Find Academic Supervisors

Louis LongchampsView profile

Assistant Professor

• Precision Agriculture
• Farm Data Management
• On-Farm Experimentation
• +5 more

Louis Longchamps is an Assistant Professor in the School of Integrative Plant Science (Soil and Crop Sciences Section) at Cornell University. He specializes in digital agronomy, precision agriculture, and farmer-centric on-farm experimentation. His work focuses on integrating farm data management systems with environmental sustainability goals. Education: PhD in Agronomy from Laval University (thesis on weed population spatial structure and site-specific weed management). Postdoctoral research at Colorado State University’s Department of Soil and Crop Sciences. Research Interests: Precision nitrogen management using remote sensing, reduction of nitrous oxide emissions via variable rate fertilization, co-creation of solutions with farmers for environmental stewardship, and leveraging AI/digital tools for farm decision-making. Recent projects include assessing on-farm experimentation practices in NY State and developing frameworks for pooling farm data across multiple sites. Outreach: Leads an integrated research-extension program focused on co-creating digital agriculture solutions with farmers. Engages in expanding on-farm experimentation through data contextualization and artificial intelligence applications. Labs/Teams: Associated with the Farmers DataLab initiative. Previously co-led a Living Laboratory project on the St. Lawrence River ecosystems.

Yunpeng LiView profile

Academic

• Machine Learning
• Bayesian Inference
• Healthcare AI
• +3 more

Yunpeng Li is a Reader in AI & Digital Oral Health at King’s College London, affiliated with the Centre for Oral, Clinical & Translational Sciences in the Faculty of Dentistry, Oral & Craniofacial Sciences. Previously, he held roles at the University of Surrey as a faculty member and the University of Oxford as a Postdoctoral Researcher. He completed his PhD in Electrical Engineering at McGill University, Canada. His research focuses on trustworthy AI, Bayesian methods, and machine learning applications in healthcare and environmental monitoring. Key projects include AI-assisted dental disease detection via radiography and acoustic monitoring of invasive species. He has secured grants from NIHR, NERC, and RAEng. Developed methodologies for uncertainty quantification in AI systems. Collaborates on projects like HumBug II for biodiversity monitoring. Supervised students such as Xiongjie Chen, Zhi Qin Tan, and Conghui Hu. Awards include the RAEng Enterprise Fellowship and recognition in BIA PULSE 2025 for his dental AI spinout BeCertain.

Adel DaoudView profile

Associate Professor

• Global Poverty
• Political Economy
• Development Studies
• +3 more

Adel Daoud is a Senior Associate Professor at Linköping University's Institute for Analytical Sociology (Department of Management and Engineering) and an Affiliated Associate Professor in Data Science and Artificial Intelligence for the Social Sciences at Chalmers University of Technology. His research focuses on global poverty, political economy, and causal inference using machine learning. He leads the AI and Global Development Lab, combining AI and earth observation to study poverty dynamics in Africa. Education: PhD in Sociology (2011, University of Gothenburg), M.A. in Sociology (2006), and pedagogical training for higher education (2011-2012). Former positions include roles at Harvard University, the University of Cambridge, and the Alan Turing Institute. Research emphasizes causal mechanisms linking economic shocks, governance, and child poverty. Methodological innovations include integrating satellite imagery with machine learning for poverty measurement. Major grants include SEK 18 million for historical poverty analysis via satellite data. Awards include the Academy Research Fellowship (2018) and Young Academy of Sweden membership (2022-2027). His work spans 150+ publications in journals like PNAS, World Development, and Sociological Methods & Research. Teaching covers quantitative methods, political economy, and social theory. Labs: AI and Global Development Lab (focusing on satellite-based poverty analytics). Podcast: 'The Journeys of Scholars' interviews top academics on research strategies. Active in policy advocacy through the Young Academy of Sweden.

