Dr. Kaibo Liu is the Grainger STAR Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison and serves as Associate Director of the UW-Madison IoT Systems Research Center. He earned his B.S. from the Hong Kong University of Science and Technology (2009), and M.S. and Ph.D. from Georgia Tech (2011/2013). His research focuses on system informatics, big data analytics, and data fusion for process modeling, monitoring, and decision-making. He has been funded by NSF, ONR, DOE, and industry partners. Notable awards include the 2024 Hromi Medal (ASQ), 2021 IISE Technical Innovation Award, and multiple early-career recognitions. Recent work emphasizes real-time cyber-physical security, reinforcement learning for data streams, and Bayesian methods for prognosis. He edits IEEE Transactions on Automation Science and Engineering and IISE Transactions on Data Science.
Prof. Ruth King is the Thomas Bayes’ Professor of Statistics at the University of Edinburgh’s School of Mathematics. Her research focuses on applying Bayesian statistical methods to ecological and public health challenges, including population estimation for hidden groups (e.g., injecting drug users, modern-day slaves) and wildlife conservation. She develops computationally efficient techniques for analyzing large datasets, such as spatial capture-recapture models for animal populations and spatio-temporal abundance models for hidden human populations. Key projects include estimating survival rates of guillemots (30,000 individuals) and improving capture-recapture models to account for animal movement dynamics. Her work bridges statistical methodology with real-world applications, emphasizing rigorous inference and scalable algorithms. King’s academic contributions span Bayesian modeling frameworks, parameter clustering in neuroscientific data, and hierarchical centering in random effects models. She collaborates with biologists and policymakers to address conservation and public health issues. Notable recent projects include incorporating memory effects into spatial capture-recapture models and developing semi-complete data augmentation for state-space models. Her interdisciplinary approach addresses challenges in ecology, epidemiology, and computational statistics, with a focus on methodological innovation for large-scale data. Her scientific contributions are highlighted through over 100 peer-reviewed articles, including work on integrated population models, animal movement dynamics, and hidden Markov models for seabird behavior. King emphasizes the importance of statistics in uncovering hidden information within datasets, advocating for robust methodologies that ‘stand up in court’ when applied to critical real-world problems.
Nikita Zhivotovskiy is an Assistant Professor in the Department of Statistics at the University of California Berkeley within the College of Letters and Science. His research spans the intersection of mathematical statistics, probability theory, and statistical learning theory with particular focus on high-dimensional data analysis and non-parametric inference. His research interests include mathematical statistics, applied probability, statistical learning theory, high-dimensional data analysis, non-parametric inference, and artificial intelligence/machine learning. Zhivotovskiy's work addresses fundamental questions in statistical learning theory, including risk bounds, algorithmic stability, and convergence rates, with applications spanning multiple domains including robust statistics and private learning. His recent publications (2021-2025) demonstrate significant contributions to theoretical machine learning, particularly in statistical learning theory, risk bounds, high-dimensional statistics, and algorithmic stability. These works appear in top venues including NeurIPS, COLT, and FOCS, reflecting the theoretical depth and importance of his contributions to the field. Among his notable achievements is a Best Paper Award at the Conference on Learning Theory (COLT) in 2020 for his work on 'Proper Learning, Helly Number, and an Optimal SVM Bound.' Zhivotovskiy has also contributed to the theoretical foundations of PAC learning, risk minimization, and statistical aggregation. Prior to his current position, Zhivotovskiy was a postdoctoral researcher at ETH Zürich (2021-2022) and Google Research (2019-2020). He completed his PhD at Moscow Institute of Physics and Technology in 2018 under the supervision of Vladimir Spokoiny and Konstantin Vorontsov.
