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Christopher AdolphView profile

Professor

• Political Economy
• Comparative Politics
• Health Policy
• +4 more

Christopher Adolph is a Professor of Political Science at the University of Washington, Seattle, and an Adjunct Professor of Statistics. He is a core faculty member of the Center for Statistics and the Social Sciences. His research focuses on political economy, health policy, and quantitative methods. He led the COVID-19 State Policy Project , analyzing pandemic responses across U.S. states. His work appears in journals like BMJ Open , The Lancet , and Nature Medicine . Education: Ph.D., Political Science, Harvard University, 2005 A.M., Political Science, Harvard University, 2000 B.A., Political Science, Economics, and History, Rice University, 1998 Research Interests: Comparative political economy, public policy design, health policy, and statistical methodology. Recent Articles: Focus on pandemic policy analysis, global health equity, and methodological contributions to social science research. Awards: Charles H. Levine Prize for Bankers, Bureaucrats, and Central Bank Politics: The Myth of Neutrality . Lab/Teams: Leads the COVID-19 State Policy Project and collaborates with IHME on pandemic modeling.

• Robotics Perception
• Alternative Sensing
• Inertial Fusion
• +6 more

Teresa Vidal-Calleja is an Associate Professor and Research Director at the Robotics Institute, University of Technology Sydney (UTS). She holds a Deputy Head of School (Research) role in the School of Mechanical and Mechatronic Engineering. Her research focuses on robotics perception, alternative sensing, inertial fusion, SLAM, and applications in manufacturing, agriculture, mining, and construction. Education: B.Sc. Mechanical Engineering (UNAM, 2000), M.Sc. Electrical Engineering (CINVESTAV-IPN, 2002), Ph.D. Automatic Control (Polytechnic University of Catalonia, 2007). Postdoctoral Experience: LAAS-CNRS (France), Australian Centre for Field Robotics (Sydney). Research Interests: Teresa’s work spans robotics perception, sensor fusion, autonomous navigation, and collaborative robotics. She leads projects on next-gen infrastructure robotics (ARC Discovery Grant) and is co-lead of the Australian Cobot Centre. Her innovations target hazardous environments, infrastructure maintenance, and human-robot collaboration. Funded Research: Includes grants for robotic sensors, satellite docking systems, and multi-robot collaboration. Recent projects involve Navantia, the NSW Space Research Network, and industry partnerships. Professional Roles: IEEE Senior Member, Treasurer of the Australian Robotics and Automation Association, and Associate Editor for IEEE Transactions on Robotics and other journals. She chairs conferences like the Australasian Conference on Robotics and Automation. Labs/Teams: UTS Robotics Institute, collaborating with global institutions like ETH Zürich and DLR. Her work integrates robotics with industries such as agriculture and construction, emphasizing sustainability and safety.

Sebastian KurtekView profile

Professor

• Shape Analysis
• Functional Data Analysis
• Image Analysis
• +4 more

Sebastian Kurtek is a Professor of Statistics at The Ohio State University, affiliated with the College of Arts and Sciences. He holds roles in the Interdisciplinary PhD Program in Biostatistics and Translational Data Analytics. His research focuses on statistical shape analysis, functional data analysis, and Bayesian statistics, addressing challenges in medical imaging, computer vision, and biometrics. Education: PhD in Biostatistics, Florida State University (2012) M.S. in Biostatistics, Florida State University (2009) B.S. in Mathematics, Tulane University (2007) Research Interests: Development of statistical models for complex functional data, including shapes and images Applications in medical imaging, biology, and computer vision Computational tools for Riemannian manifold analysis Publications & Trends: Recent work emphasizes geometric frameworks for analyzing eye tracking heat maps, radiogenomics in tumors, and Bayesian approaches for functional data registration. His articles highlight interdisciplinary applications, combining statistics with fields like computer vision and medical imaging. Awards: Best Paper Awards in computer vision, pattern recognition, and medical imaging NSF funding (multiple grants since 2016) IMS and AMS Travel Awards Grants & Collaborations: NSF DMS-1613054 (2016-2020) NIH R37-CA214955 (2017-2024) Collaborations on endometrial tissue modeling and fossil classification Labs/Teams: Involved in CBMS Conference on Elastic Functional and Shape Data Analysis (EFSDA), advancing methodologies for shape and functional data analysis.