Mireya Diaz-InsuaView profile

Professor

• Biostatistics
• Epidemiology
• Medical Guidelines Development
• +5 more

Mireya Diaz-Insua is a Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University's School of Medicine. She holds leadership roles as Director of the PQHS Biostatistics Core and Case CCC Biostatistics Shared Resource. Her career includes over 21 years in biostatistics and medical research, with expertise in surgical outcomes, evidence synthesis, and guideline development. She has pioneered oncologic surveillance schemes post-robotic prostatectomy and restructured the Division of Epidemiology and Biostatistics at Western Michigan University's Homer Stryker M.D. School of Medicine. Education: PhD in Biostatistics (Case Western Reserve, 2002), MS in Biomedical Engineering (2002), and BS in Electrical Engineering (1994). Professional roles span academic leadership, hospital collaborations (e.g., Henry Ford Health System), and guideline work with the American Urologic Association. Research focuses on statistical methods for healthcare data, including causal inference, meta-analysis, and clustered data modeling. Key contributions include developing risk-based prostate cancer surveillance protocols and analyzing long-term outcomes of robotic surgery. Her work bridges methodological rigor with clinical translation, notably impacting guidelines for urologic care and cancer management. Publications span 74 peer-reviewed articles (H-index 30/31), with top-tier contributions in epidemiology, urology, and biostatistics. Awards include institutional recognition for cancer care innovations. Current initiatives emphasize evidence synthesis, surgical innovation evaluation, and aging-related cancer outcomes.

Wei Ji MaView profile

Professor

• Decision-making
• Planning
• Reinforcement Learning
• +4 more

Wei Ji Ma is a Professor of Neural Science and Psychology at New York University, affiliated with the Faculty of Arts and Science. He holds dual appointments in the Departments of Neural Science and Psychology. His research focuses on decision-making, planning, reinforcement learning, and perception, combining human behavioral experiments, computational modeling, and collaborations in neuroimaging and electrophysiology. He earned his PhD in Physics from the University of Groningen, followed by postdoctoral work at Caltech and the University of Rochester before joining NYU in 2013. His affiliations include the Neuroscience Institute, Institute for the Study of Decision Making, Center for Data Science, and NYU-ECNU Institute of Brain and Cognitive Science in Shanghai. Research Interests: Dr. Ma’s lab explores cognitive processes such as decision-making under uncertainty, working memory, and social cognition. He develops computational models to understand how neural systems integrate sensory information and prior knowledge. His work bridges neuroscience, psychology, and artificial intelligence, emphasizing Bayesian approaches and resource-rational theories. Key Collaborations & Roles: He co-directs the NIH-funded Training Program in Computational Neuroscience with Xiao-Jing Wang and leads initiatives like the Growing up in Science seminar series. His mentorship extends to advocacy through organizations like the Scientist Action and Advocacy Network and NeuWrite NYU. Beyond academia, he co-founded the Rural China Education Foundation. Grant & Mentorship: His work is supported by NIH grants, and he mentors students and postdocs in computational neuroscience. His research on planning, uncertainty, and neural representations has been published in top journals like Nature Neuroscience , Neuron , and Nature Communications .

• Public Health
• Child and Adolescent Health
• Intervention Development
• +3 more

Dr Jeremy Segrott is a Reader in the School of Medicine at Cardiff University, affiliated with the Centre for Trials Research and the DECIPHer Centre. His research focuses on improving public health through interventions targeting schools and families, with expertise in process evaluations of randomized controlled trials. He leads the Learning and Development Group at the Centre for Trials Research and oversees undergraduate teaching. His work emphasizes qualitative methods, public involvement, and interdisciplinary collaboration. Key areas include bullying prevention, mental health resilience in children, and interventions for families with intellectual disabilities. Dr Segrott has held roles at Cardiff University since 2007, with a PhD in human geography. He chairs initiatives like the KiVa anti-bullying trial and the Peer MIST trauma intervention project. His teaching includes leading a research methods module in the School of Medicine’s intercalated degree course. Recent projects include evaluating the KiVa program (£2.5M), fostering changes training (£828K), and peer-led trauma support (£218K). He advocates for accessible, evidence-based interventions and has contributed to policy through grants from Wellcome Trust, NIHR, and others. Dr Segrott is committed to public engagement and supervises postgraduate students. His work spans Wales and internationally, addressing health disparities through innovative trial methodologies and participatory approaches.