Dr. Giacomo Crisenza is a Lecturer in Catalysis at the Department of Chemistry, University of Manchester. His research focuses on developing sustainable electrochemical and photocatalytic methods for converting carbon feedstocks into value-added chemicals. He holds a PhD from the University of Bristol and has held academic positions at Manchester since 2020. Education: PhD in Chemical Synthesis, University of Bristol (2013–2017) MSc and BSc, Università degli Studi di Milano (2007–2012) Chemical Synthesis CDT, University of Bristol (2012–2013) Research Interests: Electrochemical synthesis of novel organic compounds Photocatalytic activation of aromatic systems Design of sustainable catalytic protocols Radical-mediated C–C bond formations Development of carbon feedstock valorization strategies Articles Trends: His recent work emphasizes photocatalytic C–H functionalization strategies, asymmetric total syntheses, and metal-free arylation approaches. Key themes include visible-light-driven reactions and transition-metal catalyzed transformations. Advising & Grants: Supervised 2 PhD students (specific names not listed). Actively seeks external funding through schemes like MSCA and NIF for postdoctoral researchers. Labs & Teams: Leads the Crisenza Group within the Organic Chemistry Group at Manchester, focusing on net-zero catalysis and sustainable chemical synthesis.
Dr. Hengrui Cai is an Assistant Professor of Statistics at the University of California Irvine (UCI), affiliated with the Donald Bren School of Information and Computer Sciences. She holds a Ph.D. in Statistics from North Carolina State University (NCSU) and a B.S. in Statistics from Zhejiang University. Her research focuses on causal inference, reinforcement learning, and graphical models, with applications in precision medicine, healthcare analytics, and epidemiology. She develops interpretable solutions for individualized decision-making, particularly in healthcare settings such as ICU patient treatment optimization and pandemic analysis. Notable achievements include the NSF CDS&E-MSS Award (2024), ICS Research Awards (2023–2024), and recognition for contributions to causal discovery and policy evaluation. Dr. Cai advises graduate and undergraduate students on projects involving causal AI, machine learning, and healthcare data analysis. She teaches courses like 'Causal Machine Learning' and 'Introduction to Probability and Statistics,' emphasizing interdisciplinary approaches to real-world problems. Her work integrates statistical theory with practical applications, exemplified by software tools like ANOCE-CVAE for causal mediation analysis and the Sepsis EHR Benchmark Environment for reinforcement learning. Dr. Cai collaborates widely, contributing to projects such as quantifying the impact of the 2020 Hubei lockdowns on virus spread in China through causal graph analysis.
Keming Yu is a Professor and Chair in Statistics at the Department of Mathematics, Brunel University London, within the College of Engineering, Design and Physical Sciences. He is also the Impact Champion for REF in Mathematical Sciences. He joined Brunel in 2005 after holding positions at the University of Plymouth, Lancaster University, and The Open University. He earned his PhD from The Open University and earlier degrees in Mathematics and Statistics from Chinese institutions. PhD in Statistics – The Open University, UK MSc in Statistics – China BSc in Mathematics – China His research centers on quantile regression, Bayesian modeling, survival analysis, and statistical methods for big data . His work spans applications in health, finance, environment, and social sciences. He has made significant contributions to robust and flexible regression methods, including expectile, mode, and censored quantile regression. His recent publications (2023–2025) show a strong focus on streaming data, spatiotemporal modeling, high-dimensional data, and Bayesian methods . He frequently publishes in top-tier journals such as the Journal of the Royal Statistical Society Series A, B, and C , Statistica Sinica , and Computational Statistics and Data Analysis . His work often involves collaboration with international researchers, especially in China and Europe. He has contributed to methodological discussions in leading statistical journals, demonstrating active engagement with the academic community. His work on financial risk, environmental statistics, and health data analysis reflects interdisciplinary impact. Reviewed and contributed to discussions on safe testing, confidence sequences, and betting-based inference. Active in developing methods for nonignorable missing data, censored models, and functional covariates. He supervises PhD students and is involved in teaching and curriculum development, including as Course Director for the MSc Statistics with Data Analytics. His research is supported by extensive publication output and academic service. He leads or contributes to research on Bayesian models, robust regression, and scalable methods for big data , often involving collaborations in interdisciplinary teams. His lab or research group focuses on statistical methodology development with real-world applications.
