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Xu ShiView profile

Associate Professor

• Biostatistics
• Causal Inference
• Healthcare Data Analysis
• +5 more

Xu Shi is an Associate Professor in the Department of Biostatistics at the University of Michigan. Previously, they held a postdoctoral fellowship at Harvard’s Data Science Initiative (2017) under Tianxi Cai and Eric Tchetgen Tchetgen. Their research focuses on statistical methods for administrative healthcare data, electronic health records (EHR), and causal inference. Key projects include developing scalable pipelines for EHR curation, causal inference methods for comparative effectiveness studies, and co-leading the FDA’s Sentinel Initiative Innovation Center’s Causal Inference Core. Education: Ph.D. in Biostatistics from University of Washington (201?), B.S. in Mathematics & Applied Mathematics from Zhejiang University, China. Research emphasizes harmonizing distributed EHR data, addressing unmeasured confounding in air pollution studies, and rare adverse event analysis through flexible propensity score methods. They also explore medical knowledge extraction from ICD code patterns and natural indirect effect estimation robust to measurement errors. Awards: None explicitly listed. Grants & Advising: No specific grants or students listed, but their work involves collaborative initiatives like the FDA Sentinel Program. Labs/Teams: Active in the Causal Inference Core and distributed healthcare data partnerships with institutions like Henry Ford Health System and Kaiser Permanente.

Kyle T SpikesView profile

Associate Professor

• Seismic Reservoir Characterization
• Rock Physics Modeling
• Geophysical Inversion Techniques
• +4 more

Kyle T Spikes is an Associate Professor in the Department of Earth and Planetary Sciences at the Jackson School of Geosciences, The University of Texas at Austin. His research focuses on integrating geologic data with quantitative tools for seismic reservoir characterization, emphasizing forward and inverse problems in rock physics, stochastic modeling, and seismic inversion. He works across scales from sub-micron rock samples to surface seismic data, developing effective medium models and numerical techniques to estimate heterogeneous and anisotropic rock properties. His work spans applications in carbonates, shales, and fractured reservoirs, with a strong emphasis on Bayesian methods, stochastic inversion, and machine learning for data analysis. Recent studies include fluid flow effects in porous media, distributed acoustic sensing (DAS), and rock physics modeling of unconventional reservoirs like the Haynesville Shale. He has contributed to CO2 sequestration monitoring through inversion of 3D VSP data at the Cranfield site. Notable trends in his publications include advancements in Bayesian-based rock physics modeling, integration of multi-scale data (laboratory to field scale), and innovative applications of DAS technology for seismic monitoring. His research bridges geophysics, petrophysics, and reservoir engineering, addressing challenges in reservoir characterization and monitoring under varying fluid and stress conditions. Dr. Spikes advises postdoctoral researchers and graduate students in the Jackson School, though specific advisee names are not listed. His work is supported by collaborative projects with industry and academic partners, focusing on practical solutions for subsurface reservoir challenges.

• Nuclear Engineering
• Reactor Design and Safety
• Thermal-Hydraulics
• +3 more

Tomasz Kozlowski is an Associate Professor and Associate Head for Undergraduate Programs at the University of Illinois at Urbana-Champaign's Grainger College of Engineering, Department of Nuclear, Plasma, and Radiological Engineering (NPRE). He holds additional positions as Associate Professor at Poland's National Centre for Nuclear Research (NCBJ) and Affiliated Professor in Computational Science and Engineering at UIUC. His research focuses on multi-physics modeling, reactor design/safety, computational methods, and thermal-hydraulics. He has taught courses like NPRE 200 (Mathematics), NPRE 455 (Neutron Transport), and advanced modeling topics. Education: B.S., M.S., and Ph.D. in Nuclear Engineering from Purdue University (2000–2005), followed by a Docent Habilitation in Nuclear Power Safety from the Royal Institute of Technology (KTH, 2011). He has collaborated on a $2M DOE grant for fuel storage solutions and contributed to UIUC's submission for a micro-reactor license application. His work includes advanced reactor design, uncertainty quantification, and computational tools like TRACE and MCNP-ORIGEN. Research emphasizes reactor analysis methods, numerical solver development, and inverse uncertainty quantification. Over 100 publications span topics like TRISO fuel performance, BWR instability, and hydrogen production integration with microreactors. He serves as Associate Editor for Nuclear Technology and actively engages in international benchmarks (e.g., BEAVRS, OECD/NEA).

