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Scott W Linderman is an Assistant Professor of Statistics at Stanford University with courtesy appointments in Electrical Engineering and Computer Science. He serves as an Institute Scholar in the Wu Tsai Neurosciences Institute and is affiliated with Stanford Bio-X and the Stanford AI Lab. Education: PhD in Computer Science (2016) - Harvard University SM in Computer Science (2013) - Harvard University BS in Electrical and Computer Engineering (2008) - Cornell University 3 years as Microsoft software engineer before graduate school His research focuses on machine learning and computational neuroscience , developing: Advanced state space models (rSLDS, GP-SLDS) behavioral time series methods (GIMBAL, Keypoint MoSeq) deep state space architectures (S5, ELK) point process models (PP-Seq) scalable inference algorithms (SIXO, Structure-exploiting VI) Key collaborations include: Prof. David Anderson (Caltech) - hypothalamic dynamics Prof. Bob Datta (Harvard Medical School) - behavioral sequencing Prof. Chris Ré (Stanford) - biomedical ML Prof. David Sussillo (Stanford) - neural network theory Scientific contributions: Developed SSM and Dynamax software packages Leonard J. Savage Award recipient (2016) Bridging reinforcement learning and neural dynamics Advancing 3D keypoint tracking and behavioral syllable analysis Labs & teams: Linderman Lab - computational neuroscience Stanford AI Lab - machine learning Wu Tsai Neurosciences Institute - interdisciplinary research Stanford Bio-X - cross-departmental collaboration

• Machine Learning
• Computational Neuroscience
• Statistical Modeling
• +5 more

Kristen Underwood is a Research Professor in the Department of Civil and Environmental Engineering at the University of Vermont (UVM), affiliated with the College of Engineering and Mathematical Sciences. Her work bridges water resources engineering, aquatic ecology, and advanced computational methods to address environmental challenges. She holds a Ph.D. in Environmental Engineering and an M.S. in Geosciences from UVM and Penn State University, respectively. Education: Ph.D., Environmental Engineering (UVM); M.S., Geosciences (Penn State) Her research focuses on applying machine learning, Bayesian inference, and geostatistical tools to study catchment dynamics, fluvial geomorphology, and sediment-nutrient flux in rivers. She explores sustainable infrastructure design and floodplain restoration to mitigate hazards and enhance ecological compatibility. Recent work emphasizes floodplain functionality, including sediment deposition patterns and phosphorus retention in restored wetlands. She collaborates on projects like the Functioning Floodplain Initiative and Vermont’s Water Resources Monitoring Network. Publications highlight innovative applications of AI in turbidity forecasting, flood routing models, and snow hydrology using LIDAR data. She also investigates the impact of climate change on water systems and interdisciplinary approaches to Critical Zone science. Dr. Underwood teaches courses in Applied River Engineering and Data Analytics for Water Resources, reflecting her commitment to integrating theory and practical solutions in environmental engineering.

• Water Resources
• Fluvial Geomorphology
• Hydrology
• +8 more

Leah Johnson is an Associate Professor in the Department of Statistics at Virginia Polytechnic Institute and State University (Virginia Tech), within the College of Science. Her research focuses on the intersection of statistics, biology, and mathematics, particularly in understanding how individual variability in populations influences broader ecological and epidemiological patterns. Education: Ph.D. in Applied Mathematics and Statistics and Physics, University of California Santa Cruz (2006), Dissertation: Mathematical Modeling of Cholera M.S. in Physics, University of California Santa Cruz (2003) B.S. with Honors in Physics, The College of William and Mary (2001) Research Interests: Dr. Johnson’s work centers on infectious disease epidemiology, vector-borne disease dynamics, and the impact of climate change. She employs Bayesian statistical methods and mechanistic models to study how environmental drivers, population structure, and individual behaviors shape disease transmission and persistence. Key areas include malaria, dengue, and amphibian pathogens. Recent Article Trends: Her publications emphasize the role of temperature and humidity in vector performance, the development of databases for vector traits, and ecological forecasting under climate change. Studies highlight the importance of integrating empirical data with theoretical models to address public health challenges. Awards and Honors: Early Career Fellow, Mathematical Biosciences Institute (2013) Finalist, Kings College Junior Research Fellowship (2008) College Research Associate, University of Cambridge (2006–2009) Grants and Funding: Principal Investigator: Vector Behavior in Transmission Ecology (VectorBiTE) (NIH-NSF-USDA, 2015–2020) Co-PI: Effects of Temperature on Vector-Borne Disease Transmission (NSF-NIH-USDA, 2015–2020) Labs and Teams: She leads the QED Lab, which explores quantitative methods for ecological and epidemiological systems. The lab collaborates on global initiatives like the VectorByte platform for vector trait data and climate-driven disease forecasting.

