Andrew Markham is a Professor of Computer Science at the University of Oxford , affiliated with Kellogg College . He leads a research group focusing on Cyber Physical Systems (CPS) , specializing in sensors, signal processing, and machine learning to enable machines to better perceive the physical world. His work emphasizes cross-disciplinary collaboration, notably in wildlife tracking and indoor positioning systems. He has held roles as a Postdoctoral Fellow (2008-2012), Associate Professor (2013), and Full Professor (2021). Education : PhD in Electrical Engineering (University of Cape Town, 2008), BSc (Hons) in Electrical Engineering (2004). Research Interests : Tracking and localization in GPS-denied environments (e.g., underground, indoors), magneto-inductive systems, physics-informed machine learning, and data-driven approaches for noisy sensor data. His projects include wildlife monitoring via wireless sensor networks and mmWave radar for human motion capture. Key Projects : CARACAL acoustic monitoring system, mmPoint dense human tracking, and RandLA-Net for large-scale point cloud segmentation. His work spans robotics, environmental sensing, and biomedical applications. Advising & Grants : Supervises over 30 students and collaborates with industrial partners. Research teams include Cyber Physical Systems, Autonomous Ubiquitous Sensing, and Wildlife Monitoring initiatives. Labs/Teams : Leads the CPS research group, focusing on sensor networks, inertial navigation, and multimodal fusion systems. Collaborates with zoology and earth science disciplines on applied projects.
Leena Järvi is a Professor at the Institute for Atmospheric and Earth System Research (INAR) and Helsinki Institute of Sustainability Science (HELSUS) , University of Helsinki. Her work bridges urban climate science , air pollution , and greenhouse gas dynamics through experimental and theoretical approaches. Research Interests : Urban micrometeorology, carbon sequestration in green spaces, climate mitigation strategies, and air quality modeling. Key Projects : CO-CARBON (Strategic Research Council), GHUGS (Research Council of Finland), and PAUL (EU Horizon 2020). Her recent publications focus on urban CO2 fluxes , carbonyl sulfide as a carbon proxy , and climate impacts of urban vegetation . She has supervised 12 PhD students, 7 postdocs, and 17 undergraduates, while serving on editorial boards and organizing international workshops. Scientific Awards : Timothy Oke Award 2021 (IAUC). Teaching : Courses on Urban Climate and Atmospheric Sciences .
Dr. Amir Hakami is a Professor in the Department of Civil & Environmental Engineering at Carleton University , where he leads the Carleton Atmospheric Modelling Group . His research focuses on advanced air quality modeling techniques to inform environmental policy. Degrees: B.Sc. (Polytechnic of Tehran), M.Sc., Ph.D. (Georgia Tech), Postdoc (Caltech) Contact: Office 3454 Mackenzie Building, Phone: 613-520-2600 ext. 8609, Email: amir.hakami@carleton.ca Research Interests: Air quality modeling at multiple spatial scales Adjoint sensitivity analysis for atmospheric response Inverse modeling and data assimilation techniques Uncertainty quantification in environmental systems Interdisciplinary applications in policy, public health, and economics Teaching: Courses include Environmental Engineering Systems Modeling , Contaminant Transport , and Air Pollution & Emissions Control at undergraduate and graduate levels. Research Group: The group includes Ph.D. candidates, postdoctoral fellows, and alumni working on topics ranging from atmospheric chemistry to sustainable energy systems. Members come from diverse backgrounds in engineering, science, and policy disciplines.
Prof. Hens Runhaar is a Full Professor of Sustainable Food System Governance at Utrecht University's Copernicus Institute of Sustainable Development. His research focuses on reconciling food production with biodiversity recovery, environmental sustainability, and health through innovative governance frameworks. He emphasizes connectivity issues in food systems, such as disconnects between production and environmental conditions, and advocates for transformative practices like agroecology and nature-inclusive agriculture. Key roles include chairing the Board of Examiners for Sustainable Development and membership in Future Food Utrecht. His projects include NWO-funded initiatives like COMBINED (grassland biodiversity) and REWIRE (circular agricultural missions). He actively engages in policy dialogues, such as advising BirdLife Netherlands and contributing to the Dutch Biodiversity Council. Research highlights systemic barriers to sustainability transitions in agriculture, with recent work analyzing dairy farming and farmer collectives. His outreach includes media appearances on nitrogen policy, nature restoration, and sustainable food systems. Professional memberships span international networks like Earth System Governance and the Wageningen School of Social Sciences.
