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David LozinskiView profile

Associate Professor

• Combustion Science
• Fluid Dynamics
• Financial Mathematics
• +3 more

David Lozinski is an Associate Professor in the Department of Mathematics and Statistics at McMaster University, Faculty of Science. His research spans combustion science, fluid dynamics, and financial mathematics. He has published extensively on topics like smoldering combustion, flame stability, and quantitative risk modeling. Teaching responsibilities include courses on risk management, actuarial mathematics, financial markets, and applied statistics. Research interests combine theoretical and applied mathematics with practical applications in combustion engineering and financial systems. Key contributions include studies on reverse smoldering mechanisms (1995), vapor diffusion flames (1995), and modern financial risk models (2024). Teaching spans undergraduate and graduate programs, covering mathematical finance, probability, and statistical inference. No scientific awards are listed in the profile. Active in curriculum development for financial mathematics programs, instructing courses like MFM 714 (Risk Management) since 2025 and STATS 3G03 (Actuarial Mathematics I) since 2024.

Rajen ShahView profile

Professor

• Statistical Inference
• High-Dimensional Data Analysis
• Machine Learning
• +3 more

Professor Rajen Shah is a faculty member in the Statistical Laboratory at the University of Cambridge, part of the Department of Pure Mathematics and Mathematical Statistics (DPMMS) within the Faculty of Mathematics. His research focuses on statistical methodology, particularly in high-dimensional data analysis, machine learning, and robust statistical inference. He is known for contributions to areas such as change-point regression, inverse propensity score weighting, and efficient estimation techniques in complex models. Key research interests include developing novel methods for variable selection, robust hypothesis testing, and scalable algorithms for large-scale data. He has collaborated on interdisciplinary projects, such as functional genomics studies (e.g., screening conserved genes of unknown function). His work often emphasizes theoretical rigor alongside practical applications in fields like causal inference and computational statistics. Prof. Shah has published extensively in top-tier journals like The Annals of Statistics , Bernoulli , and Journal of the Royal Statistical Society Series B . His recent articles address challenges in cross-validation for change-point detection, rank-transformed subsampling, and sandwich boosting methods. He is actively involved in the academic community, contributing to the Cambridge Statistics Clinic and supervising research in statistical methodology. His research group is affiliated with the Statistical Laboratory at the Centre for Mathematical Sciences, Cambridge. The lab focuses on advancing statistical theory and applications, with a strong emphasis on high-dimensional and assumption-lean methods.

Jonathan ScarlettView profile

Associate Professor

• Machine Learning
• Information Theory
• High-Dimensional Statistics
• +3 more

Jonathan Scarlett is an Associate Professor jointly appointed in the Department of Computer Science, Department of Mathematics, and Institute of Data Science at the National University of Singapore (NUS). He also serves as Assistant Dean (Graduate Studies) in the School of Computing. His research focuses on information theory, machine learning, and high-dimensional statistics, with applications to optimization, group testing, and statistical inference. Education: Ph.D. (Information Engineering), University of Cambridge (2014) B.Eng. (Electrical Engineering) and B.Sci. (Computer Science), University of Melbourne (2010) Research Interests: Algorithmic foundations of machine learning and statistical estimation Information-theoretic limits and adaptive algorithms Applications in group testing, compressed sensing, and DNA storage Robust optimization under uncertainty and adversarial settings His work bridges theoretical guarantees with practical algorithm design, emphasizing scalable solutions for high-dimensional problems. Key Achievements: Recipient of Singapore NRF Fellowship (2018) and NUS Presidential Young Professorship Listed in MIT Technology Review's 'Innovators Under 35' Asia Pacific (2021) Over 100 publications in top venues like ICML, NeurIPS, and IEEE Transactions Advising & Grants: Supervised multiple PhD students in areas like Bayesian optimization, group testing, and compressed sensing Lead researcher on projects supported by NRF and NUS grants Developed novel frameworks for safe Bayesian optimization and robust bandit algorithms Labs & Collaborations: Active in the Information Theory and Statistical Learning Group at NUS, collaborating with global institutions like EPFL and MIT on data science challenges.

