Find Academic Supervisors

• High Energy Physics
• Particle Physics
• Cosmic Ray Physics
• +10 more

Prof. Dr. Martin Erdmann is a University Professor of Experimental Physics (High Energy Physics) at RWTH Aachen University, affiliated with the Department of Physics within the Faculty of Mathematics, Computer Science and Natural Sciences. He leads research in high-energy particle physics through the CMS experiment at CERN and the Pierre Auger Observatory in Argentina. His work integrates cutting-edge digital methods, including deep learning and cloud-based data analysis via the VISPA platform. PhD, University of Freiburg (1990) Habilitation, University of Heidelberg (1996) Heisenberg Fellow at DESY and University of Karlsruhe (1997–2002) Professor at RWTH Aachen since 2004 His research interests span Higgs and top-quark physics, cosmic ray detection, radio-based shower measurement, and AI-driven data analysis. He actively contributes to physics education through textbooks and open-access video lectures. His recent publications reflect strong trends in applying deep learning to particle and astroparticle physics, particularly in event reconstruction and simulation. He leads major initiatives like the ErUM-Data-Hub and DIG-UM, advancing digital transformation in fundamental research. Heisenberg Fellowship (DESY and Karlsruhe) Chair, DPG Working Group on Physics, Modern IT, and AI (2019–2021) Project Leader, ErUM-Data-Hub (since 2021) Chair, DIG-UM Community Organization (2021–2024) Prof. Erdmann advises students at all levels and fosters innovation in data science for physics. He has secured leadership roles in international collaborations and promotes sustainable, resource-aware computing in research. His lab develops advanced detector technologies and simulation tools like CRPropa for cosmic ray propagation. Future work includes probing Higgs self-coupling, identifying cosmic ray sources, and refining AI models for physics discovery.

• Statistics
• Sports science and exercise
• Ecological applications
• +7 more

Paul Wu is an Associate Professor in the School of Mathematical Sciences at Queensland University of Technology (QUT), Faculty of Science, where he also serves as an industry research fellow in the strategic partnership between the Centre for Data Science (CDS) and AIS/QAS. He leads the sports systems domain within CDS and applies statistical and machine learning models to complex systems across sports, marine science, and defence sectors. PhD, Queensland University of Technology Master of Engineering Science (Computer and Comm Engineering), Queensland University of Technology Bachelor of Engineering (Electrical and Computer Engineering), Queensland University of Technology His research focuses on Bayesian statistics, dynamic Bayesian networks, state space modelling, and simulation techniques. He works closely with domain experts to solve real-world problems in sports performance, ecological resilience, and human systems. His interdisciplinary work spans sports science , marine ecology , and defence applications . Paul’s recent publications demonstrate a strong trajectory in predictive analytics for elite sports and ecosystem modelling, particularly using Bayesian frameworks. His work on swimming performance prediction has informed national training strategies and contributed to competitive success. He has also advanced methods in clustering, model adaptation, and psychosocial risk assessment. His scientific contributions have been recognized through impactful collaborations with elite sports organizations including the Australian Institute of Sport, Swimming Australia, and the West Coast Eagles. Testimonials highlight his role in transforming data analytics in sports injury recovery and performance optimization. Applied Bayesian models in elite sports decision-making Developed predictive tools for marine ecosystem resilience Collaborated on over 30 industry and government projects Supervises research in complex sports data analytics Paul leads a dynamic research environment focused on translating statistical innovation into practical impact across diverse domains.

Daniël LakensView profile

Associate Professor

• Meta-Science
• Research Methods
• Applied Statistics
• +10 more

Daniël Lakens is an Associate Professor in the Human Technology Interaction group at Eindhoven University of Technology (TU/e), with expertise in meta-science, research methods, and applied statistics. His empirical research focuses on conceptual thought, similarity, meaning, study design, and reward structures in science. He has contributed to replication research and open science initiatives. Academic Background: PhD in Experimental Psychology (2010) from Utrecht University Grants: NWO VIDI grant (2017), pilot project for replication research grants Teaching: MOOC on 'Improving Your Statistical Inferences', TU/e Teacher of the Year (2014), over 40 workshops on open science His research spans methodologies to optimize study structures, meta-statistics, and interdisciplinary collaborations. Recent publications include datasets and interventions in esports, behavioral modeling, and methodological frameworks. Scientific Awards: Ammodo Science Award for Fundamental Research (2023) Leamer-Rosenthal Prize for Open Social Science (2017) NWO Vidi Award (2017) Datasets and projects highlight reproducibility in psychology, computational models for goal-directed behavior, and open data practices. He serves on editorial boards and has supervised 23 students, with current courses in advanced research methods and ethics.

