Find Academic Supervisors

• Formal Methods
• Software Engineering
• Model-Based Testing
• +3 more

Petra van den Bos is an Assistant Professor at the University of Twente, affiliated with both the Digital Society Institute and the Formal Methods and Tools department. Her research focuses on advancing software engineering through formal methods, model-based testing, and automated testing techniques. She has contributed to integrating Behavior-Driven Development (BDD) with model-based approaches, as well as developing tools like VeyMont for choreography-based concurrent programming. Her work emphasizes practical applications in video game development, web testing, and user story-driven methodologies. Notably, she received the FORTE 2023 Best artefact award for outstanding contributions to formal techniques. Petra collaborates on datasets archived on Zenodo, showcasing reproducible research in testing frameworks and formal verification. Research Interests: Formal Methods, Model-Based Testing, Automated Testing, Concurrent Programming, and Software Verification. Awards: FORTE 2023 Best artefact (2022). Advising & Grants: No formal advisees listed; contributions focus on collaborative research projects and open-source tool development. Labs/Teams: Active in the Formal Methods and Tools research group, advancing software reliability through interdisciplinary approaches.

Stefano MartinianiView profile

Assistant Professor

• Computational and Statistical Physics
• Materials Science
• Artificial Intelligence for Science
• +5 more

Stefano Martiniani is an Assistant Professor of Physics, Chemistry, Mathematics, and Neuroscience at New York University, affiliated with the Center for Soft Matter Research and the Simons Center for Computational Physical Chemistry. His interdisciplinary research explores computational physics of complex systems, including neural circuit theories, non-equilibrium statistical mechanics, and AI-driven materials discovery. He has pioneered methods for analyzing high-dimensional energy landscapes and received prestigious awards like the NSF CAREER Award (2024) and IUPAP Early Career Prize (2023). Education: PhD in Physics (2017), University of Cambridge MPhil in Physics (2013), University of Cambridge BSc in Physics (2012), Imperial College London Research Interests: His work bridges statistical physics and artificial intelligence, focusing on: Engineering disordered materials with tailored spectral properties Quantifying entropy production in active matter Developing open science frameworks like ColabFit for machine learning interatomic potentials Neural circuit models for cortical communication Grants & Collaborations: Funded by NSF, NIH, Chan Zuckerberg Initiative, and Simons Foundation. Leads interdisciplinary teams in computational physics, AI, and materials science. Labs/Initiatives: Core member of NYU's Center for Soft Matter Research; develops software tools like FReSCo and KLIFF-Torch for computational materials science.

Wang-Hung TseView profile

Researcher

• Applied Mathematics
• Pattern Formation
• Computational Biology
• +3 more

Dr. Wang-Hung Tse is a researcher and faculty member in the Department of Mathematics at Trinity Western University (TWU), affiliated with the Faculty of Natural & Applied Sciences. He holds a PhD from the University of British Columbia (2016) and an MPhil from The Chinese University of Hong Kong (2009). His primary research focuses on pattern formation in biological systems, nonlinear dynamics, and the integration of machine learning with experimental data analysis. He has contributed to studies on crime hotspot dynamics, biological morphogenesis, and satellite data assimilation. Dr. Tse has taught courses including Differential Equations (MATH 321), Calculus III (MATH 223), and Business Mathematics (MATH 101). His interdisciplinary research emphasizes reproducible outcomes and involves agent-based modeling, computational simulations, and mathematical analysis of complex systems. Recent work includes collaborations on sea surface temperature modeling and crime pattern stability. His research methodologies span applied mathematical analysis of singularly perturbed systems, numerical bifurcation methods, and high-performance computing for large-scale simulations. Key contributions include studies on localized stripe stability in reaction-diffusion systems and the interplay between Gaussian curvature and pattern formation on Riemannian manifolds.

