Find Academic Supervisors

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.

Frank MaurerView profile

Professor

• Extended Reality (XR)
• Immersive Analytics
• Human-Computer Interaction (HCI)
• +7 more

Dr. Frank Maurer is a Professor in the Department of Computer Science at the University of Calgary, specializing in Extended Reality (XR) and Immersive Analytics. His work focuses on serious applications of XR in healthcare, energy, and engineering sectors. He leads the SEER Group, advancing cross-reality prototyping tools and digital twin technologies. Maurer holds a Diplom and a Dr. rer. nat. in Computer Science from the University of Kaiserslautern (1989 and 1993). Research areas include XR prototyping , immersive analytics , UX design for 5G-XR , and healthcare applications like pre-surgical epilepsy evaluations. He collaborates on strategic initiatives such as Brain/Mental Health and Energy Innovations. Recent work explores hydrogen pipeline monitoring, seismic data analysis (Siera tool), and generative AI integration in XR. Publications emphasize technical challenges in 3D medical imaging, reservoir engineering workflows, and cross-reality application testing. Maurer teaches Research Methods in Computer Science and has pioneered tools like NiwViw (immersive analytics authoring) and SpyREST (API documentation). His lab develops solutions for multi-surface environments and emergency response systems.

Gregorio IglesiasView profile

Professor

• Marine Renewable Energy
• Coastal Engineering
• Wave Energy Conversion
• +5 more

Prof. Gregorio Iglesias is a Professor of Marine Renewable Energy at University College Cork (UCC) and Honorary Professor of Coastal Engineering at the University of Plymouth. His expertise lies in Marine Renewable Energy and Coastal Engineering, with a focus on wave and tidal energy systems, offshore wind integration, and coastal protection strategies. He has secured over €12M in research funding as Principal Investigator and authored/edited key texts such as Wave and Tidal Energy (Wiley) and Ocean Energy and Coastal Protection (Springer). Education: BEng (Civil Engineering, 1992), MEng (Civil Engineering, 1993), PhD (Engineering, 2001). Research Interests: Advanced modeling of wave energy converters, floating offshore wind turbines, coastal erosion mitigation, and climate change impacts on marine energy resources. His work bridges theoretical advancements and practical applications, including coasts like the Port of Gijón (Spain) and the Shannon Estuary. Recent publications highlight climate-driven renewable energy transitions, multi-hazard coastal resilience frameworks, and techno-economic assessments of offshore systems. He chairs the IEC Standards panel for wave energy device testing and serves as Subject Editor for Energy (Elsevier) . Professional Activities: Lead of Marine Renewable Energy research at MaREI (Ireland), former Head of the COAST Engineering Group at Plymouth (2012–2018), and member of PIANC’s Universities Consortium. His work has generated 5,459 citations with an h-index of 41. Teaching: Delivers modules on Ocean Energy (NE4003/NE6005) and Hydraulics (CE3007).

Annie K. LamarView profile

Assistant Professor

• Computational Linguistics
• Digital Humanities
• Classical Studies
• +5 more

Annie K. Lamar is an Assistant Professor of Classics and Director of the Low-Resource Language (LOREL) Lab at the University of California, Santa Barbara. Her research focuses on computational models of ancient Mediterranean languages, low-resource computational linguistics, and digital humanities. She specializes in Homeric epic and oral tradition analysis, with projects exploring geospatial approaches to catastrophe narratives and computational tools for language resources. Education: PhD in Classics, Stanford University MA in Education Data Science, Stanford Graduate School of Education BA in Classical Languages & BS in Computer Science, University of Puget Sound Research Interests: Combines classical philology with machine learning to analyze low-resource languages, oral poetry composition, and georeferenced cultural narratives. Current work includes a monograph redefining Homeric formulaic composition through computational methods. Publications: Her work bridges computational linguistics and classics, with recent contributions on stop-word lists for classical Latin, data augmentation in low-resource NMT, and geospatial models of displacement narratives. These studies highlight interdisciplinary approaches to humanities challenges. Labs & Teams: Leads the LOREL Lab, focusing on advancing computational methods for understudied languages and cultural heritage preservation.

