Peng Shige is a Professor of 1st class at the School of Mathematics, Shandong University, China. He has held the Distinguished Professor title under the Ministry of Education (Cheung Kong Scholarship) since 1999. His academic journey includes degrees from Shandong University (Physics diploma, 1971-1974), Paris-IX (1985), and Aix-Marseille University (PhD 1986, Habilitation 1992). Research focuses on nonlinear expectations, stochastic calculus, partial differential equations, and financial mathematics. Key contributions include foundational work on backward stochastic differential equations (BSDEs), the g-expectation framework, and the G-expectation theory extending probability axioms to nonlinear settings. These innovations have advanced stochastic control, financial risk modeling, and differential games. Honors include the 2020 Future Science Award, 2011 Princeton Global Scholar, and 2005 Chinese Academy of Sciences Academician status. He delivered a plenary lecture at the 2010 International Congress of Mathematicians. Peng's work integrates theoretical breakthroughs with applied domains like financial engineering. His research has been widely cited (~8k citations) and shaped modern stochastic analysis methodologies.
Ralf Haefner is an Assistant Professor in the Departments of Brain & Cognitive Sciences and Physics & Astronomy at the University of Rochester, holding this joint appointment since 2014. His interdisciplinary research bridges neuroscience and physics to investigate computational principles of perception and decision-making. Education and professional background: PhD, Oxford University, 1999 Visiting Research Fellow, Department of Neurobiology, Harvard Medical School Swartz Fellow, Sloan-Swartz Center for Theoretical Neurobiology, Brandeis University Haefner's research program centers on computational neuroscience , with primary focus on how the brain forms perceptual beliefs and uses them for decisions through Bayesian modeling . He employs machine learning tools to construct mathematical models explaining neural responses and behavior, particularly in the visual domain. His work addresses neural representation of uncertainty, causal inference mechanisms, and probabilistic computation in cortical circuits. Analysis of recent publications (2023-2025) reveals three dominant trends: (1) causal inference frameworks applied to motion perception and segmentation, (2) Bayesian modeling of perceptual biases and confidence computations, and (3) integration of generative and discriminative neural computations. His work extends beyond traditional neuroscience into scientific methodology through 'Generative Adversarial Collaborations' for improving research discourse. Honors and Awards: Swartz Fellowship, Sloan-Swartz Center for Theoretical Neurobiology NSF CAREER Award (2022) for 'Approximate inference at the intersection of neuroscience and machine learning' Haefner secured significant research funding through his NSF CAREER award, which supports foundational work on probabilistic inference at the neuroscience-ML interface. While specific students aren't listed, his active publication record and lab infrastructure suggest ongoing mentorship of graduate students and postdocs. His research has clinical relevance as shown by studies on perceptual abnormalities in autism spectrum disorder, indicating translational potential for understanding neurological conditions.
Yiyu Yao is a Professor in the Department of Computer Science at the University of Regina, Faculty of Science. He holds a B.Eng. from Xi'an Jiaotong University and earned both his M.Sc. and Ph.D. from the University of Regina. His office is located in College West 308.6, and he can be reached at Yiyu.Yao@uregina.ca or by phone at (306) 585-5226. Dr. Yao's research spans multiple interconnected domains in intelligent systems. His primary focus is on three-way decisions, which serves as a unifying framework for his work in granular computing, rough sets, and decision-theoretic models. He has developed significant theoretical contributions to decision-theoretic rough sets (DTRS) and probabilistic rough sets, creating bridges between uncertainty management and practical decision-making applications. His work extends to web intelligence, information retrieval systems, and multiview data analysis, where he applies his theoretical frameworks to real-world problems in data science and artificial intelligence. Analysis of Dr. Yao's recent publications reveals a strong continuing focus on three-way decision theory, with increasing applications across diverse domains. His work demonstrates evolution from foundational theoretical contributions to sophisticated applications in multi-criteria decision making, conflict analysis, and explainable AI. The research shows integration of granular computing principles with modern machine learning techniques, particularly in handling uncertainty and developing interpretable models. Recent publications indicate growing interest in the intersection of three-way decisions with fuzzy sets, shadowed sets, and cognitive approaches to data analysis. Dr. Yao has mentored numerous graduate students and has hosted many visiting scholars, primarily from Chinese institutions including Nanjing University Posts and Telecommunication, Harbin Normal University, Shaanxi Normal University, and others. He serves as Area Editor on Rough Sets for the International Journal of Approximate Reasoning and as Associate Editor for Information Sciences. He is also Associate Editor-in-Chief for the Journal of Emerging Technologies in Web Intelligence and serves on multiple editorial boards including LNCS Transactions on Rough Sets and Web Intelligence and Agent Systems. He has organized significant conferences including the International Joint Conference on Rough Sets (IJCRS 2017) and served on the Steering Committee for the International Symposium on Fuzzy and Rough Sets.
