Alexander Fisher is an Assistant Professor of the Practice of Statistical Science at Duke University's Department of Statistical Science, affiliated with the Trinity College of Arts & Sciences. He holds a Ph.D. (2021), M.S. (2018) from UCLA, and a B.S. (2016) from Florida State University. His research focuses on Bayesian statistical methods applied to phylogenetics, divergence time estimation, and epidemiological modeling, with publications in journals like Systematic Biology and Molecular Biology and Evolution. Fisher teaches advanced courses in Bayesian inference, statistical computing, and data science methodologies. Education: Ph.D. in Statistics (UCLA, 2021), M.S. (UCLA, 2018), B.S. (Florida State University, 2016) His work integrates computational statistics with biological problems, emphasizing scalable Bayesian approaches for analyzing large genomic datasets. Recent projects include developing methods for divergence time estimation and applying phylogeographic frameworks to trace viral transmission dynamics. Fisher's teaching portfolio includes courses like Bayesian Statistical Modeling and STA 323D (Statistical Computing).
Partha Dey is an Associate Professor in the Department of Mathematics at the University of Illinois at Urbana-Champaign (UIUC), where he also serves as Director of the NetMath Program. He holds affiliations in both Mathematics and Statistics departments. His research focuses on Probability Theory and its intersections with Statistical Physics, emphasizing First/Last Passage Percolation, Random Growth Models, Stein’s Method, Spin Glasses, and Random Matrix Theory. Education: Ph.D. in Statistics from UC Berkeley (2010), supervised by Sourav Chatterjee and Steve Evans. Prior to UIUC, he was a Courant Instructor/Simons Fellow at NYU (2010-2013) and a Harrison Early-Career Assistant Professor at the University of Warwick (2013-2014). Undergraduate and Master’s studies at Indian Statistical Institute Kolkata (Mathematical Statistics & Probability). Research interests include analyzing stochastic processes in complex systems, with recent work on fluctuation phenomena in percolation models, spin glasses under external fields, and Stein’s method applications. His publications span high-impact journals like ALEA , Annals of Probability , and Communications in Mathematical Physics . Awards/Funding: No explicitly listed honors, though his positions suggest sustained academic recognition. Grants/Advising: No detailed grant info provided; no advisee names listed in texts. Labs/Teams: Leads NetMath Program, a distance-learning initiative in mathematics education. Collaborates widely on interdisciplinary projects in probability and statistical physics.
Jose Eos Trinidad is an Assistant Professor of Education Policy at the University of California, Berkeley , affiliated with the Berkeley School of Education. A sociologist with a joint PhD in Sociology and Comparative Human Development from the University of Chicago, his work bridges organizational theory and education policy. Education: PhD (University of Chicago, 2017), MA (University of Chicago), BA (Ateneo de Manila University) Research Interests center on cross-sector partnerships between schools and external organizations, policy implementation in decentralized systems, and causal inference. His 2025 book Subtle Webs: How Local Organizations Shape US Education (Oxford) argues for an "outside-in" theory of educational change. Scientific Recognition includes the Earl S. and Esther Johnson Prize for Best MA Thesis. His work spans peer-reviewed articles in journals like Sociology of Education and Social Science & Medicine , with a focus on organizational dynamics, equity in education, and methodological innovation. Teaching includes courses on causal inference, organizational theory, and education inequality/policy. He actively advises graduate students in cross-sector partnerships and policy research, utilizing mixed methods including quantitative causal inference and network analysis.
Ramin Zabih is a Professor at Cornell Tech with a joint appointment in Weill Cornell Radiology. His research focuses on computer vision, medical imaging, and advanced algorithms like graph cuts, which have garnered prestigious awards such as the Test of Time Award (ICCV 2011) and Koenderink Prize (ECCV 2012). He has held leadership roles, including Program Chair for CVPR 2007 and Editor-in-Chief of IEEE TPAMI (2009-2012). His work bridges medical imaging reconstruction and AI ethics, addressing challenges in MR image analysis and workflow optimization. Education details are not explicitly provided in the text, but his professional trajectory indicates a strong academic background in computer science and engineering. His research interests emphasize algorithmic innovation for medical applications and foundational computer vision problems. Key scientific contributions include advancements in graph cuts for inverse problems and adversarial methods for clinical text anonymization. His publications span medical imaging, machine learning, and vision, reflecting interdisciplinary impact. Awards highlight his transformative work in algorithmic efficiency and medical applications. Zabih has contributed to organizing major conferences (e.g., CVPR) and serves as President of the Computer Vision Foundation. His current work at Cornell Tech integrates computational methods with radiology, advancing both technical and clinical outcomes.
