Professor Luke Harding is a faculty member at the Department of Linguistics and English Language , Lancaster University , within the School of Social Sciences . His work bridges applied linguistics, language assessment, and critical discourse studies, with a focus on the ethical and societal implications of testing. Research Interests : Language testing and assessment, World Englishes and English as a Lingua Franca (ELF), second language listening and pronunciation assessment, diagnostic approaches to language evaluation, and language assessment literacy. Recent projects integrate digital technology and corpus linguistics into testing frameworks. Publications : Published extensively in Language Testing , Applied Linguistics , and Language Assessment Quarterly . Co-edited the Routledge Handbook of Language Testing (Second Edition) (2022), a key reference work in the field. Teaching : Leads modules in Language Test Construction and Evaluation , Issues in Language Testing , and Statistical Analysis for Language Testing within the university's distance MA program. Leadership : Convened the Language Testing Research Group with colleagues Tineke Brunfaut and John Pill, advancing interdisciplinary approaches to assessment.
Dr. Sander Los is an Associate Professor at the Faculty of Behavioural and Movement Sciences (Department of Cognitive Psychology), Vrije Universiteit Amsterdam. He earned his PhD in 1994 with a thesis on 'On the origin of mixing costs: Exploring information processing in pure and mixed blocks of trials' under Prof. Andries Sanders. His research focuses on temporal dynamics of preparatory processes, co-developing the formalized Multiple Trace Theory (fMTP) to explain temporal preparation across time scales (seconds to days). His work integrates cognitive psychology, neuroscience, and computational modeling to explore attentional mechanisms, statistical learning, and spatiotemporal dynamics. Education: PhD in Cognitive Psychology (VU Amsterdam, 1994), postdoctoral research at VU Amsterdam, progressing to Assistant Professor before his current role. Key research areas include visual attention, response inhibition, and long-term memory. He has published over 40 peer-reviewed articles and serves on editorial boards for journals like Attention, Perception, and Psychophysics and Acta Psychologica . Research Interests: His studies investigate how humans prepare for upcoming events temporally and spatially, with recent work on statistical learning guiding visual attention and computational frameworks for temporal preparation. Collaborations emphasize interdisciplinary approaches to understanding attention allocation and neural underpinnings of timing. Grants & Advising: No explicit grants listed, but active in training students (1 supervised PhD thesis). His courses include Methodology, Research Methods, and Practical Skills for Researchers at VU Amsterdam. Labs/Teams: Works closely with colleagues on the fMTP model and statistical learning projects, emphasizing team-based computational and experimental psychology.
Xiaocheng Shang is an Associate Professor in Mathematical Optimisation and Data Science at the University of Birmingham's School of Mathematics. His research focuses on numerical methods for stochastic differential equations, with applications in computational mathematics, statistics, physics, and data science. He is affiliated with the Optimisation and Numerical Analysis Group, Statistics and Data Science Group, and the Institute for Data and AI. Shang holds a PhD in Applied and Computational Mathematics from the University of Edinburgh (2016) and completed postdocs at the University of Edinburgh, Brown University, and ETH Zurich before joining Birmingham in 2019. His academic achievements include fellowships from The Alan Turing Institute, the LMS Emmy Noether Fellowship, and the EUniWell Leadership Fellowship. He has secured funding from EPSRC, the Royal Society, and the Isaac Newton Institute. Shang is actively involved in supervising PhD students and co-organizing research initiatives such as the Data Science and Computational Statistics Seminar. Research interests include structure-preserving integrators, Bayesian sampling techniques, and machine learning applications in dynamical systems. His work bridges numerical analysis, probability theory, and multiscale modeling in materials science. Recent projects involve neural networks for complex dynamical systems and numerical algorithms for deterministic/stochastic systems.