• Decision Theory
• Game Theory
• Economic Learning Models
• +4 more

Nabil Al-Najjar is the John L. and Helen Kellogg Professor of Managerial Economics & Decision Sciences at Northwestern University's Kellogg School of Management. He holds a PhD in Economics from the University of Minnesota (1989), preceded by an MA from the University of Ottawa (1982) and a BA from Al-Mustansiriah University (1979). Prior to joining Kellogg in 1995, he taught at the University of Quebec in Montreal. His research focuses on decision theory, game theory, and learning-based models in markets and contracts. Notable contributions include frameworks for uncertainty modeling, aggregate risk analysis, and recursive utility theory. He has published in top journals such as the Journal of Economic Theory, Econometrica, and the American Economic Review. Award-winning educator, Al-Najjar twice received the Sidney J. Levy Teaching Award (1996-97, 2006-07) for excellence in microeconomics and competitive strategy instruction. He also holds editorial roles at the International Journal of Game Theory and Journal of Mathematical Economics. His work bridges theoretical economics with practical applications, addressing issues like market fundamentals, contractual complexity, and policy design under uncertainty. Over 49 peer-reviewed publications and 12 case studies reflect his interdisciplinary impact across economic theory and applied fields.

Anton WestveldView profile

Senior Lecturer

• Bayesian Methodology
• Network Analysis
• Game Theoretic Data
• +3 more

Anton Westveld is a Senior Lecturer in the Department of Statistics at the Australian National University (ANU), within the Research School of Finance, Actuarial Studies & Statistics (RSFAS). He also serves as an Affiliate Associate Professor at Virginia Commonwealth University since August 2023. His research focuses on Bayesian methodology, network analysis, game theoretic data, and statistical causality, with notable contributions to ecological modeling and agent-based stochastic simulations. Westveld holds a Bachelor’s in Economics and Political Science from the University of Michigan (Ann Arbor), a Master’s in Applied Economics and Statistics from the same institution, and a PhD in Statistics from the University of Washington. His work has been published in prestigious journals like the Annals of Applied Statistics and Proceedings of the National Academy of Sciences . His research interests span Bayesian inference, relational data analysis, and causal modeling, with applications in ecological and health sciences. Recent work includes developing Bayesian methods for ecological drivers in marine viral communities and latent socioeconomic health indices for policy evaluation. Notable articles include analyses of menstrual disorder surveys using Gaussian copulas, ecological metagenomics studies, and Bayesian-optimized bootstrap techniques for uncertainty quantification. His interdisciplinary collaborations bridge statistics with environmental science, public health, and economics.

• Uncertainty Quantification
• Bayesian Inverse Problems
• Data Science
• +7 more

Dr. Aretha Teckentrup is a Lecturer in the Mathematics of Data Science at the University of Edinburgh's School of Mathematics. Her research focuses on integrating mathematical models with observational data, particularly in areas like uncertainty quantification, Bayesian inverse problems, and computational methods for partial differential equations (PDEs). She holds a PhD in Mathematics from the University of Bath and has held postdoctoral positions internationally, including in Florida. Her work emphasizes interdisciplinary approaches, blending statistics, numerical analysis, and applied mathematics. Notably, she has contributed to advancing Gaussian processes, multilevel Monte Carlo techniques, and sparse grid methods for high-dimensional problems. Her academic journey reflects a strong commitment to bridging theoretical foundations with practical applications. She has published extensively on topics such as probabilistic numerical methods, error estimation in Bayesian inference, and adaptive sampling strategies. Dr. Teckentrup is an active member of the SIAM community, having received the prestigious SIAG/UQ Early Career Prize in recognition of her contributions to uncertainty quantification. Her research continues to explore innovative solutions for data-driven modeling challenges in science and engineering. Dr. Teckentrup’s work often addresses the growing importance of combining data with physical models, exemplified by her development of numerical methods for weather prediction and stochastic dispersion modeling. She advocates for collaboration across disciplines and emphasizes the transformative potential of integrating computational tools with real-world data. Her career trajectory underscores the dynamic and collaborative nature of modern academic research in applied mathematics and data science.