Michael LandisView profile

Assistant Professor

• Computational Biology
• Statistical Inference
• Phylogenetics
• +9 more

Michael Landis is an Assistant Professor in the Department of Biology at Washington University in St. Louis, with a courtesy appointment in the Department of Computer Science & Engineering. He obtained his PhD in Biology from UC Berkeley, followed by postdoctoral fellowships at Iowa State University and Yale University. His research focuses on computational and statistical approaches to understand evolutionary processes, particularly phylogenetics, biogeography, and trait evolution. He develops probabilistic models and open-source software (e.g., RevBayes) to analyze evolutionary patterns in biological datasets. His work spans topics like historical biogeography, phylogenetic biome shifts, and the evolution of ecological interactions. Landis also teaches courses in bioinformatics and computational biology. His lab actively collaborates on projects involving plant radiations (e.g., Hawaiian plants) and viral phylogeography. Funded by NSF and other grants, his research integrates methods from computer science, statistics, and evolutionary biology. Education: PhD in Biology, UC Berkeley; Postdocs at Iowa State and Yale. Research interests include phylogenetic modeling, Bayesian inference, and deep learning applications in evolutionary studies. His lab emphasizes open-source tools and community-driven science. Current students and postdocs work on topics like chromosome evolution, viral spread dynamics, and phenotypic diversification. Lab values prioritize collaboration, mentorship, and balancing personal-professional life.

Weihua AnView profile

Professor

• Network Analysis
• Causal Inference
• Bayesian Analysis
• +3 more

Dr. Weihua An is a Professor of Sociology and Quantitative Theory and Methods at Emory University, with affiliations at The East Asian Studies Program, Goizueta Business School, and Rollins School of Public Health. He holds a Ph.D. in Sociology and A.M. in Statistics from Harvard University, with postdoctoral training at Harvard Kennedy School. His research focuses on network analysis, causal inference, Bayesian methods, and their applications to social inequality, health, and organizations. He has authored influential statistical software packages like 'fglsnet' and 'keyplayer' and edited special issues in top journals. His work bridges methodological innovation with substantive social science topics, including peer effects, policy diffusion, and network-based interventions. He has advised over 27 Ph.D. dissertations and received the Emory Faculty Teaching Award and the Clifford Clogg Award. Education: PhD in Sociology, Harvard University, 2011 MA in Statistics, Harvard University, 2009 MA in Economics, Claremont Graduate University, 2005 MA in Political Science, Peking University, 2002 BA in Public Administration, Wuhan University, 1999 Research Interests: Network Analysis, Causal Inference, Bayesian Analysis, Social Inequality, Health Policy, Organizational Studies. His methodological work emphasizes rigorous statistical frameworks for analyzing complex social systems. Grants & Awards: Funded by NIH, NSF, and U.S. Census Bureau Editor of Sociological Methodology Recipient of Clifford Clogg Award (ASA) Labs & Collaborations: Involved in interdisciplinary projects at Emory’s Institute for Quantitative Theory and Methods and collaborations with institutions like The Carter Center and Harvard University.

