNetOps multi-layer networking scheme. He has pioneered datasets like Satellite Object Detection (SOD) and developed anomaly detection methods using genetic algorithms and Monte Carlo dropout. Peng Hu’s recent work addresses global connectivity gaps via non-terrestrial networks and reviews reinforcement learning algorithms for space-air-ground integration. His technical leadership is reflected in workshops like the 5th IEEE ICC 2025 Satellite Mega-Constellations workshop.

George ShakerView profile

Adjunct Associate Professor

• Wireless Sensor Technologies
• Biomedical Engineering
• Antennas and Propagation
• +5 more

George Shaker is an Adjunct Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada, and Lab Director of the Wireless Sensors and Devices Laboratory at the Schlegel-UW Research Institute for Aging. He is also Chief Scientist at Spark Technology Labs. His research focuses on wireless sensor technologies for healthcare, autonomous systems, and IoT. He earned his bachelor's from Cairo University and master's/PhD from the University of Waterloo. Education: Bachelor’s degree, Cairo University, Egypt Master’s degree, University of Waterloo, Canada PhD, University of Waterloo, Canada Research Interests: Dr. Shaker’s work spans advanced wireless sensor systems for healthcare monitoring, UAVs, and automotive applications. His lab developed the MIRADA initiative for aging populations and pioneered radar-based non-invasive glucose monitoring. Key areas include mm-wave radar, antenna design, bioelectromagnetics, and machine learning integration. He has co-authored over 200 publications and holds 35+ patents, collaborating with companies like Google, Apple, and Toyota. Recent Article Trends: His 2025 work emphasizes AI-driven radar systems for activity recognition, bio-sensing metasurfaces, and UAV classification using digital twins. Projects include 4D radar imaging, low-cost milk quality monitoring, and smart furniture for cardiac health. Awards: IEEE AP-S Best Paper Award IEEE MTT-S Graduate Fellowship arXiv Top Downloaded Medical Article URSI Young Scientist Award Multiple student awards (see full list above) Advising & Grants: He advises graduate students in ECE and has led projects funded by NSERC and industry partners. His students have won Velocity Fund, NASA Tech Briefs, and Canadian Space Agency awards. Collaborates with over 40 companies including Amazon, Microsoft, and Medella Health. Labs & Initiatives: Leads the Wireless Sensors & Devices Lab and co-founded MIRADA, a smart apartment for aging healthcare. Active in Spark Labs for wireless innovation.

• Signal Processing
• Wireless Communications
• Networking
• +3 more

Philippe Ciblat is a Professor at TELECOM Paris Tech, affiliated with the Department of Signal Processing and Communications. His research spans signal processing, wireless communications, and machine learning applications in networking. He has collaborated extensively with institutions like the University of Paris-Saclay and international researchers in areas such as cooperative communication protocols, resource allocation, and coding theory. Research Interests: Machine learning for signal processing, wireless channel modeling (Rician fading), lattice decoding, caching strategies, and distributed optimization. Notable Work: Pioneered transformer-based packet scheduling, neural network approaches to lattice decoding, and effective capacity analysis in fading channels. His contributions include over 170 publications in top venues (IEEE Trans. Signal Process., IEEE Trans. Wireless Commun.) and collaborations with industry partners on practical implementations like cache-aided polar coding. He has advised multiple researchers in distributed systems and wireless resource management.