Guang ChengView profile

Professor

• Generative Data Science
• Trustworthy AI
• Machine Learning Theory
• +5 more

Guang Cheng is a Full Professor of Statistics and Data Science at the University of California, Los Angeles (UCLA), and serves as the Graduate Vice Chair. He leads the Trustworthy AI Lab, focusing on generative data science, privacy-preserving synthetic data, and the theoretical foundations of machine learning. His research explores generative AI, trustworthy synthetic data, and high-dimensional statistics. Education : Ph.D. in Statistics, University of Wisconsin-Madison (2003-2006) B.A. in Economics, Tsinghua University (1998-2002) Research Interests : Generative Data Science, trustworthy AI, machine/deep learning theory, privacy-preserving techniques, high-dimensional statistics, and business intelligence applications in finance, healthcare, and marketing. His lab develops tools like artificially generated tables for privacy-preserving data sharing. Publications : Recent work includes advancements in generative models (e.g., TimeAutoDiff, MissDiff), fair classification algorithms, and theoretical analyses of deep learning (e.g., attention mechanisms, minimax analysis). Key themes include synthetic data utility/privacy trade-offs and adversarial robustness. Awards : IMS Fellow (2020), Adobe Data Science Award (2020), NSF CAREER Award (2012). Advising & Grants : Supervises PhD/Master’s/postdoc researchers in trustworthy AI and generative data science. Alumni hold roles at Meta, Amazon, and academia. Active in editorial roles for JASA - Theory & Methods and Canadian Journal of Statistics . Labs & Teams : Leads the Trustworthy AI Lab at UCLA, organizing workshops on synthetic data in finance and healthcare. Collaborates with Amazon as an Amazon Scholar.

• Stroke Treatment
• Neurology
• Translational Medicine
• +4 more

Neil Spratt is a Professor at the University of Newcastle, affiliated with the School of Biomedical Sciences and Pharmacy (Human Physiology). He is a clinical neurologist at John Hunter Hospital and leads the Stroke Translation Laboratory. His research focuses on translational stroke therapies, including hypothermia, intracranial pressure management, and advanced CT imaging. Spratt holds an NHMRC fellowship and has secured over $422,000 in grants for his work. His educational background includes a PhD from the University of Melbourne and undergraduate degrees from the University of Newcastle. Collaborations with researchers like Professors Mike Calford and Chris Levi emphasize bridging laboratory and clinical research. Education: PhD in Medicine (Neurology), University of Melbourne Bachelor of Medical Science (Honours), University of Newcastle Bachelor of Medicine (Honours), University of Newcastle Research Interests: Developing therapies to reduce brain injury post-stroke Optimizing stroke triage and imaging protocols Short-duration hypothermia for intracranial pressure management Collaborations in environmental enrichment for stroke recovery Grants & Awards: NHMRC Early Career Fellowship (2012–2015) NHMRC Practitioner Fellowship (2007–2011) $422,000 NHMRC grant for hypothermia research Labs & Teams: Stroke Translation Laboratory (University of Newcastle/HMRI), Priority Research Centre for Brain and Mental Health.

Maria De-ArteagaView profile

Assistant Professor

• Algorithmic Fairness
• Human-AI Complementarity
• Machine Learning
• +1 more

Maria De-Arteaga is an Assistant Professor at the Information, Risk and Operation Management Department within the McCombs School of Business at the University of Texas at Austin. She holds joint appointments as a core faculty member in the Machine Learning Laboratory and as a researcher for Good Systems. She earned her PhD in Machine Learning and Public Policy from Carnegie Mellon University. Her research focuses on algorithmic fairness, human-AI complementarity, and the societal impacts of machine learning. Key areas include characterizing how historical biases are reproduced in ML systems, developing bias mitigation techniques, and improving human-AI collaboration frameworks. Her work bridges technical ML methods with public policy considerations. Recent publications demonstrate trends in fair decision-making systems, including studies on label indeterminacy problems, expert consistency modeling, and toxicity detection. Her interdisciplinary work spans healthcare, social computing, and human-AI interaction. She advises multiple graduate students including Terry Neumann (IROM), Yunyi Li (IROM), and Soumyajit Gupta (CS), along with undergraduate student Riya Cyriac. Previously advised researchers include postdoc Jakob Schoeffer and undergraduate Jennifer Mickel.