Barnabas Poczos is an Associate Professor in the Machine Learning Department at the School of Computer Science, Carnegie Mellon University. He is a member of the Auton Lab and has established himself as a leading researcher in theoretical machine learning with applications spanning numerous scientific domains. Carnegie Mellon University, School of Computer Science Machine Learning Department Auton Lab member Dr. Poczos earned his M.Sc. in applied mathematics from Eotvos Lorand University in Budapest, Hungary in 2001, followed by a Ph.D. in computer science from the same institution in 2007. He completed postdoctoral training at the University of Alberta (2007-2010) in the RLAI group and at Carnegie Mellon University (2010-2012) in the Auton Lab. His research focuses on theoretical questions of statistics and their applications to machine learning. Dr. Poczos develops machine learning methods for advancing automated discovery and efficient data processing across diverse scientific fields including health-sciences, neuroscience, bioinformatics, cosmology, agriculture, robotics, civil engineering, and material sciences. His work bridges theoretical foundations with practical applications, making significant contributions to both machine learning methodology and domain-specific scientific problems. Analysis of his recent publications reveals a strong emphasis on diffusion models and generative AI techniques applied to scientific discovery. His work spans drug design, genomics, cosmology, and materials science, demonstrating a consistent pattern of developing novel machine learning methodologies that address specific challenges in scientific domains. The interdisciplinary nature of his research is particularly evident in the application of advanced ML techniques to solve complex problems in biology, physics, and engineering. Yahoo! ACE award Dr. Poczos has served as PI or co-Investigator on 15+ federal and non-federal grants, supporting his research in theoretical machine learning and its scientific applications. His teaching portfolio at CMU includes advanced courses in optimization, convex optimization, and machine learning with large datasets. While specific students aren't mentioned in the provided information, as an Associate Professor at CMU, he undoubtedly mentors graduate students in the Machine Learning Department. As a core member of the Auton Lab at Carnegie Mellon University, Dr. Poczos contributes to a research environment focused on developing machine learning methods for real-world applications, particularly in healthcare and scientific discovery. The lab's work emphasizes both theoretical foundations and practical implementations of machine learning systems.
Prof. Hubert P. H. Shum is a Professor of Visual Computing and Director of Research in the Department of Computer Science at Durham University. He specializes in Responsible AI, Computer Vision, and AI in Healthcare. As a Co-Founder of the Durham University Space Research Centre and Fellow of the Wolfson Research Institute for Health and Wellbeing, he leads interdisciplinary research in healthcare, space technology, and autonomous systems. His work includes over 200 publications in top venues like CVPR, ICCV, and MICCAI, focusing on spatio-temporal data modeling. He has secured £10M+ in grants from EPSRC, MoD, and Innovate UK, supervising over 30 PhD students. Key projects include NortHFutures (digital health hub) and counter-drone research. Research interests span AI ethics, medical imaging, autonomous vehicles, and space applications. His lab develops technologies for surgical workflow anticipation, LiDAR segmentation, and human-AI interaction. Notable awards include Best Paper Awards in CVPR and exceptional teaching/supervision accolades from Durham University. Key Grants : EPSRC Impact Acceleration (£2.76M), EPSRC Digital Health Hub (£4.17M), Royal Society (£143k). Professional Activities : Conference chairs for Pacific Graphics, BMVC; editorial roles in Computer Graphics Forum and IJCV. Labs/Initiatives : Durham University Space Research Centre, Centre for Visual Arts and Culture.
Elena Beretta is an Assistant Professor at the Faculty of Science, Department of Computer Science at Vrije Universiteit Amsterdam, affiliated with the Network Institute. Her research focuses on ethical AI, human-machine interaction, and sociotechnical bias in AI systems. She explores how AI systems encode identity, process visual data, and influence decision-making, advocating for inclusive and accountable technologies. Key research areas include: Human-Machine Interaction & Ethical Design AI, Identity & Representation (e.g., gender and LGBTQI+ representation) AI & Visual Data (e.g., facial recognition and image processing) Her work bridges computer science and social sciences, addressing fairness in algorithms and mitigating bias through interdisciplinary methods. Recent publications highlight studies on vision transformers, non-binary representation in computer vision, and discriminatory risks in automated systems. Prof. Beretta collaborates on projects like the Open Data Infrastructure for Social Science (ODISSEI), enhancing data accessibility and ethical AI practices. She has contributed to agent-based simulations analyzing retailer behavior and cultural technology adoption, reflecting her broader interest in societal impacts of technology.
