Find Academic Supervisors

Neil D. B. BruceView profile

Associate Professor

• Computer Vision
• Deep Learning
• Machine Learning
• +6 more

Neil D. B. Bruce is an Associate Professor in the School of Computer Science at the University of Guelph, Canada. His research focuses on computer vision, deep learning, and computational neuroscience, with a strong emphasis on visual saliency, neural networks, and semantic segmentation. He holds a BSc in Computer Science & Pure Math from the University of Guelph, an MASc in Systems Design Engineering from the University of Waterloo, and a PhD in Computer Science from York University. Prior to Guelph, he held academic positions at Ryerson University and the University of Manitoba. Dr. Bruce leads the Vision Lab, exploring topics like attention mechanisms, image processing, and AI-driven solutions for visual computing challenges. His work bridges theoretical models with practical applications, including real-world gaze behavior analysis and exposure blending techniques. Key contributions include saliency prediction frameworks (e.g., AIM model) and semantic segmentation networks (EML-Net, Iterative Gating Networks). His research also intersects with interdisciplinary fields such as neuroscience and healthcare informatics, as seen in recent studies on avian influenza outbreak detection using social media data. Teaching highlights include courses in neural networks, data science, and machine learning. He actively supervises graduate and undergraduate research projects, emphasizing computational methods and AI innovation.

Kevin A. HaasView profile

Professor

• Coastal Engineering
• Numerical Modeling
• Sediment Transport
• +5 more

Kevin A. Haas is a Professor of Civil Engineering and Associate Chair for Undergraduate Programs at the Georgia Institute of Technology's School of Civil and Environmental Engineering, part of the team developing Coastal Engineering programs in Savannah. He holds a B.S. (Ohio State University, 1994), M.S. (Ohio State University), and Ph.D. (University of Delaware, 2001) in Civil/Coastal Engineering, followed by a postdoctoral fellowship at the University of Delaware. Education: B.S. in Civil Engineering (Distinction), The Ohio State University, 1994 M.S. in Civil Engineering, The Ohio State University, 1997 Ph.D. in Coastal Engineering, University of Delaware, 2001 Research Interests: Focuses on coastal dynamics, numerical modeling of nearshore circulation, sediment transport, and hydrodynamics of rip currents. Explores wave energy extraction, tidal marsh sediment transport, and applications of video-based field observations. His work bridges environmental engineering and renewable energy, emphasizing sustainable coastal systems. Key Achievements: Recipient of the Delaware Sea Grant Award for outstanding Ph.D. research on rip currents Lead in securing a $1M educational grant for undergraduate programs at Georgia Tech Active in developing tidal and wave energy assessment methodologies Grants & Labs: Principal investigator on projects funded by federal agencies and industry partnerships Conducts fieldwork and modeling at Georgia Tech's Savannah campus and coastal sites Collaborates with USGS, NOAA, and international teams on marine energy initiatives

• Remote Sensing
• Geographic Information Systems (GIS)
• Environmental Monitoring
• +7 more

Giorgos Mountrakis is a Professor in the Department of Environmental Resources Engineering at SUNY College of Environmental Science and Forestry (ESF). His research focuses on environmental monitoring using remote sensing, environmental modeling through geographic methods, and decision support systems for ecological and urban challenges. He holds a Dipl. Eng. from the National Technical University of Athens (1998), an M.S. (2000), and Ph.D. (2004) from the University of Maine. His work integrates advanced technologies like satellite imagery, LiDAR, and machine learning to address land cover dynamics, climate impacts, and wildlife conservation. Current advisees include Atef Amriche (PhD candidate in Geospatial Information Science), Babak Haji Seyed asadollah (PhD in Environmental Resources Engineering), Ahmadreza Safaeinia (PhD in Environmental Resources Engineering), and Zhixin Wang (PhD in Geospatial Information Science). Key research themes include: land use/cover classification using deep neural networks, climate change impacts on forests and rangelands, and optimizing spatial-temporal models for large-scale environmental analysis. His projects span global datasets (e.g., Landsat, MODIS) and regional case studies in the US, Mongolia, and Algeria. Publications emphasize methodological advancements in remote sensing, such as fusion of multisensor data, accuracy assessment frameworks, and applications in biodiversity conservation. His work bridges technical innovation with practical environmental decision-making, addressing issues like urban growth prediction and wildlife-vehicle collision mitigation.