Federico MariniView profile

Professor

• Chemometrics
• Analytical Chemistry
• Food Chemistry
• +3 more

Federico Marini is a Full Professor at the Department of Chemistry, University of Rome La Sapienza. He holds editorial roles including Editor-in-Chief of *Chemometrics and Intelligent Laboratory Systems* and membership in multiple journal boards. His research focuses on chemometrics, particularly method development and application in food chemistry, metabolomics, and multi-block data analysis. He has authored over 300 publications and edited books like *Chemometrics in Food Chemistry* (Elsevier 2013). Education: PhD in Analytical Chemistry (University of Rome La Sapienza, 2004) with Marie Curie Fellowship (2000–2004). Postdoctoral roles included visiting positions at universities in Copenhagen, Stellenbosch, and Lille. Research interests span advanced chemometric methods for food authentication, omics data processing, and non-linear modeling. Notable contributions include SO-PLS(-LDA) algorithms and multi-block fusion strategies. He coordinates the Chemometrics Task Group of DAC-EuCheMS and serves as Vice President of the Italian Society of NIR Spectroscopy. Awards include the 2006 Young Researcher Award (Italian Chemical Society) and 2012 Chemometrics Award. His work bridges analytical chemistry with data science, addressing real-world problems in agriculture, food safety, and biomedicine through innovative spectroscopic and statistical techniques.

Morten Thaysen-AndersenView profile

Associate Professor

• Glycomics
• Glycoproteomics
• Immunology
• +7 more

Associate Professor Morten Thaysen-Andersen leads the Analytical Glycoimmunology group at Macquarie University's School of Natural Sciences. His research focuses on advancing understanding of the human innate immune system through glycomics and glycoproteomics technologies, leveraging mass spectrometry and multidisciplinary approaches. He is an ARC Future Fellow and directs the ARC Training Centre for Facilitated Advancement of Australia's Bioactives (FAAB). His work addresses microbial infections, inflammation, and cancer by elucidating glycobiological mechanisms in immune-related diseases. Research interests include N-glycan analysis, tumor microenvironment profiling, immune cell glycosylation dynamics, and septic shock pathogenesis. He collaborates on projects like high-content oncoproteomics and cystic fibrosis immune cell studies. Key awards include the ARC Future Fellowship (2022–2027). His lab develops glycan-based biomarkers and investigates glycoprotein function in health and disease. Key Projects: FAAB Training Centre (2022–2027) High-Content Oncoproteomics (2017–present) Neutrophil Glycosylation in Cystic Fibrosis (2017–present) Labs: Analytical Glycoimmunology Group Biomolecular Discovery Research Centre Grants & Awards: ARC Future Fellowship (2022–2027) Multiple project grants from NHMRC and industry partners His work bridges analytical chemistry with clinical applications, contributing to diagnostics and therapeutics in immunology and oncology. Collaborations span microbiology, structural biology, and systems biology.

Stefano BattistonView profile

Associate Professor

• Sustainable Finance
• Systemic Risk
• Climate-Related Financial Risk
• +3 more

Stefano Battiston is an Associate Professor in Sustainable Finance and Networks at the University of Zurich (UZH) and holds a part-time position as Associate Professor at the University of Venice Ca' Foscari. He serves as vice-chair of the Center of Competence for Sustainable Finance and is a Lead Author for the IPCC’s Sixth Assessment Report on Finance and Investment. His research focuses on systemic risk, climate-related financial risks, and sustainable finance, with methodologies like DebtRank and climate stress-tests widely adopted in policy and practice. Education includes a PhD in Statistical Physics from École Normale Supérieure, Paris, and a Master’s in Neuroscience from Brandeis University. He has directed EU-funded projects such as SIMPOL and DOLFINS, totaling over €9 million in funding. His work bridges academia and policy, engaging with institutions like the ECB, ESMA, and the European Commission. Key research interests include financial networks, climate policy integration, and the societal impact of complex systems. His awards include the Swiss National Science Foundation (SNSF) Professorship and a CNRS Post-Doctoral Fellowship. Battiston has supervised over 10 PhD students and 8 postdocs, contributing to roles in academia and financial regulatory bodies. Teaching roles span courses on systemic risk, sustainable finance, and climate change at UZH, Venice, and ETH Zurich. He organizes major conferences on financial networks and sustainability, including the FINEXUS series. His interdisciplinary approach addresses challenges at the nexus of finance, climate, and policy.