Wenrui Hao is an Associate Professor in the Department of Mathematics at The Pennsylvania State University, part of the Eberly College of Science. His research focuses on interdisciplinary applications of mathematics in biology, medicine, and engineering, with particular emphasis on mathematical modeling of complex systems and numerical methods for partial differential equations. Dr. Hao’s work integrates computational methods with real-world biological and medical challenges, including Alzheimer’s disease modeling, cancer progression simulation, and cardiovascular dynamics. He develops innovative numerical algorithms such as homotopy methods, neural operators, and finite element techniques to solve nonlinear systems and free boundary problems. His recent publications highlight contributions to optimal control strategies for neurodegenerative diseases, causal network discovery in Alzheimer’s biomarkers, and computational frameworks for practical identifiability analysis in biological models. His research bridges theoretical mathematics with applied sciences, addressing critical questions in health and engineering through rigorous computational approaches. Dr. Hao’s lab focuses on advancing mathematical tools for understanding complex biological phenomena, with applications in personalized medicine and predictive modeling of disease progression. His collaborative efforts span multiple disciplines, reflecting his commitment to translating mathematical insights into impactful scientific solutions.
Yuyuan Ouyang is an Associate Professor in the Department of Mathematical and Statistical Sciences at Clemson University. He holds a Ph.D. in Mathematics from the University of Florida (2013). His research focuses on nonlinear optimization, stochastic approximation, and algorithm design for big data analytics. His work bridges theoretical foundations with practical applications in machine learning, network flow programming, and convex optimization. Dr. Ouyang teaches advanced courses including Machine Learning I/II, Network Flow Programming, and Nonlinear Programming. His recent publications emphasize gradient sliding methods, decentralized optimization, and saddle-point problem analysis. He has contributed to SIAM Journal on Optimization, Mathematical Programming, and Operations Research Letters. His research trends show a strong emphasis on algorithmic innovation for large-scale systems, with notable work on complexity bounds, convex reformulations, and statistical estimation techniques. He has collaborated widely on topics ranging from variational inequalities to MRI image reconstruction.
Dr. Qian (Michelle) Zhou is an Associate Professor of Statistics in the Department of Mathematics and Statistics at Mississippi State University (office Allen 454, phone 662-325-7160). Her research develops advanced statistical methods addressing fundamental questions about model misspecification in clinical and genetic studies. Research interests include: Model diagnosis and selection Risk prediction and biomarker evaluation Survival and longitudinal data analysis Developing robust statistical procedures Her work creates methods for survival analysis, longitudinal data, risk prediction, and model diagnostics that accommodate complications in clinical/genetic studies. Recent publications focus on copula models, survival analysis methods, and agricultural statistics applications. She maintains active research profiles on Google Scholar and MathSciNet. Before joining MSU, Dr. Zhou was an Assistant Professor at Simon Fraser University (2012-2015) and Postdoctoral Fellow at Harvard T.H. Chan School of Public Health (2009-2012). She earned her Ph.D. from University of Waterloo in 2009.
Dr. Daniel Rowe is a Professor of Data Science and Co-Director in the Department of Mathematical and Statistical Sciences at Marquette University. His research focuses on Bayesian statistics, computational neuroscience, and medical imaging, particularly in fMRI analysis. He has contributed extensively to improving fMRI signal processing techniques, including Bayesian approaches for brain activity mapping and parallel imaging reconstruction methods like GRAPPA and SENSE. His work emphasizes statistical methodologies to address challenges in neuroimaging, such as noise reduction, artifact correction, and the development of complex-valued analysis frameworks. Notable contributions include the Bayesian merged utilization of GRAPPA and SENSE (BMUGS) for enhanced fMRI detection and efficient fully Bayesian approaches for brain activity mapping. Dr. Rowe collaborates on interdisciplinary projects, including cardiac chamber modeling using Bayesian neural networks and studies on radiation-induced cardiotoxicity. His publications span high-impact journals like the Annals of Applied Statistics, Magnetic Resonance Imaging, and the Journal of the Royal Statistical Society.