Jonathan KingslakeView profile

Associate Professor

• Geophysics
• Arctic Studies
• Cryosphere
• +3 more

Jonathan Kingslake is an Associate Professor in the Department of Earth and Environmental Sciences at Columbia University and a member of the Lamont-Doherty Earth Observatory. His research focuses on glaciology, subglacial and supraglacial hydrology, and ice sheet dynamics. He holds a BSc in Physics from the University of York and a PhD in Glaciology from the University of Sheffield. Prior to Columbia, he worked as a Glacier Geophysicist at the British Antarctic Survey. His work integrates field observations, remote sensing, and mathematical modeling to improve understanding of glacial processes and climate impacts. Education: B.Sc. Physics, University of York (2009) Ph.D. Glaciology, University of Sheffield (2013) Key Research Themes: Subglacial and supraglacial water dynamics West Antarctic Ice Sheet evolution Ice shelf stability and meltwater mechanisms Glacial lake formation and drainage Fieldwork Experience: Antarctica (2013–2015): Radar and GPS measurements of ice flow Greenland (2017): Meltwater refreezing studies Juneau Icefield (2018): Firn compaction and ice core sampling Grants & Collaborations: NSF-funded projects on Antarctic ice shelf dynamics Lenfest Junior Faculty Development Grant (Juneau Icefield fieldwork) Part of the Polar Geophysics Group at Lamont Lab/Team: Advises a group of students and postdocs in the Polar Geophysics Group, focusing on interdisciplinary approaches to glacial processes.

Barry RawnView profile

Academic

• Renewable Energy Integration
• Power Systems Modeling
• Sustainable Energy
• +5 more

Barry Rawn is an Associate Teaching Professor in Electrical and Computer Engineering at Carnegie Mellon University (CMU), affiliated with the College of Engineering. He focuses on stressed centralized electricity infrastructure and off-grid energy systems, particularly in Nigeria. His research contributes to the IEEE Working Group on Sustainable Energy for Developing Communities and emphasizes renewable energy integration, grid stability, and smart grid technologies. Education: All degrees from the University of Toronto: Ph.D. in Electrical Engineering MASc in Electrical Engineering BASc in Engineering Science Research Interests: Rawn explores the interplay between centralized grids and decentralized energy solutions in developing regions. His work spans transmission/distribution system optimization, renewable energy modeling, and circular economy frameworks for plastics in Africa. He also investigates battery reuse and smart grid innovations, often collaborating with industry and international partners. Key Article Trends: Recent work emphasizes circular plastics economies in Africa, vehicle-to-grid integration, and Nigerian grid modernization. Earlier research focused on wind power integration, grid stability under reverse power flows, and HVDC transmission planning. Awards: No specific awards listed, though his work aligns with IEEE sustainability initiatives. Advising/Grants: No grant details provided; maintains affiliations with CMU Africa and Brunel University London’s Smart Power Networks Theme. Collaborative activities include industrial partnerships and cross-sectoral projects in Africa. Labs/Teams: Engaged with the Institute of Energy Futures (UK) and leads fieldwork in Nigeria/Rwanda. Active in IEEE committees and African energy policy dialogues.

Navin RamankuttyView profile

Professor

• Sustainable Food Systems
• Environmental Science
• Agricultural Sustainability
• +3 more

Navin Ramankutty is a Professor and Tier 1 Canada Research Chair in Data Science for Sustainable Global Food Systems at the School of Public Policy and Global Affairs (SPPGA) and the Institute for Resources, Environment and Sustainability (IRES), University of British Columbia. He serves as Director of IRES. His research focuses on global land use change, sustainable agriculture, and minimizing agriculture's environmental footprint while addressing food security. He has contributed to major initiatives like the Millennium Ecosystem Assessment and IPCC reports, and received the Wihuri International Prize (2020). Research interests include climate change impacts on crop yields, smallholder farming biodiversity, and sustainable land-use policies. Key contributions include developing global datasets on cropland use and analyzing trade-offs between food production and environmental conservation. Scientific awards include the Wihuri Prize, Leopold Leadership Fellowship, and editorial roles in journals like Environmental Research Letters and Global Food Security . His work emphasizes the intersection of data science, policy, and ecological modeling to drive sustainable food systems.

