Tom Kirchmaier is Professor of Governance, Risk, Regulation and Compliance at Copenhagen Business School's (CBS) Department of Accounting and Centre for Corporate Governance. He concurrently serves as a Professorial Fellow and Director of Policing and Crime at the London School of Economics' Centre for Economic Performance. He is also a Faculty Affiliate at LSE's Data Science Institute and member of the UK Home Office Scientific Advisory Council. Research Interests: Kirchmaier's work empirically analyzes organizational governance using large datasets. Primary domains include: Law/economics intersections Policing systems and crime patterns Anti-money laundering (AML) and organised crime networks Corporate boards with emphasis on gender dynamics Risk management and compliance frameworks Publication Trends: Recent articles (2021-2025) demonstrate strong focus on empirical crime analysis (e.g., terrorism impacts, domestic abuse recidivism) and corporate governance in banking. Methodologies leverage big data to study spatial crime patterns, behavioral economics, and organizational efficiency across financial and public sectors. Awards: 2024 LSE Innovation Challenge Winner Teaching & Advising: Supervises 1 PhD and 1 Master's student. Teaches graduate/executive courses: Corporate governance and finance MBA compliance/risk management Board training programs
Gideon Nave is the Carlos and Rosa de la Cruz Associate Professor of Marketing at The Wharton School, University of Pennsylvania. He holds a prominent position within the Department of Marketing and contributes significantly to the school's research and teaching missions in consumer behavior, marketing analytics, and decision sciences. His academic journey began with B.Sc and M.Sc degrees in Electrical Engineering from the Technion – Israel Institute of Technology, specializing in Signal Processing. He then pursued and earned his PhD in Computation & Neural Systems from the California Institute of Technology (Caltech), establishing the interdisciplinary foundation for his current research program. Nave's research focuses on how technological developments in measurement instruments have granted unprecedented access to individual-level data. His work spans several interconnected domains: Consumer neuroscience and neuroeconomics Applications of genetic data in marketing AI's impact on creativity and consumer research Ethical implications of new data collection technologies Decision-making processes across various contexts He investigates how digital footprints of online behavior, MRI, genotyping, and hormonal assays can help understand consumer preferences and behavior, while critically evaluating the ethical challenges these technologies present. His publication record demonstrates a clear trajectory toward understanding the intersection of emerging technologies and consumer behavior. Recent work particularly emphasizes AI applications in marketing research, genetic data utilization for personalization, and neuroscientific approaches to understanding decision-making processes. His methodological approach often combines large-scale datasets with rigorous experimental designs, contributing significantly to both theoretical advancement and practical applications in marketing. Nave's contributions to the field have been recognized with several prestigious awards: National Science Foundation Faculty Early Career Development (CAREER) Award (2020-2025) Association For Consumer Research (ACR) Early Career Award (2024-2025) APS Rising Star Award (2020) Poets & Quants Selection as one of the "World's Best 40 B-School Professors under the Age of 40" (2021) As a recipient of the NSF CAREER Award, Nave leads significant research initiatives examining the applications and ethical implications of new measurement technologies in marketing and consumer behavior. His work has attracted considerable media attention, with features in major outlets including The New York Times, The Guardian, CNN, and The Economist, reflecting the broad relevance and impact of his research. While specific lab names aren't prominently featured in his public profile, Nave's research appears to involve extensive interdisciplinary collaborations spanning neuroscience, genetics, computer science, and marketing. His publications frequently include co-authors from diverse disciplinary backgrounds, suggesting a team-based research approach that bridges traditional academic boundaries.
Kenza Kellou-Menouer is a researcher affiliated with the ETIS Laboratory at ENSEA, France, and part of the MIDI research group . Her work focuses on schema discovery for Semantic Web data, data mining, and big data optimization. Research: Semantic schema discovery, clustering/classification algorithms, and association rules. Teaching: Semantic Web technologies, database design, algorithms, and programming languages (Java, C++, C#, C). Research Interests center on Semantic Web data integration, RDF schema inference, and hybrid machine learning approaches. She has contributed to scalable schema discovery systems and real-time profiling techniques for large datasets. Publications include work on schema inference tools (SchemaDecrypt++, HInT) and methodological frameworks presented at top-tier venues like VLDB (A*), ICDE (A*), SSDBM (A), and ISWC . Her research bridges theoretical advancements with practical implementations for RDF datasets. Community Contributions include organizing tutorials at the International Semantic Web Conference (ISWC) 2022 and developing educational materials for database and programming courses.