Florian HoffmannView profile

Associate Professor

• Labor Economics
• Macroeconomics
• Applied Econometrics
• +11 more

Florian Hoffmann is an Associate Professor and BIE Director at the Vancouver School of Economics within the Faculty of Arts at the University of British Columbia. He received his Ph.D. from the University of Toronto in 2010 and has established himself as a leading researcher in labor economics. His professional affiliations include membership in the Human Capital and Economic Opportunity Working Group within the Becker-Friedman Institute at the University of Chicago since 2012, Research Fellow at the Halle Institute for Economic Research, Director of the IAB Research Data Center, and Board Member of the Canadian Labour Economics Forum (CLEF). Ph.D., University of Toronto, 2010 Hoffmann's research focuses on the determinants of life-cycle earnings and career dynamics, dynamic discrete choice models of human capital formation, estimation of equilibrium search models, and the importance of student-instructor interactions for academic achievement. His work bridges theoretical econometrics with empirical applications in labor markets. His research has identified significant effects of demographic matching between instructors and students, showing that the performance gap between white and underrepresented minority students falls by roughly half when taught by an underrepresented minority instructor. Hoffmann's publications appear in top economics journals including the American Economic Review, Journal of Economic Perspectives, The Review of Economics and Statistics, and Journal of Human Resources. His recent work has examined growing income inequality across advanced economies, complex-task biased technological change effects on labor markets, and Burdett-Mortensen models of on-the-job search. Growing Income Inequality in the United States and Other Advanced Economies (2020) Complex-Task Biased Technological Change and the Labor Market (2017) Burdett-Mortensen Model of On-the-Job Search with Two Sectors (2016) As an educator, Hoffmann teaches several courses at UBC including ECON 326 (Methods of Empirical Research), ECON 360 (3rd Year Labour Economics), and ECON 460 (4th Year Labour Economics). He has also been invited to teach labor economics courses at the Bavarian Graduate Program in Economics and the University of Halle-Wittenberg. His research methodology combines sophisticated econometric techniques with large administrative datasets to address fundamental questions about labor market dynamics.

Valerie VenturaView profile

Professor

• Computational Neuroscience
• Time Series Forecasting
• Graphical Models
• +6 more

Valerie Ventura is a Professor in the Department of Statistics and Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. She serves as Co-Director of the Ph.D. Program and maintains dual affiliations as affiliated faculty in the Machine Learning Department and the Center for the Neural Basis of Cognition (CNBC). As a graduate advisor for the Program in Neural Computation (PNC), she bridges statistical methodology with neuroscience applications. Her research focuses on computational neuroscience, particularly time series forecasting, networks, algorithms, and nonparametric methods. Key research areas include computational neuroscience, biostatistics & epidemiology, graphical models & networks, and DELPHI lab projects. Her work integrates advanced statistical techniques to solve complex problems in neural data analysis, motor control, and neural decoding. Analysis of her publication record reveals consistent contributions to neural decoding methodologies, spike train analysis, and cortical covariance modeling. Her recent work emphasizes practical applications in brain-machine interfaces and neural prosthetics, with a strong focus on improving decoding accuracy through novel statistical approaches to spike sorting and waveform analysis. The integration of machine learning with traditional statistical methods characterizes her interdisciplinary approach. Scientific Awards: 2006 Canadian Journal of Statistics Best Paper Award As an educator, Ventura serves as Co-Director of the Ph.D. Program in Statistics and Data Science while advising students through the CNBC's Program in Neural Computation. Her collaborations span multiple departments and research centers, particularly through the DELPHI Lab Group which focuses on computational neuroscience applications. Current research directions suggest continued innovation in neural decoding algorithms and applications to motor control systems.

Toan T. NguyenView profile

Professor

• Partial Differential Equations
• Fluid Dynamics
• Kinetic Theory
• +4 more

Toan T. Nguyen is a Professor in the Department of Mathematics at Pennsylvania State University, affiliated with the Eberly College of Science. He holds a Ph.D. from Indiana University (2009). His research focuses on Partial Differential Equations, Mathematical Physics, Fluid Dynamics, and Kinetic Theory, with contributions to boundary layer stability, inviscid limits, and plasma physics. Education: Ph.D. in Mathematics, Indiana University, 2009. Affiliations: Editorial Board member of Kinetic & Related Models and SIAM Journal on Mathematical Analysis. Grants & Support: Recipient of Simons Fellowship (2019), Centennial Fellowship (2018). His work bridges theoretical analysis and applications, including nonlinear wave dynamics, fluid-structure interactions, and quantum kinetic models. He has organized international conferences, such as the VIASM Summer School in Mathematical Physics (2023–2024), and advised Ph.D. students like Chanjin You and Trinh T. Nguyen. Awards: T. Brooke Benjamin Prize (2022), highlighting his contributions to nonlinear waves and Landau damping. His research also explores instabilities in boundary layers and the mathematical foundations of plasma physics. Nguyen collaborates internationally, contributing to journals like the Journal of the American Mathematical Society and Communications in Mathematical Physics. His teaching includes advanced graduate courses on PDEs and kinetic theory.