Ronojoy AdhikariView profile

Lecturer

• Statistical Physics
• Soft Matter
• Stochastic Processes
• +2 more

Dr Ronojoy Adhikari is a Lecturer in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Faculty of Mathematics. His research focuses on statistical physics, soft matter, stochastic processes, Bayesian inference, and machine learning. He has taught Mathematical Biology (2018–2021) and Electrodynamics (2021–2023). His work bridges theoretical frameworks with experimental insights, addressing phenomena such as active matter dynamics, non-equilibrium thermodynamics, and stochastic modeling of biological systems. Key contributions include studies on autophoretic particles, path probabilities in stochastic systems, and Bayesian approaches to epidemiological modeling. His research group, part of the Soft Matter program at DAMTP, explores interdisciplinary topics like colloidal crystallization and enzymatic network kinetics. Notable publications highlight investigations into fluctuating hydrodynamics, entropy production measurements, and the mechanics of rigid inclusions on curved surfaces. His interdisciplinary approach integrates computational methods (e.g., lattice Boltzmann simulations) with mathematical rigor to understand complex systems. While no awards are explicitly listed, his extensive publication record underscores sustained academic impact. Ongoing research includes projects on path probabilities, active particle dynamics, and the interplay between geometry and material behavior in Cosserat solids. Advising and grants are not explicitly detailed in the provided texts, but his role as a faculty member suggests involvement in student supervision and collaborative projects. His work frequently appears in top journals like Physical Review Letters , Journal of Fluid Mechanics , and Science Advances , reflecting high-quality contributions to theoretical and applied physics.

• Applied Mathematics
• Stochastic Differential Equations
• Nonequilibrium Statistical Mechanics
• +7 more

Greg Pavliotis is a Professor of Applied Mathematics at the Department of Mathematics, Imperial College London. His research focuses on stochastic processes, nonequilibrium statistical mechanics, and homogenization theory. He develops analytical, computational, and statistical methods for multiscale systems. His work includes inference/control for multiscale systems, computational techniques for transport coefficients, and sampling methods in molecular dynamics. Position: Professor of Applied Mathematics Affiliations: Applied Mathematics and Mathematical Physics, Dynamical Systems, Fluid Dynamics, Stochastic Analysis Research interests span stochastic differential equations, kinetic theory, and high-dimensional sampling. He contributes to both theoretical advancements and practical computational tools for complex systems analysis. No specific articles were listed in the provided text. His research trends emphasize multiscale analysis, statistical mechanics applications, and computational methodology development. No awards or grants were explicitly mentioned in the text. His professional activities include leading research projects and maintaining active affiliations across multiple interdisciplinary centers at Imperial College.

Duygu Uygun TuncView profile

Assistant Professor

• Philosophy of Science
• Epistemology
• Philosophy of Mind
• +3 more

Duygu Uygun Tunc is a Collegiate Assistant Professor in the Department of Philosophy at the University of Chicago, where she has been teaching since September 2023. She is also affiliated with the Society of Fellows and holds the position of Harper-Schmidt Fellow. Her academic journey includes a dual PhD from Heidelberg University in Germany and the University of Helsinki in Finland, which she completed in October 2020 with her dissertation titled 'Communication and the Origins of Personhood'. Her research spans multiple dimensions of epistemic value in scientific research, investigating what makes individuals good researchers, groups good scientific collectives, epistemic systems good scientific communities, and research procedures good empirical studies. She works extensively on intellectual virtues in science, scientific communities, experimental methodology (including testability, theory choice, and underdetermination), and applied philosophy of science (addressing the replicability crisis, science policy, and scientific norms). A central question guiding her work is how the social nature of scientific inquiry can best be utilized to advance scientific knowledge. Her publications from 2019-2024 demonstrate a consistent focus on the intersections of philosophy of science, epistemology, and values in science. Her work often examines distributed cognition in scientific communities, the nature of scientific expertise in contemporary research environments, and the relationship between scientific methodology and social values. A notable trend across her publications is the exploration of how scientific knowledge production has become increasingly social and collaborative, requiring new conceptual frameworks to understand epistemic responsibility in team science. Marie Curie Actions Fellowship for 'Extended Scientific Virtue' project Dr. Uygun Tunc currently teaches 'Science and Values' (PHIL 23404/33404) at the University of Chicago, examining debates about the value-free ideal of science and the relationship between scientific objectivity and social values. She is actively working on a series of articles on values in science and her first book manuscript on scientific expertise, which develops a novel conception addressing distributed epistemic labor and technological extension of epistemic processes while maintaining notions of epistemic responsibility. She maintains a blog on the epistemology of science where she explores topics like trust and criticism in scientific communities.