Adam J Rothman is a Professor in the Department of Statistics at the University of Minnesota, Twin Cities campus, specializing in high-dimensional statistical methodologies. His research focuses on covariance estimation, multivariate analysis, and developing innovative regression frameworks for complex data structures. His primary research interests include High-Dimensional Statistics, Covariance Estimation, Multivariate Analysis, and Statistical Machine Learning. Rothman develops penalized likelihood methods and shrinkage estimators to address challenges in matrix-valued predictors, categorical responses, and large covariance matrices, with applications spanning scientific domains requiring scalable high-dimensional analysis. Rothman's recent publications (2019-2024) demonstrate consistent innovation in high-dimensional regression and classification. Key trends include covariance matrix regularization, sufficient dimension reduction techniques, and likelihood-based approaches for categorical multivariate responses. His work emphasizes computational efficiency and theoretical guarantees for datasets where variables exceed sample sizes. He has secured major National Science Foundation funding as Principal Investigator for two projects: Sufficient Dimension Reduction of High-Dimensional Data (2011-2015) and New methods for multivariate analysis in high dimensions (2015-2021). These grants supported foundational work in dimension reduction and covariance estimation, advancing methodologies for modern statistical challenges.
Dr. Michel Dumontier is a Distinguished Professor of Data Science at Maastricht University, where he serves as the founder and Director of the Institute of Data Science. He is internationally recognized as a co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles, which have transformed scientific data management globally. His academic background includes: BSc in Biochemistry from the University of Manitoba (1999) PhD in Bioinformatics from the University of Toronto (2004) Assistant/Associate Professor at Carleton University (2005-2013) Associate Professor at Stanford University (2013-2016) Distinguished Professor at Maastricht University (2017-present) Dr. Dumontier's research focuses on unlocking data potential for scientific discovery, with expertise in knowledge graphs for drug discovery and personalized medicine. His work spans FAIR data principles, generative AI, machine learning, semantic technologies, ontology, and data integration. His recent publications reveal a strong trend toward applying generative AI to healthcare data, with increasing focus on synthetic health data generation, privacy-preserving techniques, and knowledge graph applications in drug repurposing. His work bridges computer science, biomedical informatics, and clinical applications across multiple medical domains. Dr. Dumontier has secured significant research funding as a principal investigator: NWO (Dutch Research Council) Horizon Europe MCSA NIH/NCATS ARPA-H He coordinates the AIDAVA and REALM projects, leads the NCATS Biomedical Data Translator, and directs the GENIUS AI lab. As editor-in-chief of the journal Data Science, he shapes discourse in the field. Dr. Dumontier maintains active industry connections through: Minderheidsaandeelhouder at Data2Discovery Inc Scientific advisor and minority shareholder at OntoForce NV Scientific advisor, board member, and minority shareholder at Comunicare Editor-in-chief of Data Science Journal at Sage Publishing
Professor Tracey Rogers is a distinguished academic at the University of New South Wales, Faculty of Science, School of Biological, Earth and Environmental Sciences. Her research focuses on marine mammals and their adaptation to changing environments, with particular expertise in Antarctic wildlife, predator-prey interactions, and wildlife communication. Her research interests span marine mammal ecology, behavioral ecology, evolutionary biology, and animal behavior. Professor Rogers leads the MammalLab at UNSW, conducting cutting-edge research on various marine mammal species including whales, seals, and dolphins. Her work combines field observations, bioacoustic analysis, and physiological studies to understand how mammals overcome environmental challenges. Professor Rogers's publication record shows a strong focus on marine mammal vocalizations, Antarctic ecosystems, and conservation biology. Her recent work has increasingly incorporated advanced analytical techniques for studying large audio datasets and examining physiological responses to environmental change. 2015: Finalist Australian Academy Science, Nancy Millis Medal 2011: Finalist Eureka Prize People's Choice Science Award 2011: Finalist Eureka Prize Animal Protection, Research & Innovation 2008-2010: Ambassador, Australian Institute of Policy & Science Tall Poppy Program 2009: F.G. Wood Memorial Prize, International Society Marine Mammalogy 2007: Research Award, Australasian Association of Zoological Parks & Aquaria 2005: NSW Tall Poppy Award Professor Rogers has successfully supervised over 20 PhD students to completion, with many now holding prominent positions globally in academia, government agencies, and conservation organizations. She currently supervises five doctoral candidates working on diverse projects related to marine mammal acoustics and ecology. She is an active participant in Scientists in Schools and conducts workshops at the Powerhouse Museum and Taronga Conservation Society Australia.
