Find Academic Supervisors

Gregory ParadisView profile

Assistant Professor

• Sustainable Forest Management
• Operations Research
• Forest Economics
• +7 more

Gregory Paradis is an Assistant Professor in the Department of Forest Resources Management at the University of British Columbia (UBC) Faculty of Forestry. His research focuses on sustainable forest management, integrating operations research, mathematical optimization, and systems modeling to address complex interactions between ecosystems, industries, and society. He works with the FRESH Lab and collaborates with the Integrated Remote Sensing Studio, emphasizing ecological and economic integration in forest planning. Sustainable Forest Management Operations Research Forest Economics Data Science Risk Assessment GIS-based Methods His research spans forest inventory optimization, climate change adaptation strategies, wildfire risk modeling, and decision support systems for invasive species. He develops computational frameworks to enhance wood supply planning, carbon management, and ecological resilience. Recent work includes machine learning applications for fire safety in timber structures and automated road planning tools for wildlife conservation. Paradis’s publications highlight trends in applying optimization methods to sustainable forestry, with a focus on biodiversity, climate adaptation, and value chain innovation. He advocates for interdisciplinary approaches that bridge silviculture, industrial engineering, and data science to tackle emerging challenges in forest ecosystems. As an educator, he seeks motivated students with quantitative and creative problem-solving skills. His lab collaborates on remote sensing integration, risk assessment models, and policy-relevant forest management strategies, ensuring plans account for uncertainties like insect infestations or windthrow events.

• Computer Vision
• Autonomous Driving
• Robotics
• +7 more

Dominik Schörkhuber is a PreDoc Researcher at the Vienna University of Technology (TU Wien) in the Computer Vision department. With a background in Informatics (BSc, Dipl.-Ing.), he focuses on computer vision applications for autonomous driving, robotics, and human-machine interaction. His work spans driver action recognition, pedestrian prediction, and adaptive lighting systems. Current projects: Empathic Vehicle (2024–2026), SyntheticCabin (2021–2025), SmartProtect (2020–2025) Research themes: Video transformers, synthetic data transfer learning, multi-task learning, and sensor-lighting integration Specializes in 3D sensing, nighttime driving analysis, and mobile video creation tools

George BritovsekView profile

Professor

• Catalysis
• Sustainable Carbon Management
• Transition Metal Complexes
• +3 more

Professor George Britovsek (FRSC) is a leading figure in catalysis and sustainable carbon management at Imperial College London . As Director of the MRes in Catalysis & Engineering and Head of Teaching in Inorganic Chemistry, he bridges academic leadership with cutting-edge research. His work focuses on transition metal complexes for converting ethylene , alkanes , biomass , and CO₂ into valuable chemicals and fuels through industrial collaborations. Education : M.Sc. (Technical University of Aachen, 1990), Ph.D. (Aachen, 1993) under Prof. W. Keim Postdoctoral Training : University of Tasmania (1994-1996), Imperial College London (1996-2000) His research interests span: Selective oxidation of alkanes using bio-inspired iron complexes Alkene conversions to functional polymers via novel catalysts CO₂ valorization into polymers and cyclic carbonates Biomass-derived feedstocks for chemical synthesis Recent catalysis trends highlight his work on: Designing Fe-N/C catalysts for epoxidation Developing PN3P pincer ligands for H₂ activation Creating degradable polyethylene via iron-catalyzed chain growth Modeling alternating α-olefin distributions in chromium systems Awards : Fellow of the Royal Society of Chemistry (FRSC) Students & Collaborators actively engage in: Photocatalytic polymer degradation Electrocatalytic CO₂ conversion Functionalized polymeric materials 3D-printed catalytic scaffolds His Britovsek Research Group operates at the Molecular Sciences Research Hub, White City Campus, advancing both homogeneous and heterogeneous catalysis through experimental and computational approaches.

