Find Academic Supervisors

• Plant Ecology
• Restoration Ecology
• Ecological Modeling
• +3 more

Daniel Laughlin is a Professor in the Department of Botany at the University of Wyoming, part of the College of Agriculture, Life Sciences, and Natural Resources. His research focuses on quantitative approaches to understand plant responses to global change, including trait-based models for ecological restoration and community assembly. He holds a Ph.D. in Forest Science from Northern Arizona University (2009), an M.S. in Ecology from Penn State (2002), and a B.S. in Biology from Calvin College (1999). His lab, plant-traits.net , explores functional traits, ecological modeling, and restoration ecology. Key research interests include plant community assembly, trait-environment interactions, and the application of statistical frameworks to predict species responses. His work integrates ecological processes with statistical models to inform ecosystem restoration strategies. Notable contributions include studies on functional diversity, trait covariance, and the adaptive value of plant traits across environmental gradients. Laughlin teaches courses such as Vegetation Ecology and Ecological Modeling. His lab actively publishes on topics like root traits, climate impacts on plant strategies, and biodiversity drivers. While no awards are explicitly listed, his extensive publications and academic contributions highlight his leadership in ecological research. Advising and grants are not detailed in the provided text, but his lab’s focus on collaborative consortia and workforce development underscores his commitment to training future scientists. His work bridges theory and practice, aiming to reconcile ecological understanding with real-world restoration challenges.

Renata DividinoView profile

Assistant Professor

• Graph Knowledge Representation
• Machine Learning
• Web Science
• +5 more

Renata Dividino is an Assistant Professor in the Department of Computer Science at Brock University, Canada. She holds a BSc from the University of Campinas (Brazil), an MSc from Universität des Saarlandes (Germany), and a PhD from Universität Koblenz – Landau (Germany). Her research focuses on graph knowledge representation, machine learning, and their applications in web science, semantic web foundations, and provenance systems. She has worked at institutions like DFKI, Fraunhofer IGD, and the Big Data Analytics Lab at Dalhousie University, bridging academic and industrial sectors. Her industry experience includes roles as an AI Scientist and Director of Data Science in the maritime sector, where she developed patented technologies for AI-driven maritime operations and risk assessment systems. Key research contributions include improving AI system reliability via provenance analysis and advancing knowledge graph applications. Education: BSc in Computer Science, University of Campinas MSc in Computer Science, Universität des Saarlandes PhD in Computer Science, Universität Koblenz – Landau Research Interests: Provenance systems, semantic web foundations, knowledge graphs, graph-based AI, maritime AI applications, and federated learning. Her work emphasizes practical applications in complex networks, web-scale data, and social networks. Awards: No specific awards mentioned, but her contributions include patented technologies and peer-reviewed publications on provenance-driven AI transparency. Advising & Grants: Secured industry grants for R&D projects in maritime AI and data science. Her work on vessel risk assessment and infectious disease prediction demonstrates applied research impact.

Sun-ha HongView profile

Assistant Professor

• Critical Data Studies
• Surveillance Studies
• Science and Technology Studies (STS)
• +5 more

Sun-ha Hong is an Assistant Professor in the School of Communication at Simon Fraser University, with affiliations at Stanford’s Humanities Center, SFU’s Digital Democracies Institute, and the VINCI Institute. Her work critically examines data-driven technologies, surveillance practices, and the epistemological shifts in a machine-readable society. Hong holds a PhD from the Annenberg School for Communication (2016), and has held fellowships at MIT and Microsoft Research. Her research focuses on the limits of datafication, exploring how predictive technologies and algorithmic systems reshape knowledge production. Key themes include speculative inequality, the politics of prediction, and the societal implications of AI bias. Hong’s monograph Technologies of Speculation (2020) analyzes state surveillance and self-tracking technologies, arguing that datafication creates gaps between technological promises and social realities. Hong has secured over $280,000 in SSHRC grants and serves on editorial boards for Media Peripheries and Big Data & Society . Her teaching spans AI ethics, surveillance studies, and media historiography, with courses like AI, Algorithms and Power . She advises 11 graduate students researching topics from biopolitical surveillance in China to gendered AI violence. Her recent work explores ‘speculative drama’ in generative AI and the ritualized temporality of technological futures. Hong frequently advises Canadian government bodies on AI policy and digital governance, balancing academic rigor with public engagement.

