Cong Ling is a Professor of Information Theory and Cryptography at Imperial College London's Department of Electrical and Electronic Engineering, within the Faculty of Engineering. His research focuses on lattice theory and its applications in coding, cryptography, quantum information, and number theory. Key affiliations include the Academic Centre of Excellence in Cyber Security Research and the Engineering Secure Software Systems group. Education details are not explicitly provided in the text, but his professional experience indicates advanced qualifications in electrical engineering and mathematics. Research interests span lattice-based cryptography, post-quantum security, algebraic coding theory, and quantum-resistant algorithms. His work bridges information theory and number theory, with contributions to MIMO systems, secure communication protocols, and cryptographic protocol design. Recent publications emphasize lattice reduction techniques, quantum algorithms for the shortest vector problem, and advancements in polar codes. Notable trends include exploration of non-commutative algebras for cryptography, Gaussian sampling optimizations, and hybrid quantum-classical approaches to hard integer problems. Over 50+ articles published since 2018 reflect his leadership in lattice-based research and quantum-safe technologies. Awards: None explicitly listed in the text. Grants/Advising: No specific grants or student advisees mentioned; focus remains on collaborative research outputs. Labs/Teams: Associated with Imperial's Cyber Security Research groups and quantum engineering initiatives.
Qimin Liu is an Assistant Professor at Boston University, serving as Lab Director of the Quantitative Psychopathology Laboratory. He holds a PhD in Psychological Sciences from Vanderbilt University with specializations in Clinical Science and Quantitative Methods. His research focuses on emotional disturbances across development, statistical methodology development, and health equity with an emphasis on intersectional marginalization. He has expertise in analyzing intensive longitudinal data and has published extensively on topics like irritability, suicidality, and mental health disparities among sexual and gender minority populations. Dr. Liu’s work frequently integrates advanced statistical techniques such as latent variable modeling, network analysis, and machine learning. His recent studies explore the temporal dynamics of affect, the impact of stigma on mental health, and the role of childhood adversity in psychiatric outcomes. Notable contributions include developing methods for analyzing zero-inflated longitudinal data and creating algorithms for digital phenotyping of mood disorders through mobile device usage patterns. His scholarship emphasizes bridging clinical phenomena with rigorous quantitative approaches, addressing gaps in understanding how social determinants and individual differences shape mental health trajectories. He has collaborated on large-scale datasets like the Collaborative Psychiatric Epidemiological Surveys and contributed to interdisciplinary research on public health outcomes among aging sexual minority men. Dr. Liu’s methodological innovations include the DACF framework for ceiling/floor effect data and the lamme package for log-analytic multiplicative effects modeling. He actively publishes in high-impact journals such as Psychological Methods and Journal of Abnormal Psychology , focusing on both empirical findings and statistical best practices.
Frank L. H. Brown is a Professor at the University of California, Santa Barbara with joint appointments in the Department of Physics and Department of Chemistry and Biochemistry. His research focuses on theoretical and computational approaches to understanding biomembrane dynamics and related biophysical phenomena, situated within the College of Letters and Science. Dr. Brown's research interests span the interface between physical chemistry and biophysics. He employs a variety of theoretical tools including statistical mechanics , hydrodynamics , elasticity theory , and quantum mechanics to study complex biological systems. His work particularly emphasizes the dynamics and structure of biomembranes and the interpretation of various spectroscopy experiments including single molecule fluorescence, neutron spin echo, and flicker spectroscopy. Analysis of his publication record reveals a consistent focus on computational modeling of lipid bilayers, membrane proteins, and related phenomena, with particular emphasis on developing novel theoretical frameworks for understanding membrane behavior across multiple scales. Dr. Brown leads an active research group that includes current members Ehsan Noruzifar (Postdoctoral Researcher) and Sean Cray (Graduate Student). His former group members include numerous successful scientists such as Grace Brannigan, Brian Camley, Lawrence Lin, and Max Watson who completed their graduate studies under his supervision, along with several postdoctoral researchers. His research has been supported by funding that enables theoretical and computational investigations of biomembrane systems. The Brown Research Group operates at the intersection of physics, chemistry, and biology, with facilities connected to the Biomolecular Sciences & Engineering Program and the California NanoSystems Institute (CNSI) at UCSB. Their work combines advanced computational techniques with theoretical physics to address fundamental questions about soft and living matter systems, particularly at biological interfaces.
