Find Academic Supervisors

Jonathan GammellView profile

Assistant Professor

• Autonomous Systems and Control
• Robotics
• Artificial Intelligence
• +4 more

Jonathan Gammell is an Assistant Professor at Queen's University's Department of Electrical and Computer Engineering and a member of the Ingenuity Labs Research Institute. He holds an adjunct fellowship at the Oxford Robotics Institute (University of Oxford), where he previously taught. His expertise spans autonomous systems, robotics, and AI, with a focus on motion planning for diverse applications, including medical devices, self-driving cars, and aerial robotics. He leads the Estimation, Search, and Planning (ESP) group, which develops algorithms for autonomous systems. Educations: BASc in Mechanical Engineering and Physics from the University of Waterloo MASc and PhD in Robotics from the University of Toronto Institute for Aerospace Studies Research Interests: Dr. Gammell's work addresses fundamental motion planning challenges in robotics, emphasizing asymptotically optimal algorithms like BIT*, AIT*, and FCIT*. His research integrates robotics with medical applications (e.g., knee replacement implants) and autonomous systems (e.g., aerial mapping for emergency response). His algorithms are widely adopted in industry and academia, including collaborations with NASA JPL and Oxford orthopaedic surgeons. Publications: His recent work explores advanced motion planning frameworks (e.g., AORRTC, Osprey), multimotion visual odometry (MVO), and medical robotics applications. These contributions highlight his dual focus on theoretical algorithm development and practical, high-impact applications. Awards: CIPPRS Award for Best Canadian PhD Thesis (Medical Imaging/Robotics) NASA Group Achievement Award (2021, 2022) IROS Best Paper Shortlist (2020) Advising & Grants: He advises on projects involving autonomous systems, medical robotics, and AI. His grants support collaborations with industry and academic partners, including NASA JPL and Oxford's robotics and medical teams. The ESP group's open-source tools (e.g., Planner Developer Tools) enable reproducible motion-planning research. Labs/Teams: He leads the ESP research group at Queen's University and collaborates with the Oxford Robotics Institute and Ingenuity Labs, focusing on cutting-edge robotics solutions for real-world challenges.

Simona PicardiView profile

Assistant Professor

• Wildlife ecology
• Movement ecology
• Quantitative ecology
• +4 more

Simona Picardi is an Assistant Professor of Wildlife Ecology & Management at the University of Idaho's College of Natural Resources. She holds a PhD from the University of Florida (2019), an M.S. and B.S. from the University of Rome La Sapienza (2015, 2012). Her research focuses on wildlife responses to environmental change and anthropogenic pressures, integrating movement ecology and quantitative methods. She leads the Picardi Lab, which emphasizes reproducible science and data-driven conservation strategies. Key research interests include animal movement analysis, spatial ecology, and conservation biology. Her work spans topics such as site fidelity null models, human-wildlife interactions, and migratory behavior in wading birds. She teaches data science and programming for ecologists, reflecting her commitment to advancing quantitative methods in the field. Recent publications highlight her contributions to understanding fitness consequences of anthropogenic subsidies, sex-based movement differences in striped hyenas, and partial migration patterns in wood storks. Her research integrates field data with computational modeling to address applied conservation challenges.

George T. HeinemanView profile

Associate Professor

• Software Engineering
• Component-Based Software Engineering
• Software Architecture
• +3 more

George T. Heineman is an Associate Professor of Computer Science at Worcester Polytechnic Institute (WPI). He holds a BS from Dartmouth College (1989), an MS (1990), and a PhD (1996) from Columbia University. His research focuses on software engineering, component-based systems, and modularity, with notable contributions to type-safe modular software evolution through the CoCo design pattern. Heineman emphasizes professional software engineering practices in teaching, challenging students with industry-relevant projects to foster best practices. His work has been published in leading venues like ECOOP, with a 2021 paper on CoCo gaining attention for its impact on Java language design. He received the WPI Trustees' Award for Outstanding Teaching in 2022, reflecting his dedication to education. His research spans algorithm design, system architecture, and cybersecurity, with publications ranging from foundational theory to practical applications in automated assessment and network security. Education: BS in Computer Science, Dartmouth College, 1989 MS in Computer Science, Columbia University, 1990 PhD in Computer Science, Columbia University, 1996 Research interests include software evolution, design patterns, and modular software systems. His recent work addresses challenges in maintaining stable APIs and enabling cohesive extensions in object-oriented systems. Collaborations with institutions like the University of Copenhagen and TU Dortmund highlight his international academic engagement. Beyond research, Heineman contributes to curriculum development, including WPI's new graduate programs in computing and workforce development initiatives.