Professor Lucy Kimbell is Professor of Contemporary Design Practices at Central Saint Martins, University of the Arts London, where she convenes the Policy Futures Studio. She is Co-Director of the Sustainable Transitions through Democratic Design Doctoral Network funded by the EU Marie Curie programme and serves as Visiting Professor at University of Ulster and Honorary Adjunct Professor at RMIT University. Her work bridges design practice, public policy, and social innovation across multiple international contexts. Professor Kimbell's educational background includes a PhD in Design from Lancaster University (2013), an MA in Computing in Art and Design from Middlesex University (1996), and a B. Engineering in Design and Appropriate Technology from the University of Warwick (1989). Her academic journey reflects a consistent integration of technical, creative, and social perspectives. Kimbell's research focuses on service design, social design, and design for policy, with increasing emphasis on sustainable transitions and democratic innovation. She explores how design methods can address complex societal challenges, particularly through transdisciplinary approaches that connect creative practice with public policy contexts. Her work examines the role of design in government, professional services, and community settings, with particular attention to AI readiness in professional service firms and antimicrobial resistance policy in India. She has pioneered frameworks for measuring design's social and environmental value, notably through the Design Value framework developed with the Design Council. Her recent publications demonstrate a clear trajectory toward understanding design's role in public policy, sustainable transitions, and democratic innovation. The research shows increasing integration of practice-based research methods with policy development, emphasizing transdisciplinary collaboration and the application of design thinking to complex societal challenges. There's a notable focus on developing frameworks for measuring design's social and environmental value and advancing democratic innovation through design practice. Notable recognitions include: AHRC Research Fellow in Policy Lab in the Cabinet Office (2014-15) Principal research fellow at University of Brighton (2013-15) Clark Fellow in Design Leadership at Said Business School, University of Oxford (2005-10) Professor Kimbell supervises transdisciplinary PhD research connecting design with public policy, with six completions to date including four UAL-KCL joint studentships. She examines PhDs internationally and serves on the supervisory board for the PhD in Service Design for Public Sector at Sapienza University, Rome. Her grant portfolio includes major projects funded by EU Marie Curie, AHRC, ESRC, and Creative Europe, totaling millions in research funding for design-led approaches to societal challenges, including the Sustainable Transitions through Democratic Design Doctoral Network (2024-28) and the AHRC Design & Policy Research Network (2022-23). She previously directed UAL's Social Design Institute (2019-2022), which brought together university expertise in design for society through capacity building, joint research, publications, and public events. Her collaborative work extends to knowledge exchange projects with Design Council, Department of Work and Pensions, Northern Ireland Social Care Council, and Bite Back 2030, demonstrating her commitment to applying design thinking to real-world policy and social challenges.