Nicholas Kortessis is an Assistant Professor in the Department of Biology at Wake Forest University, within The Undergraduate College. His research lies at the intersection of ecology, evolution, and theoretical modeling, focusing on how environmental variability shapes biological diversity. Research Interests: His primary areas include statistical and theoretical ecology, adaptation in variable environments, population and community dynamics, and ecosystem modeling. He uses mathematical frameworks to simulate ecological processes across large spatial and temporal scales, bridging experimental data with predictive theory. Publication Trends: His recent work spans topics such as habitat fragmentation, disease in invasive species, character displacement, seed dormancy evolution, and pandemic transmission dynamics. These reflect a strong emphasis on theoretical and synthetic approaches to ecological problems, often involving collaboration across institutions. Scientific Awards: No awards are mentioned in the provided text. Advising and Grants: While no specific students or grants are listed, Dr. Kortessis leads an active research lab and collaborates widely, indicating ongoing mentorship and likely grant-supported research. His lab engages in theoretical and data-driven ecological studies, suggesting funding from agencies supporting environmental and theoretical biology. Labs and Teams: He runs the Kortessis Lab at Wake Forest University, which focuses on modeling ecological and evolutionary processes. The lab emphasizes mathematical and conceptual tools to explore biodiversity, coexistence, and ecosystem responses to environmental change.
Thomas M. Antonsen Jr. is a Distinguished University Professor at the University of Maryland, holding joint appointments in the Department of Electrical and Computer Engineering and the Department of Physics. He is affiliated with the Institute for Research in Electronics & Applied Physics (IREAP), Maryland Energy Innovation Institute, and the Institute of Physical Science and Technology. His research focuses on plasma physics, nonlinear dynamics, and high-power coherent radiation sources. Antonsen earned his B.S., M.S., and Ph.D. in electrical engineering from Cornell University (1973–1977) and has held visiting positions at institutions such as the University of California, Santa Barbara, and the École Polytechnique in France. **Education:** B.S., Electrical Engineering, Cornell University, 1973 M.S., Electrical Engineering, Cornell University, 1976 Ph.D., Electrical Engineering, Cornell University, 1977 **Research Interests:** Antonsen’s work spans magnetically confined plasmas, laser-plasma interactions, and advanced vacuum electronics. He has pioneered adjoint methods for optimizing beam-wave interaction systems and contributed to the development of high-power microwave amplifiers. His recent projects include wave chaos in complex systems and machine learning applications in nonlinear dynamics. **Awards & Honors:** James Clerk Maxwell Award (American Physical Society, 2023) IEEE Marie Sklodowska-Curie Award (2022) University of Maryland Distinguished University Professor (2017) IEEE Fellow (2012) **Teaching & Mentorship:** Antonsen teaches courses such as Physics 132 (Biophysics), Electrodynamics, and Plasma Physics. He mentors graduate students in plasma physics and vacuum electronics through his research groups at IREAP and the Bright Beams Collective. **Labs & Collaborations:** His research is supported by grants from the Department of Energy, NASA, and the Office of Naval Research. Key collaborations include the National Institute of Standards and Technology (NIST) and the European XFEL facility.