Dr. Yolanda Gil is a Research Professor in Computer Science and Spatial Sciences at the University of Southern California, where she serves as Principal Scientist and Senior Director for Strategic Initiatives in Artificial Intelligence and Data Science at the Information Sciences Institute (ISI). She is also the Director of AI and Data Science Initiatives in the Viterbi School of Engineering and leads the USC Center for Knowledge-Guided Interdisciplinary Data Science (CKIDS). Dr. Gil received her Licenciatura in Computer Science from the Polytechnic University of Madrid and her M.S. and Ph.D. in Computer Science from Carnegie Mellon University, with a focus on artificial intelligence and cognitive science. Her research focuses on developing AI approaches that use knowledge to accelerate scientific discovery processes. Her key research interests include knowledge capture and representation, semantic workflows, ontology tools, scientific discovery methods, task-based collaboration, provenance tracking, knowledge networks, reproducibility in science, and machine learning for data analysis. She collaborates with scientists across multiple domains to improve how scientific knowledge is created, shared, and used. Dr. Gil's work has significant impact across multiple scientific domains including climate science, neuroscience, and omics research. Her projects demonstrate her commitment to building knowledge-guided systems that transform how scientists conduct research. She has pioneered approaches to capture the provenance of scientific experiments and to automate the analysis of complex scientific data. Fellow of the Association for Computing Machinery (ACM) Fellow of the Association for the Advancement of Science (AAAS) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) 24th President of the Association for the Advancement of Artificial Intelligence Co-chair of the CRA/AAAI 20-Year Artificial Intelligence Research Roadmap for the US Initiator and leader of the W3C Provenance Group that resulted in a widely-used standard for web trust As an educator and leader, Dr. Gil directs the Data Science Program in Computer Science and serves as Co-Director of multiple joint MSc programs including Communication Data Science, Spatial Data Science, Environmental Data Science, Public Policy Data Science, and Healthcare Data Science. She also leads the new dual degree USC-Tsinghua University on Communication Data Science. Her leadership extends to mentoring numerous students and researchers in AI and data science. Through the USC Center for Knowledge-Guided Interdisciplinary Data Science (CKIDS), Dr. Gil organizes DataFest events each semester, fostering collaboration and innovation in data science across disciplines.
John H. Shaw is the Harry C. Dudley Professor of Structural and Economic Geology and Professor of Environmental Science & Engineering at Harvard University's School of Engineering and Applied Sciences (SEAS). He specializes in structural geology, earthquake hazards, and geomechanics, with a focus on thrust fault systems, fault-related folding, and seismic risk assessment in regions like California and China. His research integrates field observations, 3D modeling, and geomechanical simulations to understand fault dynamics and their implications for societal safety. Shaw's work emphasizes quantitative analysis of fault geometry, slip rates, and rupture processes. Key projects include modeling ground deformation during earthquakes, assessing seismic hazards in fold-thrust belts, and investigating reservoir-induced seismicity. He leads the Structural Geology & Earth Resources Group and contributes to collaborative initiatives like the Southern California Earthquake Center (SCEC). His articles highlight advancements in fault system modeling, including 3D structural reconstructions, distinct element method applications, and coupling geomechanical models with fluid flow simulations. Recent studies focus on the Wilmington blind-thrust fault beneath Los Angeles, the Ventura fault system, and tectonic evolution of the Canadian Rockies and Qaidam Basin. Shaw's research also addresses interdisciplinary challenges such as stochastic velocity modeling for earthquake ground motion prediction and developing open-source tools like the SCEC Unified Community Velocity Model (UCVM). His work bridges fundamental structural geology with applied seismic hazard mitigation strategies.
