Shie Mannor is a Professor at the Technion - Israel Institute of Technology in the Department of Electrical Engineering. He also holds a visiting professorship at Cornell-Tech in New York City and is affiliated with the Technion Machine Learning Center and the Grand Technion Energy Program . Key Research Interests: Machine Learning: Theory, algorithms, and applications to high-dimensional data and dynamics modeling. Reinforcement Learning and Markov Decision Processes: Adaptive control in large stochastic systems. Learning and control under uncertainty: Robust/stochastic optimization frameworks. Game Theory: Stochastic, dynamic, and network games applied to power markets and resource allocation. Multi-agent systems: Online learning and designing economic systems with optimal equilibria. Power Grid: Data-driven reliability, pricing, and decision-making in smart grids (e.g., EU-funded GARPUR project). Applications: Communication network optimization, mobile health, LDPC codes, and large-scale optimization problems. He actively seeks postdocs, graduate, and undergraduate students with strong mathematical or programming skills for projects in mobile phone programming and complex system optimization. Contact: shie.mannor@ee.technion.ac.il | Phone: ++972-4-829-3284
Dr. Kelly Burkett is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa, within the Faculty of Science. She specializes in Statistical Genetics and Genetic Epidemiology, focusing on genealogical relationships, population substructure, and family-based study designs. Her work includes software development for genetic data analysis, such as SMLE and GENLIB. Dr. Burkett holds an MSc and PhD from Simon Fraser University. Research Interests: Dr. Burkett’s research addresses challenges in genetic epidemiology, including maternal effects, gene-environment interactions, and the impact of population substructure on study design. Her methodologies often integrate computational tools to analyze large-scale genetic datasets. She actively collaborates with biomedical researchers, inspiring new questions in statistical genetics and genomics. Publications & Software: Her recent work includes studies on orofacial clefting etiology, software for high-dimensional feature screening, and genealogical simulations in French Canadian populations. Her articles span topics from computational genetics to molecular biology, emphasizing interdisciplinary approaches. Advising & Collaborations: Dr. Burkett has mentored over 20 students and trainees across MSc, PhD, and postdoctoral programs. Current advisees include Yuewen Pan (MSc) and Yuhao Feng (PhD). Past students hold roles in academia, healthcare, and industry. She collaborates on biomedical projects, leveraging statistical methods to address genetic and environmental interactions. Labs & Teams: While no formal lab is explicitly mentioned, her software contributions (e.g., hapassoc, sampletrees) and collaborative projects suggest involvement in computational biology and genetics research networks at the University of Ottawa and beyond.
Ricardo Martinez-Botas is a Professor of Turbomachinery and Associate Dean for Industry Partnerships at the Department of Mechanical Engineering, Imperial College London. He holds affiliations with the Electrochemical Science and Engineering, Energy Materials, Grantham Institute, Mechanics of Materials, and Network of Excellence in Air Quality. His academic career includes a DPhil from the Rolls Royce University Technology Center at the University of Oxford (1993) and an MEng in Aeronautical Engineering from Imperial College London. He also serves as a Visiting Professor at University Teknologi Malaysia. His research focuses on unsteady flow aerodynamics in turbochargers, supercritical CO2 systems, and thermal-fluids engineering. Notable contributions include advancements in turbine aerodynamics, pulsating flow control, and generative AI-driven design methodologies. He leads the Thermofluids Division and the Hybrid and Electric Vehicles Theme at the Energy Futures Lab. His work integrates computational fluid dynamics (CFD), experimental methods, and one-dimensional modeling for turbomachinery optimization. Key Awards: Dugald Clerk Prize (2011), ASME Turbomachinery Best Paper Awards (2010, 2009). Editorial Roles: Associate Editor of the Journal of Turbomachinery (ASME) and Journal of Mechanical Engineering Science (IMechE). Facility Leadership: Developed the TURBODYNA dynamic simulator for radial turbomachinery and commissioned a blowdown facility for dense gas vapor research. His research portfolio spans interdisciplinary topics like battery technology, organic Rankine cycle turbines, and sustainable automotive emissions control. He actively collaborates with industry partners to translate academic innovations into real-world applications, emphasizing energy efficiency, waste heat recovery, and low-carbon transportation solutions.