Maurice Smith serves as the Gordon McKay Professor of Bioengineering at Harvard University's School of Engineering and Applied Sciences (SEAS), where he leads the Neuromotor Control Lab. His primary appointment resides within the Department of Bioengineering, focusing on the computational and neural mechanisms underlying human movement control. Smith's research centers on sensorimotor learning , motor adaptation , and neuromotor control systems . He investigates how the brain forms and retains motor memories, particularly examining cerebellar contributions to long-term sensorimotor memory and the dissociation between implicit and explicit learning pathways. His work frequently employs computational modeling to dissect neural tuning properties and motor variability regulation. Analysis of his recent publications reveals a strong emphasis on temporal dynamics in motor learning , cerebellar function in memory consolidation , and Bayesian frameworks for understanding sensorimotor adaptation . His research demonstrates consistent focus on how error processing, uncertainty, and neural plasticity shape motor memory formation across multiple timescales. Smith maintains active collaborations with researchers including Wilsaan M. Joiner, Yohsuke R. Miyamoto, and Nathan Sandholtz, as evidenced by frequent co-authorship patterns. His laboratory investigates fundamental questions in motor control with implications for neurorehabilitation and adaptive robotics.
Joseph P. Romano is a distinguished Professor of Statistics and Economics at Stanford University, where he has been on the faculty since 1986. He holds joint appointments in both the Department of Statistics and the Department of Economics, reflecting his interdisciplinary research that bridges statistical theory with economic applications. Romano has established himself as a leading scholar in mathematical statistics with significant contributions to econometrics, climate science, and multiple testing methodologies. Ph.D. in Statistics, University of California, Berkeley (1986) M.S. in Statistics, University of California, Berkeley (1983) A.B. in Statistics, Princeton University (1982), Summa Cum Laude Romano's research focuses on the theoretical foundations and practical applications of statistical methods, particularly in nonparametric statistics, bootstrap and resampling techniques, and multiple testing procedures. His work addresses the challenges of analyzing massive datasets with complex structures, such as those found in biotechnology, clinical trials, and econometrics. He has developed universal statistical tools applicable across diverse fields including climate science, genetics, finance, and education. His recent work emphasizes methods for multiple testing and multivariate inference driven by the availability of massive datasets, where he tackles issues like unknown dependence structures, heterogeneity, and high dimensionality. Analysis of Romano's recent publications reveals a consistent focus on developing robust statistical methodologies for complex data structures. His work spans theoretical advances in U-statistics with growing dimensions, practical applications in seroprevalence studies, and innovative approaches to ranking inference across various domains. The interdisciplinary nature of his research is evident in publications spanning economics journals, statistics journals, and even behavioral science preprints, demonstrating the broad applicability of his methodological contributions. 2021 LGBTQ+ Scientist of the Year, Out to Innovate Fellow, International Association of Applied Econometrics (2020) Fellow, Institute of Mathematical Statistics Presidential Young Investigator Award, National Science Foundation The Canadian Journal of Statistics Award Romano has mentored dozens of doctoral students throughout his career at Stanford, serving as dissertation advisor, co-advisor, and committee member for numerous PhD candidates in Statistics. His research has been consistently supported by National Science Foundation grants, including recent funding for computer-intensive inference with applications to social sciences (2020-2023) and randomization inference for contemporary statistical problems (2013-2016). He has served in various administrative roles at Stanford including Associate Chairman and Chair of Committee on Faculty Affairs. Beyond his academic pursuits, Romano is actively involved in the 500 Queer Scientists visibility campaign and maintains a balanced life with passions in music (having performed at Carnegie Hall), competitive tennis (ranked nationally in his age group), cooking, and architecture.