Marilyn J Smith is the David S. Lewis Professor and Director of the Vertical Lift Research Center of Excellence (VLRCOE) at the Georgia Institute of Technology's Daniel Guggenheim School of Aerospace Engineering. She leads a seven-university consortium conducting vertical lift research for the U.S. Army, Navy, and NASA, and has secured over $200 million in collaborative research funding. Computational Nonlinear Computational Aeroelasticity Lab Director NASA FUN3D development team contributor Aerospace Systems Design Lab (ASDL) affiliate Her research spans unsteady aerodynamics, computational aeroelasticity, and sustainable energy applications across rotary-wing, fixed-wing, and launch vehicles. She serves on the Vertical Lift Consortium (VLC) Board of Directors and Vertical Flight Society (VFS) Board, while acting as VFS Deputy Technical Director for Aeromechanics and leading international NATO AVT panels on UAV aerodynamics. Recent publications focus on galaxy cluster cosmology, ship-helicopter dynamic interface modeling, and Type Ia supernova analysis. She has won prestigious awards including the AIAA Aerodynamics Award and multiple American Helicopter Society honors for research, mentoring, and service. 2022 AIAA Aerodynamics Award 2015 Best Paper Awards at AHS Forum 2014 & 2012 AHS Agusta-Westland International Fellowships Her laboratory work integrates high-performance computing with aerospace design and develops advanced turbulence models through partnerships with Georgia Tech Research Institute (GTRI). She contributes to public science communication with appearances on National Geographic, PBS, NPR, and local media.
Keely Dugan serves as an Assistant Professor in the Department of Psychology at the University of Missouri, directing the Personality, Attachment, and Change (PAC) Lab in McReynolds Hall. She holds a PhD in Social/Personality Psychology from the University of Illinois at Urbana-Champaign (2023) and completed an NIMH T32 Postdoctoral Fellowship at the University of Minnesota (2024). Her research investigates dynamic changes in personality traits and attachment styles across time, life experiences, and social contexts. Using advanced statistical methodologies, she examines how individual differences manifest in everyday environments, emphasizing how cumulative "little moments" shape long-term development. This work bridges personality psychology, attachment theory, and contextual behavioral science. Recent 2024 publications reveal three interconnected research strands: quantifying life events' impact on personality trajectories, testing attachment theory's canalization hypothesis through within-subject variations, and conducting systematic reviews of queer/minority identities in relationship science. These studies demonstrate her interdisciplinary approach combining longitudinal analysis, computational modeling, and inclusive relationship research. Dr. Dugan's scientific recognition includes: NIMH T32 Postdoctoral Fellowship (2024) She actively recruits graduate students for Fall 2025 and teaches PSYCH 9330 (Graduate Research Methods), PSYCH 8620 (Graduate Seminar in Personality Psychology), and PSYCH 2320 (Introduction to Personality Psychology). Current projects include NIH-funded personality-environment interaction studies and development of AI-assisted coding methodologies for behavioral research. The PAC Lab, located in McReynolds Hall's Lower Level, currently spearheads a groundbreaking project analyzing 3D living room scans to predict personality traits through environmental cues. This initiative employs both human coders and machine learning algorithms to examine how physical spaces reflect and influence individual differences in attachment and personality expression.
Quoc Thong Le Gia is an Associate Professor in the School of Mathematics & Statistics at the University of New South Wales (UNSW), Sydney. He holds a PhD in Mathematics from Texas A&M University (2003), an MS in Mathematics from Texas A&M University (2000), and a BSc in Mathematics and Computer Science from UNSW (1998). His research focuses on Numerical Analysis , Approximation Theory , Partial Differential Equations , and Stochastic Processes , with particular expertise in problems on spherical domains. His work bridges theoretical mathematics with practical applications in computational science, data science, and machine learning. Le Gia's recent publications demonstrate a strong focus on numerical methods for PDEs on spheres, stochastic analysis, and machine learning applications. His work shows consistent progression from theoretical foundations to practical implementations, with increasing interdisciplinary applications in recent years. L. F. Guseman Prize in Mathematics, Texas A&M University (2003) As a dedicated academic mentor, Le Gia has supervised numerous PhD, Master's, and Honours students across computational mathematics and data science topics. He has secured significant research funding through ARC Discovery Projects including DP220101811 (2022-2024) and DP180100506 (2018-2020). Professionally, he serves as External Associate Editor for Frontiers in Applied Mathematics and Statistics , Secretary for ANZIAM's Computational Mathematics Group, and Co-chair of Mathematics of Computation and Optimisation (AustMS Special Interest Group).