• Statistical Signal Processing
• Pattern Recognition
• Machine Learning
• +5 more

Ulisses Braga-Neto is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from The Johns Hopkins University (2002), with earlier degrees including an M.S. from the Federal University of Pernambuco (1992). His research focuses on statistical signal processing, pattern recognition, and machine learning, with applications in bioinformatics, materials informatics, and environmental modeling. He leads the TAMIDS Scientific Machine Learning Lab and has authored over 100 publications. Key research areas include physics-informed neural networks for environmental and engineering problems, error estimation in classification systems, and gene regulatory network inference. His recent work emphasizes machine learning applications in agriculture (e.g., cotton detection in corn fields using UAS), CO2 sequestration modeling, and wildfire prediction. He has contributed to foundational texts like Error Estimation for Pattern Recognition (Wiley-IEEE, 2015). Prof. Braga-Neto's interdisciplinary approach bridges computer science, engineering, and biology. His lab develops algorithms for data-poor environments, with applications in genomics, proteomics, and metagenomics. He advises graduate students through Texas A&M's ECE program, emphasizing rigorous statistical methods alongside machine learning innovation.

Takahiro YabeView profile

Assistant Professor

• Computational Social Science
• Urban Resilience
• Human Mobility
• +5 more

Dr. Takahiro Yabe is an Assistant Professor at the Department of Technology Management and Innovation (TMI) and the Center for Urban Science + Progress (CUSP) within New York University's Tandon School of Engineering. His research focuses on computational social science and network science approaches to model urban resilience against disasters, pandemics, and technological disruptions. He holds a Ph.D. from Purdue University (2021) and degrees from the University of Tokyo (BS 2015, MS 2017). Previously, he was a Postdoctoral Associate at MIT's IDSS and Media Lab under Sandy Pentland and Esteban Moro. Research interests include urban resilience, human mobility, socioeconomic networks, inequality, and computational modeling. He leads the Resilient Urban Networks (RUN) Lab, an interdisciplinary group developing data-driven tools for urban systems analysis. His work has been published in top journals like Nature Human Behaviour, PNAS, and Nature Machine Intelligence. Recent grants include NSF funding for EV charging infrastructure planning (SAI 2024) and post-disaster mobility governance (HDBE 2024). Notable awards include the NICE STEP Researchers recognition (2024). His lab collaborates with urban planners, policymakers, and global institutions to advance equitable urban resilience strategies. Current projects involve open mobility data standards, disaster recovery policy assessment, and AI-driven urban modeling. Students supervised include PhD candidates Vaidehi Raipat (urban policy), Callie Clark (mobility equity), and DongHak Lee (socioeconomic networks). The lab also hosts visiting scholars like Mavin De Silva (EV charging systems) and supports master's students in mobility analysis and disaster response.

Tania MendoView profile

Lecturer

• Marine Spatial Planning
• Small-Scale Fisheries Management
• Socio-Economic Indicators
• +3 more

Dr. Tania Mendo is a Lecturer at the University of St Andrews, School of Geography and Sustainable Development. She specializes in interdisciplinary approaches to small-scale coastal fisheries management, focusing on policy-informed research, socio-economic indicators, and marine spatial planning. Her work emphasizes equitable representation of fishers in the blue economy and employs statistical methods paired with user-friendly data visualization tools for stakeholder engagement. Current research interests include machine learning applications for decision-making, blue justice frameworks, climate change adaptation in hyper-arid regions, and El Niño impacts on food systems. Dr. Mendo leads projects such as the Digital Transition of Catch Monitoring in European Fisheries and Conserving Atlantic Biodiversity through Co-management. She collaborates internationally with organizations like Cefas and engages with communities in Peru and Scotland. Her research integrates quantitative methods with participatory approaches to address fisheries governance challenges and environmental sustainability. Grants include funding from UKRI, BBSRC, and EPSRC for initiatives like improving stock assessment technologies and assessing fishing communities' resilience during crises. Her publications highlight innovations in vessel tracking analysis, spatial distribution modeling, and socio-economic frameworks for fisheries. She advises PhD students Miguel Delos Santos and Tamsin Rigold. Mendo is affiliated with the Bell-Edwards Geographic Data Institute and contributes to UN SDGs related to life below water and sustainable communities.