Steve GoodmanView profile

Professor

• Meta-research
• Research reproducibility
• Clinical trial methodology
• +5 more

Steve Goodman is a Professor of Epidemiology and Population Health and Medicine at Stanford University, serving as Associate Dean of Clinical and Translational Research. He co-founded and co-directs the Meta-research Innovation Center at Stanford (METRICS) and leads the Stanford Program on Research Rigor and Reproducibility (SPORR). His work focuses on improving research methodology, reproducibility, and evidence synthesis, particularly in clinical trials and biomedical research. Goodman has held leadership roles in editorial boards, including Annals of Internal Medicine and Clinical Trials , and chairs the Methodology Committee at PCORI. Education: AB in Biochemistry and Applied Math (Harvard), MD (NYU), Pediatrics residency (Washington University), MHS in Biostatistics and PhD in Epidemiology (Johns Hopkins). Former faculty at Johns Hopkins from 1989-2011, where he directed the Division of Biostatistics and Bioinformatics. Research interests include meta-research, Bayesian methods, clinical trial reporting standards (CONSORT/SPIRIT guidelines), and educational initiatives to promote rigorous scientific practices. His articles emphasize reproducibility, transparency, and methodological improvements in biomedical research. Awards: 2016 Spinoza Chair in Medicine, 2019 Abraham Lilienfeld Award, National Academy of Medicine (2020) Grants/Initiatives: CTSA KL2/TL1 training programs, SPORR curriculum development Labs/Teams: METRICS, SPORR, and collaborations in global research integrity networks

Bertram LudäscherView profile

Professor

• Data and Knowledge Management
• Scientific Workflows
• Data Provenance
• +4 more

Bertram Ludäscher is a Professor at the School of Information Sciences at the University of Illinois, with affiliate appointments at the National Center for Supercomputing Applications (NCSA) and the Department of Computer Science. He leads the Center for Informatics Research in Science and Scholarship and directs the NSF-funded Euler project focused on logic-based taxonomic alignment. His research emphasizes scientific workflow systems, data provenance, and knowledge representation. Ludäscher co-founded the Kepler workflow system and contributed to DataONE’s provenance initiatives. He previously held roles at UC Davis and the San Diego Supercomputer Center. Education: PhD in Computer Science, University of Freiburg (1998) MS in Computer Science, Technical University of Karlsruhe (1992) Research Interests: Data and knowledge management, scientific workflows, provenance tracking, biodiversity data curation, and taxonomic reconciliation using logic-based methods. Ludäscher’s work bridges computational methods with biological and environmental sciences, focusing on reproducibility and interoperability in data-driven research. Recent Work Trends: His articles address provenance unification in databases, workflow automation recovery, and resolving taxonomic conflicts. Key themes include reproducibility frameworks (e.g., the Whole Tale project), ontology alignment, and scalable workflow systems for diverse scientific domains. Awards: 2018 ProvenanceWeek Best Paper, Google Scholar Classic Papers recognition. Advising & Grants: Leads NSF-funded projects like Euler and Kurator. Active in collaborative initiatives such as DataONE and the Whole Tale. Courses taught include data curation, workflow design, and data cleaning methodologies. Labs/Teams: Directs the Center for Informatics Research in Science and Scholarship and collaborates with NCSA on cyberinfrastructure projects. Key partnerships include the San Diego Supercomputer Center and UC Davis Genome Center.

Joshua SunshineView profile

Assistant Professor

• Software Engineering
• Programming Languages
• Human-Computer Interaction (HCI)
• +5 more

Joshua Sunshine is an Assistant Professor in the S3D Software and Societal Systems Department at Carnegie Mellon University's School of Computer Science. His research focuses on the intersection of programming languages, software engineering, and human-computer interaction (HCI), emphasizing usability-driven language design and tools for developers. He leads the REU Site in Software Engineering (REUSE), a National Science Foundation-funded program fostering undergraduate research and equity in computing. Key research areas include: (1) Formal methods for software testing and verification (e.g., TerzoN, Gradual C0), (2) Domain-specific languages for mathematical visualization (Penrose), (3) Secure blockchain programming (Obsidian), and (4) Automated test generation tools (Nanofuzz). His work bridges theory and practice through user-centered methodologies, evident in projects like PLATEAU workshops and Glacier's transitive immutability. Awards include the NSF CAREER Award (2024) and sustained leadership in REU programs. His grants emphasize reproducible scientific programming, framework debugging, and interdisciplinary tool development. Notable contributions include frameworks like Rose for web-based autodiff and Edgeworth for scalable visual activity authoring. Labs/Teams: Core contributor to the S3D group, collaborating with HCI and PL communities. Active in organizing PLATEAU workshops (2018–2023), fostering evaluation and usability research in programming languages.