Professor Shaun Gregory is the Director of the Centre for Biomedical Technologies at Queensland University of Technology (QUT), where he also serves as Co-Director of the Artificial Heart Frontiers Program, Founder and Director of the Heart Hackathon student team competition, and Director of the CardioRespiratory Engineering and Technology Laboratory. He holds appointments in the Faculty of Engineering, School of Mechanical, Medical & Process Engineering. His educational background includes Bachelor, Masters (research), and PhD degrees, all awarded by QUT. He also holds both NHMRC and Heart Foundation fellowships, demonstrating his significant contributions to cardiovascular research. Professor Gregory's research applies a translational approach to cardiovascular engineering with a particular focus on devices used to support or replace the heart. His work brings together multidisciplinary teams of engineering, biomedical science, design, and medicine to develop novel technical solutions for clinically relevant problems. His research has changed clinical practice on numerous occasions and assisted with the regulatory approval of medical devices. His areas of interest include mechanical circulatory support, artificial heart development, cardiovascular device engineering, and hemodynamics. His publication portfolio demonstrates a strong focus on extracorporeal membrane oxygenation (ECMO), ventricular assist devices, and cardiovascular device testing. His recent work has explored computational fluid dynamics in blood flow analysis, novel cannula design for circulatory support, and the hemodynamic effects of various cardiovascular devices. His research often bridges engineering principles with clinical applications, resulting in practical innovations in cardiac support technologies. NHMRC Fellowship Heart Foundation Fellowship President-Elect of the International Society for Mechanical Circulatory Support Professor Gregory has successfully secured more than $65 million in research funding and has published over 100 research articles in his field. He is actively involved in mentoring the next generation of researchers, currently accepting Honours, Masters, and PhD students. His CardioRespiratory Engineering and Technology Laboratory serves as a hub for interdisciplinary research that brings together engineering, biomedical science, and clinical expertise to address critical challenges in cardiovascular medicine.
Christine Mahoney is a Professor of Public Policy and Politics and Chief Innovation Officer at the Frank Batten School of Leadership and Public Policy at the University of Virginia. She also serves as Director of the Tadler Program in Impact Investing in Appalachia, the UVA Environmental Institute-funded Climate Collaborative on Appalachian Renewable Energy & Resilience, and the EPA-funded Community Change project for economic development in Appalachia. Previously, she was an Assistant Professor at the Maxwell School of Syracuse University and Director of the Center for European Studies and the Maxwell EU Center. Dr. Mahoney earned her Ph.D. in Political Science from Pennsylvania State University in 2006, with fields of study in American politics, Comparative politics, and Research Methods & Statistical Analysis. She also completed her M.A. (2003) and B.A. (2001) in Political Science and International Politics, respectively, at Pennsylvania State University. Professor Mahoney specializes in social justice advocacy, activism, and direct action through social entrepreneurship. Her research focuses on how advocates shape public policy, with particular attention to lobbying strategies in powerful political systems like the United States and the European Union. She has conducted extensive fieldwork in seven conflict zones across Asia, Africa, Eastern Europe, and Latin America, studying the rights of forcibly displaced people and proposing innovative solutions through social entrepreneurship. Her work bridges political science with practical applications in impact investing and refugee integration, creating tangible pathways for social change through entrepreneurial approaches. Professor Mahoney's scholarly output reveals an evolution from comparative studies of advocacy systems to practical applications of social entrepreneurship for forcibly displaced populations. Her early work established foundational knowledge about lobbying in transatlantic political systems, while her more recent research has shifted toward actionable solutions for global displacement crises and sustainable community development. This trajectory demonstrates her commitment to translating academic insights into real-world impact, particularly through the lens of social entrepreneurship and impact investing. Fulbright Fellow Visiting Scholar at Oxford National Science Foundation grant recipient Emerging Scholar award from the American Political Science Association UVA's Public Impact-Focused Research Award Through Social Entrepreneurship at UVA (SE@UVA), which she founded and led for a decade (2011-2021), Professor Mahoney has mentored over 80 student social enterprise teams and supported more than 100 social entrepreneurs with over 38,400 hours of pro-bono consulting. She has secured $37.7 million in funding for programs addressing social and environmental challenges, including the Tadler Program in Appalachia that has made significant strides in advancing rural policy and economic development. Her work demonstrates a consistent commitment to connecting academic research with practical applications that address pressing social problems. Professor Mahoney leads several significant initiatives including the Refugee Investment Network, where she serves as fellow and advisor, and multiple UVA-based programs focused on social entrepreneurship, impact investing, and community development. Her interdisciplinary approach brings together policy experts, social entrepreneurs, impact investors, and community stakeholders to develop innovative solutions to complex social problems, particularly in the areas of refugee integration and rural economic development in Appalachia.