James Anderson is an Assistant Professor in the Department of Electrical Engineering at Columbia University, with affiliations to the Data Science Institute (DSI) and multiple research centers including the Computing Systems for Data-Driven Science and Foundations of Data Science. Prior to Columbia, he was a Senior Research Scientist at Caltech’s Computing + Mathematical Sciences division (2016–2019) and held a Junior Research Fellowship at the University of Oxford’s Department of Engineering Science (pre-2012). He earned his DPhil (PhD) in Engineering Science from Oxford in 2012. His research focuses on optimal/robust control theory, mathematical programming, data privacy, and cyber-physical systems, with applications in smart grids, systems biology, and power systems. Recent work emphasizes energy storage strategies, distributed control algorithms, and cybersecurity in critical infrastructure. His publications span advanced control methodologies (e.g., reinforcement learning for LQR problems), energy market dynamics, and resilient system designs. Notable contributions include frameworks for decision-focused energy storage arbitrage and defenses against false data attacks in power grids. He actively collaborates on federated learning approaches for distributed systems and has pioneered techniques for system-level synthesis in cyber-physical architectures. Anderson’s affiliations include the Data Science Institute (DSI) and specialized centers focused on data-driven science and energy systems. His work bridges theoretical control advancements with real-world applications in energy and healthcare.
Mick Cooper is a Professor of Counselling Psychology at the University of Roehampton, leading the School of Psychology's Centre for Research in Psychological Wellbeing (CREW). He holds qualifications including a DPhil and is a chartered psychologist with fellowships from the British Association for Counselling and Psychotherapy (BACP) and the Academy of Social Sciences (AcSS). His work focuses on humanistic, existential, and pluralistic therapies, emphasizing shared decision-making, therapy personalization, and youth mental health. Education: DPhil in Psychology. Professional Affiliations: BACP Fellow, Associate Fellow of the British Psychological Society (BPS), and member of the Society for Humanistic Psychology (APA). Research Interests Cooper's research explores preferences in therapy, relational depth, goals in therapy, and the interface between psychological practice and social change. His projects include school-based counselling trials and evaluations of humanistic therapy efficacy. Key contributions include co-developing the pluralistic therapy approach with John McLeod and validating tools like the Relational Depth Frequency Scale (RDFS). Awards & Recognition 2023: BACP Outstanding Research Award for the ETHOS Project. 2014: Carmi Harari Mid-Career Award from APA's Division 32. 2005: Recognised Achievement in Counselling and Psychotherapy. Grants & Projects Current projects include randomized trials on school-based humanistic counselling and nutrition's role in mental health. He leads the CREST Research Clinic and collaborates on studies like 'Puzzle Therapy and Mental Health.' Labs & Teams As Acting Director of CREW, Cooper oversees research on psychological wellbeing, therapy personalization, and youth mental health interventions. His team includes postgraduate researchers like Sally-Ann Adams and Charlotte Jakson.
Ye Zhisheng is the Dean’s Chair and Associate Professor in the Department of Industrial Systems Engineering & Management at the National University of Singapore (NUS). His research focuses on reliability engineering, inventory control, emergency response systems, and statistical modeling. He holds a PhD in Industrial and Systems Engineering from NUS, along with a BEng in Material Science and Engineering and a BEco in Economics from Tsinghua University. His work emphasizes practical applications in mission-critical systems, predictive maintenance, and data-driven decision-making. Current research initiatives include optimal maintenance policies for manufacturing systems, degradation analysis of bearings, and federated learning approaches for battery lifecycle prediction. He has pioneered methods for integrating physics-informed neural networks into prognostics and health management (PHM) systems. Key technical contributions span advanced statistical methodologies like sieve estimation for survival data, phase-type distributions modeling, and condition-based maintenance optimization. His interdisciplinary approach bridges operations research, mechanical engineering, and computer science to address complex reliability challenges. Recent projects include resilient consensus-based power grid management and contamination source identification frameworks. Notable collaborations involve developing intelligent cross-domain fault diagnosis systems using transformer networks and advancing the Internet of Federated Things (IoFT) for distributed data analytics. His work has been applied in aerospace, telecommunication infrastructure, and medical emergency response systems.