Dimitrios ZekkosView profile

Professor

• Geotechnical Engineering
• Landslides
• Unmanned Aerial Vehicles (UAVs)
• +7 more

Dimitrios Zekkos is a Professor of Civil and Environmental Engineering at the University of California, Berkeley, and serves as Vice Chair for Strategic Planning and Implementation. He is also Founder and CEO of ARGO-E LLC, focusing on geotechnical and geoenvironmental solutions. Ph.D., University of California, Berkeley (2005) M.S., Geoengineering, University of California, Berkeley (2002) Joint B.S./M.Eng Degree, University of Patras, Greece (2001) Zekkos specializes in infrastructure resilience, landslide analysis, earthquake engineering, and UAV/satellite-enabled geohazard reconnaissance. His work combines experimental, computational, and autonomous systems to model earth material response to static/dynamic loads. His recent publications emphasize landslide inventory statistics, gravelly soil liquefaction, MSW landfill behavior, and erosion dynamics in mountainous regions. Key themes include remote sensing integration, machine learning applications, and 3D modeling of geohazards. Outstanding Innovator Award (ISSMGE, 2015) ASCE Arthur Casagrande Professional Development Award (2012) ASCE Collingwood Prize ASCE Middlebrooks Award Zekkos leads the Center for Land-Surface Hazards (CLaSH) and has secured EPA grants for landfill methane mitigation. His research spans geotechnical, geoenvironmental, and earthquake engineering domains with a focus on autonomy-enabled data acquisition.

Elliot J. CrowleyView profile

Senior Lecturer

• Machine Learning
• Automated Machine Learning
• Low-Resource Deep Learning
• +3 more

Dr Elliot J. Crowley is a Senior Lecturer in Electronics and Electrical Engineering at the University of Edinburgh, serving as Discipline Programme Manager. He co-leads the Bayesian and Neural Systems research group. His research focuses on simplifying machine learning, automated ML, low-resource deep learning, and engineering applications. He holds an MEng in Engineering Science and a DPhil (PhD) from the University of Oxford, with postdoctoral experience at Edinburgh's School of Informatics. He leads the EPSRC New Investigator Award and participates in the dAIEdge Horizon Network. Notable contributions include foundational work in neural architecture search (NAS), probabilistic methods for model efficiency, and applications in computer vision. His courses, such as the Data Analysis and Machine Learning module, emphasize practical Python-based learning for engineering students. Key awards include an EPSRC grant and recognition through distinguished papers at ASPLOS 2021. His team includes current PhD students (Linus Ericsson, Miguel Espinosa) and former advisees (Chenhongyi Yang at Meta, Jack Turner at Qualcomm). Research spans from NAS algorithms to ethical machine learning practices, with a focus on bridging theoretical advances and real-world engineering challenges.

Daniel HarrisView profile

Senior Lecturer

• Climate Change Impacts
• Coastal Morphodynamics
• Coral Reef Geomorphology
• +3 more

Dr Daniel Harris is a Senior Lecturer in the Department of Geography at the School of the Environment, University of Queensland (UQ). His research focuses on coastal and coral reef morphodynamics, integrating physical processes like waves and tides with ecological and geological systems. Prior to UQ, he held positions at the University of Sydney and the Leibniz Center for Tropical Marine Ecology (ZMT). He leads The BeachLab, dedicated to developing tools for coastal resilience in a warming world. Research interests include coral reef structural complexity, coastal protection under climate change, and surf zone processes. His work combines field data, remote sensing (e.g., LiDAR, drones), and numerical modeling to address both fundamental and applied questions. Notable projects involve quantifying coral rubble mobility, analyzing Holocene reef evolution, and assessing shoreline change via satellite imagery. His expertise spans marine geoscience, physical oceanography, and environmental adaptation strategies. Publications emphasize coral reef dynamics, coastal geomorphology, and climate impacts. He collaborates with ecologists, geologists, and coastal engineers to advance interdisciplinary solutions. Teaching focuses on geography and marine science, reflecting his commitment to educating future researchers and practitioners. Key contributions include advancing methods for shoreline monitoring using Bayesian networks and Google Earth Engine. His research highlights the critical role of coral reefs in coastal protection, particularly under rising sea levels and extreme weather events. The BeachLab’s work bridges academic inquiry with practical management strategies for vulnerable coastal ecosystems.