Prof. Dr. Florian Scholz is a Professor of Geology and Biogeochemistry at the University of Hamburg , affiliated with the Institute of Geology within the Department of Earth System Sciences . He joined the university in 2024, following leadership of an Emmy-Noether research group at GEOMAR Helmholtz Centre for Ocean Research Kiel. His academic training includes a doctorate (summa cum laude) and habilitation from Kiel University, and a Marie Curie postdoctoral fellowship at Oregon State University. Ph.D., Geomar Helmholtz-Zentrum & University of Kiel (2010) Habilitation, University of Kiel (2019) Diploma in Geoecology, Karlsruhe Institute of Technology & University of Bordeaux (2007) His research lies at the intersection of marine geochemistry , biogeochemistry , and paleoceanography , with a focus on sediment-water interactions , early diagenesis , and the impact of environmental change on biogeochemical cycles . He investigates trace metal cycling (e.g., Fe, Mo, W, Ni), redox processes, and authigenic mineral formation in marine sediments, using field data, laboratory analyses, and geochemical modeling. His recent publications (2024–2025) reveal a strong trend in paleo-redox proxy development (Mo, W, Ni isotopes), trace metal flux quantification in oxygen minimum zones, carbon cycle impacts of sediment disturbance, and ocean-based carbon dioxide removal via seafloor alkalinity enhancement. His work frequently appears in high-impact journals such as Earth and Planetary Science Letters , Geochimica et Cosmochimica Acta , and Communications Earth & Environment . Emmy-Noether-Gruppenleiter, GEOMAR (2016–2023) Marie Curie Fellow, Oregon State University & GEOMAR (2012–2015) Postdoctoral Researcher, SFB 574 & 754 (2010–2012) Prof. Scholz teaches courses including Marine Biogeochemistry , Environmental Geochemistry , and Field Geology at both undergraduate and graduate levels. He leads an active research group focused on marine sediment geochemistry and collaborates widely across international oceanographic projects. His work contributes significantly to understanding past and present ocean chemistry and developing strategies for climate change mitigation.
Prof. Stefan Wiemer is the Director of the Swiss Seismological Service (SED) and holds the Chair of Seismology at ETH Zurich's Department of Earth and Planetary Science. He obtained a geophysics diploma from Ruhr University Bochum (1992) and a PhD from the University of Alaska Fairbanks (1997). His research focuses on earthquake processes, probabilistic hazard assessments, induced seismicity, and geothermal energy applications. He has published over 200 articles and supervised 30 PhD students, while also lecturing at the University of Bern. Key Roles: Member of ERC Grants Evaluation Panel (2015–present) Scientific Advisor to GFZ German Research Center for Geosciences (2021–present) President of Swiss Geophysical Commission Projects: Leader of Switzerland's national earthquake risk model Coordinator of EU-funded RISE (risk assessments) and DEEP (geothermal de-risking) projects Principal Investigator for ERC Synergy grant FEAR (BedrettoLab fault experiments) His research spans operational earthquake forecasting, CO2 storage monitoring, and the BedrettoLab underground experiments. He has won the Humboldt Foundation Fellowship (1997) and contributed to international initiatives like the Dutch Mining Effects Panel. Teaching includes ETH's Geophysics I course. Scientific contributions include developing seismic hazard models (ERM-CH23), real-time induced seismicity forecasting frameworks, and innovative techniques for fault dynamics analysis. His work integrates field data, laboratory experiments, and computational modeling to address both natural and human-induced seismic risks.
Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.
Jiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University. He holds multiple affiliations including membership in Bio-X, Faculty Affiliate status at the Institute for Human-Centered Artificial Intelligence (HAI), and membership in both the Wu Tsai Human Performance Alliance and Wu Tsai Neurosciences Institute. Dr. Wu earned his Ph.D. and S.M. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology before joining Stanford. Dr. Wu's research program focuses on creating AI systems that understand and interact with the physical world through the integration of computer vision, machine learning, robotics, and cognitive science. His work emphasizes physics-based modeling combined with deep learning to develop systems capable of perceiving, reasoning about, and predicting physical interactions. Key research areas include 3D scene understanding, neurosymbolic AI approaches, multimodal perception (combining vision, sound, and language), and embodied intelligence for robotics applications. His lab develops novel frameworks that bridge the gap between neural networks and symbolic reasoning to create more interpretable and robust AI systems. Analysis of Dr. Wu's recent publications reveals a strong trajectory toward integrated multimodal understanding for embodied AI. His work increasingly combines vision, sound, and language processing with physical reasoning to create systems that can interact meaningfully with the physical world. There's a clear progression from foundational computer vision research toward practical robotics applications, with significant emphasis on foundation models for robotics, sim2real transfer techniques, and creating comprehensive datasets for embodied AI research. Dr. Wu's exceptional contributions have been recognized with numerous prestigious awards including the NSF CAREER award (2024), Young Investigator Programs from ONR (2024) and AFOSR (2023), the Okawa research grant (2024), and being named to IEEE Intelligent Systems' 'AI's 10 to Watch' (2024). He has received multiple best paper awards at leading conferences including ICRA (2024), SIGGRAPH Asia (2023), and CoRL (2023). Dr. Wu actively mentors a large cohort of students across multiple levels, serving as primary advisor for doctoral candidates, master's students, and numerous independent researchers. His research is supported by substantial funding from major technology companies including Google, Meta, Amazon, Samsung, and J.P. Morgan, as well as government agencies like NSF, ONR, and AFOSR, reflecting the significance and impact of his work in physical AI and multimodal perception systems. Dr. Wu leads a dynamic research group at Stanford that collaborates extensively with the Wu Tsai Neurosciences Institute and Institute for Human-Centered AI. Current projects include developing neurosymbolic models for computer graphics, creating multisensory datasets like OBJECTFOLDER 2.0 for sim2real transfer in robotics, and building foundation models for embodied intelligence that can understand and manipulate objects with human-like physical intuition.