• Visual Physiology
• Neurobotics
• Insect Visual Systems
• +4 more

Associate Professor Steven Wiederman holds the position of Associate Dean Research in the Faculty of Health and Medical Sciences at the University of Adelaide. He leads the Visual Physiology & Neurobotics Laboratory within the School of Biomedicine. His research focuses on understanding visual processing in insects, particularly dragonflies, and translating these insights into applications for autonomous robotics and neuro-inspired systems. Key areas include target detection, optic flow processing, and selective attention mechanisms in biological systems. His interdisciplinary work combines electrophysiological techniques, computational modeling, and robotics engineering. Current projects explore how dragonfly neurons enable precise tracking of moving targets amidst clutter, with applications in developing advanced vision systems for drones and robots. He has secured significant funding through ARC fellowships and industry partnerships, including projects on neuro-inspired deep learning for defense applications. Academic achievements include an ARC DECRA Fellowship (2011–2015) and an ARC Future Fellowship (2016–2020). He is a member of the ARC College of Experts and actively supervises PhD and Master’s students in neuroscience, robotics, and computational biology. His lab collaborates with engineers and computer scientists to bridge biological insights with technological innovation. Publications highlight breakthroughs in understanding neuronal mechanisms underlying predictive vision, selective attention, and adaptive processing in dragonfly visual systems. Media coverage includes features in Wall Street Journal , New York Times , and Science Daily , emphasizing the potential of insect-inspired solutions for future technologies.

Ahmedullah AzizView profile

Assistant Professor

• Mixed-Signal VLSI Circuits
• Non-Volatile Memory
• Beyond CMOS Device Design
• +4 more

Ahmedullah Aziz is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, within the Tickle College of Engineering. He earned his Ph.D. in Electrical and Computer Engineering from Purdue University (2019), an M.S. from Pennsylvania State University (2016), and a B.S. from Bangladesh University of Engineering & Technology (2013). His research focuses on mixed-signal VLSI circuits, non-volatile memory, and beyond-CMOS device design, emphasizing emerging technologies like ferroelectrics and spintronics. He leads the NorDIC Lab and has published over 60 articles in journals, conferences, and patents. Education: PhD, Electrical & Computer Engineering, Purdue University, 2019 MS, Electrical Engineering, Pennsylvania State University, 2016 BS, Electrical & Electronic Engineering, BUET, 2013 Research Interests: Aziz explores device-circuit-system co-design techniques, particularly in cryogenic neuromorphic systems, superconducting memory, and ferroelectric-based circuits. His work bridges material innovation with practical applications, aiming to advance energy-efficient and high-performance computing. Awards: EDAA Outstanding Dissertation Award (2019/2020) Outstanding Graduate Student Research Award (Purdue, 2019) Samsung 'Icon' Award (2013) Best Publication Awards (SRC-DARPA STARnet, 2015/2016) Advising & Grants: While specific grants are not detailed, his research has been supported by prestigious institutions. He has advised/co-advised projects in emerging technologies but no listed students. His work extends to reviewing IEEE journals, conference TPC roles, and editorial contributions. Labs & Teams: He directs the NorDIC Lab, focusing on next-generation computing and device innovation.

Richard MolyetView profile

Senior Lecturer

• Automatic Control
• Robotics
• Sensor Development
• +7 more

Dr. Richard Molyet is a Senior Lecturer and Undergraduate Director in the Department of Electrical Engineering and Computer Science at the University of Toledo's College of Engineering. After retiring as Associate Professor in 2002, he returned to academia in 2005 as Visiting Professor and transitioned to Associate Lecturer in 2008. Education: Ph.D. in Engineering Science (1981) from University of Toledo His research spans Automatic Control , Robotics , Smart-Grid Systems , and Biomedical Applications . Recent publications focus on deep learning for medical diagnostics and hybrid power network optimization , while earlier work explored repetitive control algorithms and microprocessor-based motion analysis . Scientific Recognition: IEEE Third Millennium Medal (2000) IEEE-USA Professional Achievement Award (2002) University of Toledo Outstanding Teacher Award (2016) Currently advising 3 PhD students and multiple Master’s candidates, Dr. Molyet has served on numerous academic committees since the 1980s. He maintains an active role in IEEE Toledo Section's executive board for 39 years .