• Soil-Landscape Modelling
• Digital Soil Mapping
• Climate Change Mitigation
• +5 more

Professor Budiman Minasny is a leading academic in soil-landscape modeling at the University of Sydney, affiliated with the School of Life and Environmental Sciences. He holds roles as Theme Leader of Soil, Carbon, and Water at the Sydney Institute of Agriculture and is a member of the Net Zero Institute, China Studies Centre, and Sydney Southeast Asia Centre. His expertise spans digital soil mapping, climate change mitigation, and soil security. Minasny has over 160 international publications and has pioneered methodologies in spectral soil analysis and peatland assessment. He earned his undergraduate degree from Universitas Sumatera Utara and advanced degrees in soil science from the University of Sydney. Research interests include soil carbon dynamics, peatland management, and the integration of AI/remote sensing in soil science. Awards include the Australian Research Council’s QEII and Future Fellowships, and recognition as a Web of Science Highly Cited Researcher (2019). Current projects focus on soil carbon auditing, real-time soil moisture monitoring, and global peatland mapping. Minasny leads multidisciplinary teams addressing climate resilience, with grants from institutions like the National Soil Carbon Innovation Challenge and Australia-India Strategic Research Fund. Awards: QEII Fellowship, Future Fellowship, Highly Cited Researcher 2019 Grants: Includes initiatives on soil carbon platforms, continental-scale soil assessments, and viral diversity studies. His work bridges environmental science and policy, advocating for soil security frameworks to balance agricultural productivity with ecological preservation.

Javier IrizarryView profile

Professor

• Unmanned Aerial Systems (Drones) in Construction
• Construction 4.0
• Reality Capture Technology
• +3 more

Javier Irizarry is a Professor and Interim Chair in the School of Building Construction at Georgia Tech's College of Design. He also serves as Associate Dean for Academic Affairs and Outreach , overseeing academic programs, diversity initiatives, and recruitment efforts. As Director of the CONECTech Lab, he pioneers research in Construction 4.0 and UAV applications for infrastructure challenges. Education : Ph.D. (Civil Engineering), Purdue University Masters in Engineering Management, Polytechnic University of Puerto Rico B.S. (Civil Engineering), University of Puerto Rico, Mayagüez Research Focus : Irizarry’s work integrates cutting-edge technologies into construction, including Unmanned Aerial Systems (UAS), Reality Capture, Virtual/Augmented Reality, and Human Factors in Robotics. His lab develops frameworks for Construction 4.0 , emphasizing technology-driven solutions for safety and efficiency. Lab & Impact : The CONECTech Lab focuses on Construction 4.0 , advancing drone-based inspection systems, digital twins, and safety analytics. His research has led to over 100 publications, with applications in infrastructure monitoring, safety management, and educational tools. Leadership : As Associate Dean, Irizarry drives academic innovation and outreach, including pre-college programs and diversity strategies. He is a licensed Professional Engineer (PE) and FAA-licensed drone pilot, bridging academia and industry.

Carlos Cámara-MenoyoView profile

Lecturer

• Urban Studies
• Open Science
• Urban Commons
• +3 more

Carlos Cámara-Menoyo is a Senior Research Software Engineer at the University of Warwick's Centre for Interdisciplinary Methodologies, where he bridges technical expertise with urban research. He holds a PhD in Information and Knowledge Society from the Universitat Oberta de Catalunya (2018), awarded the IND+I Science Prize. His academic roles include Lecturer at Universidad San Jorge and Associate Lecturer at Oxford Brookes University and Universitat Oberta de Catalunya. Education : PhD in Information and Knowledge Society (2018), Universitat Oberta de Catalunya MSc in Information and Knowledge Society (2012), Universitat Oberta de Catalunya MSc Architecture (2004), Universitat Politècnica de Catalunya Research Interests : Urban Studies, Open Science, Spatial Justice, Data Visualization, and Urban Commons. His work explores commodifications between cities, technology, and society, focusing on equitable urban development and participatory methods. Articles : His recent work addresses the Food-Water-Energy Nexus, participatory urban governance, and inclusive GIS applications. Key themes include sustainability transformations and co-design processes. Awards : IND+I Science Prize (2018), Cum Laude PhD distinction. Advising & Grants : Supervised Master’s theses on urban commons and co-production. Active in collaborative projects like Zaragoza Accesible and the Creating Interfaces initiative. Labs & Teams : Leads research software engineering efforts at CIM, promoting open science and reproducible workflows.