• Neuroimaging
• Pain Mechanisms
• Placebo/Nocebo Effects
• +4 more

Christian Büchel is a Professor in the Department of Neurology and Experimental Neurobiology at the University Medical Center Hamburg-Eppendorf (UKE). He holds a faculty position within the Medical Faculty of Hamburg University. His research focuses on understanding brain mechanisms underlying pain, placebo/nocebo effects, and psychiatric disorders using advanced neuroimaging techniques. He leads studies on brain-body interactions, developmental neuroscience, and translational neurology. Key research areas include structural and functional MRI investigations of pain modulation, addiction, and stress responses. He has contributed to multi-center neuroimaging studies and pioneered methods for spinal cord imaging. His work integrates cognitive neuroscience principles with clinical applications, particularly in chronic pain management and mental health. Büchel's recent studies explore how environmental factors (e.g., urban green spaces) influence brain structure, and he investigates neural correlates of resilience to substance use in adolescents. He collaborates internationally on projects like the IMAGEN study and the ENIGMA consortium, emphasizing reproducibility in neuroimaging research. His publications span over 20 years, with a focus on brain network dynamics, decision-making processes, and translational neuroscience. He actively contributes to developing open-access neuroimaging standards and has authored seminal papers on placebo mechanisms and brain plasticity.

Rachel AnkenyView profile

Professor

• History and Philosophy of Life Sciences
• Agricultural and Food Studies
• Bioethics
• +3 more

Rachel Ankeny is a Professor & Chair of Philosophy at Wageningen University & Research (WUR) and holds honorary/affiliate roles at the University of Exeter and University of Adelaide. Her interdisciplinary work spans history/philosophy of life sciences, agricultural studies, bioethics, and science policy. She leads the Philosophy Group at WUR and oversees research at the ARC Training Centre for Future Crops Development. Ankeny holds advanced degrees from the University of Pittsburgh (BA, MA, PhD) and University of Adelaide (MGAstronomy, online education certificate). Research Interests: Epistemic issues in agricultural and medical science Regulatory frameworks for biotechnology Animal welfare and consumer ethics Indigenous knowledge integration Professional Activities: Board member, Ann Johnson Institute (USA) Advisory roles on Australian Commonwealth committees (FSANZ, OGTR) Editor-in-Chief, Studies in HPS Current Projects: Focus on genomic ethics, livestock welfare, and transdisciplinary science policy. Active in public engagement via initiatives like the EchidnaCSI conservation project.

• Recommender Systems
• Context-aware Recommendation
• Explainable AI
• +5 more

Markus Zanker is a Professor in the Department of Knowledge Engineering within the Faculty of Computer Science at Free University of Bozen-Bolzano, Italy. With an extensive publication record spanning over two decades, he has established himself as a leading expert in recommender systems research, with particular expertise in context-aware, group, and knowledge-based recommendation approaches. His work bridges theoretical foundations with practical applications across diverse domains including tourism, healthcare, and e-commerce. Zanker's research interests center on advancing the theoretical underpinnings of recommender systems while addressing practical challenges in real-world deployments. His recent work has focused on causal decision-making frameworks for recommendation, intent-aware systems, and the integration of generative AI in group recommendation scenarios. He has made significant contributions to explainable recommendation systems, medical recommendation applications, and tourism recommendation systems, demonstrating both theoretical rigor and practical impact. His recent publication portfolio reveals a strong trend toward addressing fundamental challenges in recommendation science, including the growing emphasis on causal reasoning to move beyond correlation-based approaches, the integration of generative AI capabilities for more sophisticated group decision support, and the development of frameworks for ethical and socially beneficial recommendation systems. His work increasingly bridges the gap between traditional recommendation algorithms and emerging AI paradigms while maintaining focus on real-world applicability. Zanker is actively involved in the academic community as a workshop organizer, having co-chaired the Knowledge-aware and Conversational Recommender Systems (KaRS) workshop and the Recommenders in Tourism (RecTour) workshop for multiple consecutive years at major conferences including RecSys. His research has been supported through collaborations with numerous international researchers and institutions, focusing on both theoretical advances and practical implementations of recommendation technology across multiple application domains.