Anna Kende is a University Professor at Eötvös Loránd University, serving as Director of the Institute of Psychology and Head of the Department of Social Psychology. She actively contributes to academic governance as a member of both the Faculty Honors Committee and Faculty Council. Her research centers on the psychology of intergroup relations, with specialized expertise in social change processes, prejudice reduction mechanisms, and collective action dynamics. A defining focus of her work examines anti-Gypsyism and the social inclusion challenges faced by Roma communities across Europe, integrating both theoretical frameworks and applied interventions. Her research methodology frequently employs large-scale international collaborations spanning dozens of countries. Analysis of her 15 most recent publications (2025) reveals three dominant research trajectories: pandemic-induced shifts in intergroup relations (particularly migration attitudes), cross-cultural analyses of allyship motivations for marginalized groups (LGBTQ+ and Roma), and context-dependent interventions for reducing structural prejudice. Her work consistently utilizes multinational datasets and addresses contemporary societal challenges through psychological lenses. Professor Kende has secured substantial funding from European Union programs and other international organizations for numerous research projects. She leads the Social Groups and Media Research Workshop, which investigates group dynamics, media influences on social attitudes, and psychological mechanisms of social change. Her work bridges academic research with real-world applications in prejudice reduction and social cohesion.
Jieyang Chen is an Assistant Professor in the Department of Computer Science at the University of Oregon's School of Computer and Data Sciences, where he leads research in high-performance computing and data-intensive scientific applications. His work bridges theoretical computer science with practical solutions for large-scale computational problems. Research Focus: Developing energy-efficient algorithms for CPU-GPU heterogeneous systems Creating fault-tolerant frameworks for scientific computing Designing advanced data compression techniques with error control Optimizing distributed machine learning workflows Dr. Chen's research portfolio demonstrates a consistent focus on performance, reliability, and energy efficiency in scientific computing. His recent publications show increasing sophistication in handling scientific data through techniques like multigrid frameworks, progressive retrieval methods, and adaptive compression algorithms that preserve critical features in climate and other scientific datasets. Education: PhD in Computer Science, University of California, Riverside (2019) MS in Computer Science, University of California, Riverside (2014) BE in Computer Science, Beijing University of Technology Dr. Chen previously worked as a Computer Scientist at Oak Ridge National Laboratory before joining the University of Oregon faculty. His collaborations span national laboratories and industry partners, contributing to real-world applications in scientific computing infrastructure.
Dr. Chia-Wen Chen is a Psychometrician at the Psychometrics Centre within the Cambridge Judge Business School , specializing in Executive Education. Originally from Taiwan, he holds a PhD in Psychometrics from the Education University of Hong Kong (2018), preceded by a master's degree in Psychology from National Chung Cheng University (2012). His career spans institutions in Taiwan, Hong Kong, Norway, and the UK, including postdoctoral research at the University of Oslo's Centre for Educational Measurement (2019-2023). PhD in Psychometrics (2018), Education University of Hong Kong MSc in Psychology (2012), National Chung Cheng University Postdoctoral Researcher (2019-2023), University of Oslo Chia-Wen's research focuses on Item Response Theory (IRT) models for forced-choice and compositional items , with applications in computerized adaptive testing (CAT) , differential item functioning (DIF) , and multilevel modeling . His work includes developing novel IRT methods for ranking items and Most-Least formats, addressing reliability/validity in educational scales like the Principal Instructional Management Rating Scale (PIMRS), and analyzing large-scale datasets (PISA) for cross-national educational insights. Recent publications demonstrate expertise in ipsative testing , online parameter estimation , and factor mixture modeling for educational diagnostics. He has also contributed to methodologies for CAT algorithms in educational evaluation and fault line analysis in school leadership teams.
Renee Luthra is a Professor in the Department of Sociology and Criminology at the University of Essex, serving as Director of the Essex Centre for Migration Studies and Assistant Director of the ESRC Research Centre on Micro-Social Change. Her research expertise spans international migration, social stratification, education, and quantitative methods, with current investigations focusing on: Migration-driven inequalities in parenting, education, work, and health Second-generation immigrant integration and educational trajectories Policy impacts on migrant communities (including Brexit consequences) Immigrant health paradox mechanisms and mental health outcomes Political socialization in transnational families Analysis of her 15 most recent publications reveals methodological consistency in quantitative analysis of large-scale datasets like Understanding Society, with thematic emphasis on contextual factors shaping migrant integration. Her work consistently examines how structural forces—particularly discrimination, family dynamics, and policy environments—mediate outcomes across generations. She leads strategic research initiatives through the Essex Centre for Migration Studies and ESRC Research Centre, directing teams that investigate contemporary migration patterns and their societal implications across European contexts.