Sai RavelaView profile

Researcher

• Nonlinear Stochastic Dynamics
• Coherent Fluids
• Uncertainty Quantification
• +6 more

Sai Ravela is a Principal Research Scientist in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at the Massachusetts Institute of Technology (MIT). His research focuses on nonlinear stochastic dynamics, coherent fluid systems, uncertainty quantification, and autonomous observing technologies. He specializes in developing data-driven methodologies for natural hazard detection, climate change impacts, and environmental risk assessment. Ravela’s work integrates computational science with geophysical applications, including storm surge modeling, extreme rainfall analysis, and geothermal exploration. He pioneers techniques like neural dynamical systems and adversarial learning to improve predictive accuracy in nonstationary climate regimes. His contributions span environmental monitoring systems, autonomous aircraft resilience frameworks, and policy-informed climate vulnerability assessments. Key research areas include: Coastal flood risk in Bangladesh and Vietnam Dynamic data-driven applications systems (DDDAS) Machine learning for geosciences and environmental systems Uncertainty quantification in complex fluid dynamics He leads interdisciplinary projects at MIT’s Computational Science and Engineering (CSE) program, advancing methods for data assimilation, surrogate modeling, and real-time environmental observatories. His innovations bridge theoretical frameworks with practical solutions for climate adaptation and disaster resilience.

Richard MA TianbaiView profile

Associate Professor

• Systems & Networking
• Computer Networks
• Distributed Computing
• +3 more

Richard MA Tianbai is an Associate Professor at the School of Computing, National University of Singapore (NUS), and serves as IT Coordinator for Computing Facilities. He holds a B.Sc. (First-Class Honors) and M.Phil. from the Chinese University of Hong Kong, followed by an M.Sc. and Ph.D. from Columbia University, USA. His research focuses on systems & networking, distributed computing, and internet economics, with projects addressing cloud-based big data systems and internet peering agreements. Richard has received multiple awards, including the Best Paper Award Runners-up at ACM Mobihoc 2020 and the Teaching Excellence Award from NUS School of Computing in 2019. His work bridges theoretical models with practical applications in network economics and distributed systems. Education: B.Sc. (First-Class Honors), Computer Science & Engineering, Chinese University of Hong Kong (2002) M.Phil., Computer Science & Engineering, Chinese University of Hong Kong (2004) M.Sc., Columbia University (2008) Ph.D., Columbia University (2010) Research Interests: Cloud-Based Big Data Systems: Innovating serverless paradigms for real-time analytics (e.g., Stream as a Service). Internet Economics: Modeling peering agreements, premium peering dynamics, and regulatory alternatives to net neutrality. Distributed Computing: Auto-scaling frameworks (e.g., Elasticutor, DRS) for real-time stream processing. His research emphasizes performance guarantees and resource optimization in dynamic networked systems. Awards: Best Paper Award Runners-up (ACM Mobihoc 2020) Teaching Excellence Award (School of Computing 2019) Bell Labs Best Paper Award (IEEE SDP 2015) Best Paper Awards at ICNP 2014 and IC2E 2013 Grants & Advising: Recipient of research grants supporting projects like “Testbed for Innovative Inter-Networking Research.” Advises on doctoral projects related to stream processing and network economics. His work is funded by industry collaborations and institutional grants. Labs & Teams: Part of the Distributed Network Analysis (DNA) Research Group and Advanced Networking & Systems Research Group, fostering interdisciplinary collaborations in networking and distributed systems.

Graham DobereinerView profile

Associate Professor

• Homogeneous Catalysis
• Organometallic Chemistry
• Synthetic Chemistry
• +2 more

Graham Dobereiner is an Associate Professor and Robert L. Smith Early Career Professor in the Department of Chemistry at Temple University's College of Science and Technology. He received his Ph.D. from Yale University (2011) and completed postdoctoral research at MIT (2012-2014) after earning his B.S. from Brandeis University (2007). His research group develops novel homogeneous transition metal catalysts for synthetic chemistry applications spanning fine chemicals manufacturing, petrochemical processing, and drug discovery. The work integrates organometallic chemistry principles, combining organic molecular diversity with inorganic compound reactivity. Research areas include catalytic isomerization, oxidative synthesis, ligand design, and mechanistic studies of transition metal complexes. Analysis of his recent publications demonstrates strong emphasis on reaction mechanism elucidation, catalyst design for stereoselective transformations (particularly Z-selective isomerizations), and development of novel catalytic systems for sustainable synthesis. His group employs computational and experimental approaches to advance synthetic methodology.