Francis Bach is a Professor and researcher at INRIA, leading the SIERRA project-team since 2011, which is part of the Computer Science Department at Ecole Normale Supérieure (ENS) within PSL Research University. His work bridges CNRS, ENS, and INRIA as a joint research effort. Elected to the French Academy of Sciences in 2020, he currently runs the ERC project SEQUOIA following his previous ERC project SIERRA (2009-2014). His research spans statistical machine learning with focus on optimization, sparse methods, kernel-based learning, neural networks, graphical models, and signal processing. Bach completed his Ph.D. in Computer Science at U.C. Berkeley under Professor Michael Jordan, followed by work at Ecole des Mines de Paris and the WILLOW project-team at INRIA/ENS/CNRS (2007-2010). His recent book "Learning Theory from First Principles" was published by MIT Press in December 2024. Bach's publication record shows consistent high-impact contributions across machine learning theory and applications, with recent work focusing on conformal prediction, diffusion models, optimization theory, and learning theory foundations. His research demonstrates strong connections between theoretical guarantees and practical algorithms, with applications spanning generative modeling, robust optimization, and statistical inference. Elected to French Academy of Sciences (2020) ERC project SIERRA (2009-2014) ERC project SEQUOIA (current) Author of "Learning Theory from First Principles" (MIT Press, 2024) Bach actively mentors numerous PhD students and postdocs, with many alumni now holding faculty positions at institutions like EPFL, Ecole Polytechnique, University of Washington, and University of Montreal. His teaching includes advanced courses on learning theory at ENS's Master's programs. He regularly presents tutorials at major conferences including COLT, NeurIPS, and ICML, demonstrating his leadership in the theoretical machine learning community.
Pekka Marttinen is a tenured Associate Professor of Machine Learning at Aalto University, Department of Computer Science, and leads the Machine Learning for Health (Aalto-ML4H) group within the Helsinki Institute for Information Technology HIIT. Education: M.Sc. in Applied Mathematics, University of Helsinki (2004) Ph.D. in Statistics, University of Helsinki (2008) Title of Docent in Information and Computer Science, Aalto University (2015) Research Focus: His methodological work spans large language models, reinforcement learning, deep learning, probabilistic machine learning, and causal inference . These techniques are applied to critical domains of healthcare, bioinformatics, statistical genetics, epidemiology, and personalized medicine . The group develops novel algorithms, theoretical guarantees, and open-source software that enable data-driven discovery and decision-making in medicine and biology. Publication Trends: Across 2022-2024 the lab has concentrated on (i) rigorous causal reasoning over temporal clinical data, (ii) principled uncertainty quantification in LLMs, (iii) representation learning for neural network comparison, and (iv) translational projects that turn raw EHRs into actionable clinical insights. Earlier work integrated high-dimensional genomics with metabolomics and mapped evolutionary forces in bacterial pathogens. Scientific Awards & Recognition: While no specific awards are listed, his sustained publication record in top-tier venues (NeurIPS, ICML, AISTATS, Nature Genetics, PLOS CB) and his role as responsible professor of the Machine-Learning, Data-Science and AI major signify significant peer recognition. Advising & Grants: Prof. Marttinen currently mentors 8 PhD students as primary supervisor and an additional 7 PhD students as co-supervisor. He has already graduated 8 PhDs since 2014. The group is supported through competitive funding including the Finnish Center for Artificial Intelligence (FCAI) doctoral program. Labs & Teams: He directs the Machine Learning for Health (Aalto-ML4H) research group, comprising postdocs Hans Moen, Ti John, Alexander Nikitin, Negar Safinianaini, Linli Zhang, Zhiyuan Li, and the above-mentioned PhD cohort.
Keith Zengel is an Assistant Professor in the Department of Sciences at the School of Sciences and Humanities. His work emphasizes interdisciplinary education in applied sciences, particularly blending biochemistry, biophysics, and physical chemistry. He advocates for programs that foster adaptability and innovation through cross-disciplinary collaboration. His research focuses on experimental and theoretical physics, including electromagnetism, quantum mechanics, and classical mechanics. He frequently contributes to academic journals, often editing or authoring monthly issues that highlight current trends in physics education and research. Dr. Zengel's research interests span a broad spectrum of physics disciplines, with a particular emphasis on practical experiments and foundational theories. Notable areas include eddy currents, uncertainty principles, and the application of Fourier transforms in quantum mechanics. His work often bridges theoretical concepts with real-world phenomena, such as the motion of objects under various physical forces and electromagnetic effects. His publications reflect a commitment to both pedagogy and cutting-edge research, with contributions ranging from experimental setups to historical analyses of scientific paradoxes. Despite no awards explicitly listed, his active role in academic publishing underscores his influence in shaping physics discourse.