Vitor S. MartinsView profile

Assistant Professor

• Remote Sensing
• Water Quality
• Deep Learning
• +5 more

Dr. Vitor S. Martins is an Assistant Professor in the Department of Agricultural and Biological Engineering at Mississippi State University (MSU). He leads the Geospatial Computing for Environmental Research (GCER) Lab , focusing on satellite remote sensing, deep learning, and environmental monitoring. His research integrates Earth observation data with AI to advance digital agriculture, water resource management, and climate resilience. Education: Ph.D., Agricultural and Biosystems Engineering, Iowa State University M.Sc., Remote Sensing, Brazilian Institute for Space Research (INPE) B.Sc., Agriculture and Environmental Engineering, Federal University of Viçosa Research Interests: Remote sensing of water quality and pollution Land cover/land use change mapping Satellite and aerial image processing Soil & water conservation engineering Deep learning for geospatial data analysis Climate change impacts on environmental systems Recent Research Trends (Articles): His work emphasizes algal bloom monitoring , crop type mapping , and global water transparency using advanced satellite platforms like Sentinel-2/3 and Landsat. He also develops frameworks for burned area mapping and soil spectral analysis , leveraging cloud computing and AI-driven models. Awards & Honors: 2019 Reverent P.T. Taiganides Award (Outstanding Ph.D. Student) 2019 Harold & Katherine Guy Fellowship (Soil & Water Conservation Research) 2012 CAPES-FIPSE International Exchange Award Lab & Collaborations: The GCER Lab collaborates with multiple institutions to promote AI solutions for agriculture and water management. Projects include developing AlgaeMap for Latin American water quality monitoring and creating scalable frameworks like FieldSeg for agricultural field extraction.

Qilun ZhuView profile

Research Professor

• Optimal Control
• Vehicle Dynamics
• Powertrain Systems
• +4 more

Dr. Qilun Zhu is an Associate Research Professor at Clemson University's Department of Automotive Engineering. His research focuses on optimal control and estimation of automotive powertrain systems, vehicle dynamics, and autonomous driving technologies. He integrates cross-domain solutions for future mobility systems using rapid prototyping techniques. Dr. Zhu teaches courses in Advanced and Electrified Powertrains and Automotive Control Systems. He earned his B.S. from Jilin University (2010), M.S.E. from Cornell University (2011), and Ph.D. from Clemson University (2015). His publications demonstrate a consistent focus on predictive control strategies for thermal management and energy optimization in electrified and autonomous vehicles. Recent work emphasizes reinforcement learning applications and model-based control of power electronics. Honors include: 2019 SAE Young Professional Technical Paper Competition Best Paper award at the 2014 American Control Conference Dr. Zhu serves on technical committees including the Automotive and Transportation Systems Technical Committee and Automotive Controls Technical Committee, and as associate editor for IEEE Conference on Control Technology and Applications.

Narendra GoelView profile

Professor

• Computational Modeling
• Computer Simulation
• Remote Sensing
• +2 more

Narendra Goel is Professor of Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. His research spans computational modeling of physical, biological, and engineering systems, with applications in computer graphics and remote sensing of vegetation. Research interests focus on: Simulation frameworks for ecological and biological systems Remote sensing algorithms for vegetation analysis Theoretical foundations of computer modeling Publications demonstrate long-standing expertise in radiative transfer modeling (2000-2004) and computational biology (1972-2013). Current teaching includes Introduction to Computer Science (CSC1000), Fundamental Structures in Computer Science (CSC1500), and Introduction to Theoretical Computer Science (CSC4500). Contact at aa2671@wayne.edu or Google Scholar profile.