Benjamin C. LeeView profile

Professor

• Computer Architecture
• Energy Efficiency
• Environmental Sustainability
• +4 more

Benjamin C. Lee is a Professor at the University of Pennsylvania, affiliated with both the Department of Electrical and Systems Engineering and the Department of Computer and Information Science. He also serves as Associate Department Chair and Co-Director of the NSF Expedition in Computing: Carbon Connect. His research spans computer architecture, energy efficiency, and environmental sustainability, with interdisciplinary links to machine learning and algorithmic economics. Education: Ph.D. and S.M. from Harvard University, B.S. from UC Berkeley, postdoctoral work at Stanford University. His research integrates computer architecture with datacenter-scale systems, focusing on energy-efficient designs, statistical learning for performance analysis, and sustainable computing. Past projects include the Hound framework for straggler diagnosis in datacenters and the CORE library for regression modeling in microarchitecture. The 15 most recent articles reflect expertise in datacenter architecture, mobile computing, and regression modeling for hardware. Awards include IEEE Fellow (2024), ACM Distinguished Scientist (2019), and multiple best paper/prize recognitions from SIGMETRICS, ASPLOS, and HPCA. Doctoral and Masters alumni have pursued roles at institutions like Meta, Microsoft, and University of Waterloo. Current affiliations include the Distributed Systems Laboratory (DSL) and PRECISE center, with industry collaborations at Google, Meta, and Intel.

• Power Systems
• Smart Grids
• Renewable Energy
• +7 more

Claudio Canizares is a University Professor and Hydro One Endowed Chair in the Department of Electrical and Computer Engineering at the University of Waterloo. He also serves as Executive Director of the Waterloo Institute for Sustainable Energy (WISE). With a career spanning over 30 years, his research focuses on power systems stability, smart grids, microgrids, and renewable energy integration. He has secured nearly $118 million in grants and supervised 180+ researchers/students. Education: PhD (1991) and MSc (1988) in Electrical Engineering from University of Wisconsin-Madison; Electrical Engineering Diploma (1984) from Escuela Politécnica Nacional, Ecuador. Research Interests : Nonlinear systems theory, FACTS/HVDC applications, energy storage systems, microgrid stability/control, renewable integration in remote communities, and smart grid analytics. His work emphasizes bridging academic research with industrial applications through collaborations with utilities and tech firms. Key Achievements : IEEE Transactions on Smart Grid Editor-In-Chief; multiple IEEE Fellowships (IEEE, Royal Society of Canada, Canadian Academy of Engineering); 2017 IEEE PES Outstanding Educator Award; 2016 IEEE Canada Electric Power Medal. His publications (370+) include landmark papers on microgrid stability definitions and control frameworks, cited over 29,000 times. Teaching: Recently taught ECE 140 (Linear Circuits), ECE 467 (Power Systems Analysis), and graduate courses ECE 6601PD/ECE 6613PD on power systems modeling and analysis.

Hongye ZhangView profile

Lecturer

• Superconductor Technology
• Cryogenic Techniques
• Electric Machines and Drives
• +3 more

Dr. Hongye Zhang serves as a Lecturer in Superconducting and Cryogenic Electric Machines at the School of Engineering, University of Edinburgh, while maintaining a Visiting Research Fellow position at the University of Manchester. He actively contributes to the European Society for Applied Superconductivity (ESAS) as a Board Member and chairs the international HTS 2026 workshop. His educational foundation includes: BSc and MSc in Electrical Engineering from Xi’an Jiaotong University (2015, 2018) Diplôme d’ingénieur (MEng) from École Centrale de Lyon (2018) PhD in Applied Superconductivity from the University of Edinburgh (2021) Dr. Zhang’s research centers on decarbonizing transport through superconducting/cryogenic electric machines for hydrogen-powered aircraft, integrating artificial intelligence with superconductor technology and cryogenic techniques. His work targets net zero emissions by developing high-power-density propulsion systems that leverage hydrogen energy and advanced numerical modeling of superconductors. Analysis of his 2022-2025 publications reveals dominant themes in superconducting machine design for wind energy and electric aviation, with significant contributions to loss mitigation, flux pump technology, and trapped field magnet applications. His research bridges fundamental superconductor characterization with practical system integration for renewable energy. Recognized with the 2021 IEEE Council on Superconductivity Graduate Study Fellowship, his professional engagements include: Early Career Editorial Board Member for Elsevier’s Superconductivity journal Technical Editor for IEEE Transactions on Applied Superconductivity Program Committee Member for SMT 2023 He leads critical research within the £54-million H2GEAR project developing hydrogen-electric aircraft propulsion, while teaching Power Engineering 2 and Electrical Machines courses. His advisory roles span doctoral supervision and industry collaboration through Energy Systems research institute. Based at the University of Edinburgh’s Faraday Building, Dr. Zhang directs a research group focused on hydrogen energy applications and superconducting machine testing, with strong ties to the H2GEAR consortium and ESAS working groups.