Youssef MarzoukView profile

Professor

• Uncertainty Quantification
• Bayesian Computation
• Inverse Problems
• +4 more

Youssef Marzouk is a Professor of Aeronautics and Astronautics at MIT, serving as co-director of the MIT Center for Computational Engineering and director of the Aerospace Computational Design Laboratory. His research focuses on integrating physical modeling with statistical inference, emphasizing Bayesian computation, uncertainty quantification, and optimal experimental design. He holds a SB, SM, and PhD from MIT and has been recognized with prestigious awards including the DOE Early Career Award and the Junior Bose Teaching Prize. Education: PhD in Aeronautics and Astronautics, MIT SM in Aeronautics and Astronautics, MIT SB in Aeronautics and Astronautics, MIT Research Interests: Uncertainty Quantification techniques for complex systems Bayesian computational methods and inverse problem solutions Optimal experimental design strategies Interdisciplinary applications in geophysics, environmental science, and engineering Awards: 2022: Report to the President, Center for Computational Science and Engineering 2021: Bayesian Inference Software Framework (hIPPYlib-MUQ) 2012: MIT School of Engineering Junior Bose Award 2010: DOE Early Career Research Award Labs & Leadership: Aerospace Computational Design Laboratory (Director) MIT Center for Computational Engineering (Co-Director) Editorial Board roles: SIAM Journal on Scientific Computing, Advances in Computational Mathematics

Pierre LermusiauxView profile

Professor

• Ocean Data Assimilation
• Numerical Ocean Modeling
• Uncertainty Quantification
• +7 more

Pierre Lermusiaux is the Nam Pyo Suh Professor of Mechanical Engineering and Associate Department Head for Operations Research at MIT. His research focuses on numerical ocean modeling, data assimilation, uncertainty quantification, and applications to ocean dynamics and engineering. He holds a B/M.Eng. from University of Liège, M.Sc. and Ph.D. from Harvard University. Recognized with awards like the MIT Doherty Chair and Spira Teaching Award, his work bridges computational science and marine systems. Education: B/M.Eng., University of Liège, 1992 M.Sc., Harvard University, 1993 Ph.D., Harvard University, 1997 Research Interests: Combines oceanographic modeling with advanced computational methods. Specializes in stochastic ocean prediction, autonomous systems optimization, and interdisciplinary applications of uncertainty quantification. His work enables real-time environmental forecasting and marine robotics navigation strategies. Publications: Focus on developing computational frameworks for probabilistic ocean forecasts, acoustic propagation modeling, and machine learning-based environmental prediction systems. Recent work emphasizes adaptive sampling strategies and energy-efficient path planning for underwater vehicles. Awards: Fulbright Foundation Fellowship (1993-96) Ruth and Joel Spira Award for Teaching (2010) MIT Doherty Chair in Ocean Utilization (2009-2011) Advising & Grants: Leads MIT’s MSEAS group and has secured funding from NSF, ONR, and international collaborations. Advises projects on ocean robotics, climate modeling, and underwater acoustics. Key contributions include the MIT Red Sea Modeling System and real-time forecasting platforms. Labs/Teams: Directs MIT’s multidisciplinary ocean modeling efforts, integrating mechanical engineering, computer science, and environmental science. Co-leads initiatives like the Ocean Predictions with Ensembles (OPEN) project and the METEOR autonomous observatory system.