Zhuang Liu is an Assistant Professor of Computer Science at Princeton University, where he leads a research group focused on deep learning and computer vision. His work spans vision and language, unified by a focus on deep learning methods, representations, and architectures. Prior to joining Princeton, he was a Research Scientist at Meta AI Research (FAIR) in New York City. He received his Ph.D. from UC Berkeley and his B.E. from Tsinghua University, both in Computer Science. His educational background includes: Ph.D. in Computer Science, University of California, Berkeley, 2022 B.E. in Computer Science, Tsinghua University (Yao Class) Liu's research focuses on empirical approaches to understanding how deep learning models work and behave. He explores simple approaches to gain empirical insights into neural networks, often challenging existing beliefs in architectures, training, pruning, and datasets. His work spans multiple domains including computer vision, natural language processing, and multimodal learning. He has made significant contributions to the field, including DenseNet and ConvNeXt architectures, which have influenced modern neural network design. His recent publications reveal a strong focus on understanding and improving large language models and vision-language systems. His work examines idiosyncrasies in LLMs, pruning approaches for efficient inference, visual shortcomings of multimodal systems, and bias in large-scale visual datasets. He also investigates fundamental questions about neural network architectures, exploring the relationship between ConvNets and Transformers, and developing normalization-free transformer variants. His research consistently bridges theoretical insights with practical applications, as evidenced by his numerous conference publications and industry collaborations. His notable scientific achievements include: CVPR Best Paper Award NeurIPS'18 Compact Neural Networks Workshop Best Paper Award Professor Liu actively mentors students and postdoctoral researchers, currently advising seven Ph.D. students including Wenhao Chai, Tony Chen, Sachin Konan, Taiming Lu, Zhuorui Ye, David Yin, and Boya Zeng. He has successfully guided graduates such as Jiachen Zhu (now at Skild AI) and Mingjie Sun (now at Thinking Machines Lab). His research has practical implications for improving the efficiency, interpretability, and fairness of deep learning systems, with applications across multiple domains. His research group maintains active collaborations with industry partners including Meta FAIR, NVIDIA Research, and Adobe, and regularly hosts research interns. The group's work has established Zhuang Liu as a leading voice in empirical deep learning research, with invitations to speak at top academic institutions including Harvard, Stanford, Columbia, and Boston University.
Dr. Matthew Barclay is a Principal Research Fellow (Statistician) in Cancer Healthcare Epidemiology at University College London's Behavioural Science and Health department. With expertise spanning medical statistics, epidemiology, and health services research, his work focuses on analyzing cancer registry data, primary care electronic health records, and healthcare claims data to improve understanding of cancer diagnosis and treatment pathways. Dr. Barclay's educational background includes: PhD in "The design of composite indicators of healthcare quality: a multi-method analysis" from University of Cambridge (2021) MSc in Statistics with Medical Applications from University of Sheffield (2015) MMath in Mathematics from Durham University (2011) Dr. Barclay's research spans the entire cancer diagnostic and management pathway, with particular focus on risk of cancer in primary care patients, cancer characteristics at diagnosis (including socio-demographic variations in staging), treatment patterns, and short-term outcomes. His methodological expertise lies in applied statistics, particularly in cohort design within electronic health record datasets and developing consistent data resources for research. His work frequently employs cancer registry data, primary care records, and healthcare claims to address critical questions in cancer epidemiology and healthcare quality. Analysis of Dr. Barclay's recent publications reveals a strong focus on cancer diagnosis pathways, particularly how symptoms present in primary care lead to cancer diagnosis. His work spans multiple cancer types but has particular emphasis on lung, colorectal, and breast cancers. Methodologically, his research combines epidemiological approaches with advanced statistical techniques applied to large-scale datasets including UK Biobank, cancer registries, and primary care records. International comparative studies through the International Cancer Benchmarking Partnership represent another significant strand of his recent work. Dr. Barclay advises postgraduate and PhD students on topics related to his research interests in cancer epidemiology and health services research. His collaborative network spans multiple institutions and countries, reflecting the international nature of his research on cancer diagnosis and treatment pathways.