• Financial Risk Management
• Extreme Value Theory
• Systemic Risk
• +3 more

Chen Zhou is a Professor at the Erasmus School of Economics, Erasmus University Rotterdam. His academic career is centered around quantitative finance and risk management, with significant contributions to extreme value theory applications in financial contexts. His research spans banking stability, market risk, and statistical methodologies for financial analysis. Chen Zhou's research interests focus on Financial Risk Management, Extreme Value Theory, Systemic Risk, Banking Stability, Financial Econometrics, and Asset Pricing. His work bridges theoretical statistical methods with practical financial applications, particularly in understanding tail risks and systemic vulnerabilities in financial markets. He has developed innovative approaches to measuring and managing financial risks using extreme value statistics, with applications across banking, equity markets, and energy markets. Analysis of Zhou's publication record reveals a strong focus on applying extreme value theory to financial risk measurement. His research shows a progression from foundational work in extreme value statistics to practical applications in banking stability and market risk. The papers demonstrate expertise in both theoretical statistical methods and their implementation in real-world financial contexts, with particular emphasis on systemic risk measurement and tail event analysis. Chen Zhou has collaborated extensively with researchers across Europe and globally, as evidenced by his co-authorship on the highly cited 'Nonstandard Errors' paper with over 50 co-authors. His research has been published in top finance journals including the Journal of Finance, and he has contributed to central bank research through working papers with institutions like De Nederlandsche Bank.

• Applied Microeconomics
• Economics of Education
• Labour Economics
• +1 more

Jeffrey Penney, PhD, is an Associate Professor in the Department of Economics at the University of Alberta, Faculty of Arts. His research focuses on Applied Microeconomics, Economics of Education, Labour Economics, and Law and Economics. He teaches courses such as ECON 378 (Law and Economics) and ECON 399 (Introductory Econometrics). Penney’s work explores topics like educational interventions, sentencing policies, and labor market dynamics. He is affiliated with the Laboratorio de Economía de la Educación and can be reached via penney1@ualberta.ca or dr.jeffrey.penney@gmail.com. His research interests emphasize empirical analysis of education systems, labor markets, and legal frameworks. Notable studies include investigations into teacher diversity impacts, scholarship efficacy, and migration effects on earnings. Penney’s methodologies often involve econometric analysis and experimental designs to evaluate policy outcomes. His recent work addresses issues like test score normalization challenges and the long-term effects of cramming on academic performance. Publications span topics from criminal sentencing distributions to the socioeconomic implications of educational programs, reflecting his interdisciplinary approach to microeconomic research. He maintains an active scholarly profile with ongoing contributions to peer-reviewed journals and policy-relevant studies.

Stefan StenbomView profile

Associate Professor

• Digital Learning Environments
• Online Education
• Community of Inquiry Frameworks
• +3 more

Stefan Stenbom is an Associate Professor in Learning in Engineering Sciences at the Division of Digital Learning within the ITM School at KTH Royal Institute of Technology. He is affiliated with Digital Futures, a cross-disciplinary research center focusing on digital technologies for societal challenges, established by KTH, Stockholm University, and RISE. His research emphasizes digital learning environments, online education frameworks, and the Community of Inquiry theoretical model. He explores topics such as cognitive presence in online tutoring, blended learning design, and teacher training in digital transformation. Research interests include the application of evidence-based practices in educational technology, analysis of student and teacher roles in digital spaces, and the impact of hybrid learning models. His work bridges pedagogy, technology, and institutional change, particularly in K-12 and higher education contexts. Recent articles highlight frameworks for online learning quality, assessment strategies in computer science education, and post-pandemic educational normalization. Publications consistently address digital pedagogy innovations, with a focus on frameworks like the Community of Inquiry and Relationship of Inquiry. His work contributes to teacher professional development through digital badges and blended learning courses for faculty. The research has implications for global educational systems and policy-making.