Laurence C. SmithView profile

Professor

• Arctic Studies
• Hydrology
• Remote Sensing
• +3 more

Laurence C. Smith is the John Atwater and Diana Nelson University Professor of Environmental Studies at Brown University, affiliated with the Institute at Brown for Environment & Society (IBES) and the Department of Earth, Environmental and Planetary Sciences (DEEPS). Previously, he served as UCLA's Geography Department Chair and Professor. His research focuses on Arctic environments, water resources, and remote sensing, with over 150 peer-reviewed publications and $16M in NSF/NASA grants. He has contributed to IPCC reports and leads NASA's SWOT mission for global water monitoring. Key awards include the Guggenheim Fellowship (2006-07), AGU Fellow (2015), and Walter P. Kistler Book Award (2012) for *The World in 2050*. Education: PhD, Cornell University (1996) MS, Indiana University (1991) BS, University of Illinois at Urbana-Champaign (1989) Research Interests: Arctic systems, hydrology, satellite remote sensing, and climate change impacts. His work bridges natural and social sciences, addressing topics like Greenland meltwater dynamics and northern resource development. Recent projects include methane emissions from Arctic lakes and river avulsion monitoring via SWOT. Awards: Phi Beta Kappa Visiting Scholar (2020-21) AGU Fellow (2015) Walter P. Kistler Book Award (2012) Guggenheim Fellowship (2006-07) Teaching & Grants: Teaches courses on water systems and scientific writing at Brown. Secured major grants from NASA and NSF, totaling over $16M. Advises graduate students in environmental studies and geosciences. Labs/Teams: Collaborates with IBES, DEEPS, and global institutions like the World Economic Forum on Arctic development and NASA's SWOT mission. Active in interdisciplinary research networks.

Wesley J. MarreroView profile

Assistant Professor

• Machine Learning
• Inferential Statistics
• Stochastic Simulation
• +4 more

Dr. Wesley J. Marrero is an Assistant Professor of Engineering at Dartmouth College's Thayer School of Engineering. He holds core affiliations with the Health Equity and Advocacy Lab at The Dartmouth Institute for Health Policy and Clinical Practice and is a faculty affiliate in the Quantitative Biomedical Sciences Graduate Program at the Geisel School of Medicine. His research integrates operations research and statistics to address healthcare challenges in substance use disorder, cardiovascular disease, and organ transplantation, collaborating with institutions like Massachusetts General Hospital and the University of Michigan. Dr. Marrero received a B.S. in Industrial and Management Engineering from the University of Turabo (2014), an M.S. in Industrial and Operations Engineering (2017), an M.A. in Statistics (2021), and a Ph.D. in Industrial and Operations Engineering (2021) from the University of Michigan. His research focuses on stochastic simulation and optimization for decision-making in healthcare. Key areas include machine learning applications in predictive modeling, inferential statistics for medical decisions, and sequential decision-making frameworks for health policy optimization. His work emphasizes practical implementations in clinical settings and health systems. Dr. Marrero's publications demonstrate a consistent focus on healthcare optimization, with recent trends in machine learning for organ transplantation and genetic testing strategies. His articles frequently appear in high-impact journals and conference proceedings, reflecting interdisciplinary collaborations. Awards: INFORMS MIF Best Paper Award (2024) INFORMS Annual Meeting Minority Issues Forum Poster Competition Best Poster Award (2020) INFORMS Judith Liebman Award (2020) National Science Foundation Graduate Research Fellowship (2017) Michigan Student Symposium Best Poster Award (2017) Rackham Merit Fellowship (2015) He actively advises graduate and undergraduate students, with current PhD candidates in engineering and biomedical sciences. His research group has secured grants from organizations including the National Science Foundation and the American Medical Association. Dr. Marrero leads a dynamic research group focusing on healthcare decision analytics. He collaborates with medical institutions and government agencies, including the U.S. Department of Veterans Affairs, to translate research into clinical practice.