Ilan Shomorony is an Assistant Professor at the University of Illinois at Urbana-Champaign, affiliated with the Grainger College of Engineering, Electrical and Computer Engineering Department, and Coordinated Science Lab. He also holds an affiliation with the Carl R. Woese Institute for Genomic Biology. His research focuses on genomic data science, information theory, and their applications in DNA storage, bioinformatics, and machine learning. He has received an NSF CAREER Award for his work on genomic data science. Shomorony’s academic journey includes roles in multiple departments and labs, reflecting his interdisciplinary approach. His recent publications explore topics such as molecular communication channel capacity, metagenomic binning, and efficient sequence alignment algorithms. Education: Not explicitly stated in provided text, but his academic roles suggest advanced degrees in electrical engineering or computer science. Research Interests: His work bridges theoretical information theory and practical genomic applications. Key areas include DNA storage systems, algorithmic improvements for sequence analysis, and the application of machine learning to biological data. He develops novel coding schemes for molecular data storage and explores fundamental limits of genomic data reassembly. Grants & Awards: NSF CAREER Award (2021): Supported research on genomic data science, integrating informational theory and algorithm design. Labs & Teams: Active in the Coordinated Science Lab and collaborates with the Carl R. Woese Institute for Genomic Biology, emphasizing interdisciplinary research in genomics and computational biology.
Skirmantas Janusonis is an Associate Professor in the Department of Psychological and Brain Sciences at the University of California, Santa Barbara (UCSB). He is a core faculty member of the UCSB Neuroscience Research Institute and the Interdepartmental Graduate Program in Dynamical Neuroscience, and a member of the California NanoSystems Institute. His research program lies at the intersection of neuroscience, complex systems, and computational modeling. Education: Ph.D. in Neuroscience and Behavior, University of Massachusetts Amherst Postdoctoral Research, Department of Neuroscience, Yale University School of Medicine B.S./M.S. in Biology, Vilnius University, Lithuania Dr. Janusonis's research focuses on the stochastic (random walk-like) behavior of serotonergic axons in the brain, particularly within the ascending reticular activating system and the broader serotonergic matrix. His work integrates molecular neurobiology, comparative neuroanatomy (from sharks to rodents to humans), advanced microscopy, and supercomputing simulations. He investigates how these complex systems self-organize and their relevance to mental disorders, especially autism and the enigma of platelet hyperserotonemia. His lab collaborates with physicists, mathematicians, and engineers to model anomalous diffusion and fractional Brownian motion in 3D brain spaces. His recent publications reveal a strong trend toward computational and theoretical neuroscience, using high-resolution data and mathematical generalizations to model axonal distributions. Key themes include reflected fractional Brownian motion, self-organization of serotonergic densities, and the interface between central and peripheral serotonin systems. His work challenges traditional views of the blood-brain barrier and proposes interdisciplinary solutions involving immunology, physiology, and computer science. Scientific Awards and Recognition: Elected to the Board of Directors of the Organization for Computational Neurosciences (2024) NSF, NIMH, and California NanoSystems Institute grant funding Multiple student awards under his mentorship, including the Harry J. Carlisle Award and NIH IRTA NSF CRCNS and Frontera supercomputing grants UCSB Art of Science People's Choice Award (awarded to lab member) Dr. Janusonis actively mentors PhD students such as Justin Haiman and Dahyana Arroyo, and has advised alumni including Dr. Angela Chen, Dr. Kasie Mays, and Dr. Melissa Hingorani. His lab has received numerous grants from the NSF and NIH, supporting research on stochastic axon systems and super-resolution imaging. He teaches graduate and undergraduate courses including Neuroanatomy (Psy 269), Neurobiology of Brain States (Psy 136), and Complex Systems (Psy 113L). Research Team and Collaborations: The Janusonis Lab is an interdisciplinary group combining neuroscience, mathematics, and engineering. It collaborates with institutions such as UC San Diego, the University of Pisa, and MIT. The lab is equipped with advanced imaging tools and has access to Frontera, a leading NSF supercomputer. Outreach includes science nights at local schools and public lectures at the Santa Barbara Museum of Natural History.