• Dark Matter
• Black Holes
• Gravitational Waves
• +3 more

Gianfranco Bertone is a Professor at the Faculty of Science, University of Amsterdam, specializing in astrophysics and theoretical physics with a focus on dark matter, black holes, and gravitational waves. His work bridges cosmology and particle physics through multi-messenger approaches. Research Interests: Dark matter detection via gravitational wave signatures Black hole binary dynamics in dark matter environments Relativistic simulations of extreme mass ratio inspirals Multi-messenger astronomy and fundamental physics Cosmological simulations for dark matter distribution Publication Trends: Recent works emphasize gravitational wave astronomy's role in dark matter studies, including waveform distortions from dark matter spikes, boson cloud effects in black hole binaries, and simulation-based inference for astrophysical observations. His research spans theoretical modeling, computational astrophysics, and observational constraints.

Henry ChaiView profile

Academic

• Machine Learning
• Bayesian Methods
• Probabilistic Numerics
• +3 more

Henry Chai is an Assistant Teaching Professor in the Machine Learning Department at Carnegie Mellon University (CMU). His role focuses on teaching core machine learning courses such as Introduction to Machine Learning (10-301/601) and Bayesian Methods in Machine Learning (10-424/624). He co-teaches courses with faculty like Matt Gormley and Charlie Garrod. Chai holds a PhD from Washington University in St. Louis (2021), advised by Roman Garnett. His research explores Bayesian machine learning, probabilistic numerics, and active learning, aiming to address computational challenges in reasoning about intractable quantities. He has contributed to methods like BINOCULARS and Bayesian quadrature optimization, published in top venues like ICML and AISTATS. He actively engages in pedagogical research and K-12 CS education advocacy. His teaching responsibilities include advanced graduate courses (e.g., 10-701) and undergraduate introductory courses, with a focus on scalable Gaussian processes and Bayesian optimization. Office hours are held in GHC 8133.

Jason SwansonView profile

Associate Professor

• Stochastic Differential Equations
• Probability Theory
• Mathematical Logic
• +2 more

Jason Swanson is an Associate Professor in the Department of Mathematics at the University of Central Florida, specializing in stochastic processes and probability theory. His research encompasses stochastic differential equations, fractional Brownian motion, and the foundations of probability. Recent work includes developing the iterated Dirichlet process for Bayesian inference and extending representations for row-exchangeable arrays. He maintains an active research program connecting mathematical logic with probability theory. Teaching responsibilities include graduate courses in Measure and Probability (MAA 6238) and undergraduate probability (MAP 4113). His lecture notes on measure-theoretic probability are publicly available.

Cory SimonView profile

Associate Professor

• Machine Learning
• Bayesian Optimization
• Computational Materials Science
• +3 more

Cory Simon serves as Associate Professor in the Department of Chemical, Biological, and Environmental Engineering within Oregon State University's College of Engineering. His research integrates machine learning, optimization, and chemical engineering to advance materials discovery and environmental sensing systems. His academic foundation includes a Ph.D. in Chemical Engineering from the University of California, Berkeley and a B.S. in Chemical Engineering from The University of Akron. Simon's work centers on Bayesian methodologies for scientific challenges, featuring: Bayesian optimization for adaptive materials synthesis Statistical inversion of physical systems with uncertainty quantification Computational design of nanoporous sensor arrays Stochastic algorithms for robotic environmental monitoring Recent publications demonstrate accelerating focus on multi-fidelity optimization for molecular design and atmospheric water harvesting, bridging chemical engineering with computational science through data-driven approaches. Leading The Simon Ensemble research group, Simon champions a versatile 'buffet-style' research philosophy—drawing from mathematics, statistical mechanics, and machine learning to address interdisciplinary problems across chemistry, materials science, and environmental engineering.