Jun YanView profile

Professor

• Survival Analysis
• Spatial Statistics
• Network Analytics
• +2 more

Dr. Jun Yan is a Professor in the Department of Statistics at the University of Connecticut. His research spans network analytics, spatial extremes, survival analysis, and statistical computing with applications in public health, finance, and environmental science. His core research interests include: network modeling and analysis, spatial statistics for climate extremes, survival analysis methodologies, statistical computing frameworks, and applications in interdisciplinary domains including sports analytics. Dr. Yan has developed significant statistical methodologies for network analysis, climate change detection, financial modeling, and health analytics. His recent publications demonstrate innovation in modeling complex network structures, analyzing climate extremes, developing computational approaches for massive datasets, and creating specialized statistical methods for health and finance applications. He maintains active collaborations across disciplines and contributes to open-source statistical software. Honors include: Guggenheim Fellowship, multiple Fromm Foundation commissions, and Barlow Endowment recognition.

• Emotional Communication
• Linguistics
• Cognitive Science
• +5 more

Dr. Martijn Goudbeek is an Associate Professor at Tilburg University's Tilburg School of Humanities and Digital Sciences (TSHD), specifically within the Department of Communication and Cognition. His research focuses on the intersection of emotion, language, and cross-cultural communication, with a strong emphasis on vocal and linguistic expression of affect. He has contributed significantly to understanding how emotional states influence language production and perception, particularly across cultural and linguistic boundaries. His work spans experimental psychology, computational linguistics, and human-robot interaction. He is affiliated with the Communication and Cognition department and teaches courses like 'The Emotional Brain' and 'Statistics and Experimental Methods.' Key research themes include vocal emotion recognition in multilingual contexts, the role of rhythm and acoustic cues in affective communication, and the reproducibility of language evaluation methods. He has led projects such as the Multilingual Emotional Football Corpus (MEmoFC) and explored how cultural factors shape nonverbal expressions like smiles and gestures. His studies often combine empirical experiments with computational modeling, addressing topics ranging from politeness in mood-influenced communication to the design of socially intelligent chatbots and robots. Dr. Goudbeek’s interdisciplinary approach bridges cognitive science, linguistics, and digital technologies. While no specific grants or awards are listed, his extensive publication record reflects sustained contributions to understanding human emotion expression and its technological applications. His work frequently engages with questions of reproducibility in NLP and dialogue systems, emphasizing methodological rigor in computational linguistics.

Van Anh Huynh-ThuView profile

Assistant Professor

• Machine Learning
• Bioinformatics
• Gene Regulatory Network Inference
• +3 more

Vân Anh Huynh-Thu is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Liège (Belgium). Her research focuses on improving machine learning techniques with an emphasis on model interpretability. She is based at B28: Systems and Modeling in Quartier Polytech, Allée de la Découverte 10, 4000 Liège, Belgium. Her primary research interests span Machine Learning , Bioinformatics , and Gene Regulatory Network Inference . Dr. Huynh-Thu has developed several influential methods including GENIE3, dynGENIE3, and Jump3 for inferring gene regulatory networks from expression data. Her work bridges the gap between machine learning theory and biological applications, particularly in understanding complex disease mechanisms through computational approaches. She has made significant contributions to interpretable machine learning models that maintain high predictive accuracy while providing insights into feature importance and model behavior. Her research demonstrates a progression from purely computational methods toward translational applications in medical research. Analysis of her recent publications reveals a clear trajectory from foundational work on gene regulatory network inference toward broader applications in medical research, particularly in Crohn's disease. Her research increasingly integrates machine learning with clinical applications, demonstrating a shift from purely computational methods to translational research with direct medical implications. The consistent emphasis across her work is on interpretability, rigorous validation, and the application of tree-based methods to complex biological systems, with a growing focus on proteomics and biomarker discovery for inflammatory bowel diseases. Dr. Huynh-Thu maintains an active GitHub presence with implementations of her methods, demonstrating her commitment to open science and reproducibility. Her software repositories have garnered significant attention from the research community, with GENIE3 alone having 88 stars and 37 forks on GitHub. She has developed multiple implementations of her algorithms in Python, MATLAB, and R, making them accessible to researchers across different computational environments. Her work has been influential in the DREAM challenges, where GENIE3 was the best performer in two network inference competitions.