• Statistics
• Machine Learning
• Psychology of Music
• +3 more

Dr. Linh Nghiem is a Lecturer in Statistics at the School of Mathematics & Statistics, University of Sydney. She specializes in both methodological and applied statistical research, focusing on measurement error modeling, dimension reduction, and graphical models. Her applied work involves collaborations with scientists exploring human perception of music and the societal impact of music on social empathy. Her research interests include longitudinal data analysis, privacy in data science, and experimental psychology of music at behavioral and neural levels. She is affiliated with the Sydney Southeast Asia Centre and actively contributes to interdisciplinary projects. Dr. Nghiem has secured grants such as the 2023 'Methodologies for complex datasets' under the Faculty Startup Scheme. She collaborates with institutions globally and maintains an active presence in academic communities through her ORCID profile and personal website.

• International Relations
• Political Science
• Global Governance
• +3 more

Inken von Borzyskowski is a Professor of International Relations at the University of Oxford's Department of Politics and International Relations (DPIR) and a Fellow at St Catherine’s College. She serves as Co-Coordinator of the DPIR International Relations Colloquium and holds roles on the editorial boards of International Studies Quarterly and Global Governance , as well as the Board of Directors at the Academic Council on the United Nations System (ACUNS). Her academic journey includes previous positions at University College London (UCL), Florida State University, and postdoctoral research at institutions like the Free University Berlin, Duke University, and the Hertie School. Dr. von Borzyskowski earned her PhD in International Relations, Methodology, and Comparative Politics from the University of Wisconsin-Madison in 2014. She holds an M.A. from Free University Berlin and has conducted extensive research and teaching across various institutions, including the Social Science Research Center (WZB) in Berlin and Duke University. Her research focuses on the intersection of international organizations and domestic governance, particularly the effects of international organizations on domestic conflict and elections. Key areas include international democracy assistance, election violence, and membership politics (withdrawals and suspensions). Her first book, The Credibility Challenge: How Democracy Aid Influences Election Violence (Cornell University Press, 2019), explores how international election monitoring and assistance impact electoral violence dynamics. Her second book, Exit from International Organizations (Cambridge University Press, forthcoming), examines state withdrawal from international bodies. Dr. von Borzyskowski’s research has garnered prestigious recognitions, including the British Academy Mid-Career Fellowship, the Philip Leverhulme Prize, and the APSA Best Article Award. Her work has been supported by grants from institutions like the U.S. Institute of Peace and the World Bank. She advises graduate students such as Rohan Watt and Christian Uta and contributes to policy-related initiatives, including co-authoring USIP Peace Briefs on election violence prevention in Liberia and Kenya. She is actively involved in academic networks, including roles at the American Political Science Association (APSA) and International Studies Association (ISA), and advocates for rigorous methodological approaches in studying international organizations and their domestic impacts.

Zheng (Tracy) KeView profile

Associate Professor

• High-dimensional statistics
• Machine learning
• Social network analysis
• +3 more

Zheng (Tracy) Ke is an Associate Professor of Statistics at Harvard University. She holds a Ph.D. from Princeton University (2014) and a B.S. from Tsinghua University (2009). Her research focuses on high-dimensional statistics, machine learning, social network analysis, text mining, and bioinformatics. Notable contributions include advancements in network data analysis (e.g., SCORE normalization), text analysis methodologies, and statistical genetics pipelines. Dr. Ke has received prestigious awards such as the COPSS Emerging Leader Award (2024) and the Sloan Research Fellowship (2023). She has organized major conferences like the Workshop on Statistical Network Analysis and Beyond (2024) and contributed to the MADStat dataset analyzing statisticians' co-authorship networks. Her research interests span theoretical and applied domains, with a focus on developing scalable algorithms and rigorous statistical frameworks for complex data. Recent work emphasizes challenges like severe degree heterogeneity in networks and rare/weak signal detection in high-dimensional settings. Dr. Ke is an Associate Editor for the Journal of the American Statistical Association and actively collaborates on interdisciplinary projects.