Brandon Lucia is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. He holds the Kavčić-Moura Professorship and leads the Abstract research group. As CEO and co-founder of Efficient Computer Corp., he bridges academic research with commercial applications in energy-efficient computing. Dr. Lucia received his Ph.D. in Computer Science and Engineering from the University of Washington in 2013, following an MS from the same institution in 2010 and a BS in Computer Science from Tufts University in 2007. His research focuses on the intersection of computer architecture, computer systems, and programming languages, particularly in energy-constrained environments. His primary research interests include intermittent computing, energy harvesting computers, orbital edge computing, and parallel computing systems. Lucia's work addresses fundamental challenges in creating programmable, reliable computing devices that operate without batteries by harvesting energy from their environments, with applications in sensing, medical implants, and space systems. He also investigates software systems and architectures for making parallel computing correct, reliable, and efficient in the post-Moore's Law era. Lucia's publication record shows a clear trajectory toward orbital edge computing and nanosatellite systems, with recent work focusing on computational constellations, visual navigation for satellites, and energy-efficient processing in space. His research spans both theoretical foundations of intermittent computing and practical implementations in hardware and software. 2021 Sloan Research Fellowship 2018 NSF CAREER Award 2018 ASPLOS Best Paper Award IEEE MICRO Top Picks in Computer Architecture (2009, 2010, 2016) 2015 OOPSLA Best Paper Award 2019 IEEE TCCA Young Computer Architect Award 2022 Engineering Faculty Award As an advisor, Lucia has mentored numerous graduate students including Brad Denby, Zhuo Cheng, and Kyle McCleary, many of whom have become co-authors on his significant publications. His lab developed the world's first batteryless PocketQube nanosatellite (Tartan-Artibeus-1), which was deployed to low-Earth orbit aboard the SpaceX Transporter-3 Rocket. Lucia's research has received funding from sources including NSF, DARPA, Google, and VMware, supporting both fundamental research and practical implementations of energy-harvesting computing systems.
Dr. Jianguo Wang is a Professor in the Department of Earth and Space Science Engineering at York University's Lassonde School of Engineering. He has been a faculty member since 2006 and is a founding member of the Lassonde School. With over 35 years of academic and industrial experience, he specializes in multisensor integration, GNSS technology, and precision engineering surveying. He holds a Dr.-Ing. in Geomatics Engineering from Universität der Bundeswehr München, Germany, alongside Bachelor’s and Master’s degrees from Wuhan Technical University of Surveying and Mapping (WTUSM). His research focuses on advanced data processing methodologies, including Kalman filtering, error analysis, and LiDAR systems. He has authored/co-authored over 60 publications, including textbooks like Error Theory and Foundation of Surveying Adjustment and Foundation of Geodesy . He is a Fellow of Engineers Canada and licensed as a Professional Engineer in Ontario. Education: Dr.-Ing., Geomatics Engineering, Universität der Bundeswehr München (Germany) M.Sc., Surveying Engineering, Wuhan Technical University of Surveying and Mapping B.Sc., Surveying Engineering, Wuhan Technical University of Surveying and Mapping Dr. Wang teaches courses such as Advanced Optimization and Applications , GNSS , and Global Geophysics and Geodesy . He leads the Earth Observation Laboratory (PSE 432), focusing on multisensor integration for navigation and positioning. His work explores innovative solutions for sensor calibration, data fusion, and geospatial applications. Grants & Labs: Active in lab-based research with collaborators like Baoxin Hu, his laboratory integrates GNSS, IMUs, LiDAR, and cameras for precision navigation. His recent work addresses challenges in sensor error calibration, LiDAR point cloud accuracy, and Kalman filter enhancements.