Professor Chun-Hung Chen is a distinguished academic at George Mason University ’s Volgenau School of Engineering , where he holds the rank of Professor in the Department of Systems Engineering and Operations Research . He has also held professorships at National Taiwan University and visiting roles at institutions like University of Pennsylvania and Microsoft Research Asia . Education: PhD in Decision and Control, Harvard University (1994) MS in Electrical Engineering, National Taiwan University (1989) BS in Control Engineering, National Chiao-Tung University (1987) Research Interests focus on Stochastic Simulation Optimization , particularly his pioneering Optimal Computing Budget Allocation (OCBA) methodology. OCBA enhances simulation efficiency by dynamically allocating computational resources to critical design alternatives, reducing computation time by orders of magnitude. Applications span air transportation , healthcare , power grids , and semiconductor manufacturing . His 15 most recent articles (2022–2025) explore intersections of simulation optimization , artificial intelligence , reinforcement learning , and personalized medicine , emphasizing computational efficiency and stochastic systems in domains like microgrids and organ transplant logistics . Scientific Awards include: IEEE Fellow (2015) K.D. Tocher Medal (2017) Best Paper Awards at IEEE CASE (2019), LOGMS (2019), and IEEE ICC (2021) Harvard’s Eliahu I. Jury Award (1994) Advisory roles include editorial leadership in IIE Transactions , Journal of Simulation , and IEEE Transactions series. He has coordinated graduate programs at George Mason (2006–11, 2015–19) and led conferences like INFORMS International Meeting (2025) and Harvard Control Workshop (2024). His work is funded by organizations such as the National Science Foundation , National Institutes of Health , and Department of Energy , with applications in healthcare logistics and microgrid control .
Chicheng Zhang is an Assistant Professor in the Computer Science Department at the University of Arizona, where he conducts research in the theory and applications of interactive machine learning. He earned his Ph.D. in Computer Science from the University of California, San Diego (UCSD) in 2017 under the supervision of Professor Kamalika Chaudhuri, and was previously an undergraduate student at Peking University working with Professor Liwei Wang. From 2017 to 2019, he was a postdoctoral researcher at the Machine Learning Group at Microsoft Research NYC. His research lies at the intersection of learning theory and practical algorithm design, focusing on interactive machine learning paradigms such as reinforcement learning, contextual bandits, active learning, and imitation learning. He aims to develop algorithms that are data-efficient, computationally tractable, and robust, with applications in healthcare, wireless communication, and fair AI systems. His work emphasizes principled algorithm design with theoretical guarantees and empirical validation. The most recent publications reflect a strong trend in developing efficient, theoretically grounded methods for sequential decision-making and interactive learning. Key themes include sample efficiency, robustness to noise, fairness in algorithmic decisions, and application-driven research in domains like oral cancer detection and mmWave network optimization. His work frequently bridges theoretical analysis with real-world deployment considerations. While no scientific awards are mentioned in the provided text, Dr. Zhang actively mentors prospective PhD students and encourages collaboration. He has contributed to interdisciplinary projects involving fairness-aware bandit algorithms for network coexistence, interpretable classifiers for cancer detection, and LLM-based initialization for reinforcement learning. His lab focuses on developing intelligent agents that actively learn from environments and human experts. He can be reached at chichengz@arizona.edu .
Baris Ata is the Sigmund E. Edelstone Distinguished Service Professor of Operations Management at the University of Chicago Booth School of Business. His work bridges theoretical operations management with practical applications, focusing on dynamic decision-making under uncertainty. Research Interests Ata’s research spans stochastic networks, manufacturing/service operations, healthcare delivery, and social sector innovation. Recent projects address high-dimensional stochastic control, xenotransplantation candidate selection, criminal justice logistics, and last-mile delivery challenges in Africa. Scientific Awards Best Paper in Service Science Award, INFORMS (2009) William Pierskalla Best Paper Award, INFORMS (2015) Wickham Skinner Best Paper Award, POMS (2019) Manufacturing and Service Operations Management Young Scholar Prize, INFORMS (2015) Emory Williams MBA Teaching Award (2021) Recent Publications Ata’s recent work includes topics in dynamic pricing, stochastic control, and healthcare logistics. His papers examine congestion-based pricing strategies, equilibrium analysis in queues, and policy design for organ transplantation. Broad disciplines include operations management, stochastic modeling, and healthcare analytics.