Aleksandar DimitrovskiView profile

Associate Professor

• Power Systems
• Microgrid Protection and Control
• Parallel Simulation of Power Systems
• +5 more

Dr. Aleksandar Dimitrovski is an Associate Professor at the University of Central Florida (UCF) in the Department of Electrical and Computer Engineering, College of Engineering and Computer Science. Holding a Ph.D. in Power Engineering from Saints Cyril and Methodius University (1997), his research focuses on power system modeling under uncertainty, microgrid protection, parallel simulation techniques, and renewable energy integration. Ph.D., Power Engineering, Saints Cyril and Methodius University (1997) Active research areas: Power System Protection, Parallel Simulation, Solar Integration, Magnetic Amplifiers His publications emphasize advanced simulation algorithms like Parareal, probabilistic forecasting for solar power, and magnetic reactor applications in grid control. He has led DOE-funded projects on solar integration and real-time grid protection. Fulbright Scholar (2016) R&D 100 Award (2014) Prof. Dimitrovski serves as an IEEE Senior Member, CIGRE member, and reviewer for journals like IEEE Transactions on Power Systems and Smart Grid. He developed the open-source RAPID simulation toolbox and contributed to cyber-physical security frameworks for smart grids.

Azizur RahmanView profile

Professor

• Data Science
• AI and Machine Learning
• Statistics
• +7 more

Professor Azizur Rahman is a leading academic in applied statistics and data science at Charles Sturt University (Australia), where he leads the Data Mining Research Group and directs the Data Analytics Lab. He holds a PhD in Economics and Applied Statistics from the University of Canberra, with postdoctoral experience at the University of Adelaide. His expertise spans statistical methodologies, microsimulation, AI/ML, and data-driven policy analysis. He has attracted over $4.03M in research funding and published over 200 scholarly works. Education: BSc (Honours) in Statistical Science, University of Chittagong (2000) MSc (Thesis) in Biostatistics, University of Chittagong (2003) PhD in Economics and Applied Statistics, University of Canberra (2010) Research Focus: Big data analytics and AI applications in health, environment, and policy Bayesian inference and multilevel modeling Spatial microsimulation for socioeconomic analysis Water quality modeling and environmental systems Public health challenges in developing nations Key Contributions: Developed novel computational methods for microsimulation modeling Founder of the Data Analytics Lab at CSU Editorial roles in journals like PLOS ONE and International Journal of Microsimulation Recipient of 2023 ANZRSAI Outstanding Service Award and Charles Sturt Excellence Awards Grants & Awards: $4.03M+ external research funding secured 30+ awards including travel grants, fellowships, and service recognitions Advising & Mentorship: Supervised 6 PhD/DIT students and co-supervised many more. Active in mentoring early-career researchers through editorial boards and conference organizing roles. Labs & Collaborations: Leads DaMRG (Data Mining Research Group) and collaborates with global institutions on interdisciplinary projects involving AI/ML, environmental science, and public health.

Harrison Huibin ZhouView profile

Professor

• High-Dimensional Data Analysis
• Nonparametric Estimation
• Statistical Decision Theory
• +3 more

Harrison Huibin Zhou is the Henry Ford II Professor of Statistics and Data Science at Yale University. He has held leadership roles, including Department Chair of Statistics and Data Science (2018–present) and former Chair of Statistics (2012–2017). His academic career at Yale spans over two decades, with promotions from Assistant Professor (2004–2009) to Associate (2009–2010) and full Professor (2010–present). Research Interests: Dr. Zhou specializes in high-dimensional statistical theory, including nonparametric estimation, minimax theory, and applications in network analysis, machine learning, and functional data analysis. His work bridges theoretical foundations with computational methods, addressing challenges in modern statistical decision-making. Publications: His recent work focuses on spectral clustering, quantum state tomography, and optimal estimation in high-dimensional models. Notable contributions include theoretical guarantees for algorithms like the EM method in Gaussian mixtures and advancements in community detection in networks. Teaching: He teaches advanced courses such as Functional Data Analysis, Nonparametric Estimation, and Decision Theory, reflecting his expertise in statistical methodology and theory. Professional Service: Organized workshops on topics like Empirical Processes (2015) and High-Dimensional Data (2012), underscoring his role in fostering academic collaboration.