Dr. Jannah Baker is a Research Fellow at the Sydney School of Public Health, University of Sydney. She holds a PhD in Statistics specializing in Bayesian spatiotemporal modelling of chronic diseases, alongside dual postgraduate diplomas in Public Health and Statistics. Her clinical background includes five years as a practicing physician. Her research focuses on cancer prevention (breast, endometrial, melanoma), spatiotemporal disease modeling, health economics, and clinical trial design. She has led projects on sepsis detection systems, diabetes management, and surgical outcomes analysis. Notable contributions include a seminal systematic review on fertility-sparing endometrial cancer treatments referenced in international guidelines. Jannah has attracted $6M+ in collaborative funding and oversees grants such as the 2023 NHMRC-funded ROADMAP trial and a Department of Health-funded study on pediatric sepsis management. Her work spans 50+ peer-reviewed articles across journals like Journal of Medical Internet Research and PLOS One . Professional activities include clinical trials statistics, health economics analysis, and implementation science lectures. She has mentored multiple research teams and maintains an ORCID profile (0000-0002-2208-6584).
Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence, and Medicine at the University of Cambridge, leading the van der Schaar Lab. She holds dual affiliations with the Department of Applied Mathematics and Theoretical Physics (DAMTP) and the Centre for Mathematical Imaging in Healthcare. Her research focuses on healthcare AI, machine learning, and operations research. She has authored over 250 journal articles and 275 conference papers, with notable contributions to synthetic data for privacy, causal inference, and clinical decision-making. Her work has led to 35 U.S. patents, including foundational innovations in streaming video compression (MPEG-4 standards). Awards include the Oon Prize (2018), IEEE Fellow (2009), and recognition as the UK's most-cited female AI researcher (2019). Leadership roles include Director of the Cambridge Centre for AI in Medicine and Co-Director of the European Laboratory for Learning and Intelligent Systems. She has mentored global academic leaders and pioneered initiatives like the Inspiration Exchange for early-career researchers. Key projects include predictive models for hospital resource allocation during pandemics and AI tools for personalized medicine. Publications span machine learning theory, healthcare applications, and interdisciplinary fields like network science. Her lab's impact includes tools like AutoPrognosis (automated ML for clinical prediction) and SynthCity (synthetic healthcare data generation).
Laurens Lootens is a Researcher in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge. His work focuses on theoretical physics, particularly in quantum lattice models, topological phases of matter, and mathematical structures underlying quantum systems. He is affiliated with the High Energy Physics research group within DAMTP. His research interests include dualities in quantum systems, matrix product operator symmetries, conformal field theories, and tensor network methods. Lootens explores topics such as entanglement in many-body systems, symmetry-protected topological phases, and the interplay between algebraic structures and physical phenomena. Publications highlight his contributions to understanding lattice representations of dualities, topological sectors in quantum models, and critical lattice models for conformal field theories. His work bridges theoretical frameworks with computational methods, advancing both fundamental physics and quantum information science.