Fabio Luciani is a Professor in Systems Immunology and Machine Learning at the School of Medical Sciences at UNSW Sydney, with visiting fellow positions at the Garvan Institute for Medical Research and Weill Cornell College of Medicine NY, USA. His interdisciplinary research program integrates immunology, genomics, and computational approaches to develop novel immunotherapies for cancer and autoimmune diseases. With a background spanning physics, theoretical biology, and biophysics, Luciani leads an interdisciplinary team with expertise in immunology, mathematical modeling, statistics, and bioinformatics. His research focuses on T cell responses using single-cell genomic technologies to understand immune responses in viral infections, autoimmunity, and CAR T cell therapies. He has developed landmark bioinformatic methods for combining single-cell transcriptome data with antigen receptor sequences and computational models for haplotype reconstruction. Luciani's work demonstrates a strong emphasis on translating molecular and genomic discoveries into clinical interventions, particularly in predicting side effects and identifying solutions for unmet clinical needs in immunotherapy. His research spans multiple collaborative projects including single-cell multi-omics analysis in coeliac disease with Prof Chris Goodnow's team at the Garvan Institute, and CAR T cell therapy response studies with clinicians at Westmead Hospital and Weill Cornell. His scientific contributions include over 140 peer-reviewed publications and successful acquisition of more than $20 million in research funding from organizations including ARC, NHMRC, JDRF, NIH, and private industry partners. He serves as Principal Investigator of the UNSW Future Institute of Cellular Genomics, which has secured $4.5 million over seven years for single-cell technologies in cellular genomics. As an academic supervisor, Luciani currently mentors 3 PhD students, 2 honours students, and 2 postdoctoral researchers, welcoming applications from students interested in bioinformatics, statistics, data science, immunology, and genomics. His work bridges theoretical approaches with experimental immunology to advance precision medicine and transform current immunotherapies into more precise and accessible solutions.
Xing Wang is a Scientist at the Laboratory for Materials Simulations, Paul Scherrer Institute. Research focuses on computational materials science including high-throughput computing, first-principles calculations, nanoscale simulations, and heterogeneous catalysis. Specializes in phase stability and transformations in transition metals and oxides. Develops scientific data management platforms for materials simulations. Contributes to understanding atomic-scale processes in catalytic systems and nanomaterial behavior under various conditions.
Bas Donkers is a full Professor of Marketing Research at the Department of Business Economics within Erasmus School of Economics (ESE), Erasmus University Rotterdam. Affiliated with ERIM (Erasmus Research Institute of Management) since 2000, he holds a prominent position in the field of consumer behavior and marketing analytics. His research examines consumer decision-making from a behavioral perspective, building on advanced market research and machine learning techniques to generate groundbreaking insights. His research interests center on consumer behavior , choice modeling , and marketing analytics , with significant contributions to healthcare decision-making and financial investment contexts. Donkers has published extensively in leading journals including Journal of Marketing Research, Marketing Science, and Journal of the Academy of Marketing Science. His recent work demonstrates a clear trajectory toward integrating machine learning with traditional choice modeling, particularly in healthcare applications (35% of recent publications) and digital consumer behavior (25%), with growing emphasis on AI-driven decision support systems. ERIM Top Article Junior Award (2017) ERIM postdoc fellowship (2002) Donkers has supervised 13 PhD candidates to completion, serving as promotor or co-promotor on diverse topics spanning retirement planning, charitable giving, healthcare choice modeling, and digital marketing analytics. His research has been supported through ERIM frameworks and collaborative projects with healthcare institutions. He actively coordinates academic events including the Invitational Choice Symposium and regularly presents at specialized research seminars. As a core member of ERIM's Marketing Group, Donkers contributes to the institute's research infrastructure focused on behavioral decision modeling and choice experimentation. His work bridges theoretical marketing research with practical applications in healthcare policy and financial services, maintaining strong connections with industry partners through ERIM's business engagement initiatives.
Jean Fan, PhD is an Assistant Professor of Biomedical Engineering at Johns Hopkins University, affiliated with the Center for Computational Biology and Institute for Computational Medicine. Her research focuses on developing machine learning methods to analyze spatially resolved and single-cell omics data. Her team, the JEFworks Lab, creates open-source tools for analyzing high-dimensional biological data to understand cellular identity, tissue organization, and disease progression. Dr. Fan's work bridges computational biology with clinical applications, particularly in leukemia and pediatric brain cancers. Education: BS in Biomedical Engineering & Applied Math from Johns Hopkins (2013); PhD in Bioinformatics from Harvard Medical School (2018); Postdoc in Chemical Biology/Physics at Harvard (2018–2020) under Xiaowei Zhuang, focusing on spatial transcriptomics. Research emphasizes spatial genomics and computational tool development, with key contributions to spatial alignment algorithms (STalign), normalization techniques, and cell-type deconvolution methods. Her lab's work has advanced understanding of kidney ischemic injury, glioblastoma spatial dynamics, and CLL pathogenesis. Awards include Forbes 30 Under 30, NSF CAREER Award, and 2025 PECASE. She founded CuSTEMized, a nonprofit providing STEM storybooks for girls. Recent collaborative projects include HuBMAP 3D reference atlas construction and Discovery Award-funded interdisciplinary initiatives. Labs/Teams: JEFworks Lab (primary); active collaborations with Dana-Farber Cancer Institute and Harvard Medical School. Current efforts focus on spatially resolved multi-omic integration and clinical translation of computational tools.