Adam KhalifaView profile

Assistant Professor

• Low-Power Electronics
• Biomedical Engineering
• Neural Engineering
• +4 more

Adam Khalifa is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Florida. His research focuses on low-power analog/RF/Mixed-mode ASIC design, miniaturization of biomedical devices, wireless powering solutions, and neural stimulation/recording techniques in animal models. He holds a PhD from Johns Hopkins University and degrees from The Hong Kong University of Science and Technology. His work emphasizes implant packaging, electrode microfabrication, and coil design for medical applications. Key research areas include developing energy-efficient wireless systems for implanted devices, such as magnetoelectric antennas and galvanic body-coupled powering. He has pioneered advancements in miniaturized implantable devices, including the 'Microbead' stimulator. His NIH T32 Fellowship (2019) and Ferdinand H. Fellowship (2018) reflect his impactful contributions. Publications highlight innovations in wireless power transfer, metamaterials for biomedical implants, and injectable microdevice fabrication. His work spans from circuit-level modeling to in vivo validation, emphasizing both technical and biological integration challenges. Collaborative efforts address challenges like implant migration tracking via MRI and energy harvesting for battery-free systems.

Lan WeiView profile

Associate Professor

• Nanoelectronic devices
• Cryogenic CMOS electronics
• GaN-based devices and circuits
• +5 more

Lan Wei is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada. She leads the Waterloo Emerging Integrated Systems Group, focusing on device-circuit co-optimization, cryogenic CMOS for quantum computing, and emerging technologies like GaN, RRAM, and low-dimensional materials. Her work bridges nanoelectronics and system-level applications, with notable contributions to the MIT Virtual Source GaN HEMT (MVSG) compact model, an industry-standard tool. Education: B.S. in Microelectronics and Economics, Peking University (2005) M.S. and Ph.D. in Electrical Engineering, Stanford University (2007, 2010) Research Interests: Nanoelectronic devices Cryogenic CMOS for quantum computing GaN-based circuits and systems RRAM-based neuromorphic computing Device-circuit interactive design Publications reflect her expertise in GaN modeling, quantum computing hardware, and RRAM applications. Recent work emphasizes scalable quantum control circuits and error-resilient neural networks using emerging technologies. Awards include the 2019 Ontario Early Researcher Award and the 2020 UWaterloo President's Excellence Award in Research. She has served on technical committees for IEDM, DATE, and ICCAD, and contributed to the ITRS roadmap. Teaching includes courses like ECE 240 (Electronic Circuits) and ECE 730 (Solid State Devices). Her group actively seeks graduate students with interest in integrated systems and nanoelectronics.

Matthew DunbabinView profile

Professor

• Environmental Robotics
• Vision-Based Perception
• Marine Robotics
• +4 more

Matthew Dunbabin is a Professor at Queensland University of Technology (QUT) and Chief Investigator at the Australian Centre for Robotic Vision (ACRV). His expertise spans environmental robotics, with a focus on vision-based autonomous systems for marine conservation, water quality monitoring, and greenhouse gas management. He holds a PhD from QUT and a BEng (Aerospace) from RMIT. Dunbabin has led projects at CSIRO and QUT, developing robots like COTSBot and RangerBot to combat marine pests and promote reef restoration. His work has earned national and international awards, including the 2019 Australian Water Association Award and 2016 Google Impact Challenge. Education: PhD in Engineering, Queensland University of Technology (Queensland, Australia) BEng (Hons) in Aerospace Engineering, Royal Melbourne Institute of Technology (Melbourne, Australia) Research Interests: Environmental robotics and autonomous systems Vision-based perception and classification Marine habitat restoration and pest control Greenhouse gas monitoring via autonomous vehicles Cooperative robotics and sensor networks Awards & Recognition: 2019 Australian Water Association Award (SAMMI Project) 2019 Good Design Award - Sustainability (RangerBot) 2016 Google Impact Challenge People’s Choice Award (RangerBot AUV) 2010 Australian ICT Industry Association National iAward (iSnet) 2006 Queensland Engineering Excellence Innovation Award (Starbug Project) Grants & Projects: ARC Centre of Excellence for Robotic Vision (ACRV): Leading robotic vision research (2014–present) Revolutionising Protection Against Air Pollution: Air quality monitoring networks (2015–present) Establishing Advanced Networks for Air Quality Sensing: Sensor development for urban environments (2017–present) Labs & Collaborations: ACRV at QUT Institute for Future Environments (QUT) CSIRO Autonomous Systems Laboratory (2001–2013)