Jacob Gardner is an Assistant Professor in the Department of Computer & Information Science at the School of Engineering and Applied Science, University of Pennsylvania. His research bridges machine learning and scientific discovery with emphasis on computational biology and molecular design. His primary research interests include: Machine Learning Bayesian Optimization Computational Biology Molecular Design Artificial Intelligence Gaussian Processes Analysis of his 2024-2025 publications reveals a dominant focus on Bayesian optimization techniques integrated with large language models for biological applications. Key trends include therapeutic design using knowledge distillation from scientific literature, RNA splicing prediction, antibiotic development, and scalable Gaussian process methods. His work consistently addresses dimensionality challenges in molecular modeling while improving computational efficiency for high-dimensional biological data. No scientific awards were mentioned in the provided text. No information regarding student advising or research grants was provided in the source material. His research appears supported by institutional initiatives including Penn AI, Innovation in Data Engineering and Science (IDEAS), and the Data Driven Discovery Initiative (DDDI).
Prof. Dr. Urs Fischbacher is a full-time Professor at the Department of Economics at the University of Konstanz, holding the Chair of Behavioral Economics. He is also affiliated with the Thurgau Institute of Economics in Switzerland. Research Focus: Behavioral Economics, Experimental Economics, Game Theory, Social Preferences, Public Goods, Trust and Cooperation, Decision-Making under Stress, and Neuroeconomics. Key Projects: Evaluation of educational interventions in Colombia, analysis of leadership selection in inequality perception, and studies on social pension targeting in Bangladesh. Academic Contributions: His recent articles focus on super-additive cooperation mechanisms, responsibility attribution in decision chains, stress-induced prosociality, and diversity policy impacts. Work spans interdisciplinary collaborations with psychologists and neuroscientists. Contact: Based at the University of Konstanz (Room F315) and Thurgau Institute of Economics. Office hours require appointment via email.
Johanna Ziegel is a Professor of Statistics at ETH Zurich, Switzerland, since 2024, and a Visiting Scientist at the Heidelberg Institute for Theoretical Studies (HITS). Previously, she held positions at the University of Bern, where she was promoted to Full Professor in 2023. Her research focuses on decision-theoretically sound methods for forecast evaluation, probabilistic forecasting, risk measures in finance, and applications in meteorology, medicine, and climate science. She is actively involved in editorial roles for journals like Bernoulli , JASA: Theory & Methods , and SIAM Journal on Financial Mathematics . Education: PhD in Stereological Analysis of Spatial Structures from ETH Zurich (2010), supervised by Paul Embrechts and Eva B. Vedel Jensen. Postdoctoral research at the University of Melbourne and Heidelberg University. Research Interests: Forecast evaluation, elicitable functionals, risk measures, isotonic regression, statistical calibration, and applications in finance, climate science, and biostatistics. Her work bridges theoretical statistics with practical challenges in uncertainty quantification and decision-making under uncertainty. Advising & Collaborations: Supervised 7 PhD students and mentored several postdocs. Collaborates with the Computational Statistics group at HITS and the Oeschger Centre for Climate Change Research. Her group explores distributional regression under order constraints and novel methods for forecast comparison. Recognition: Credit Suisse Award for Best Teaching (2022), H.I.T. Program for Academic Leadership (2021–2022). Active in professional service, including the Bernoulli Society Council and editorial boards.
Junier Oliva is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill and Lead Faculty of the Master of Applied Data Science program. His research focuses on machine learning, artificial intelligence, and nonparametric statistics, particularly in high-dimensional density estimation, sequential modeling, and learning from complex/structured data. He holds a B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University, with prior industry experience at Yahoo! and Uber ATG. Research Interests: Machine learning, artificial intelligence, nonparametric statistics, deep learning, statistical data mining, signal processing, kernel methods, and scalability. His work bridges machine and human learning via collective approaches, emphasizing simple yet flexible models for massive datasets. Awards/Grants: $592K AIM-AHEAD/NIH Grant for Human+AI Collaboration $594K NSF Grant for Scientific Discovery $500K NSF Grant for 'Machine Detectives' Project ACM BCB Best Paper Award (2022) for transparent single-cell classification work Labs/Teams: Director of the LUPA Lab, which develops machine learning techniques for holistic data understanding across domains like healthcare, earth science, and computer vision.