Stephanie RossitView profile

Associate Professor

• Neuroscience
• Clinical Psychology
• Cognitive Rehabilitation
• +4 more

Dr. Stephanie Rossit is an Associate Professor in Psychology at the University of East Anglia (UEA), leading the Neuropsychology Laboratory (Neurolab). Her research bridges neuroscience, psychology, and clinical practice, focusing on perception, attention, and action in healthy and clinical populations. She investigates aging and brain disease impacts and develops cognitive rehabilitation interventions. Rossit holds roles as Editor for Cogent Psychology and consulting Editor for Journal of Experimental Psychology: Human Perception and Performance , alongside British Psychological Society fellowships. Education: PhD from the University of Glasgow (stroke and visual neglect), postdoc at Western University (Canada) studying visuomotor control via fMRI. BA from University of the Algarve (Portugal). Research Interests: Stroke rehabilitation (spatial neglect), tool use neuroscience, aging effects on attention, and neural mechanisms of action perception. Recent work includes the SIGHT trial for stroke neglect therapy and EyeFocus app development. Grants & Awards: Multiple NIHR grants (e.g., £1M for SIGHT trial), UEA Reproducibility Network leadership, and awards including 2020 Associate Dean of Innovation. Labs/Teams: Director of Neurolab, collaborating on projects like EyeFocus spatial neglect rehabilitation app validation and intuitive physics deficits post-stroke studies.

• Structural identifiability
• Observability analysis
• Nonlinear systems
• +3 more

Alejandro F. Villaverde is a Ramón y Cajal research fellow at the Department of Systems & Control Engineering, Universidade de Vigo. His research focuses on structural properties of nonlinear systems, particularly structural identifiability and observability analysis. His primary research interests include: Structural identifiability of dynamic models Observability analysis of nonlinear systems Control theory and systems engineering Development of computational tools for model analysis Applications in systems biology and pharmacokinetics Villaverde's work centers on developing mathematical methods and software tools to analyze structural properties of nonlinear models. His most notable contributions include the STRIKE-GOLDD MATLAB toolbox and its Python counterpart StrikePy, which implement the FISPO algorithm for simultaneous assessment of state observability and parameter identifiability. His research has practical applications in systems biology, biochemical network analysis, and pharmacokinetic modeling where model validation and parameter estimation are critical. Among his scientific recognitions, he holds the prestigious Ramón y Cajal research fellowship, a competitive program in Spain supporting experienced researchers. Villaverde actively supervises students, with David Rey Rostro having developed the StrikePy toolbox under his supervision. His work demonstrates strong connections between theoretical control engineering and practical computational implementations, making structural analysis methods more accessible to researchers across various disciplines.

• Computational Neuroscience
• Neural Network Simulation
• Large-Scale Simulation
• +3 more

Hans Ekkehard Plesser is a Professor at the Department of Data Science, Norwegian University of Life Sciences (NMBU). His work focuses on computational neuroscience, large-scale neural network simulations, and advancing reproducibility in computational research. He is a key contributor to the NEST simulator, a widely used open-source tool for modeling spiking neural networks. His research spans theoretical and applied aspects of neuroscience, including neuron-astrocyte interactions, connectivity patterns in neural networks, and high-performance computing strategies for brain-scale simulations. He emphasizes reproducible computational practices through initiatives like the ReScience project, promoting transparency and validation in scientific software. His publications highlight advancements in simulation methodologies, algorithm optimization, and the integration of supercomputing resources for neuroscience. He has contributed to foundational work on spiking neuron dynamics, firing-rate models, and the theoretical underpinnings of neural network behavior.