Osama MahmoudView profile

Lecturer

• Predictive Modelling
• Health Data Science
• Machine Learning
• +6 more

Dr. Osama Mahmoud is a Lecturer (Assistant Professor) in Data Science and Statistics at the University of Essex, affiliated with the School of Mathematics, Statistics and Actuarial Science (SMSAS) and the Department of Mathematical Sciences. He holds dual roles as the Director of the BSc Data Science and Analytics programme and Deputy Director of Research at SMSAS. His academic journey includes a PhD in Statistics from the University of Essex and an Honorary Senior Researcher position at the University of Bristol Medical School (2020–2024). Dr. Mahmoud's research focuses on Predictive Modelling, Health Data Science, Machine Learning, Bio and Medical Statistics, and Explainable AI. He pioneered the Slope-Hunter method (Nature Communications, 2022) for bias correction in genome-wide studies and developed tools like the Proportion Overlapping Score (POS) and open-source packages on CRAN/GitHub. His work bridges statistical methodology with applications in healthcare, environmental epidemiology, and industrial collaboration. Key contributions include studies on sleep-breast cancer mortality linkages (2025), lung function-cardiovascular risk relationships (2024), and smoking's role in depression recovery (2022). He has secured grants supporting collaborations with UNDP, Rolls Royce, and Recruitment Smart. Teaching innovations include pioneering programming and text analytics modules. Education: PhD in Statistics, University of Essex Affiliations: UK Reproducibility Network (UKRN) Institutional Lead, British Data Science Society Member Grants: Projects in Data Science, theoretical/applied Statistics, and industrial partnerships Tools: ESKNN, OTE, propOverlap packages on CRAN His work emphasizes reproducibility, with over 50 peer-reviewed publications and international teaching engagements at ICTP (Italy), EuADS (Luxembourg), and UK institutions.

Steven C. DeCaluweView profile

Associate Professor

• Clean Energy Systems
• Electrochemistry
• Interfacial Processes
• +5 more

Steven C. DeCaluwe is an Associate Professor in the Department of Mechanical Engineering at Colorado School of Mines, where he combines experimental and computational approaches to study clean energy systems. His research focuses on electrochemistry and interfacial processes in batteries (Li-ion, Li-O 2 , Li-sulfur) and fuel cells. He also serves as Director of Graduate Studies and chairs the university's Diversity, Inclusion, and Access Committee. PhD in Mechanical Engineering (University of Maryland, 2009) NRC Postdoctoral Fellowship at NIST (2009-2012) BS in Elementary Education and Mathematics (Vanderbilt University, 2000) DeCaluwe's work bridges advanced diagnostics (neutron scattering, operando XPS) with multiscale simulations to improve energy storage technologies. His research spans solid-electrolyte interphase formation , electrochemical degradation mechanisms , and materials optimization for sustainable energy . He advocates for open science practices and integrates conservation biology principles into engineering design. His recent publications demonstrate expertise in battery modeling, phase segregation in electrodes, and sustainability-driven materials selection. Key themes include parameter uncertainty quantification , interface stability , and multi-modal characterization of energy storage systems. NRC Postdoctoral Fellowship Associate Editor, ASME Journal of Electrochemical Energy Conversion and Storage Chair, Mechanical Engineering Diversity Committee Director, Rocky Mountain Environmental XPS Facility DeCaluwe actively mentors students through the CORES Research Group , emphasizing inclusive learning environments and growth mindsets. His service extends to organizing neutron scattering conferences and managing shared instrumentation facilities.

Djamel E. KhelladiView profile

Researcher

• Software Engineering
• Model-Driven Engineering
• Software Evolution
• +6 more

Djamel E. Khelladi is a CNRS researcher affiliated with the IRISA research lab and the DIVERSE team at University of Rennes 1 , France. Previously, he held postdoctoral and PhD positions at Johannes Kepler University (JKU) Linz, Austria, and Université Pierre et Marie Curie (UPMC), France. Research interests include: Model-Driven Engineering Software Evolution & Co-evolution AI and Generative AI Applications Polyglot Programming Digital Twins Recent article trends focus on integrating Large Language Models (LLMs) for code-metamodel co-evolution, polyglot programming challenges, incremental build optimization in configurable systems, and empirical studies on software evolution. His work often bridges theoretical modeling with practical implementation in industrial contexts. Academic service roles include: Proceedings Co-Chair @MODELS 2025 Co-Organizer of Models and Evolution (ME) workshops (2023-2025) Co-Editor for special issue on Model Driven Engineering for Digital Twins (SoSym 2024/25) PC member in top venues: ICSE , ASE , MODELS , ECMFA , MSR , FSE

• Gastroenterology
• Epidemiology
• Cardiovascular Health
• +3 more

Shrikant Bangdiwala , Professor at McMaster University 's Faculty of Health Sciences in the Department of Health Research Methods, Evidence, and Impact , leads groundbreaking research in disorders of gut-brain interaction, cardiovascular epidemiology, and statistical methodology. 2025 Research Focus: Gut-brain disorder subtyping, dietary pattern analysis, and machine learning applications in clinical trials 2024 Innovations: Development of GROOVE tool for symptom overlap visualization and exposome-DNA damage correlation studies Longitudinal Impact: 20+ years tracking GI comorbidities, cardiovascular outcomes, and educational interventions Research Clusters Gut-Brain Disorders: Prevalence modeling across 26+ countries, symptom stratification, and psychological comorbidities Cardiovascular: Mortality prediction models, antithrombotic risk assessment, and ACE-inhibitor effectiveness Epidemiological Innovation: Hybrid trial adjudication systems, statistical frameworks for complex interventions, and exposome analysis tools Methodological Expertise Specializes in biostatistical frameworks for cardiovascular trials, machine learning detection of trial irregularities, and epidemiological modeling of gut-brain disorders through the Rome Foundation Global Epidemiology Study.