Professor Dylan Jones is a Professor of Operational Research at the University of Portsmouth within the School of Mathematics and Physics. He holds dual affiliations with the Centre for Operational Research and Logistics and the Centre of Excellence in Defence, Risk & Resilience. His academic journey includes a BSc (Hons) in Mathematics with Operational Research from the University of Southampton and a PhD in Operational Research from the University of Portsmouth. Specializing in Multi-Criteria Decision Making (MCDM), his research spans logistics, healthcare, renewable energy, and defense applications. He has led over 18 PhD theses and secured EU funding for projects focused on offshore wind energy and sustainable logistics. Professor Jones is also the Director of the Centre for Operational Research and Logistics, emphasizing strategic port development and disaster risk reduction. His work integrates advanced methodologies like goal programming and mixed modeling to address complex real-world challenges. Recent contributions include frameworks for offshore wind farm logistics, sustainable port selection, and resilience-based maintenance strategies. Education: BSc (Hons) in Mathematics with Operational Research, University of Southampton PhD in Operational Research, University of Portsmouth Research interests revolve around applying operational research principles to solve multi-objective problems in logistics, healthcare systems, and renewable energy sectors. His work emphasizes sustainability, decision-making under uncertainty, and optimizing resource allocation. Key projects include developing methodologies for offshore wind energy infrastructure and analyzing risk in maritime logistics. His research outputs (109+ publications) focus on advancing operational research techniques, with notable contributions to goal programming, logistics optimization, and multi-criteria decision analysis. He collaborates internationally, particularly in Brazil, France, Spain, and Portugal, to address global challenges in sustainable energy and infrastructure. Labs/Teams: Centre for Operational Research and Logistics Centre of Excellence in Defence, Risk & Resilience
Vera Liao is a Principal Researcher at Microsoft Research, where she is part of the FATE (Fairness, Accountability, Transparency, and Ethics of AI) group. She will join the University of Michigan Computer Science and Engineering department as an Associate Professor in fall 2025. Her work focuses on human-AI interaction, explainable AI, and responsible computing. Liao has made significant contributions to IBM products such as AI Explainability 360 and Uncertainty Quantification 360 during her time at IBM T.J. Watson Research Center. Dr. Liao received her education from the University of Illinois at Urbana-Champaign and Tsinghua University. Her academic journey has positioned her at the intersection of human-computer interaction and artificial intelligence, with a strong emphasis on creating AI systems that are transparent, accountable, and user-centered. Vera Liao's research primarily centers around human-centered AI explainability and transparency. She investigates how to design AI systems that effectively communicate their capabilities, limitations, and decision-making processes to users. Her work examines the intersection of AI transparency with trust, control, and user experience. Liao has pioneered approaches to bridging the socio-technical gap in AI evaluation and has developed frameworks for contextualized evaluation of explainable AI systems. Her research spans multiple domains including conversational interfaces, data storytelling, and creative work with generative AI. Liao's publications reveal a clear trend toward addressing the challenges of large language models and their impact on human-AI interaction. Her recent work focuses on understanding how uncertainty communication affects user trust, how to design for appropriate reliance on AI systems, and how to create authentic co-creation experiences with generative models. She has been examining the risks in AI-infused information ecosystems and developing methods for human-centered evaluation of language technologies. Her scientific contributions have been recognized with multiple honors: Best Paper Award, Honorable Mention at CHI 2025 (two papers) Best Paper Award at CHI 2024 Best Paper Award, Honorable Mention at FAccT 2023 Best Paper Award, Honorable Mention at HCOMP 2022 Best Paper Award, Honorable Mention at CHI 2021 Best Paper Award, Honorable Mention at CHI 2014 Outstanding Paper Award at IUI 2019 Dr. Liao is an active mentor, having guided numerous research interns from top universities including Cornell, Princeton, CMU, Stanford, and MIT. She serves in editorial roles as Co-Editor-in-Chief of the Springer Human-Computer Interaction Book Series and as an Editor for ACM CSCW. Liao has secured research funding through her work at Microsoft Research and previously at IBM, focusing on projects related to AI explainability, transparency, and responsible AI development. As part of Microsoft Research's FATE group, Liao collaborates with a multidisciplinary team of researchers focused on the ethical implications of AI technologies. Her work bridges the gap between technical AI development and human-centered design principles, ensuring that AI systems are developed with user needs and societal impacts in mind.