Prof. Franziska Matthäus is a Professor at Goethe University Frankfurt and a Fellow at the Frankfurt Institute for Advanced Studies (FIAS). Her research focuses on mathematical modeling of spatiotemporal processes in biological systems, particularly cell motility, cancer migration, and developmental biology. She leads a multidisciplinary group collaborating with experimental partners to integrate data analysis, agent-based models, and partial differential equations (PDEs) into theoretical frameworks. Notable contributions include the 2020 book The Art of Theoretical Biology , which showcases visually striking scientific images from biological research, and the development of QuickPIV software for 3D particle image velocimetry. Education: PhD in Biophysics (University of Warsaw, 2005), postdoctoral work at Heidelberg University, and a junior professorship at the University of Würzburg before joining FIAS in 2016. She currently holds the Giersch Endowed Professorship. Research Interests: Agent-based modeling of collective cell behavior, reaction-diffusion systems in developmental patterning, and force inference in epithelial tissues. Her work bridges computational methods with experimental data, addressing questions in organoid morphogenesis, cancer metastasis, and embryonic development. Teaching: Offers courses in theoretical biology and bioinformatics, including modules on data analysis, mathematical modeling, and programming for biological systems. Courses are taught in German and English. Labs/Teams: Active in FIAS's Life and Neurosciences group, focusing on multiscale analyses of biological systems. Collaborates with institutions globally, including the University of Leeds and the University of Alberta.
Dr. Mubarak Shah is the Trustee Chair Professor of Computer Science and Founding Director of the Center for Research in Computer Vision (CRCV) at the University of Central Florida. His research focuses on computer vision, including video surveillance, visual tracking, human activity recognition, and UAV video analysis. He has held prestigious fellowships from ACM, NAI, AAAS, IAPR, and IEEE. Roles: Trustee Chair Professor, Director of CRCV, Graduate Faculty Member Affiliations: University of Central Florida, College of Engineering and Computer Science Research interests span visual analysis of crowded scenes, video registration, and privacy-aware diffusion models. He has pioneered geolocalization techniques and multimodal learning frameworks. His work impacts security, autonomous systems, and medical imaging. Recent publications emphasize diffusion models, 3D object detection, and adversarial learning. His work has been presented at CVPR, ECCV, and NeurIPS. Awards: Pegasus Professor (2006) ACM SIGMM Technical Award (2019) Multiple fellowships and recognitions from IEEE, AAAS, and others Advising and mentorship include over 50 graduate students and NSF REU programs. He leads CRCV, a hub for interdisciplinary vision research, and contributes to high-impact datasets like MAVREC. Labs/Teams: CRCV hosts cutting-edge projects in geolocalization, action recognition, and medical vision. Collaborative efforts include global partnerships in AI and robotics.
Fei He is an Associate Professor at Coventry University's Centre for Computational Science and Mathematical Modelling. He leads the 'Digital Neuroscience' Cross Cutting Theme and holds editorial roles at journals like IEEE Transactions on Neural Systems and Rehabilitation Engineering. His research focuses on nonlinear system identification, signal processing, and computational neuroscience applied to neurological disorders like Alzheimer’s and epilepsy. He received his PhD and MSc from the University of Manchester. Key projects include EPSRC-funded studies on neurological disorder analysis and brain-computer interfaces. Education: PhD and MSc (Distinction) in Control Engineering from University of Manchester Research Interests: Nonlinear systems, EEG analysis, network inference, and statistical machine learning Grants: EPSRC grants (EP/X020193/1, EP/W036770/1, EP/W035030/1) totaling £1.2M+ for neuroscience and systems biology projects Publications span 63 peer-reviewed articles, with recent work on EEG-based Alzheimer’s diagnosis and graph neural networks. Supervises 8 PhD students and co-leads the feihelab research group. Teaches modules like Introduction to Statistical Methods for Data Science and Bio-systems Engineering.
Michael Pesko is a Professor and J. Rhoads Foster Chair of Economics at the University of Missouri's Department of Economics, housed within the College of Arts and Science. His research focuses on applied microeconomics and health economics, emphasizing the evaluation of health policy changes through causal research methods and data analysis. He has secured over $10 million in research funding, including NIH R01 grants and American Cancer Society awards. His work includes evaluating e-cigarette policies, health insurance mandates for hearing aids, and cancer prevention/detection services. He directs the Tobacco Online Policy Seminar and serves on the National Center for Health Statistics' Board of Scientific Counselors and the Canadian Scientific Advisory Board on Vaping Products. Dr. Pesko's research spans topics like the impact of legal abortion on maternal mortality, paid sick leave mandates on cancer screening, and e-cigarette taxation effects. His recent publications in top journals such as the New England Journal of Medicine and American Economic Journal highlight his contributions to health policy evaluation. His work integrates experimental and quasi-experimental designs to address public health challenges, emphasizing evidence-based policy recommendations. He has advised multiple grants totaling $10M+, with a focus on tobacco policy and healthcare access. His leadership in interdisciplinary forums ensures his research bridges academia and policy-making, influencing public health strategies nationally and internationally.