• Infectious Disease Epidemiology
• Vector-Borne Disease Dynamics
• Climate Change Impact Analysis
• +3 more

Hongxiao Zhu is an Associate Professor in the Department of Statistics at Virginia Tech. She holds a Ph.D. in Statistics from Rice University (2009), an M.S. in Mathematics from the University of Arkansas at Little Rock (2004), and a B.S. in Finance from Wuhan University (2002). Her research focuses on Bayesian methods, functional data analysis, and statistical machine learning, with applications in medicine, bioinspired sensing, neuroimaging, finance, and genomics. She has developed methods for analyzing high-dimensional data, including biosonar signals, genomic loci, and medical imaging data. Zhu has been recognized with awards such as the Travel Award from the Sixth International Workshop on Statistical Analysis of Neuronal Data (2012) and the SBSS Student Paper Competition (2008). Her teaching includes advanced courses on regression, statistical inference, Bayesian statistics, and statistical computing. She is affiliated with the Institute of Mathematical Statistics and the American Statistical Association. Zhu collaborates with researchers in engineering, biology, and computer science, notably in bioinspired sonar sensing frameworks and UAV applications. Her work bridges statistical theory and interdisciplinary applications, emphasizing robust modeling and computational innovation. Zhu's lab explores sonar terrain analysis, functional mixed models, and sensor-based environmental sensing. Recent projects include simulating bat-inspired sonar systems and analyzing DNA methylation patterns in brain development. Her research trends emphasize integrating Bayesian methods with real-world data challenges in healthcare and robotics. Her awards highlight contributions to statistical methodologies and interdisciplinary research. Grants and collaborations are evident through her work with institutions like SAMSI and Duke University. She advises on advanced statistical techniques for diverse datasets, though no specific advisee list is provided here.

• Bayesian Methods
• Functional Data Analysis
• Statistical Machine Learning
• +6 more

Abhranil Das is a Postdoctoral Fellow at the University of Texas at Austin with interdisciplinary expertise in computational neuroscience, vision science, and the intersection of psychedelics with psychology. He holds a PhD in Physics and Neuroscience from UT Austin and explores topics ranging from human perception to transpersonal psychology through both academic research and personal reflection. Education: PhD in Physics/Neuroscience from University of Texas at Austin Current Role: Postdoctoral Fellow at UT Austin His research interests span computational modeling of human vision , camouflage detection mechanisms , and psychedelic-assisted therapy , with a focus on interdisciplinary connections between physics, neuroscience, and cognitive science. He also maintains a blog ( One Life ) where he discusses transpersonal psychology, climate anxiety, and personal philosophical reflections. Recent publications highlight his work on Bayesian models of visual perception , self-supervised learning in texture discrimination , and statistical methods for signal classification . Das is affiliated with the Center for Psychedelic Research and Therapy at UT Austin and explores the energetic and transpersonal aspects of consciousness in both scientific and anecdotal contexts.

• Computational neuroscience
• Vision science
• Psychedelics
• +5 more

Stephen J. Wright is a Professor at the University of Wisconsin–Madison, affiliated with the Department of Industrial and Systems Engineering, Computer Sciences, and the Wisconsin Institutes for Discovery. He holds a Ph.D. (1984) and B.Sc. (First Class Honors, 1981) from the University of Queensland. Research Interests: Wright specializes in optimization algorithms, machine learning, and computational mathematics. His work spans convex/nonconvex optimization, stochastic methods, algorithm complexity, and applications in data science and scientific computing. Publication Trends: Recent research focuses on nonconvex optimization with complexity guarantees, federated learning with privacy constraints, gradient flow in experimental design, and hybrid neural-symbolic systems. He also explores randomized algorithms, variance reduction, and distributed computing frameworks. Lab Affiliations: Wright contributes to the Wisconsin Institutes for Discovery, advancing interdisciplinary research in computational methods and data-driven modeling.