Bermond ScogginsView profile

Research Fellow

• Political Behavior
• Survey Experiments
• Democratic Backsliding
• +1 more

Bermond Scoggins is a Research Fellow at the School of Politics and International Relations, College of Asia and the Pacific, Australian National University. He holds a BA (Honours) from ANU and specializes in political behavior research using survey experiments and meta-analytic methods. His research investigates democratic backsliding mechanisms, causal inference in political systems, and voter decision-making processes in democratic contexts, with special focus on Southeast Asian political systems. Recent publications examine global science communication patterns, public trust in scientists, and methodological transparency in social sciences, utilizing large-scale international datasets and cross-national comparative frameworks.

• Systems and Networking
• Computer Architecture
• Machine Learning and Artificial Intelligence
• +3 more

Andrew W. Moore is a Professor of Networked Systems at the University of Cambridge's Department of Computer Science and Technology. His research focuses on networked systems design, high-performance prototyping (e.g., NetFPGA), and latency measurement across scales from data centers to wide-area networks. He has contributed to SDN architectures, reconfigurable networking, and energy-efficient interconnects. He teaches courses including Computer Networking (Part IB), Scientific Computing (Part IA), and advanced modules on network measurements and system performance. His work emphasizes open-source tools and reproducible research, with publications in top venues like IEEE Transactions and ACM SIGCOMM. Research interests span Systems and Networking, Computer Architecture, Machine Learning applications in networking, and Security. Recent work addresses data center latency optimization, SDN-enabled IXPs, and FPGA-based network prototyping. Collaborations include projects on energy-aware networks and hardware-software co-design. He leads the NetFPGA initiative, a widely used open-source FPGA platform for networking research and education. His lab also explores emerging storage media and applications of software-defined networking in critical infrastructure.

Yumin LinkView profile

Adjunct Associate Professor

• Medical Imaging
• Neurological Disorders
• Ophthalmology
• +3 more

Yumin Link is an Adjunct Associate Professor at Linköping University's Department of Biomedical and Clinical Sciences (BKV), affiliated with the Faculty of Medicine and Health Sciences. He is part of the Center for Social and Affective Neuroscience (CSAN) and the Division of Cell and Neurobiology (CNB). His research focuses on medical imaging techniques like Optical Coherence Tomography (OCT), neurological disorders, and clinical neuroscience. Key areas include retinal imaging in idiopathic intracranial hypertension, multiple sclerosis progression, and intermachine reproducibility in OCT measurements. Publications (2023–2025) highlight advancements in neuroimaging and diagnostic reliability. No explicit awards or grants are listed, though ongoing collaborations with clinical teams suggest active research participation. Yumin Link advises via two emails: yumin.link@liu.se (academic) and yumin.link@regionostergotland.se (clinical).

• Behavioral Economics
• Economics
• Incentive Design
• +3 more

Dr. Victor Gonzalez Jimenez is a Visiting Professor at the Erasmus School of Economics, Erasmus University Rotterdam, specializing in Behavioral Economics. His research focuses on incentive design, decision-making under uncertainty, and the psychological underpinnings of economic behavior. He contributes to teaching and has been involved in one documented teaching activity. His work bridges theoretical economic models with experimental methodologies, addressing topics such as reference-dependent preferences, poverty's impact on productivity, and the unintended consequences of monetary incentives. Education : Not explicitly detailed in the provided text. Research interests include behavioral contract theory, reproducibility in management science, and the interplay between social status and belief formation. His recent work (2024) explores how agents distort probabilities in incentive contracts and the implications for optimal design. Other studies address loss aversion in bonuses and the effects of poverty exposure on productivity. He has also examined how self-chosen goals influence performance across genders. Notably, his articles emphasize non-expected utility frameworks and experimental validation of economic theories, contributing to both theoretical and applied domains in behavioral economics.

Erik RosenqvistView profile

Assistant Professor

• Educational Inequality
• School Segregation
• Peer Effects
• +2 more

Erik Rosenqvist is Assistant Professor at Linköping University's Institute for Analytical Sociology, examining how educational systems reproduce social inequality. Research investigates school composition effects, peer influence on educational decisions, and socioeconomic achievement gaps. Current project compares educational reforms' impact on segregation in Scandinavian countries. Methodological expertise includes quasi-experimental designs analyzing large-scale educational datasets. Published in leading sociology journals on grading equity and school choice impacts.