Rafael Ruiz De CastañedaView profile

Researcher

• One Health
• Global Health
• Epidemiology
• +5 more

Rafael Ruiz De Castañeda is a Researcher and Lecturer at the Faculty of Medicine, University of Geneva, leading the One Health Unit in collaboration with Dr. Isabelle Bolon. His work focuses on interdisciplinary global health challenges, including emerging infectious diseases, zoonotic pathogens, and conservation biology. He has extensive field experience in Africa and Latin America, coordinating projects like the EU-funded Africa Build and RAFT Network. He is also a key figure in global health education, developing MOOCs such as the Coursera course on Global Health at the Human-Animal-Ecosystem Interface . His research integrates ecological, veterinary, and human health perspectives, emphasizing snakebite envenoming epidemiology, AI-driven species identification, and planetary health. He collaborates globally with institutions like Institut Pasteur and the Geneva Science Policy Interface. Key contributions include policy briefs on humanitarian action in planetary crises and frameworks for One Health diplomacy. Recent publications highlight transdisciplinary approaches to snakebite prevention, geospatial modeling of health risks, and leveraging AI for wildlife health surveillance. His work bridges academia, policy, and community engagement to address systemic health challenges at the human-animal-environment nexus.

Cristian Román-PalaciosView profile

Assistant Professor

• Phylogenetics
• Biodiversity Drivers
• Paleoclimatic Reconstructions
• +7 more

Cristian Román-Palacios is an Assistant Professor in the Department of Ecology and Evolutionary Biology at the University of Arizona, where he also serves as Coordinator and Advisor for the Master of Science in Data Science (MSDS) and Master of Science in Information Systems (MSIS) programs. He is a core faculty member in Artificial Intelligence and Machine Learning, Data Management, Analysis and Visualization, and Environmental, Health and Biological Sciences. Education: PhD in Ecology and Evolutionary Biology, University of Arizona (2020) BS in Biology, Universidad del Valle, Colombia (2015) His research lies at the intersection of phylogenetics, biodiversity modeling, and machine learning, focusing on large-scale biodiversity patterns, the impacts of climate change on species survival, and the development of statistical tools for paleoclimatic reconstructions. He employs computational and data-intensive methods to explore evolutionary and ecological questions across diverse taxa. His recent publications (2024–2025) demonstrate a strong trend toward interdisciplinary research, combining computational biology, geochemistry, climate science, and open-source software development. Key themes include biodiversity informatics (e.g., Animal Culture Database), paleoclimatic modeling (e.g., clumped isotope thermometry), reproducibility in science, and tools for collaborative research (e.g., LabOps, SSARP). His work increasingly integrates data science with biological and environmental applications. Scientific Contributions: Published over 25 peer-reviewed papers, many as first author Research featured in Science News, Popular Science, CNN, USA Today Developed open-source tools: phruta , treedata.table , SSARP , LabOps Cristian advises graduate students through the infosci-msadvise@arizona.edu email and Calendly appointments. He was previously a staff researcher at UCLA’s Tripati Lab. He leads initiatives such as the Southwest Center on Resilience for Climate Change and Health and promotes inclusive, collaborative science through online toolkits and leadership ecosystems aimed at addressing climate and social inequities. His lab, the Román-Palacios Lab, and involvement with the Data Diversity Lab reflect his commitment to open, reproducible, and equitable research practices in data-intensive biology.