Alan Grafen is an Honorary Fellow at Jesus College, University of Oxford, and holds Tutorial Fellow roles at St John’s College in Quantitative Biology. His primary affiliation is the University of Oxford’s Faculty of Biology and Medicine. He is a leading evolutionary biologist specializing in mathematical and logical models of evolutionary processes. Research Interests: Grafen’s work focuses on evolutionary theory, particularly the formalization of Darwinism, inclusive fitness, signal evolution, and sexual/kin selection. He leads the Formal Darwinism Project to mathematically formalize natural selection’s role in evolution. His models include the mathematical underpinning of Zahavi’s handicap principle and advancements in reproductive value theory. Key Contributions: Published influential works like Modern Statistics for the Life Sciences (2002) and co-authored analyses of Richard Dawkins’ evolutionary ideas. His recent articles (2018–2024) explore natural vs. sexual selection distinctions, kin recognition stability, and extensions to Fisher’s fundamental theorem. Awards and Grants: Not explicitly listed, but his work has shaped evolutionary theory. His research spans theoretical models in Nature -level journals and interdisciplinary collaborations with statisticians and biologists. Labs/Teams: Directs the Formal Darwinism Project and collaborates with evolutionary theorists globally. His work bridges mathematics and empirical biology, influencing both academic and popular science discourse.
Patrick Tomlin is a Professor of Philosophy at the University of Warwick and Director of the Politics, Philosophy, and Law (PPL) undergraduate degree. He holds affiliations with the Philosophy Department and the Centre for Ethics, Law, and Public Affairs (CELPA). His academic journey includes roles at the University of Oxford (Corpus Christi College) and University of Reading, where he was Associate Professor in Political Philosophy. Education: B.A. (Hons) European Politics (University of Nottingham, 2002), M.A. Legal and Political Theory (University College London, 2004), D.Phil. Political Theory (University of Oxford, 2010). Research interests span moral, political, and legal philosophy, with a focus on distributive ethics, equality, criminal law, children's wellbeing, and moral uncertainty. His recent work includes exploring transferred malice in criminal law, aggregation in normative ethics, and proportionality in warfare. Publications highlight contributions to journals like Philosophical Quarterly , Law and Philosophy , and Journal of Applied Philosophy . Notable works include critiques of punishment theory, analyses of parental rights, and ethical frameworks for warfare. Awards include the Berger Memorial Prize (APA, 2017) and a Leverhulme Trust Research Fellowship (2020-21). He co-founded Free & Equal: A Journal of Ethics and Public Affairs in 2024 after resigning from Philosophy & Public Affairs . Teaching includes modules on ethics, applied ethics, and political economy. He advises on the PPL degree, established in 2018, and contributes to academic governance at Warwick.
Jesse Hoey is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo and leader of the Computational Health Informatics Lab (CHIL). He serves as a Faculty Affiliate at the Vector Institute and is Editor-in-Chief of the IEEE Transactions on Affective Computing. His research spans affective computing, health informatics, and socially assistive robotics, with a particular focus on developing technologies for elderly care and cognitive assistive applications. Hoey's research interests center around affective intelligence, Bayesian affect control theory (BayesACT), and decision-theoretic planning in uncertain domains. His work integrates social psychology with artificial intelligence to create emotionally aware systems that can interact naturally with humans, particularly those with cognitive impairments such as Alzheimer's disease. He has developed models for social interaction, emotion recognition, and uncertainty management in human-robot collaboration. His recent publications demonstrate a strong trend toward medical applications of AI, particularly in ultrasound analysis and healthcare technology. Many of his papers focus on self-supervised learning techniques for medical imaging and the application of affective computing principles to assistive technologies for dementia care. His work bridges theoretical AI with practical healthcare applications, showing increasing emphasis on real-world implementation. Editor-in-Chief of IEEE Transactions on Affective Computing Hoey has supervised numerous PhD and Master's students through the Computational Health Informatics Lab, with research spanning socially assistive robotics, affective computing, and health informatics. His lab has received funding for projects related to AI for dementia care, smart home technologies, and emotion-aware systems. The CHIL lab collaborates with healthcare institutions including the Toronto Rehabilitation Institute. The Computational Health Informatics Lab (CHIL) focuses on developing intelligent systems that understand and respond to human emotions and social contexts. Current projects include emotionally aligned social robots for dementia care, self-supervised learning for medical ultrasound, and models of social organization as uncertainty management. The lab combines theoretical work in Bayesian modeling with practical applications in healthcare technology.