Natalia EfremovaView profile

Senior Lecturer

• Machine Learning
• Deep Learning
• Computer Vision
• +4 more

Dr. Natalia Efremova is a Senior Lecturer in Digital Economy at Queen Mary University of London (QMUL), School of Business and Management. She joined QMUL in November 2021 and is a member of the Centre for Globalisation Research (CGR) and a fellow of the Digital Environment Research Institute (DERI). Her research focuses on applying machine learning and deep neural networks to address sustainability challenges such as climate change, sustainable agriculture, and environmental monitoring. She holds a Ph.D. in Computer Science (Neural Networks for Computer Vision) from Kyoto University and an MBA from the University of Oxford, where she also worked as a Teradata Research Fellow at the Said Business School. Education: Ph.D. in Computer Science, Kyoto University, Japan (2012) MBA, University of Oxford, Said Business School (2021) Previous Roles: Associate Professor, Plekhanov University of Economics, Russia (2012–2016) Teradata Research Fellow, University of Oxford (2016–2021) Research Interests: Dr. Efremova’s work emphasizes developing transparent ML models for sustainable land-use and climate-related applications, as well as ethical AI frameworks for sustainable development goals. Her projects involve satellite data analysis (e.g., Sentinel imagery) for precision agriculture, soil moisture estimation, and crop monitoring. Teaching & Supervision: She teaches courses in Business Analytics (e.g., Group Projects in Business Analytics) and supervises PhD students in AI applications for sustainability. She is currently co-director of the MSc in Environmental Analytics program (2023). Key Themes in Publications: Her articles focus on AI-driven solutions for environmental challenges, including crop mapping, soil carbon estimation, and regenerative grazing monitoring. Methodologies include deep learning (e.g., Transformers, GANs) and remote sensing data fusion. Affiliations: Member of CGR and fellow of DERI, contributing to interdisciplinary research on globalization and environmental AI.

Anthony ConstantinouView profile

Senior Lecturer

• Causal Machine Learning
• Decision Systems
• Bayesian Networks
• +6 more

Anthony Constantinou is a Senior Lecturer at Queen Mary University of London, part of the School of Electronic Engineering and Computer Science. He leads the MInDS Research Group and the Bayesian AI Lab, focusing on causal machine learning for decision-making under uncertainty. His research spans healthcare, defense, sports, economics, and gaming, with a strong emphasis on Bayesian networks and causal inference. Research Interests: Causal machine learning, decision systems, Bayesian networks, uncertainty quantification, and their applications in healthcare, military defense, sports analytics, and economics. He collaborates with academia and industry to advance AI-driven decision-making systems. Grants: Noted for securing funding such as the Engineering and Physical Sciences Research Council grant (EP/S001646/1) for Bayesian AI research. His work emphasizes practical applications in diverse fields through collaborative projects. Labs & Teams: Head of the MInDS Research Group and Bayesian AI Lab, fostering interdisciplinary research in AI and decision systems.

• Privacy-Preserving Algorithms
• Differential Privacy
• Federated Learning
• +6 more

Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (QMUL), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Libera Università di Bolzano (2018) and a Diploma in Media Informatics from the University of Munich (2014). His research focuses on developing algorithms and data structures for managing large and sensitive datasets, with a particular emphasis on privacy-preserving techniques like differential privacy and federated learning. He has held postdoctoral roles at the University of Warwick and King's College London before joining QMUL in 2023. Research Interests: Privacy-preserving algorithms, differential privacy, federated learning, data management systems, randomized algorithms, and efficient query processing. His work bridges theoretical foundations with practical applications, aiming to enable secure data sharing while preserving individual privacy. Teaching: Leads undergraduate modules in Database Systems and Operating Systems at QMUL. His teaching emphasizes foundational concepts in computer science through rigorous coursework and practical projects. Grants and Funding: Currently supervises a PhD studentship titled 'Privacy-Preserving Algorithms: Unlocking Data Sharing for Medical Sciences & Machine Learning', funded by QMUL and open to UK home students. The role involves exploring privacy-preserving frameworks for collaborative data analysis. Professional Contributions: Serves as a reviewer for top-tier conferences (NeurIPS, ICML, SIGMOD, ICDE) and journals (IEEE TKDE, Data & Knowledge Engineering). Actively involved in conference organization, including NeurIPS Area Chair (2024) and ICDT Proceedings Chair (2024). Labs and Collaborations: Affiliated with the Centre for Fundamental Computer Science at QMUL, fostering interdisciplinary research in theoretical and applied computing. Engages with industry partners on privacy-enhancing technologies and data management solutions.