Giancarlo ManziView profile

Associate Professor

• Machine Learning
• Multivariate Statistics
• Bayesian Statistics
• +5 more

Giancarlo Manzi is an Associate Professor of Statistics at the Department of Methods and Models for Economics, Territory, and Finance, University of Rome La Sapienza. He holds a PhD from the University of Milan-Bicocca and conducted thesis research at the University of Toronto. His career includes roles at the Medical Research Council Biostatistics Unit in Cambridge and the University of Milan. University of Rome La Sapienza (Current) Medical Research Council Biostatistics Unit (Former Researcher) University of Milan (Former Researcher and Associate Professor) University of Milan-Bicocca (PhD) University of Toronto (Thesis collaboration) His research spans Machine Learning , Bayesian Statistics , and Smart Mobility , with a focus on Covid-19 analytics , data visualization , and epidemiological modeling . He integrates Multivariate Statistics with Public Health to address complex challenges in health systems and urban environments. The 15 most recent publications highlight expertise in quantile regression , Bayesian networks , time-series analysis , and smart mobility optimization . His methodological contributions include wavelet analysis, cross-correlation models, and SIRD modeling frameworks applied to pandemic dynamics and bike-sharing systems. Scientific awards and honors are not explicitly mentioned in the provided texts. Giancarlo Manzi has taught at the University of Verona, Catholic University of the Sacred Heart, and Universidad Carlos III in Madrid, maintaining strong ties with Italy's academic institutions.

John Derek MorganView profile

Associate Professor

• Geographic Information Systems (GIS)
• Urban Environmental Geography
• Participatory Mapping
• +12 more

John Derek Morgan is an Associate Professor in the Department of Earth and Environmental Sciences at the University of West Florida, within the Hal Marcus College of Science and Engineering. He serves as the primary faculty for the online Master of Science in GIS Administration program, contributing significantly to geospatial education and research. His work bridges urban environmental geography, GIS, cartography, and public policy. PhD in Geography, Florida State University MS in Management Information Systems, University of Central Florida BA in Economics (with Computer Science minor), University of Central Florida Dr. Morgan's research centers on the application of advanced geospatial methodologies to urban and environmental challenges. His work spans participatory mapping, crime mapping using time-geographic models, hazard modeling, land use planning, and cartographic visualization. He emphasizes user-centered design in Web GIS and explores how spatial tools can promote safer communities and equitable decision-making. His projects often integrate satellite imagery, social vulnerability indices, and community input to inform resilience and policy. The recent publications reflect a strong trend in applying GIS to social and environmental issues, including disaster response, urban park safety, illegal logging detection using GeoAI, and wildfire monitoring. His work combines technical innovation in geovisualization with real-world applications in public safety, environmental justice, and urban sustainability. Scientific Awards: NSF Doctoral Dissertation Research Grant #0927850 Dr. Morgan advises graduate students in the MS GIS program and has been involved in significant research grants, including the Florida Sea Grant 2024 project focused on training GIS graduates for coastal resilience. His collaborations span interdisciplinary teams, including environmental scientists, urban planners, and computer scientists. He has also contributed foundational content to the UCGIS Geographic Information Science & Technology Body of Knowledge. He leads research initiatives involving Web GIS development, hazard modeling post-hurricanes, and participatory mapping for community engagement. His lab work includes the use of satellite imagery, Bayesian networks for infrastructure failure, and deep learning for environmental monitoring. Future work continues to focus on transboundary viewshed changes, GeoAI applications, and improving spatial tools for public decision-making.