Kostas MagoutisView profile

Professor

• Distributed computer systems
• Scalable data stream processing
• Internet of Things
• +3 more

Kostas Magoutis is Professor and Chair of the Computer Science Department at the University of Crete and collaborating researcher with FORTH-ICS. His research focuses on distributed systems, scalable data processing, IoT, and cloud computing, with projects including GreenInCities for urban regeneration and STREAMSTORE for stateful stream processing systems. Research interests include: Distributed computer systems architecture Elastic stream processing platforms Quantum-enhanced computing applications Multi-cloud application lifecycle management Recent publications demonstrate strong focus on federated data systems, quantum computing applications, and IoT-enhanced infrastructure, with consistent output in high-impact conferences and journals. Awards and distinctions: Multiple best paper awards from USENIX conferences Grand Challenge Audience Award at DEBS 2022 Marie Curie Fellowship and IBM Research awards Advises over 20 PhD and MSc students in distributed systems research. Leads multiple EU-funded projects and serves on program committees for top conferences including SOSP, EuroSys, and IEEE BigData. Directs research groups in distributed systems and cloud computing at FORTH-ICS.

Azizur RahmanView profile

Professor

• Data Science
• AI and Machine Learning
• Statistics
• +7 more

Professor Azizur Rahman is a leading academic in applied statistics and data science at Charles Sturt University (Australia), where he leads the Data Mining Research Group and directs the Data Analytics Lab. He holds a PhD in Economics and Applied Statistics from the University of Canberra, with postdoctoral experience at the University of Adelaide. His expertise spans statistical methodologies, microsimulation, AI/ML, and data-driven policy analysis. He has attracted over $4.03M in research funding and published over 200 scholarly works. Education: BSc (Honours) in Statistical Science, University of Chittagong (2000) MSc (Thesis) in Biostatistics, University of Chittagong (2003) PhD in Economics and Applied Statistics, University of Canberra (2010) Research Focus: Big data analytics and AI applications in health, environment, and policy Bayesian inference and multilevel modeling Spatial microsimulation for socioeconomic analysis Water quality modeling and environmental systems Public health challenges in developing nations Key Contributions: Developed novel computational methods for microsimulation modeling Founder of the Data Analytics Lab at CSU Editorial roles in journals like PLOS ONE and International Journal of Microsimulation Recipient of 2023 ANZRSAI Outstanding Service Award and Charles Sturt Excellence Awards Grants & Awards: $4.03M+ external research funding secured 30+ awards including travel grants, fellowships, and service recognitions Advising & Mentorship: Supervised 6 PhD/DIT students and co-supervised many more. Active in mentoring early-career researchers through editorial boards and conference organizing roles. Labs & Collaborations: Leads DaMRG (Data Mining Research Group) and collaborates with global institutions on interdisciplinary projects involving AI/ML, environmental science, and public health.

Babak MehranView profile

Associate Professor

• Transportation Network Resilience
• Big Data Analytics in Transportation
• Autonomous and Connected Vehicles
• +4 more

Babak Mehran is an Associate Professor in the Department of Civil Engineering at the University of Manitoba (Price Faculty of Engineering). He holds a PhD in Civil Engineering from Nagoya University (2009). His research focuses on transportation network resilience, big data analytics, and AI-driven solutions for traffic management. He leads the Urban Mobility and Transportation Informatics Group (UMTIG), collaborating with government and industry on applied transportation research. Key research areas include autonomous vehicle integration, cold-region traffic vulnerability, and optimization of public transit systems. Dr. Mehran has advised graduate students on topics like traffic safety, sensor placement, and semi-flexible transit design. His work bridges theoretical models (e.g., reinforcement learning algorithms) with real-world applications such as winter road maintenance strategies and transit demand analysis. Recent publications emphasize AI-driven traffic prediction, climate adaptation for infrastructure resilience, and safety metrics for truck operations in harsh environments. He collaborates internationally on transportation policy and has contributed to methodologies for evaluating congestion relief strategies using travel time reliability analysis. Lab: Urban Mobility and Transportation Informatics Group (UMTIG) Key Partners: Government agencies, transportation industries, academic collaborators Current Focus: Autonomous shared mobility, cold-climate traffic systems, data fusion for traffic monitoring