Amit Singer is a Professor of Mathematics at Princeton University, specializing in computational methods for structural biology and cryo-electron microscopy (cryo-EM). His work focuses on developing mathematical frameworks and algorithms for analyzing large-scale microscopy datasets, particularly in 3D reconstruction and heterogeneity analysis of molecular structures. He leads research in manifold learning, optimal transport, and harmonic analysis, with applications to cryo-EM, signal processing, and inverse problems. Research interests include: (1) Mathematical methods for cryo-EM, including particle alignment, density map analysis, and subspace-based reconstruction techniques; (2) Development of rotation-invariant representations for imaging problems; (3) Application of machine learning and optimization to biomedical imaging challenges. His contributions bridge pure mathematics (e.g., harmonic analysis, manifold theory) with applied computational techniques for real-world microscopy data. Key trends in his recent articles (2023–2025) include advancements in multi-reference alignment methods, Wasserstein distance-based image registration, and overcoming particle detection limitations in cryo-EM. He also explores sparsity constraints, autocorrelation analysis, and novel algorithms for handling heterogeneous datasets. These methods improve resolution and reduce computational costs in analyzing molecular structures at atomic scales. Notable contributions include the ASPiRE software package for steerable PCA, and foundational work on synchronization problems in cryo-EM orientation estimation. His research often addresses algorithmic scalability and robustness to noise in experimental setups.
Ramina Sotoudeh is an Assistant Professor of Sociology at Yale University with a secondary appointment in Statistics & Data Science. Her research bridges sociogenomics, the sociology of culture, and social inequality, focusing on how genetic and social environments interact to shape human behavior. Education : BA in Social Research and Public Policy from NYU Abu Dhabi, PhD in Sociology from Princeton University Postdoctoral Experience : Fellow at Nuffield College, University of Oxford Ramina’s work in sociogenomics examines how institutional, relational, and genetic contexts influence health outcomes, such as smoking behavior and peer interactions. Her sociology of culture projects use relational methods to explore cultural frameworks underlying attitudes toward science, religion, politics, and marriage. She also investigates health disparities and inequality through interdisciplinary lenses. Her most recent publications analyze genomic population structure, behavioral plasticity, and computational approaches to algorithm selection. Earlier works focus on cultural attitudes, behavioral diffusion in networks, and genetic correlations with education and longevity. These studies span journals like American Sociological Review , PNAS , and Demography .