Rhongho JangView profile

Assistant Professor

• Cybersecurity
• Artificial Intelligence Security
• Network Systems
• +3 more

Rhongho Jang is an Assistant Professor in the Department of Computer Science at Wayne State University's College of Engineering. He holds a PhD from the University of Central Florida and directs the Networked Intelligence and Distributed Security (NIDS) Lab. His research advances security and privacy in distributed systems, networks, and AI. Research spans cybersecurity, AI security, network algorithms, and distributed systems, with applications in intrusion detection, malware analysis, and in-network computing. Recent publications focus on data plane security, adversarial ML, and IoT security. Awards include NSF grants (2022-2023), Best Paper Award at DSN 2021, and KISA's Outstanding Young Researcher (2018). Current PhD advisees include Seyed Mohammad Mehdi Mirnajafizadeh, Seungsam Yang, Fatima Kamal Khaja, and Sian Kim. Manages NIDS Lab with projects on cyber-physical security and in-network computing.

Walid HubbiView profile

Associate Professor

• Power Systems
• Load Flow Analysis
• Control Systems
• +1 more

Walid Hubbi is Associate Professor in Electrical and Computer Engineering at NJIT. He holds a PhD from Queen's University Belfast and degrees from the University of London and Aleppo University. His research focuses on power system analysis and control, particularly optimization techniques for reactive power compensation, load flow methodologies, and stability enhancement. Key contributions include fuzzy logic controllers for static VAR compensators, neural network applications for load modeling, and optimal placement strategies for grid control devices. His publications consistently address practical challenges in transmission efficiency, voltage stability, and measurement accuracy using computational intelligence and optimization frameworks.

Andrew DuView profile

Assistant Professor

• Paleoanthropology
• Human Evolution
• Paleoecology
• +7 more

Andrew Du is an Assistant Professor in the Department of Anthropology and Geography at Colorado State University, within the College of Liberal Arts. His research focuses on the ecology of ancient hominins and the biases in the fossil record affecting paleoecological interpretations. He employs quantitative methods to analyze fossil assemblages and infer hominin spatiotemporal distributions. Education: Ph.D. and M.Phil. in Hominid Paleobiology, The George Washington University B.S. in Evolutionary Anthropology, Rutgers University Research Interests: Paleoanthropology, human evolution, paleoecology, macroecology, macroevolution, taphonomy, Plio-Pleistocene eastern Africa, and quantitative methods. Current projects include studying time-averaging effects in fossil assemblages, large mammal diversity in the Omo-Turkana Basin, and modeling hominin distributions. Teaching & Labs: Teaches courses on human origins, evolution, and data analysis. Maintains a lab with clear cultural norms and mentorship expectations outlined in accessible documents. Active in developing graduate-level R tutorials for statistical methods.

Louis N. Cattafesta IIIView profile

Professor

• Fluid dynamics
• Aeroacoustics
• Active and passive flow control
• +5 more

Louis N. Cattafesta III is the Carol and Ed Kaplan Dean of Armour College of Engineering at Illinois Institute of Technology, with a faculty appointment in the Department of Mechanical, Materials, and Aerospace Engineering. A Fellow of AIAA, ASME, APS, and RAeS, his research focuses on fluid dynamics, aeroacoustics, and flow control technologies. Ph.D. in Mechanical Engineering, Penn State University (1992) M.S. in Aeronautics, MIT (1988) B.S. in Mechanical Engineering, Penn State University (1986) His research bridges fundamental fluid mechanics with applied aerospace systems, specializing in active/passive flow control, cavity flow dynamics, and advanced measurement techniques. Recent work explores liquid hydrogen storage for zero-emission aviation and turbulence modeling using kinematic decomposition methods. Key article trends include flow control optimization for supersonic cavities, porous media applications for separation bubbles, and data assimilation techniques for turbulent boundary layers. His work spans experimental validations, numerical modeling, and patent-driven innovations. Fellow, American Institute of Aeronautics and Astronautics Fellow, American Society of Mechanical Engineers Fellow, American Physical Society Fellow, Royal Aeronautical Society As an active researcher, he has co-authored over 100 publications, advised numerous graduate students, and contributed to major flow control patents. His collaborations extend to wind tunnel design, cavity flow suppression, and cryogenic measurement systems.