Zohreh Sharafi is an Assistant Professor of Software Engineering in the Department of Computer and Software Engineering (GIGL) at Polytechnique Montréal. Previously, she served as a Senior Research Fellow in the Department of Electrical and Computer Engineering at the University of Michigan, Ann Arbor, where she worked with Dr. Westley Weimer and was awarded the prestigious NSERC Postdoctoral Fellowship. Prior to her academic career, she worked as a software engineer at Morgan Stanley, contributing to the firm's electronic trading platform and serving as principal architect of SURF, a market data simulator. Her educational background includes a Ph.D. in Computer Engineering from École polytechnique de Montréal under the supervision of Dr. Giuliano Antoniol and Dr. Yann-Gaël Guéhéneuc, a Master of Applied Science in Software Engineering from Concordia University, and a Bachelor of Computer Engineering from the University of Tehran. Dr. Sharafi leads the SENSE Lab, a multidisciplinary software engineering research laboratory focused on understanding problem-solving strategies developers use during software development, with particular attention to human factors such as gender and native language. Her research combines human-centric design with experimental methodologies, investigating cognitive processes involved in software development using biometric measures including eye tracking and neuroimaging. Current active projects include evaluating trustworthiness perceptions of software artifacts and studying the role of creativity in software engineering tasks. She has made significant contributions to understanding how gender influences program comprehension and code review processes. Her publication record demonstrates a strong focus on empirical methods in software engineering, particularly eye tracking and neuroimaging techniques to study developer cognition. Her work spans program comprehension, code review, requirements engineering, and the impact of human factors on software development processes. She has developed methodological frameworks for conducting eye tracking studies in software engineering and has made notable contributions to understanding how visualization techniques affect software development tasks. NSERC Postdoctoral Fellowship NSERC Discovery Grant Program and Launch Supplements (Sep 2024-Sep 2029) IVADO Startup & Operation Fund (Jan 2022-Jan 2023) Scholarship for Doctoral Studies from Fonds de Recherche du Quebec Distinguished Reviewer Awards from IEEE ICPC 2020 and ACM FSE 2024 Dr. Sharafi actively mentors students including Mahta Amini (PhD Candidate, IVADO Scientifique en résidence 2024 Laureate), Cameron Cherif (PhD Candidate), Sara Yabesi (Master's Student), and Anthonia Njoku (Graduate research intern). She serves on numerous conference organizing committees including as Local Arrangement Chair for SANER 2025, Program Co-chair for SEMLA 2024, and as a reviewer for top-tier journals including IEEE Transactions on Software Engineering and ACM Computing Surveys. Her research is supported by multiple grants focused on understanding human factors in software engineering through empirical methods. At Polytechnique Montréal, Dr. Sharafi directs the SENSE Lab which brings together computer scientists, cognitive scientists, and software engineering researchers to investigate the cognitive aspects of software development. The lab employs advanced methodologies including eye tracking, functional near-infrared spectroscopy (fNIRS), and functional magnetic resonance imaging (fMRI) to study how developers comprehend, navigate, and modify software systems. Current projects examine trustworthiness perceptions in code review, the role of creativity in software engineering tasks, and gender differences in software development processes.
Professor August Evrard is a distinguished academic at the University of Michigan, holding the Arthur F. Thurnau Professorship in Physics and Astronomy. He is affiliated with the Department of Physics within the College of Literature, Science, and the Arts. Known for his contributions to cosmology and astrophysics, he pioneered the Problem Roulette tool, recognized with the Provost's Teaching Innovation Prize. His research focuses on galaxy clusters, dark matter, and cosmological surveys like the Dark Energy Survey (DES) and XXL Survey. He has been honored as an AAS Fellow (2025) and has contributed to advancements in physics education through innovative teaching methods and technologies. In research, Prof. Evrard explores topics such as dark matter halo dynamics, galaxy cluster properties, and weak lensing analyses. His work spans observational cosmology, computational modeling, and multi-wavelength astronomy. Notable projects include studies on galaxy cluster mass distributions, the relationship between X-ray emissions and velocity dispersions, and the application of machine learning to astrophysical data analysis. His contributions to education highlight the integration of AI-driven tools to enhance learning, as seen in initiatives like the Problem Roulette and course recommendation systems. Prof. Evrard's awards include the Provost's Teaching Innovation Prize for Problem Roulette and his AAS Fellowship. His academic leadership and innovative approaches to both research and education solidify his role as a pivotal figure in astrophysics and STEM pedagogy.
Yuliya Martsynyuk is an Associate Professor in the Department of Statistics at the University of Manitoba, located within the Faculty of Science. She holds an office in 256 Parker and can be reached via email at Yuliya.Martsynyuk@umanitoba.ca. Her research interests align with core statistical disciplines, including theoretical and applied statistics, probability, and data analysis methodologies. Specific subfields are not explicitly detailed in the provided text, but her affiliation with the Statistics department suggests expertise in areas such as statistical modeling, computational statistics, and interdisciplinary applications of statistical methods. No awards, publications, grants, or student advising records are explicitly listed in the provided information. Further details on her academic contributions would require additional sources.