Richard CleggView profile

Senior Lecturer

• Complex Networks
• Temporal Networks
• Network Statistics
• +3 more

Dr. Richard Clegg is a Senior Lecturer in Technology Management at Queen Mary University of London, within the School of Electronic Engineering and Computer Science. He is affiliated with the Centre for Networks, Communications and Systems. His research focuses on temporal networks, complex systems, and graph software development, particularly through the Raphtory project. He holds a PhD and has extensive experience in network analysis, blockchain applications, and AI-driven network optimization. Research Interests: Dr. Clegg investigates temporal networks across social, transactional, and computer networks, emphasizing their mathematical and statistical foundations. He develops tools like Raphtory (www.raphtory.com) for analyzing dynamic graphs in Rust and Python. His work spans cryptocurrency transaction networks, email communication hierarchies, and energy-efficient radio access networks. Key Contributions: His recent work analyzes the Luna-Terra collapse using temporal multilayer graphs and explores social mobility in temporal networks. He has published widely on network statistics, sensor data privacy, and cloud-assisted video conferencing. Current research includes AI-ready energy modeling for next-gen networks and fraud detection in cross-chain cryptocurrency ecosystems. Grants & Collaborations: He secured grants from Cisco, Moogsoft, and the Alan Turing Institute for projects on graph analysis, network fraud detection, and temporal mobility studies. Collaborators include industry leaders like Pometry Limited and academic partners like the University of Cambridge. Labs/Teams: Leads the Raphtory software initiative and advises PhD students in network science and AI. His team includes researchers like Cheick Ba and former students Ben Steer and Naomi Arnold.

Rob SalomoneView profile

Research Fellow

• Statistics
• Machine Learning
• Computational Methods
• +3 more

Dr. Rob Salomone is a Senior Research Fellow in Data Science at the Queensland University of Technology's (QUT) Centre for Data Science. He earned his Ph.D. in Statistics from the University of Queensland in 2018, focusing on advanced Monte Carlo methodology. Before joining QUT, he held research fellowships at the University of Queensland and UNSW Sydney. His research bridges statistics and machine learning, with an emphasis on developing robust computational techniques for large datasets and complex models. He specializes in creating scientific models that integrate advanced machine learning, domain expertise, and tailored computational methods to address intricate mathematical challenges. Salomone’s work spans Bayesian computation, Monte Carlo methods, and applications in computational biology and environmental systems. His contributions include advancements in nested sampling, CRISPR guide RNA optimization, and graph neural networks for anomaly detection. He also explores federated learning and uncertainty quantification in high-dimensional settings. Notable collaborations include work on agent-based tumor growth models and spectral subsampling for time series analysis. Though no formal students are listed, his research impact is evident through interdisciplinary projects and methodological innovations.

Martin MuravchikView profile

Researcher

• Sedimentary Basin Analysis
• Tectonic Sedimentology
• Rift Basin Evolution
• +4 more

Martin Muravchik is a Researcher at the Department of Earth Science, University of Bergen (UiB). His research focuses on sedimentary basin analysis, particularly the interplay between tectonic structures and sedimentation in rift basins. Key study regions include the Corinth Rift (Greece), Suez Rift (Egypt), and Neuquén Basin (Argentina). He employs advanced field techniques like LiDAR, photogrammetry, and seismic imaging for 3D digital outcrop modeling. Teaching Roles: Assistant Professor for Sequence Stratigraphy (GEOV361) at UiB (2019) Teaching Assistant for GEOV260 at UiB (2014, 2015, 2017) Teaching Assistant at Universidad Nacional de La Plata (UNLP) for Introduction to Geology (1999-2005) and Structural Geology (2002-2009) Research Interests: His work emphasizes tectonically active basins, carbonate-evaporite-siliciclastic interactions, and structural controls on sedimentation. He integrates field data with quantitative analyses to understand basin evolution and stratigraphic architecture. Students: Supervised/co-supervised multiple PhD and MSc students, including studies on rift margin deltas, deep-water turbidites, and seismic modeling in the Corinth Rift. Key Projects: DeepRift: Analyzing deep-water depositional systems in rifted margins Syn-Rift Plays: Outcrop analogues for subsurface applications Carbonate-Siliciclastic-Evaporite systems in the Suez Rift Labs/Teams: Collaborates with international teams on rift basin dynamics, including studies in Greece, Argentina, and the Norwegian continental shelf. Active in conferences like AAPG, SEPM, and EGU.