Deborah TalmiView profile

Associate Professor

• Emotional Cognition
• Memory Mechanisms
• Neurobiology of Emotion
• +7 more

Dr. Deborah Talmi is a University Associate Professor in the Department of Psychology and a Fellow of Lucy Cavendish College at the University of Cambridge. Her research focuses on the cognitive, computational, and neurobiological mechanisms underlying emotional processes, particularly their intersection with memory, perception, and attention. She leads the Emotional Cognition Lab, which employs behavioral experiments, EEG, fMRI, and computational modeling to explore how emotions influence memory formation and retrieval. Roles: Associate Professor (Department of Psychology), Fellow (Lucy Cavendish College), Principal Investigator (Emotional Cognition Lab) Affiliations: School of Biological Sciences, Cambridge Neuroscience Her research investigates questions such as why emotional experiences feel a certain way, how emotion shapes memory, and the neural basis of similarity perception. A recent study demonstrated that emotionally charged stimuli evoke more similar neural representations than neutral ones, even when controlling for thematic content. This work was published in the Journal of Neuroscience and is part of broader efforts to model emotional memory using frameworks like the Bayesian Brain hypothesis. Dr. Talmi collaborates with institutions like the Swiss Centre for Affective Sciences and employs interdisciplinary methods, including animal models of emotional memory with Prof. Amy Milton. She actively mentors PhD students and welcomes volunteers through the SONA platform. Her lab’s findings have implications for understanding chronic pain, decision-making, and mental health. Her work has been featured in The Conversation and TEDx, emphasizing public engagement. She is also committed to advancing education, as seen in her reflections on remote teaching during the pandemic.

Milica MiočevićView profile

Associate Professor

• Quantitative Psychology
• Statistical Mediation Analysis
• Bayesian Methods
• +3 more

Milica Miočević is an Associate Professor in the Department of Psychology at McGill University. She previously held an Assistant Professor position at Utrecht University's Department of Methods and Statistics. Her research focuses on statistical mediation analysis and Bayesian methods applied to social, health, and behavioral sciences, with three main research lines: optimal methods for Bayesian mediation priors, synthesizing mediated effect findings across studies, and mediation analysis in Single Case Experimental Designs (SCEDs). Dr. Miočević earned her Ph.D. from Arizona State University under advisors Dave MacKinnon and Roy Levy. Her work emphasizes methodological innovation in small sample contexts and evidence synthesis. The Miocevic Lab at McGill explores quantitative psychology and modeling techniques. Her recent publications span Bayesian vs frequentist estimation comparisons, prior distribution development, and mediation analysis methodologies. She has contributed to edited volumes on small sample solutions and published extensively in Structural Equation Modeling journals. Dr. Miočević's research bridges statistical theory and applied practice, advancing tools for researchers working with complex data structures and limited sample sizes.

Y. Samuel WangView profile

Assistant Professor

• Causal Discovery
• Graphical Models
• High-Dimensional Statistics
• +3 more

Y. Samuel Wang is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. He holds a PhD in Statistics from the University of Washington and a BS in Applied Mathematics and Economics from Rice University. Prior to academia, he worked as a management consultant and served as a postdoctoral researcher at the University of Chicago’s Booth School of Business. His research focuses on causal discovery, graphical models, mixed membership models, and high-dimensional data analysis, with applications in network science, environmental studies, and healthcare. Education: PhD in Statistics, University of Washington BS in Applied Mathematics & Economics, Rice University Research Interests: Development of interpretable statistical methods for causal inference and graphical model structures High-dimensional data analysis, particularly in non-Gaussian settings Applications in collaborative networks, environmental microbiology, and healthcare outcomes Recent Research Trends: Recent publications emphasize causal discovery under latent confounding, functional graphical models, and gender dynamics in scholarly collaborations. Methodological contributions include robust high-dimensional inference techniques and computational tools for cyclic structural equation models. Professional Activity: Licensed on GitHub, Google Scholar, and ORCID GitHub repositories include projects on causal discovery (highDNG), gender homophily analysis (genderHomophily), and mixed membership models (mixedMem)