Dr. Ian Kane is a Reader in Earth and Environmental Sciences at The University of Manchester, leading the 'Plastics in the Natural Environment' theme within the Manchester Environmental Research Group. His expertise spans sediment transport dynamics, deep-marine processes, and microplastic pollution. He holds editorial roles for journals like Sedimentologika and previously served as Editor-in-Chief of Sedimentology (2018–2022). Education: PhD in Geology from the University of Leeds (2007), focusing on submarine channel-levee systems and turbidity currents through fieldwork in Baja California, Mexico, and flume-tank modeling. Research Interests: Transport and burial of microplastics in deep-sea environments via turbidity currents Deep-marine sedimentological processes, including submarine canyons and slope systems Stratigraphic records of sediment gravity flows and anthropogenic impacts Recent articles highlight breakthroughs in understanding microplastic distribution via deep-sea currents and their ecological effects on marine organisms such as sea turtles. His work bridges geology, environmental science, and applied research on pollution mitigation. Scientific Awards: Fearnsides Prize (2008) Roland Goldring Award (2012) Advising and Grants: Supervised over 20 PhD students, with active projects on PFAS transport, microplastic stratigraphy, and deep-marine sedimentology. His research group (SedResQ) explores terrestrial-to-deep-marine sediment transfer. Labs/Teams: Leads SedResQ and collaborates globally on projects like the NEPTUNE Canada observatory, focusing on deep-sea environmental dynamics and anthropogenic impact assessment.
Anil Madhavapeddy serves as Professor of Planetary Computing at the University of Cambridge's Department of Computer Science and Technology and directs the Cambridge Centre for Carbon Credits (4C). A Fellow of Pembroke College, he integrates systems research with environmental conservation through the Computer Laboratory's Environment and Energy Group. His career spans industry leadership (NetApp, Citrix, Intel), academic appointments (Cambridge, Imperial, UCLA), and entrepreneurial ventures (XenSource, Unikernel Systems, Docker). Madhavapeddy earned his PhD at Cambridge's Computer Laboratory in 2006. His research bridges computational systems and planetary-scale environmental challenges, with deep expertise in open-source development (OCaml, Xen, Docker, OpenBSD) and technology strategy advising for organizations including Zededa, Tezos Foundation, and Tarides. His work centers on environmental computing and climate informatics, leveraging distributed systems and functional programming to develop sensing infrastructure for conservation. Recent projects focus on carbon credit systems, AI-driven biodiversity monitoring, and sustainable computing architectures that minimize ecological footprints while maximizing analytical capability. Analysis of his 2025 publications reveals a concentrated effort on AI-integrated conservation tools, privacy-preserving carbon accounting, and energy-efficient computing. Key themes include spatial networking for ecological data, LLM-enhanced evidence retrieval in conservation science, and novel metrics for extinction risk assessment—demonstrating computational innovation applied to urgent planetary boundaries. No scientific awards were documented in the source material. Madhavapeddy advises multiple technology firms on strategic development while leading the Cambridge Centre for Carbon Credits, though specific grant funding details remain unreported. He actively contributes to the Environment and Energy Group at Cambridge's Computer Laboratory and directs the interdisciplinary Cambridge Centre for Carbon Credits (4C). His open-source leadership spans critical infrastructure projects including OCaml, Xen, and Docker, fostering collaborative development communities that underpin modern cloud and container technologies.
Irina Marinov is an Associate Professor in the Department of Earth and Environmental Sciences at the University of Pennsylvania. She specializes in climate science, focusing on the critical role of oceans in global climate dynamics and carbon cycling. Her research integrates Earth system models and satellite data to study phenomena such as Southern Ocean convection, phytoplankton ecology, and the impact of climate change on oceanic processes. Marinov’s research spans biogeochemical cycles, marine ecology, and climate modeling, with a particular emphasis on Southern Ocean dynamics. She explores teleconnections between tropical atmospheric systems and Southern Ocean processes, polynya variability, and the use of satellite data to understand phytoplankton biomass and carbon distribution. Her recent publications (2024-2025) highlight the Southern Ocean’s influence on multidecadal climate variability, nonlinear CO2 dynamics, and the development of the GLOBAL CLIMATE SECURITY ATLAS. These works bridge climate modeling, satellite remote sensing, and policy applications. Scientific Awards: Undergraduate Research Mentorship award First woman to be tenured in the Earth and Environmental Sciences Department at Penn