Wei ShiView profile

Professor

• Distributed computing
• Cloud computing
• Data center networks
• +6 more

Professor Wei Shi is a faculty member at the School of Computer Science, Carleton University. His research focuses on distributed computing, cloud networks, algorithm design for sensor/actuator systems, big data analytics, and data privacy. Notable projects include federated learning optimizations, blockchain-enabled edge intelligence for IoT/vehicle networks, and AI-driven cybersecurity solutions. His work addresses challenges in dynamic resource allocation, anomaly detection, and privacy-preserving techniques. Research Interests: Distributed Computing: Optimizing federated learning and client selection algorithms for wireless networks. Blockchain & Edge Intelligence: Developing decentralized systems for IoT and vehicular networks using AI large models. Security & Privacy: Innovating methods to detect AI-generated content, combat cyber-physical attacks, and protect user data privacy. Publications: Recent works emphasize federated learning applications, blockchain integration with edge computing, and cybersecurity for vehicular/IoT ecosystems. Key themes include energy-efficient algorithms, dynamic resource allocation, and intrusion detection in constrained environments. Email: wei.shi@carleton.ca

Manolis KoubarakisView profile

Professor

• Artificial Intelligence
• Data Science
• Linked Data
• +3 more

Manolis Koubarakis is a Professor and Director of Graduate Studies at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. He is affiliated with the Archimedes Unit at Athena Research Center and is a member of ELLIS. His research focuses on Artificial Intelligence and Data Science, particularly in Linked Geospatial Data, Earth Observation, and Knowledge Graphs. Education: PhD in Computer Science (National Technical University of Athens), M.Sc. in Computer Science (University of Toronto), and B.Sc. in Mathematics (University of Crete). Research Interests: His work spans AI applications in geospatial data, entity resolution, ontology-based data access (OBDA), and semantic web technologies. He leads projects like ExtremeEarth (AI for Copernicus data) and LEO (linked Earth Observation data). Awards: 2015: Fellow of the European Association of Artificial Intelligence (EurAI) 2022: Best Demo Award at CIKM for Copernicus App Lab Advising & Grants: Supervises students in AI and data science. Collaborates on EU-funded projects like BigDataEurope and participates in initiatives like the Standing Scientific Committee for AI in Greece's justice system. Labs & Teams: Heads the AI Lab (ai.di.uoa.gr) and contributes to the MaDgIK group, focusing on data and knowledge management systems.

Chanchal K. RoyView profile

Professor

• Software Engineering
• Software Maintenance and Evolution
• Code Clone Detection
• +3 more

Dr. Chanchal K. Roy is a Professor of Software Engineering/Computer Science at the University of Saskatchewan (USask), Canada, and Director of the NSERC CREATE SOAR program. He leads the Software Research Lab (SRLab) and is renowned for his work on code clone detection (NiCad tool) and software maintenance. His research spans software evolution, big data analytics, and quantum computing applications in software engineering. Dr. Roy holds a Ph.D. from Queen’s University, an M.Sc. from RWTH Aachen University, and a B.Sc. from Khulna University. Research interests include software clone detection, maintenance, and evolution, with emphasis on semantic analysis and cross-language clones. He has published over 240 papers (h-index 52) and attracted $6M+ in funding, including NSERC grants and CFI-JELF support. Awards include the GSA Advising Excellence Award, Outstanding Young Computer Science Researcher Award, and multiple Most Influential Paper awards. Key contributions include developing NiCad, advancing Stack Overflow search techniques, and leading collaborative projects in software analytics. His work has been featured in ACM Tech News, TechRepublic, and Stack Overflow blogs. Dr. Roy actively engages in keynotes at conferences like WCRE, IWSC, and BIM.

Abul Kalam HossainView profile

Senior Lecturer

• Renewable Energy Systems
• Biofuels Production & Utilization
• Waste Heat Recovery
• +4 more

Abul Kalam Hossain is a Senior Lecturer in the Department of Mechanical, Biomedical & Design Engineering at Aston University's College of Engineering and Physical Sciences. His research focuses on sustainable low-carbon fuels, renewable energy systems, and solar-driven desalination technologies. He has extensive industry experience as a mechanical engineer and has led numerous interdisciplinary projects in biofuel development, waste-to-energy conversion, and energy storage. Dr. Hossain has secured funding from UK Energy Catalyst, Innovate UK, and international collaborations, including projects in Pakistan, India, and UAE. He serves as an Associate Editor for Frontiers in Fuels and on the Scientific Advisory Board of SDEWES conferences. His work bridges academic research with practical applications in cleaner energy and water solutions. Education: BSc from Bangladesh University of Engineering and Technology (BUET), MSc and PhD in Mechanical Engineering from Cranfield University. Professional credentials include Chartered Engineer (CEng), Fellow of the Higher Education Academy (FHEA), and membership in the Energy Institute. Research themes include biofuel characterization, engine emissions control, solar desalination for arid regions, and biomass-solar hybrid systems. Key achievements include pioneering waste-derived biodiesel blends and advancing low-temperature combustion techniques. He currently supervises MSc and final-year engineering projects and teaches modules in thermodynamics, engineering science, and alternative fuels. Funding highlights: Leading roles in projects like 'CoolRun Malawi' (UK Energy Catalyst 2024) and 'Upgraded Flexi Biodiesel Fuel' (Aston Seedcorn 2021). Awards include grants for off-grid desalination in Jordan Valley and filtered drinking water systems in Pakistan. Labs/Teams: Active member of Aston's Energy and Bioproducts Research Institute (EBRI) and collaborator with institutions like Anna University (India) and American University of Sharjah (UAE). Current projects address global challenges in sustainable energy access and water security.