Andrea Maurino is a Full Professor at the University of Milano-Bicocca and leads the Insid&s LAB. His research focuses on data quality, knowledge graphs, machine learning, and their applications in healthcare, finance, urban planning, and organizational analysis. He explores cutting-edge techniques like Large Language Models (LLMs) for decision support systems and semantic annotation of tabular data. Key research interests include improving data quality frameworks for large RDF datasets, developing enterprise knowledge graphs for organizational insights, and applying AI to social media analysis and hate speech detection. His work bridges theoretical advancements with real-world applications such as smart city mobility prediction and nutritional strategies for healthy aging. Notable contributions include scalable tools like ABSTAT-HD for knowledge graph profiling and the 3d-clost mobility prediction model. Maurino’s interdisciplinary approach integrates data science with fields like psychology (ICD-11 decision support) and environmental science (ESG activity detection in financial texts). His lab collaborates on projects like Food NET, combining nutrition science with social network analysis. While no formal awards are listed here, his prolific publication record reflects sustained innovation in data-driven methodologies.
Ryan M. Pollyea is an Associate Professor in the Department of Geosciences at Virginia Tech’s College of Science. His research focuses on geofluids, energy resources, and geologic CO2 sequestration, with expertise in numerical modeling of fluid flow in porous media and geospatial analysis. He leads the VT Hydrogeosciences Lab, investigating processes like CO2 sequestration in basalt reservoirs and fluid-induced seismicity linked to wastewater injection. Education: Ph.D., University of Idaho (2012); B.S., University of Dayton (1999). His team includes PhD student Wu Hao and MS student Gradyon Konzen, studying topics like capillary pressure uncertainty and hydraulic effects of wastewater injection. Research interests span coupled thermal-hydro-chemical-mechanical modeling, fracture network characterization via LiDAR, and reservoir-scale permeability changes. He emphasizes applying computational methods to address energy and environmental challenges, with a focus on carbon storage and induced seismicity mitigation. Teaching includes courses like Groundwater Hydrology, Reactive Transport Modeling, and Engineering Geology. His work integrates high-performance computing and machine learning to analyze large datasets, exemplified by collaborations on exascale simulation frameworks and semantic interaction tools for ensemble analysis. Current projects include the Virginia SWIFT initiative, assessing aquifer recharge risks, and evaluating carbon storage potential in basaltic formations. His lab’s research underscores the interplay between subsurface fluid dynamics and geological processes, with applications to sustainable energy and environmental management.
Ming Yuan is a Professor in the Department of Statistics at Columbia University and serves as Associate Director of the Data Science Institute. His research focuses on high-dimensional statistics, machine learning, and statistical methodology with applications in genomics, finance, and imaging. Yuan holds a Ph.D. in Statistics from the University of Wisconsin-Madison (2004) and a B.S. in Electrical Engineering from the University of Science and Technology of China (1997). Education: 2004 Ph.D., Statistics, University of Wisconsin-Madison 2003 M.S., Computer Science, University of Wisconsin-Madison 2000 M.S., Probability and Statistics, University of Science and Technology of China 1997 B.S., Electrical Engineering, University of Science and Technology of China Research Interests: Dr. Yuan’s work bridges theoretical and applied statistics, emphasizing scalable methods for high-dimensional data. Key areas include tensor decomposition, covariance estimation, and statistical machine learning. His contributions to methods like sparse inverse covariance estimation and matrix/tensor completion have found applications in finance, genomics, and image analysis. Publications: His recent work explores tensor-based methods for high-dimensional analysis and develops optimal algorithms for compressed sensing. Articles often address statistical theory and computational challenges in modern data science, reflecting a balance between foundational and applied research. Awards: 2025 JASA Theory & Method Invited Discussion Paper 2024 William F. Sharpe Award (JFQA) 2018 Medallion Lecturer (Institute of Mathematical Statistics) 2014 Guy Medal in Bronze (Royal Statistical Society) 2007 Leo Breiman Junior Award Professional Activities: Yuan has served as Co-Editor of The Annals of Statistics (2019–2021) and Program Secretary for the Institute of Mathematical Statistics (2018–2021). His work integrates interdisciplinary collaborations, particularly in biomedical imaging and financial econometrics.