• Optimization
• Machine Learning
• Stochastic Optimization
• +4 more

Rusty Tchernis is a Professor of Economics at the Andrew Young School of Policy Studies, Georgia State University, with affiliations as a Research Associate at the National Bureau of Economic Research (NBER) and a Research Fellow at the Institute for the Study of Labor (IZA). His research focuses on Applied Econometrics, Health Economics, and Labor Economics, emphasizing program evaluation, spatial econometrics, Bayesian methods, and childhood obesity. His work addresses policy-relevant topics like food security, SNAP program impacts, and public health interventions. Education: Ph.D. in Economics from Brown University. Research interests include the interplay between economic policies and health outcomes, with particular attention to spatial analysis, risk preferences, and the evaluation of social programs. His publications span journals like the Journal of the American Statistical Association and Journal of Econometrics, reflecting methodological rigor and policy relevance. His articles explore themes such as smoking cessation’s impact on obesity, SNAP redemption patterns, and Bayesian approaches to behavioral welfare economics. Funded by the NIH and USDA, his research bridges econometric innovation with real-world policy challenges. No scientific awards are explicitly mentioned. Grants and funding sources include the National Institutes of Health and the USDA. He has advised on policy evaluations but no students are listed. No lab affiliations are noted.

• Applied Econometrics
• Health Economics
• Labor Economics
• +4 more

Klara Nahrstedt is the Maybelle Leland Swanlund Endowed Chair and Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. She also serves as Director of the Coordinated Science Lab and holds professorships in Electrical and Computer Engineering, the Information Trust Institute, and the National Center for Supercomputing Applications (NCSA). Her expertise spans cybersecurity, edge computing, IoT, and multimedia systems. Her research focuses on immersive content delivery, network security, and AI-driven optimization for edge computing environments. Notable contributions include frameworks for robust 360-degree video analytics, cybersecurity in industrial networks, and energy-efficient video streaming. Awards include AAAS Fellow, ACM Fellow, and the Humboldt Research Award. Recent work emphasizes adaptive systems for IoT, reinforcement learning in multimodal applications, and generative models for intrusion detection. Key projects include Fire360 (firefighting video benchmarks) and AquaScope (underwater image transmission). Collaborations span academia and industry, addressing challenges in smart grids, telemedicine, and urban IoT. Awards: AAAS Fellow (2019), ACM Fellow (2012), Edward J. McCluskey Technical Achievement Award (2012) Labs/Teams: Coordinated Science Lab, NCSA, Information Trust Institute

• Computer Science
• Cybersecurity
• Edge Computing
• +8 more

Tjitske Starkenburg is a Research Professor at Northwestern University's Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) and Department of Physics and Astronomy. Her research combines galactic dynamics with extragalactic astronomy to study galaxy evolution through cosmological simulations and observational data analysis. Current projects focus on stellar stream formation during galaxy mergers, synthetic observation techniques, and machine learning applications for connecting theoretical models with astronomical surveys. She develops statistical methods to analyze galaxy populations in large-scale simulations like FIRE-2.

• Galaxy evolution
• Galactic dynamics
• Extragalactic astronomy
• +4 more

Sara Solla is a Professor in the Department of Physics and Astronomy at Northwestern University, with a joint appointment in Physiology. She holds a PhD from the University of Washington (1982). Her research focuses on applying statistical mechanics to complex systems, particularly neural networks, exploring topics like associative memory, supervised learning, and neural dynamics. Solla's work bridges theoretical physics and neuroscience, with contributions to computational models of neural computation and brain-machine interfaces. Research Interests : Solla investigates neural networks through the lens of statistical mechanics, examining how systems like spin-glass models can describe associative memory and generalization in adaptive systems. She has contributed to understanding neural network dynamics, spiking neuron behavior, and the role of heterogeneity in network computations. Her recent work explores low-dimensional neural manifolds for motor control, brain-computer interface development, and neuro-inspired artificial intelligence. Key Contributions : Her research spans theoretical frameworks for neural learning algorithms, applications in motor control decoding, and interdisciplinary approaches combining neuroscience with robotics. She has advanced methods for analyzing multi-electrode neural recordings and contributed to understanding tactile perception and somatosensory processing. Labs & Affiliations : Solla is affiliated with Northwestern's interdisciplinary centers, including the Center for Interdisciplinary Research and Exploration in Astrophysics (CIERA), the Center for Network Dynamics (CND), and the Center for Applied Physics and Superconducting Technologies (CAPST). These collaborations highlight her role in fostering cross-disciplinary research in physics, neuroscience, and engineering.