Dr Shu-Ling Lu is an Associate Professor at the University of Reading , serving as Director of the MSc Project Management Programme and a Member of Senate . Her research spans Innovation Management , Quality Control , and Net Zero Transitions in construction, alongside Heritage Building Integration and Gender Dynamics in built environment sectors. Her academic journey includes a PhD , MSc in Construction Engineering , and a Diploma in Architectural Engineering , all from institutions in Taiwan and the UK, complemented by a Postgraduate Certificate in Higher Education Practice from the University of Salford. Research Interests : Innovation in construction, quality management, heritage conservation, net-zero transitions, gender equity, and system dynamics applications. Article Trends : Focus on defects analysis , heritage integration , gender dynamics , system dynamics , and net-zero strategies across 15 recent publications. Scientific Awards : Fellow of the Chartered Institute of Building (FCIOB) Fellow of Higher Education Academy (FHEA) Full member, Association for Project Management (MAPM) BSI Committee Participation (Quality Management Standards) Supervision & Grants : Mentored 7 PhD students and led/co-led projects funded by Natural Environment Research Council (NERC) , EPSRC , and COST , with total grants exceeding £500,000.
Caren Walker is an Assistant Professor in the Department of Psychology at the University of California, San Diego (UCSD), leading the Early Learning and Cognition (ELC) Lab. Her research focuses on how children learn abstract causal principles, particularly through activities like analogy, explanation, and engagement with imaginary scenarios. She explores developmental trajectories in scientific reasoning and the role of cultural context in shaping cognitive processes. Dr. Walker’s work combines interdisciplinary approaches from psychology, philosophy, and computational modeling. She investigates children’s understanding of uncertainty, causal inference, and the decline of relational reasoning with age. Her lab conducts experiments in diverse settings, including partnerships with museums, to study learning mechanisms in real-world contexts. Notable achievements include the 2024 APA Boyd McCandless Award and the 2021 NSF CAREER Award. The ELC Lab actively engages undergraduate researchers and collaborates on projects addressing cognitive diversity across cultures. Recent lab milestones include studies on US-China differences in causal reasoning and the impact of stereotypes on causal judgments.
Shurojit Chatterji is a Professor of Economics at the School of Economics, Singapore Management University, where he conducts research at the intersection of microeconomic theory and social choice. His academic foundation includes a Ph.D. in Economics from SUNY at Stony Brook (1993) and a B.A. (Honours in Economics) from the University of Delhi (1988). His educational qualifications are: Ph.D. in Economics, SUNY at Stony Brook, 1993 B.A. (Honours in Economics), University of Delhi, 1988 Professor Chatterji's research centers on Mechanism Design , Social Choice Theory , and Game Theory , with deep investigations into strategy-proofness, preference domain structures, and efficient allocation mechanisms. His work rigorously examines single-peakedness in voting systems, multidimensional choice environments, and the welfare implications of redundant assets under heterogeneous forecasts. This theoretical framework extends to dynamic economic models involving Radner equilibria and imperfect foresight, bridging foundational microeconomic principles with practical design applications. Analysis of his 15 most recent publications (2020-2025) reveals consistent focus on strategy-proof mechanisms across unidimensional and multidimensional domains, probabilistic social choice, and the decentralizability of efficient allocations under uncertainty. Key trends include the taxonomy of non-dictatorial domains, decomposability properties in fractional allocation, and the role of redundant assets in welfare economics—demonstrating how theoretical insights address real-world market imperfections and institutional design challenges. As an academic advisor, he has mentored PhD student Paulo Daniel Salles Ramos. His extensive publication record across top economic journals indicates sustained research activity and scholarly influence, though specific grant details are not documented in available sources.