• Ontologies and Terminologies
• Neuroinformatics
• Big Data Analytics
• +3 more

Licong Cui, Ph.D., is an Associate Professor at the McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth). She is also affiliated with the Center for Translational AI Excellence and Applications in Medicine (TEAM-AI) and the Texas Institute for Restorative Neurotechnologies (TIRN). Her research focuses on developing informatics methods to address biomedical data challenges, with expertise in ontologies, neuroinformatics, big data analytics, and clinical text mining. Dr. Cui has authored over 100 peer-reviewed publications and secured grants from NIH and NSF. Her work emphasizes ontology quality assurance, large language model applications in healthcare, and data integration frameworks. Notable contributions include developing the VaxBot-HPV chatbot for vaccine communication and advancing seizure frequency extraction methodologies using LLMs. Her honors include the 2022 AMIA New Investigator Award and 2021 NSF CAREER Award. Current projects involve enhancing NIH Common Data Elements with AI tools and improving EHR-based cohort querying through ontology-driven approaches. She collaborates on initiatives like the National Sleep Research Resource and Vaccine Ontology harmonization efforts.

Alexandru CernatView profile

Senior Lecturer

• Survey Methodology
• Longitudinal Data Analysis
• Latent Variable Modeling
• +7 more

Dr Alexandru Cernat is a Senior Lecturer in Social Statistics at The University of Manchester, affiliated with the Cathie Marsh Institute. His research focuses on data collection methodologies, longitudinal data analysis, and measurement error reduction. He specializes in latent variable models, mixed mode surveys, and integrating digital trace data with traditional datasets. Key projects include improving crime data accuracy through statistical modeling and analyzing socioeconomic health disparities using biomarker data. He has received the UKHLS Methods Fellowship and holds leadership roles in statistical societies. Research Interests: Survey Methodology Latent Variable Modeling Data Quality Enhancement Mixed Mode Design Digital Trace Data Integration Awards: UKHLS Methods Fellowship (2019) Vice Chair, Royal Statistical Society Social Statistics Committee (2019) Grants & Collaborations: Leads projects on crime data accuracy, longitudinal measurement equivalence, and biosocial survey methods. Collaborates with institutions like the London School of Economics and University College London on health inequality studies. Labs/Teams: Core member of the Cathie Marsh Institute's survey methodology team and leads the Longitudinal Data Analysis research group.

Timothy A. McKayView profile

Professor

• Data Science
• Learning Analytics
• Physics and Astronomy Education
• +2 more

Timothy A. McKay serves as the Arthur F. Thurnau Professor of Physics, Astronomy, and Education at the University of Michigan's College of Literature, Science, and the Arts (LSA), where he also holds the administrative role of Associate Dean for Undergraduate Education. His dual expertise bridges astrophysics research and educational innovation, with significant contributions to both observational cosmology and learning analytics. His educational background includes: B.S. in Physics from Temple University (1986) Ph.D. in Physics from the University of Chicago (1992) McKay's research spans two interconnected domains. In observational cosmology, he pioneered work with major astronomical surveys including the Sloan Digital Sky Survey (SDSS), Robotic Optical Transient Search Experiment (ROTSE), and Dark Energy Survey (DES), focusing on galaxy clusters, cosmic rays, and large-scale structure. Since 2015, he has strategically shifted toward learning analytics, applying data science to transform STEM education. His innovative projects include E 2 Coach (a personalized student support system) and the NSF-funded REBUILD initiative, which creates intergenerational research teams to develop evidence-based teaching practices across physics, chemistry, astronomy, biology, and mathematics. Analysis of his publication trajectory reveals a deliberate pivot from astrophysics to educational research around 2015. While his early work centered on galaxy clusters and cosmological phenomena, recent publications (2020-2024) overwhelmingly focus on systemic equity gaps in STEM education, data-driven interventions, and multi-institutional collaborations. This evolution demonstrates how his data science methodology transitions seamlessly between cosmic structures and educational ecosystems. His scientific recognition includes: Prestigious Arthur F. Thurnau Professorship (awarded for exceptional undergraduate teaching) McKay directs the NSF-funded REBUILD project and the Digital Innovation Greenhouse, securing substantial research funding while mentoring undergraduate and graduate students in interdisciplinary teams. His work with the Big Ten Academic Alliance (CIC) has generated cross-institutional studies on grading patterns, performance disparities, and student support systems, with practical applications implemented across multiple universities. He actively collaborates with faculty across STEM disciplines to develop scalable educational technologies. His research infrastructure includes the Digital Innovation Greenhouse (an educational technology incubator) and REBUILD project teams, which integrate undergraduates, graduate students, postdocs, and faculty in evidence-based educational research. These teams operate at the intersection of data science and pedagogy, developing tools that analyze institutional datasets to personalize student support while maintaining rigorous scientific methodology.