• Statistical Mechanics
• Neural Networks
• Complex Systems
• +3 more

Yan Xiao is an Adjunct Professor at the University of Illinois. His research focuses on federated learning, cybersecurity, and distributed systems, with an emphasis on adversarial robustness, privacy-preserving techniques, and optimization in machine learning frameworks. He has contributed to foundational tools like FedML and explores challenges in federated learning security, energy efficiency, and causal mechanisms. Key research interests include federated learning architectures, adversarial defense mechanisms, and the integration of reinforcement learning for automated feature selection. His work spans theoretical advancements (e.g., invariant aggregators for backdoor attacks) and practical applications (e.g., thermal management in HPC systems). Recent publications highlight trends in federated learning security, distributed optimization, and causal discovery. Notable contributions address straggler-free coreset-based learning and Bayesian-optimized energy efficiency in federated setups. No scientific awards or grants are explicitly mentioned in the provided texts. Advising details are not listed, though collaborations with research groups in federated learning and HPC are implied through publication topics.

• Federated Learning
• Machine Learning
• Cybersecurity
• +3 more

Dr. Siwei Liu is an Associate Professor in the Department of Human Development & Family Studies at the University of California, Davis. Their research focuses on advanced statistical methods for analyzing longitudinal data in developmental and health psychology, with an emphasis on biobehavioral synchrony in close relationships and health disparities. They lead a lab investigating multi-layer contextual factors (e.g., environment, family characteristics) affecting health behaviors, prioritizing diversity in both research subjects and student recruitment. Research Interests : Longitudinal data methodologies: multilevel modeling, time series analysis, functional data analysis Biobehavioral synchrony in parent-child and couples relationships Health disparities, environmental justice, and stress physiology Intensive longitudinal methods in health research Key Projects : GROWell randomized controlled trial for dietary interventions in prenatal/postpartum populations Multimodal assessment of biobehavioral synchrony in autism spectrum disorder Analysis of family risk dynamics and parenting stress through cortisol contagion Labs/Teams : Directs a lab prioritizing underrepresented students, focusing on innovative methods for studying emotions, well-being, and family systems. Collaborates on NIH-funded projects involving wearable devices and ecological momentary assessments.

• Research Methodology
• Developmental Psychology
• Health Psychology
• +4 more

Bala Rajaratnam serves as a Professor in the Department of Statistics at the University of California, Davis, with his office located in the Mathematical Sciences Building at One Shields Avenue, Davis, CA 95616. His primary research focuses on advanced statistical methodologies, including: Covariance Estimation for complex datasets Graphical models in probabilistic systems High dimensional inference techniques Bayesian methods for uncertainty quantification Environmental Statistics applications Positivity constraints in causal inference His work bridges theoretical statistics with practical environmental data analysis, emphasizing robust computational approaches for modern statistical challenges. Professional correspondence may be directed to brajaratnam@ucdavis.edu.

• Covariance Estimation
• Graphical models
• High dimensional inference
• +3 more

Shuang Zhao is an Associate Professor of Computer Science at UC Irvine, co-directing the Interactive Graphics & Visualization Lab (iGravi). He holds a PhD from Cornell University (2014) and a postdoc at MIT. His research focuses on physics-based computer graphics, scientific computing, and inverse rendering, with applications in material science, biomedicine, and robotics. Zhao's NSF CAREER Award (2023) supports his work on Physics-Based Differentiable and Inverse Rendering, enabling automated 3D reconstruction and medical imaging advancements. Education: Ph.D., Computer Science, Cornell University (2014); Postdoc at MIT. Research areas: Inverse rendering, differentiable rendering, Monte Carlo methods, and light transport modeling. Notable projects include Meta's digital twin creation for the Metaverse, non-line-of-sight imaging, and medical imaging applications. His lab develops algorithms for efficient inverse solutions and collaborates with industry (Meta, Nvidia, Adobe). Teaching includes advanced graphics courses like CS 114 and ICS 162. Awards include ACM programming contest championships and Best Paper recognitions. Students supervised include Cheng Zhang (Facebook Fellow), Kai Yan, and Zahra Montazeri. Hobbies include photography and Japanese anime/video games.

• Physics-Based Computer Graphics
• Scientific Computing
• Inverse Rendering
• +7 more

Perry Williams is an Associate Professor of Statistical Ecology at the University of Nevada, Reno's Department of Natural Resources & Environmental Science. He specializes in statistical methods for wildlife population dynamics and conservation, with expertise in quantitative ecology. His research interests include statistical ecology, wildlife management, population dynamics, and conservation biology. He develops mathematical models to address ecological challenges and inform conservation strategies. Dr. Williams' publications focus on wildlife population modeling, movement ecology, and conservation applications. Recurring themes include spatial statistics, hierarchical modeling, and species-specific conservation strategies.

• Statistical Ecology
• Wildlife Management
• Population Dynamics
• +1 more