• Computer Vision
• 4D Vision
• Machine Learning
• +9 more

Professor Adrian Hilton is a distinguished faculty member at the University of Surrey, serving as Director of the Centre for Vision, Speech and Signal Processing (CVSSP) and Director of the Surrey Institute for People-Centred AI. He is affiliated with the School of Computer Science and Electronic Engineering and leads the Visual Media Research Lab (V-Lab). His research focuses on pioneering next-generation 4D computer vision technologies that enable machines to understand and model dynamic real-world scenes. Key areas include 3D/4D shape capture, computer vision, machine learning, graphics, and animation for applications in sports analysis, film/TV production, virtual reality, and medical imaging. His work bridges the gap between real and computer-generated imagery, with notable contributions in volumetric capture, motion capture, and free-viewpoint video. Hilton's recent publications demonstrate a strong trend toward multimodal integration, particularly combining audio and visual processing for spatial audio applications, while advancing 4D reconstruction techniques for human performance capture. His work increasingly incorporates transformer architectures and neural rendering techniques for improved illumination estimation, shadow modeling, and multi-view consistency. Scientific Awards and Recognition Two EU IST Innovation Prizes Manufacturing Industry Achievement Award Royal Society Industry Fellowship (2008-2011) Royal Society Wolfson Research Merit Award in 4D Vision (2013-2018) Fellow of the Royal Academy of Engineering (FREng) Fellow of the International Association for Pattern Recognition (FIAPR) Fellow of the Institution of Engineering and Technology (FIET) Hilton actively mentors PhD and post-doctoral researchers through his leadership of CVSSP, which has a grant portfolio exceeding £31M and comprises 170 researchers. He has successfully commercialized several technologies, including systems used by the BBC for sports commentary visualization. His research collaborations span major industry partners including BBC, BT, Sony, Framestore, and The Foundry. He co-founded the G3 Games forum and the CVMP Conference on Visual Media Production, demonstrating strong engagement with the creative industries. Current research projects include the S3A Programme Grant in Future Spatial Audio and InnovateUK's ALIVE project for 360 video reconstruction.

• Particle Physics
• High Energy Physics
• ATLAS Collaboration
• +3 more

Jonas Strandberg is an Associate Professor at KTH Royal Institute of Technology's Department of Physics, part of the School of Engineering Sciences. His research focuses on particle physics, particularly within the ATLAS Collaboration at the Large Hadron Collider (LHC). He contributed to the Higgs boson discovery and currently studies its properties. Strandberg has been involved in detector development, including the HGTD timing detector for the LHC upgrade. He holds a PhD from Stockholm University (2006) and worked as a postdoc at the University of Michigan (2006-2011) before joining KTH. His teaching responsibilities include courses on experimental particle physics, statistical methods, and engineering skills. Research interests span high-energy physics, collider technology, and detector systems. Research Highlights: Member of the ATLAS Collaboration since 2011 Key contributor to Higgs boson measurements Developed timing detector systems for LHC upgrades Published extensively on particle physics and accelerator technology Teaching & Supervision: Course responsible for Experimental Particle Physics (SH2203) Teaching roles in Applied Modern Physics (SH1015), Embedded Systems Design (IL2232), and more Professional Activities: ATLAS Data Preparation Coordinator (2015-2017) Member of the Particle and Astroparticle Physics Group at AlbaNova University Centre

• Econometrics
• Bayesian Statistics
• Quantitative Marketing
• +2 more

Kenichi Shimizu is an Assistant Professor in Econometrics (tenure-track) at the Department of Economics, University of Alberta. He holds a PhD in Economics from Brown University (2021) and previously worked at the Adam Smith Business School, University of Glasgow. His research focuses on Bayesian econometrics, quantitative marketing, industrial organization, and time-series analysis. He teaches courses such as Introductory Econometrics (ECON 399) and Applied Econometrics (ECON 599). Education: PhD in Economics from Brown University (2021). Professional affiliations include roles at the University of Alberta and University of Glasgow. His work emphasizes methodological advancements in econometrics with applications to marketing and industrial organization. Research trends in his publications highlight Bayesian methodologies for dynamic modeling, structural breaks, and high-dimensional data. Key topics include semiparametric estimation, sparse models, and policy evaluation frameworks. Grants: Recipient of SSHRC Insight Development Grant (2024-2026) for research on Bayesian econometric methods in industrial organization and marketing. Active presenter at major conferences including the NBER-NSF Seminar, Canadian Economic Association meetings, and the World Congress of the Econometric Society. Teaching responsibilities include undergraduate and graduate econometrics courses with emphasis on applied regression methods and model specification.