Chuang GanView profile

Assistant Professor

• Artificial Intelligence
• Robotics
• Computer Vision
• +7 more

Chuang Gan is an Assistant Professor at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences and the Department of Computer Science. His work focuses on advancing artificial intelligence, robotics, computer vision, and embodied agents through interdisciplinary research combining neural networks, physical simulations, and multimodal learning. Research interests include generative models, reinforcement learning, vision-language integration, and scalable autonomous systems. He explores topics like world modeling for robots, adaptive policy learning, and physics-driven AI. His projects often involve creating systems that learn from visual, auditory, and tactile inputs to perform complex tasks such as object manipulation, navigation, and decision-making in dynamic environments. Recent research trends emphasize embodied AI systems capable of long-horizon planning, compositional reasoning, and efficient learning from limited data. His work bridges theory and practice, with applications in robotics, simulation platforms, and multimodal generation. Key contributions include frameworks for 3D scene understanding, adaptive world models, and novel training paradigms for large language models. His research has been applied to robotics platforms like RoboDreamer and UBSoft, focusing on unbounded soft environments. Collaborations involve designing benchmarks for physical scene understanding (e.g., Physion++), and creating tools like DiffTactile for tactile simulation. His work often integrates principles from differential geometry, PDE dynamics, and game theory. Chuang Gan’s research group develops open-source tools and benchmarks, such as the SoftZoo robot co-design platform and the SOK-Bench situated reasoning benchmark. His team emphasizes scalable alignment methods beyond human supervision and explores ethical AI through principles like symmetry-enhanced training.

Mariana ResenerView profile

Assistant Professor

• Power Systems Optimization
• Distributed Energy Resources
• Renewable Energy Integration
• +5 more

Mariana Resener is an Assistant Professor in the School of Sustainable Energy Engineering at Simon Fraser University (SFU). She holds a Ph.D. in Electrical Engineering (2016) from the Federal University of Rio Grande do Sul, Brazil, alongside M.Sc. (2011) and B.Sc. (2008) degrees in the same field. Her research focuses on optimizing power systems, particularly in distributed energy resources, energy storage, and volt/var control. She teaches courses like Power Electronics and Power Systems Analysis & Design. Her work emphasizes sustainable development in grid planning and energy infrastructure. As a Senior Member of IEEE and an Associate Editor for the Energy Systems Journal (Springer), she contributes to advancing smart grid technologies and renewable integration. Her research spans metaheuristic optimization, stochastic modeling, and grid resilience strategies for distributed systems. Recent projects include hybrid renewable energy systems for substations, EV charging station optimization, and fault analysis in unbalanced grids. She collaborates with industry on practical solutions for grid modernization and reliability enhancement.

• Wireless Communications
• Signal Processing
• 5G/6G Networks
• +3 more

Mikko Valkama is a Professor at the Department of Communications Engineering , part of the Faculty of Information Technology and Communication Sciences at Tampere University . His research focuses on advanced wireless communication systems, positioning technologies, and integrated sensing and communication (ISAC). He holds an Orcid ID ( 0000-0003-0361-0800 ) and can be reached at mikko.valkama@tuni.fi . Research interests span 5G/6G networks , RF antenna design , deep learning for signal processing , and millimeter-wave systems . He leads projects on positioning algorithms (e.g., mmWave SLAM, NLOS mitigation), ISAC architectures, and hardware-efficient transmitter linearization. Notable contributions include works on DECT-2020 NR standards, phase-based localization, and RIS-assisted systems. In 2025 alone, his group published over 30 articles on topics such as: Antenna array design for Ka-band and wideband applications Machine learning for power amplifier predistortion Bistatic radio SLAM and mmWave mapping Covert transmission and physical-layer security His work bridges theoretical advancements with practical implementations, often validated through experimental setups (e.g., TUJI1 dataset for indoor localization). No scientific awards were explicitly listed in the provided texts.