Tyler DerrView profile

Assistant Professor

• Data Mining
• Social Network Analysis
• Graph Representation Learning
• +5 more

Dr. Tyler Derr is an Assistant Professor in the Department of Computer Science at Vanderbilt University, with affiliate roles in the Data Science Institute and the Frist Center for Autism and Innovation. He holds a PhD from Michigan State University (2020) and focuses on data mining, machine learning, and graph neural networks with applications in drug discovery, ethical AI, and neurodiversity research. Education: PhD in Computer Science, Michigan State University (2020) M.S. in Computer Science, Pennsylvania State University B.S. in Computer Science and Mathematics, Pennsylvania State University His research emphasizes interdisciplinary social good, including drug discovery, education equity, and autism innovation. He directs the Network and Data Science (NDS) Lab, mentoring students who have won 50+ awards. Notable contributions include tools like FairNNV for fairness certification and datasets like WelQrate for drug discovery benchmarking. Awards include the NSF CAREER Award (2023), NVIDIA Academic Grant (2024), and Vanderbilt’s Career Catalyst Impact Award (2025). He co-founded the MLoG workshop series and serves on editorial boards for ACM TKDD and IEEE Transactions on Big Data . Grants & Leadership: NSF Grant for student travel to KDD2024 NVIDIA BioNeMo Platform grant for peptide design DOE Computational Science Fellowship advising His work bridges theory and practice, with tutorials on graph ML at AAAI/KDD and keynotes on ethical AI. The NDS Lab hosts interdisciplinary collaborations in computational biology, transportation, and neurodiversity advocacy.

Carlo CavicchiaView profile

Assistant Professor

• Statistics
• Data Science
• Machine Learning
• +6 more

Dr. Carlo Cavicchia is an Assistant Professor of Statistics at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. He holds a PhD in Methodological Statistics from La Sapienza University of Rome and has held roles such as Research Fellow at UnitelmaSapienza University and Consultant for NGOs in Zanzibar. His research focuses on latent variable models, composite indicators, and unsupervised classification, with applications in environmental policy, sports analytics, and teacher job satisfaction. Cavicchia teaches statistics and data science courses at undergraduate and graduate levels and actively contributes to academic communities through journal reviewing, conference organizing, and editorial roles. Education: PhD in Methodological Statistics (La Sapienza University of Rome, 2020) MSc in Statistics and Decision Sciences (La Sapienza University of Rome, 2016) BSc in Statistics (La Sapienza University of Rome, 2013) Dutch University Teaching Qualification (BKO, 2022) Research Interests: Cavicchia’s work emphasizes hierarchical models, non-parametric statistics, and data science applications. He develops methodologies for composite indicators, including ultrametric Gaussian mixture models and disjoint principal component analysis. His research bridges theoretical advancements with real-world problems, such as waste management in Italian municipalities and ranking European football teams using composite metrics. Grants & Awards: 2024: IFCS Chikio Hayashi Award 2023: ESE Starter Grant (€300,000) 2017: Research Grant for Junior Researchers (€1,270) 2016: PhD Scholarship, La Sapienza University Academic Engagement: Cavicchia serves as IASC Data Analysis Competition Officer (2023–2025), co-edits the ISI Magazine , and organizes conferences like DSSV 2020 and DSSV-ECDA 2021. He is an elected member of the International Statistical Institute and contributes to SVQS’s Sustainability initiatives. Labs & Teams: He co-organizes the Econometrics internal seminars at Erasmus University and collaborates with researchers at University of Naples Federico II on hierarchical models and convex clustering.