Srijan SenguptaView profile

Associate Professor

• Statistical Methodology for Network Data
• Bootstrap and Resampling Methods
• Big Data Analysis
• +6 more

Srijan Sengupta is an Associate Professor of Statistics at North Carolina State University (NC State) since 2020. Previously, he served as an Assistant Professor at Virginia Tech from 2016 to 2020. He holds a Ph.D. in Statistics from the University of Illinois at Urbana-Champaign (2016) and degrees from the Indian Statistical Institute (B.Stat and M.Stat with Distinction). His research focuses on statistical methodology for network data, anomaly detection, bootstrap methods, and scalable inference, with applications in healthcare analytics, epidemiology, and cybersecurity. Education: Ph.D. in Statistics, University of Illinois at Urbana-Champaign (2011–2016) M.Stat (1st Division with Distinction), Indian Statistical Institute (2007–2009) B.Stat (1st Division with Distinction), Indian Statistical Institute (2004–2007) Research Interests: His methodological work includes statistical inference in networks, anomaly detection, bootstrap techniques, and scalable algorithms for big data. Applications span social determinants of health, healthcare analytics, space physics, epidemiology, and cybersecurity. He emphasizes interdisciplinary collaborations, particularly in patient safety event analysis and medical device safety. Awards and Grants: Norton Prize for Outstanding PhD Thesis (2015) NIH R01 Grant ($890,055, Principal Investigator) for statistical algorithms in patient safety (2019–2022) Multiple grants for network inference and anomaly detection (NSF, Socially Determined Inc., Virginia Tech Foundation) Advising and Service: Advises over 20 students across PhD, master’s, and undergraduate research programs. Serves as an Associate Editor for Sankhya, Series B and peer reviewer for top journals. Active in university service roles at NC State and Virginia Tech, including faculty hiring committees and curriculum development. Labs and Collaborations: Leads research on statistical network analysis, including projects funded by NIH and NSF. Collaborates with institutions globally on topics like epidemic thresholds, cybersecurity defenses (e.g., phishing detection), and healthcare analytics.

Suhasini Subba RaoView profile

Professor

• Time Series Analysis
• Nonstationary Processes
• Nonlinear Processes
• +3 more

Suhasini Subba Rao is a Professor of Statistics at Texas A&M University, affiliated with the Department of Statistics within the College of Arts & Sciences. Her research focuses on time series analysis, nonstationary processes, nonlinear processes, and spatio-temporal models. She holds a prominent academic position and contributes to both theoretical and applied statistical methodologies. Education details are not explicitly provided in the text, but her academic career includes extensive work in statistical theory and methodology. Her research interests emphasize developing robust statistical techniques for analyzing nonstationary and complex time series data, with applications in spatio-temporal modeling and recursive algorithms. Her recent articles highlight advancements in inverse covariance estimation, graphical models for nonstationary time series, and spectral methods for small sample data. She has also explored reconciliation of Gaussian and Whittle likelihoods, enhancing estimation accuracy in frequency domains. No scientific awards are explicitly mentioned. Her advising and grants are not detailed, but her prolific publication record suggests active research collaborations and funding. She maintains a lab/team focused on time series analysis, though specific team names are not provided.

Liu YongView profile

Assistant Professor

• Applied Econometrics
• Bayesian Econometrics
• Machine Learning
• +4 more

Yong Liu is an Assistant Professor in the Department of Agricultural Economics at Texas A&M University. His research focuses on applied econometrics, Bayesian methodologies, machine learning, and agricultural policy with emphasis on crop insurance and risk management. He holds a Ph.D. in Agricultural Economics from the University of Guelph (2016), preceded by dual M.S. degrees in Economics and Agricultural Economics from Yunnan University and the University of Guelph, respectively, and a B.S. in Chemistry from Yunnan University. His research explores econometric techniques for agricultural policy evaluation, including causal inference methods and behavioral economics applications. Recent work analyzes climate change impacts on crop yields, the efficacy of insurance subsidies, and the integration of weather data into risk assessment models. Liu’s publications demonstrate a strong focus on improving crop insurance mechanisms through statistical innovations like Bayesian model averaging and spatial-temporal forecasting. His academic contributions span theoretical advancements in density estimation and practical applications in agricultural finance. While no specific awards or grants are listed, his active publication record indicates sustained research engagement in agricultural economics and quantitative methods.