Ajitha Rajan is a Professor (Personal Chair of Software Testing and Verification) at the School of Informatics, University of Edinburgh. Previously, she was a post-doctoral researcher at Oxford University's Computer Science Department and Laboratoire d'Informatique de Grenoble (LIG) in France. She earned her PhD in Computer Science from the University of Minnesota in August 2009 under the supervision of Prof. Mats Heimdahl. Her research spans two main directions: Automated Software Testing Techniques covering test input generation, test oracles, and coverage metrics; and Biomedical Artificial Intelligence focusing on cancer survival models, interpretability for biological sequences, and medical images. Her work bridges software engineering and biomedical applications, particularly in the development of trustworthy AI systems for healthcare. Professor Rajan leads several significant research projects including a Royal Society Industry Fellowship (2022-2025) on AutoTest for autonomous vehicle perception safety, the H2020 European Project KATY (2021-2025) on AI for genomics and personalized medicine where she serves as Edinburgh Lead PI, and an EPSRC Trustworthy Autonomous Systems Node project (2020-2024). Her recent publications demonstrate strong activity across software testing, explainable AI, and biomedical applications, with numerous papers accepted to top conferences in 2025 including ML for Healthcare, IJCAI, and ESEM. Among her scientific recognitions, she received the Best Reviewer Award at ISSTA'25. Her work has been consistently published in leading venues including ICSE, ICASSP, and Communications Biology. Professor Rajan actively mentors PhD students working on diverse topics from automated testing of speech recognition systems to explainable AI for medical image analysis and cancer immunotherapy. She teaches undergraduate courses in Software Testing, Computer Programming, and Embedded Systems, and has been instrumental in establishing several fully funded PhD positions through Centres for Doctoral Training at the University of Edinburgh.
Amanda Kreider, PhD , is an Assistant Professor in the Department of Health Policy and Management at the University of Pennsylvania. Her research focuses on systemic challenges in long-term care, particularly the economic and policy factors affecting the direct care workforce and Medicaid-managed care networks. Education: BS in Economics and BA in French and Francophone Studies from Penn State (2009), PhD in Health Policy (Economics Track) from Harvard University (2021), and a Postdoctoral Fellowship at the Leonard Davis Institute of Health Economics (2024) Research Interests: Kreider's work investigates workforce shortages in Medicaid home-based care systems, immigration enforcement impacts on care access, and managed care plans' economic disincentives to include specialists in networks. She employs rigorous econometric analyses of large-scale datasets to inform policy reforms. Scientific Awards: Recognized with a Program Chair Award at the 2024 ASHEcon meetings for her research on Medicaid managed care networks. Her publications reveal critical gaps between Medicaid coverage expansion and workforce capacity, as well as structural barriers to specialist care access. Current projects examine how regulatory frameworks shape care delivery systems for vulnerable populations.
Simon Ostermann serves as a Senior Lecturer at Saarland University and Senior Researcher & Deputy Director at the Multilinguality and Language Technology (MLT) lab of the German Research Center for Artificial Intelligence (DFKI). He leads the Efficient and Explainable NLP (E&E) research group and contributes to major projects including lorAI (Low Resource AI), TRAILS (Trustworthy Machines), PERKS (Procedural Knowledge), DAM-S (Semantic Search), and DisAI (Disinformation Combat). His research centers on democratizing language technology through transparent, robust models—specializing in mechanistic interpretability to reverse-engineer LLM internals and enhance efficiency for low-resource languages. Key focus areas include reducing model size for constrained environments, improving cross-lingual transfer via adapters, and developing structured input techniques. His work bridges theoretical interpretability with practical applications in resource-limited settings. 2025 publications reveal concentrated efforts in low-resource adaptation (language adapters, graph-enhanced embeddings), explainable AI (counterfactual generation, conversational XAI datasets), and multilingual fact-checking systems. Notable trends include systematic neuron manipulation frameworks, rigorous evaluation of synthetic data strategies, and cross-lingual claim verification benchmarks. Ostermann advises six PhD candidates (Anikina, Oguz, Bäumel, al Ghussin, Gurgurov, Vykopal) and multiple MSc students on topics spanning RAG hallucinations, multilabel classification, and adapter interpretability. His research receives funding through DFKI-led consortia with European and international partners focusing on trustworthy, efficient AI deployment. The E&E group under his leadership drives innovation in efficient NLP through biweekly seminars, collaborative coding sessions, and partnerships with institutions like KInIT. Current initiatives prioritize green computing for language models and real-world deployment in industrial procedural knowledge systems.