Fabio Sigrist is a Professor of Applied Statistics and Data Science at the Institute of Financial Services Zug (IFZ) , part of the Lucerne University of Applied Sciences and Arts . He also holds a Senior Scientist and Lecturer position at the Seminar for Statistics, ETH Zurich . His career spans academic research, industry consulting, and project leadership in finance and data science. PhD in Statistics (2013), ETH Zurich MSc in Mathematics with distinction (2008), ETH Zurich MEd in Mathematics Education (2008), ETH Zurich Sigrist’s research focuses on integrating Machine Learning with Spatial Statistics for applications in Financial Econometrics and Credit Risk . His work includes developing novel algorithms like GPBoost and KTBoost , advancing spatio-temporal modeling , and applying tree-based boosting to financial problems. Projects such as CreHos (credit risk in hospitality) and NISMO (interpretable real estate modeling) highlight his interdisciplinary approach. His publications address challenges in large-scale spatial data , loss given default modeling , and stock volatility prediction . He contributes to software development with tools like spate (R package) and varycoef (spatially varying coefficients).
Dr Jonathan Green is a seabird biologist and currently serves as Programme Director for the BSc Environmental Science at the University of Liverpool. He has held roles such as Head of Discipline in Ecology & Marine Biology and Deputy Assessment Officer within the School/Institute. His professional activities include chairing conferences, convening sessions, and serving on editorial boards for journals like Emu - Austral Ornithology (since 2008) and Endangered Species Research (2009–2013). Education: Zoology, University of Cambridge (1995) PhD in Zoology, University of Birmingham (2001) Postdoctoral Research, University of Birmingham Fellowship at La Trobe University, Australia (2005–2008) Research Interests: Jonathan Green's research explores the intersection of ecology, physiology, and behavior in seabirds. He investigates how seabirds adapt to contrasting marine and terrestrial environments, particularly regarding foraging strategies, energy expenditure, and moult ecology. His work addresses anthropogenic threats such as overfishing, climate change, and offshore wind farm developments. He also leads conservation projects in the Caribbean UK Overseas Territories, collaborating with local organizations and government bodies to protect seabird populations. Advising and Grants: Jonathan Green supervises PhD student Elayna Daniels through the CASE studentship grant. His research is supported by grants from NERC, Defra's Darwin+ programme, and UKRI, focusing on topics like marine bird energetics, wind farm impacts, and Caribbean conservation. These projects often involve collaboration with government agencies and industry partners to ensure applied outcomes. Labs/Teams: Jonathan Green collaborates with interdisciplinary teams and institutions, including the Joint Nature Conservation Committee (JNCC), RSPB, BTO, and renewable energy developers. His fieldwork is conducted at sites such as Puffin Island, North Wales, and various locations in the Caribbean UK Overseas Territories.
Kamal Sarabandi is the Rufus S. Teesdale and Fawwaz T. Ulaby Distinguished University Professor of Electrical Engineering and Computer Science at the University of Michigan. He leads the Radiation Laboratory, renowned for research in applied electromagnetics, radar remote sensing, and antenna technology. His academic rank is Professor, and he holds affiliations with the College of Engineering. His research spans radar systems for environmental monitoring (e.g., soil moisture, snowpack), automotive radar for autonomous vehicles, metamaterials for antenna miniaturization, and security applications like concealed weapons detection. He has advised over 60 PhD students, many of whom hold academic or industry leadership roles globally. Awards and Recognition: National Academy of Engineering member, IEEE Picard Medal, Humboldt Award, Ellis Island Medal of Honor, and Stephen S. Attwood Award. His work bridges fundamental science with practical innovations, including NASA collaborations and military/defense applications. Labs and Teams: Directs the Radiation Laboratory, a hub for applied electromagnetics and radar innovation. Collaborates with industry (e.g., Qualcomm, SAIC) and government agencies (NASA, Army Research Lab) on projects like the COMBAT center for autonomous systems. Education: Earned his PhD in Electrical Engineering from the University of Michigan (1989). Alumni of his lab include professors at UW-Madison, Purdue, and international institutions.