Wenrui LiView profile

Assistant Professor

• Statistics for network data
• Causal inference
• High-dimensional data analysis
• +3 more

Wenrui Li serves as an Assistant Professor in the Department of Statistics at the University of Connecticut, where she develops advanced statistical methodologies for complex biomedical data structures. Her work bridges theoretical statistics and practical healthcare applications, with particular emphasis on network-based modeling and high-dimensional data analysis in oncology and epidemiology. Dr. Li's research program centers on statistics for network data, causal inference under interference, and Bayesian modeling of structured high-dimensional datasets. She pioneers techniques for handling network noise in graph-guided models, with direct applications to cancer genomics, multi-omics integration, and infectious disease surveillance. Her methodological innovations address critical challenges in noisy data environments where traditional statistical approaches fail. Analysis of her 2021-2025 publications reveals three dominant research threads: (1) Digital health interventions to reduce time burden in cancer care, exemplified by her TIME text-messaging system; (2) Graph-guided Bayesian methods for multi-omics data integration with noisy network priors; and (3) Epidemiological modeling of time-varying parameters in noisy surveillance systems. These threads demonstrate consistent focus on methodological rigor applied to pressing biomedical problems. No scientific awards, student advisement records, grant funding details, or laboratory affiliations were documented in the source materials. Her collaborative work spans biostatistics, oncology, and network science, with frequent co-authorship on interdisciplinary projects addressing cancer care optimization and disease modeling.

Joshua SpeagleView profile

Assistant Professor

• Astrostatistics
• Data Science
• Machine Learning
• +7 more

Joshua Speagle is an Assistant Professor jointly appointed in the Department of Statistical Sciences and the David A. Dunlap Department of Astronomy & Astrophysics at the University of Toronto. He is also an Associate Member of the Dunlap Institute for Astronomy & Astrophysics and a Member of the Data Sciences Institute. His research lies at the intersection of statistics, astronomy, and computer science, focusing on astrostatistics and data-intensive astrophysics. His research interests include astrostatistics, data science, machine learning, statistical inference, and Bayesian methods. He develops novel statistical learning techniques to extract insights from large, complex datasets, particularly from astronomical surveys. His work emphasizes interpretability, robust inference, and computational efficiency, with applications to galaxy formation, stellar photometry, and 3D dust mapping. The trends in his recent publications reflect a strong focus on interdisciplinary methodologies, particularly in Bayesian inference, nested sampling, and machine learning applied to astrophysical problems. His work consistently bridges theoretical statistics with practical applications in astronomy, emphasizing open-source software and reproducible research. Banting Postdoctoral Fellowship Dunlap Fellowship Joshua Speagle is deeply committed to mentorship and collaboration. He co-leads the Astrostatistics Research Team (ART) with Gwen Eadie, mentoring students and postdocs across disciplines. He is involved in graduate and undergraduate research programs, including the Astronomy & Astrophysics Summer Undergraduate Research Program (SURP). He teaches courses in statistics and astronomy and serves on committees within the University of Toronto and professional societies such as the AAS, ASA-AIG, and SSC-DSA. He co-leads the interdisciplinary Astrostatistics Research Team (ART), which fosters a collaborative, inclusive environment focused on cutting-edge research at the intersection of statistics and AI. The team emphasizes open and accessible science, releasing open-source tools like dynesty and brutus , and mentoring the next generation of data scientists.

• Geometric Deep Learning
• Topological Deep Learning
• Topological Data Analysis
• +7 more

Bastian Alexander Grossenbacher is a Full Professor of Machine Learning at the University of Fribourg, where he leads the AIDOS Lab within the Department of Informatics, Faculty of Science and Medicine. He holds secondary appointments at the Institute of AI for Health and the Helmholtz Pioneer Campus of Helmholtz Munich. He is also a TUM Junior Fellow and a member of ELLIS and AI-LIFE. He co-directs the Applied Algebraic Topology Research Network (AATRN) and is involved in the Topology, Algebra, and Geometry in Data Science (TAG DS) initiative. His research lies at the intersection of geometry, topology, and machine learning, with a focus on Geometric Deep Learning, Topological Deep Learning, and Topological Data Analysis. He develops novel machine learning methods that use topological and geometric priors to improve model robustness and interpretability, particularly in biomedical applications. His work spans theoretical foundations and practical implementations in areas such as single-cell analysis, medical imaging, and network science. The 15 most recent publications (2023–2025) reveal a strong trend toward integrating algebraic and differential topology into deep learning architectures. Key themes include the use of Euler Characteristic and magnitude transforms, curvature-based analysis of graphs and data, simplicial and manifold-based neural representations, and the application of topological methods to biomedical data. His articles appear in top-tier venues such as Nature Communications, ICML, NeurIPS, ICLR, and IEEE conferences, reflecting both theoretical depth and high-impact applications. ERC Starting Grant Bastian Rieck is actively involved in mentoring and academic leadership. He supervises PhD students and postdoctoral researchers in the AIDOS Lab and is open to new collaborations, particularly with candidates from interdisciplinary backgrounds. He has secured significant research funding, including the ERC Starting Grant, and leads multiple collaborative research initiatives. He emphasizes open science, sharing code, data, and educational materials publicly. He leads the AIDOS Lab, which focuses on advancing machine learning through topological and geometric principles. He is also a co-director of the Applied Algebraic Topology Research Network (AATRN), which hosts a large online seminar series. Additionally, he maintains DONUT, a curated database of non-theoretical applications of topology, and is active in the ELLIS and AI-LIFE networks, fostering international collaboration in AI and health.