Dong S. HaView profile

Professor

• Analog IC Design
• RF ICs
• Power Management Circuits
• +7 more

Dong S. Ha is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. As Founding Director of the Multifunctional Integrated Circuits & Systems (MICS) Lab, he focuses on advanced circuit design for energy harvesting, RF systems, and high-temperature electronics. His work spans analog/RF ICs, power management circuits, and wireless IoT solutions with machine learning integration. Education: PhD (1986) and MS (1984) in Electrical and Computer Engineering from the University of Iowa; B.S. (1974) in Electrical Engineering from Seoul National University. Research interests include energy harvesting (piezoelectric, thermal, RF), high-temperature RF circuits for oil/gas/spacecraft applications, and smart IoT systems. He actively seeks students for projects in RF design, energy harvesting, and embedded systems. His lab develops cutting-edge solutions for harsh environment communication, sustainable energy systems, and smart agriculture monitoring. Awarded IEEE Fellow (2008) for contributions to VLSI design/test. Key publications span energy harvesting circuits, GaN-based high-temperature systems, and low-power IoT architectures. Current projects include self-sustaining smart farm networks using federated learning and attack-resistant sensor systems. Labs/Teams: Leads MICS Lab focusing on integrated circuits and systems. Collaborates on cross-disciplinary projects involving machine learning, embedded systems, and sustainable energy. Active in industry partnerships for aerospace and automotive applications.

Nir RotenbergView profile

Associate Professor

• Quantum Photonics
• Nanophotonics
• Quantum Optics
• +3 more

Dr. Nir Rotenberg is an Associate Professor in the Department of Physics, Engineering Physics, and Astronomy at Queen's University. He is affiliated with the Faculty of Arts and Science, the Condensed Matter Physics & Optics group, and the Centre for Nanophotonics. He holds a PhD from the University of Toronto and focuses on quantum photonics and nanophotonics, particularly exploring nonlinear phenomena in solid-state quantum systems. His lab develops novel quantum devices and circuits using nanophotonic platforms, emphasizing single quantum emitter interactions with few photons. Research Interests: Quantum photonics, nanophotonics, quantum optics, nonlinear optics, quantum devices, and solid-state quantum emitters. His work bridges fundamental physics with applications in quantum information processing and photonic technologies. Recent publications highlight advancements in quantum dot positioning, waveguide-QED systems, and reconfigurable photonic circuits. He collaborates internationally and is a member of the Quantum Nanophotonics Lab (QNL), based in Stirling 308H. Dr. Rotenberg's research has implications for quantum computing, secure communication, and advanced optoelectronic systems.

Yu ChenView profile

Professor

• Smart cities
• Intelligent surveillance
• Edge-fog-cloud computing
• +6 more

Yu Chen is a Professor in the Department of Electrical and Computer Engineering at Binghamton University, State University of New York. He leads the Ubiquitous Smart & Sustainable Computing (US2C) Lab and serves as Director of the Center for Information Assurance and Cybersecurity (CIAC). His research focuses on Trust, Security, and Privacy in Edge-Fog-Cloud Computing, IoT, and Smart Cities. Dr. Chen holds a PhD from the University of Southern California (2006), with prior research under Professors Kai Hwang and Anthony F. J. Levi. His work has been funded by NSF, DoD, AFOSR, and industrial partners, yielding over 200 publications. He is a Senior Member of IEEE and SPIE, and a member of ACM. Education: PhD in Electrical Engineering, University of Southern California (2006) Affiliations: Director, US2C Lab Associate Director, CIAC Research Interests: Smart Cities, Intelligent Surveillance, Edge-Fog-Cloud Computing, IoT Security, and Privacy-Preserving Technologies. His work emphasizes real-time systems, resilient edge architectures, and decentralized consensus protocols for IoT. Grants & Awards: Funded by NSF, DoD, AFOSR, NYS MDPI Computers 2019 Best Paper Award Best Student Poster Award (IEEE AIPR 2014) Students & Labs: Advised 19 students (PhD/Master’s). Key projects include secure edge video processing, ENF-based authentication, and blockchain for IoT. The US2C Lab explores smart city applications and edge computing resilience.