Mark P. Kritzman is a Senior Lecturer in Finance at the MIT Sloan School of Management. He concurrently serves as President & CEO of Windham Capital Management LLC and Senior Partner at State Street Associates. His roles include board memberships at the Institute for Quantitative Research in Finance, Investment Fund for Foundations, and editorial boards of journals like the Journal of Investment Management and Financial Analysts Journal. Education: MBA from New York University and Chartered Financial Analyst (CFA) designation. His research focuses on investing strategies , risk management , and predictive analytics , with recent work addressing federal spending's impact on inflation, bubble detection, and NBA draft prospect evaluation. He has authored six books, including Puzzles of Finance and The Portable Financial Analyst . Key publications from 2023–2025 explore themes like transparent predictive modeling, volatility forecasting, and algorithmic alternatives to neural networks. His work bridges academia and industry, emphasizing practical applications of quantitative methods. Awards : 2025 James R. Vertin Award, 2013 Peter L. Bernstein Award, multiple article honors. Grants/Advising : No explicit student advisees listed; professional contributions focus on institutional advisory roles. He leads Windham Capital Management and actively contributes to editorial boards, shaping discourse in finance and quantitative research.
Zhipeng Liao is a Professor of Economics at the University of California, Los Angeles (UCLA), where he contributes to the Department of Economics. He holds a Ph.D. from Yale University and specializes in econometric theory and applied econometrics. His research focuses on developing statistical methods for evaluating economic models, nonstationary time series analysis, and robust inference in semi/nonparametric frameworks. Professor Liao's work has been published in leading journals such as the Annals of Statistics , Econometrica , and the Review of Economic Studies . He serves on the editorial boards of several prestigious journals, including Econometric Reviews , Econometric Theory , and Journal of Business & Economic Statistics . His research interests span econometric theory, time series analysis, panel data modeling, and nonparametric inference, with applications to financial economics and macroeconomic modeling. His recent publications emphasize methodological advancements in hypothesis testing, model selection, and robust estimation techniques. These include contributions to the analysis of spatially dependent panel data, instrumental variables methods, and the evaluation of macro-finance models. His work bridges theoretical econometrics with practical applications, addressing challenges such as endogeneity, model misspecification, and computational efficiency. Liao’s editorial roles reflect his influence in shaping the direction of econometric research. His research has implications for policy analysis, financial market modeling, and empirical studies requiring rigorous statistical foundations. Despite the breadth of his contributions, no specific awards or grants are explicitly mentioned in the provided text.
Rina Foygel Barber is the Louis Block Professor in the Department of Statistics at the University of Chicago, where she also serves as Co-chair of the Committee on Community, Diversity, and Inclusion (CCDI) and is a member of the Committee on Computational and Applied Mathematics (CCAM). Her educational background includes: PhD in Statistics, University of Chicago (2012), advised by Mathias Drton and Nati Srebro MS in Mathematics, University of Chicago (2009) ScB in Mathematics, Brown University (2005) NSF postdoctoral fellow, Stanford University Department of Statistics (2012-13), supervised by Emmanuel Candès Professor Barber's research focuses on the theoretical foundations of statistical problems in estimation, prediction, and inference, particularly in high-dimensional settings where classical methods may not be reliable. She specializes in distribution-free inference methods such as conformal prediction, multiple testing methods, algorithmic stability, and shape-constrained inference. Her work also extends to modeling and optimization problems in medical imaging reconstruction. Her recent publications demonstrate a strong focus on distribution-free inference, with particular emphasis on conformal prediction, false discovery rate control, and algorithmic stability. Her work bridges theoretical statistics with practical applications, especially in the medical imaging domain. Professor Barber has received numerous prestigious awards: Elected to National Academy of Sciences (2025) MacArthur Fellowship (2023) IMS Fellow (2023) COPSS Presidents' Award (2020) Peter Gavin Hall Early Career Prize (2020) She actively mentors students and collaborators, with many co-authored publications across statistics, machine learning, and medical imaging. Her research has been supported by significant grants that enable her work on theoretical foundations of statistical inference and practical applications in medical imaging. Professor Barber also co-organizes the International Seminar on Selective Inference. Her research group focuses on developing and analyzing estimation, inference, and optimization tools for structured high-dimensional data problems. They work on false discovery rate control, distribution-free inference, and applications in medical imaging reconstruction.