Ye HuView profile

Professor

• Marketing analytics
• Digital marketing
• Advertising
• +7 more

Ye Hu is a Professor and Conn Fellow at the C. T. Bauer College of Business, University of Houston. He serves as Director of Bauer College PhD Programs and holds a Ph.D. in Marketing & M.A. in Statistics from The Wharton School of the University of Pennsylvania, alongside a B.E. in Economics and B.S. in Automotive Engineering from Tsinghua University. Education: Ph.D./M.A. (Wharton), B.E./B.S. (Tsinghua) Current Role: Director of Bauer College PhD Programs His research examines dynamic consumer responses to marketing mix strategies using internet search data, with expertise spanning pricing, marketing analytics, digital marketing, and Bayesian statistical modeling. He has published in top journals including Journal of Marketing , Management Science , and Marketing Letters . Ye Hu has received numerous accolades including the Lucile and Leroy Melcher Excellence in Research Award (2012), Journal of Advertising Research best paper award (2008), and multiple teaching honors from Purdue University and the University of Houston. His work has been recognized by the American Marketing Association and Journal of Interactive Marketing. Teaching Awards: Krannert Distinguished Teacher (Purdue), Bauer G.R.E.A.T. Award (UH) Research Grants: Purdue Center of International Business Education and Research (2006) Leadership: Director of Bauer College PhD Programs

Tom LindströmView profile

Senior Lecturer

• Theoretical Biology
• Ecology
• Epidemiology
• +7 more

Tom Lindström is a Senior Associate Professor and Head of the Biology Division at the Department of Physics, Chemistry and Biology (IFM), Linköping University. His interdisciplinary research integrates theoretical biology with applied modeling in ecology and epidemiology, supported by collaborations with U.S. agencies such as the USDA and DHS. His research focuses on developing advanced statistical and computational models to address complex biological challenges. Key areas include livestock epidemiology , where he simulates disease outbreaks and evaluates control strategies; movement ecology , particularly reptile movement and invasion dynamics in tropical Australia; and wildlife monitoring , where he develops Bayesian methods to estimate hunting harvests in Sweden using incomplete data. His work emphasizes handling imperfect datasets through structured modeling. Recent publications reveal a strong trend in using Bayesian inference , network modeling , and large-scale simulations to study animal movement and disease spread, particularly in U.S. livestock systems. These studies contribute to policy-making by offering predictive tools for transboundary animal diseases and food security threats. Scientific contributions include: Development of efficient algorithms for nationwide livestock movement simulation Pioneering ensemble modeling approaches in epidemiology Statistical methods for estimating wildlife harvests from area-based reporting Identification of climatic drivers in reptile movement and invasion success Tom Lindström actively mentors research projects and leads collaborative teams across international institutions. He has secured significant research funding from U.S. federal agencies, underscoring the societal impact of his work. His lab focuses on bridging data gaps in animal movement and improving predictive accuracy for disease control policies. Future work continues to advance computational tools for ecological and epidemiological forecasting.