Dr. YANG, Renchi is an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University, Faculty of Science. He earned his BEng in Software Engineering from Beijing University of Posts and Telecommunications and his PhD in Computer Science from Nanyang Technological University, followed by a postdoctoral fellowship at the National University of Singapore. His research is centered on developing efficient algorithms and systems for large-scale data management and analysis. His research interests include: Big Data Management and Analysis Graph Learning and Network Embedding Databases and Data Management (especially graph query processing and similarity search) The Web and Information Retrieval (search, ranking, recommendation, web mining) Data Mining and Machine Learning (social network analysis, text mining, large language models) Dr. Yang’s recent publications span top conferences such as KDD, SIGMOD, WWW, ICDE, and AAAI, focusing on scalable graph clustering, network embedding, GNNs, and LLM integration. His work emphasizes algorithmic efficiency, scalability, and practical applications in real-world graph data. Scientific honors include: VLDB 2021 Best Research Paper Award 2022 ACM SIGMOD Research Highlight Award Best Paper Award Nominee in WWW 2022 Honorable mention as best PC member in WWW 2022 Dr. Yang actively mentors PhD and research students, currently supervising several RPg students including LIN Xiaoyang, LAI Yurui, and ZHENG Haoran. He has secured research funding enabling PhD scholarships and research assistant positions. He serves on the program committees of major conferences like VLDB, KDD, WWW, and SIGIR, and reviews for journals including TKDE and VLDBJ. He is a key member of the Database Research Group at HKBU, which has published extensively in top venues, including 8 papers at SIGMOD 2023. His research lab, the LAGAS Group, focuses on large-scale graph analytics and systems. The team is actively working on projects involving graph clustering, embedding, GNNs, and integration with large language models. Dr. Yang is currently recruiting PhD students for 2026 and research assistants for 2025, indicating active and expanding research operations.
Elizaveta Rebrova is an Assistant Professor in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University. She is also associated with the Program in Applied and Computational Mathematics (PACM) and the Center for Statistics and Machine Learning (CSML). Education: Specialist degree from Moscow State University (2012), PhD in Mathematics from University of Michigan (2018 under Roman Vershynin) Prior Appointments: Postdoctoral Scholar at Lawrence Berkeley National Lab (2021), Assistant Adjunct Professor at UCLA Mathematics Department (2018-2021) Her research focuses on randomized numerical linear algebra , mathematics of data science , and high-dimensional probability . Key interests include developing algorithms for large-scale data with non-trivial structure, robust and interpretable learning, and stochastic optimization. Her recent work analyzes algorithmic convergence in structured settings, tensor-based data compression, and nonnegative matrix/tensor factorization under constraints. Recent publications span topics in randomized NLA , robust solvers , tensor methods , and nonnegative matrix factorization . Notable trends include improving convergence rates for iterative methods, handling adversarial noise in linear systems, and leveraging tensor structures for efficient data recovery. She supervises Ph.D. students including Jackie Lok, Shambhavi Suryanarayanan, and Sofiia Shvaiko. Her teaching at Princeton covers graduate probability theory (ORF526), convex optimization (ORF523), and network science (ORF387), with prior teaching roles at UCLA and University of Michigan.
Christophe Andrieu is a Professor in Statistics within the School of Mathematics at the University of Bristol. His research bridges theoretical probability, computational statistics, and applied mathematics, with significant contributions to Markov Chain Monte Carlo methodologies and Bayesian inference frameworks. He maintains active collaborations across engineering and data science domains. His educational background includes: M.A. from List.Natnl.Scis.App.Lyon Additional M.A. (institution unspecified) Ph.D. from Paris Andrieu's research focuses on Markov Chain Monte Carlo theory , where he develops convergence guarantees and efficiency bounds for complex samplers. His work extends to non-reversible MCMC algorithms , piecewise deterministic processes , and gradient-free optimization techniques. Recent publications demonstrate innovative approaches to state-space models and numerical integration, often addressing high-dimensional statistical challenges through stochastic approximation methods. His fingerprint reveals deep specialization in Markov chain convergence analysis and computational Bayesian statistics. His 15 most recent publications (2021-2025) exhibit consistent focus on theoretical foundations of Monte Carlo methods, particularly convergence analysis of Markov chains and novel sampler designs. Key trends include the application of weak Poincaré inequalities to pseudo-marginal MCMC, development of self-organizing state-space models, and exploration of hypocoercivity in piecewise deterministic processes. The work spans both theoretical advancements and practical implementations for engineering and statistical applications. Andrieu has secured significant research funding including: COmputational Statistical INference for Engineering and Security (COSINES) (2018-2023) New Approaches to Data Science (2018-2023) He has supervised 5 research students and maintains active collaborations in computational statistics and machine learning. His network shows strong connections with probability theory and engineering research groups.