Tingting LiView profile

Lecturer

• AI for Cyber Security
• Automated Cyber Defence
• Diversification and Deception Strategies
• +8 more

Dr. Tingting Li is a Lecturer (Assistant Professor) in Cyber Security at Cardiff University's School of Computing and Informatics and a member of the Centre for Cyber Security Research. She also holds an Honorary Research Fellow position at Imperial College London, reflecting her ongoing research collaboration. Her work bridges artificial intelligence and cybersecurity, with a focus on protecting critical systems such as cyber-physical systems (CPS), industrial control systems (ICS/SCADA), and autonomous vehicles. BEng (Hons) in Information Security, Xidian University, China MSc in Computing, Imperial College London PhD in Artificial Intelligence, University of Bath Dr. Li’s research interests lie at the intersection of AI and cybersecurity, particularly in automated cyber defense , diversification/deception strategies , and symbolic AI for knowledge representation . She investigates how AI can enhance the resilience of critical infrastructures through intelligent, adaptive defense mechanisms. Her recent work explores quantum-inspired reinforcement learning and machine learning models for proactive threat mitigation in complex systems. Her recent publications demonstrate a strong trend in applying advanced AI techniques—especially deep reinforcement learning, LSTM networks, and Bayesian models—to cybersecurity challenges in industrial and autonomous systems. These works span domains like IoT malware suppression, network diversity for ICS resilience, and automated compliance checking, reflecting a multidisciplinary approach combining security, AI, and systems engineering. Dr. Li has secured significant research funding, including a grant from the Alan Turing Institute (2024–2025) on AI safety in autonomous cyber defense, a RITICS/NCSC-funded project on diversity-based cybersecurity (2021–2022), and an EPSRC-funded project on metric-driven cybersecurity frameworks for critical national infrastructure (2021–2023). She actively supervises PhD students in areas including cybersecurity for autonomous vehicles, moving target defense, and cyber-physical system security. Her team includes Iryna Bernyk, Sanyam Vyas, Victoria Marcinkiewicz, Ellis Doran, Stephen Morris, and Sam Braithwaite. She also leads teaching modules on databases (CM6125/CM6625) and cybersecurity (CM6224/CM6724). Dr. Li’s research is conducted within the Centre for Cyber Security Research at Cardiff University, where she collaborates with experts in AI, security, and critical infrastructure protection. Her work is highly interdisciplinary, involving partnerships with institutions like Imperial College London and funding bodies such as EPSRC, NCSC, and the Alan Turing Institute.

Mingjun ZhongView profile

Lecturer

• Machine Learning
• Computational Statistics
• Non-intrusive Load Monitoring
• +12 more

Dr. Mingjun Zhong is a Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen. His research focuses on machine learning and computational statistics with applications in healthcare, energy systems, and medical imaging. He is actively engaged in teaching and academic service, including editorial roles in prominent journals. Position: Lecturer Institution: University of Aberdeen School: School of Natural and Computing Sciences Email: mingjun.zhong@abdn.ac.uk Dr. Zhong's research interests center on probabilistic and statistical machine learning methodologies applied to real-world data. He works on healthcare data analysis, non-intrusive load monitoring (NILM), spectroscopy, EEG/fMRI, and energy disaggregation. His methodological expertise includes variational inference, Markov chain Monte Carlo, Bayesian matrix factorization, and deep learning. He has developed lightweight and efficient neural network models for applications in medical imaging and smart grids. The most recent publications reflect a strong trend in applying advanced machine learning techniques—particularly deep learning, self-supervised learning, and capsule networks—to diverse domains such as medical diagnostics, energy disaggregation, and clinical decision support. There is a clear emphasis on developing efficient, interpretable, and robust models for real-world deployment, often addressing challenges like class imbalance, domain adaptation, and data scarcity. Scientific recognition includes: Fellow of the Higher Education Academy (FHEA) Associate Editor, Neural Processing Letters Review Editor, Frontiers in Applied Mathematics and Statistics Regular reviewer for top-tier journals and conferences in AI and machine learning Grant reviewer for multiple funding bodies Dr. Zhong advises a number of students, as evidenced by co-authorships on numerous publications. His research is supported by academic collaborations and likely external grants, though specific funding details are not mentioned. He teaches courses in Robotics, Machine Learning, and Knowledge Representation and Reasoning, contributing significantly to the curriculum in computing sciences. He is involved in interdisciplinary research, particularly through projects like ARCHERY (Artificial intelligence to Revolutionise the patient Care pathway in Hip and knEe aRthroplastY), which integrates AI into orthopaedic care. His work bridges computer science, statistics, and domain-specific applications, demonstrating a strong commitment to impactful, application-driven research.