W. Brent Lindquist is a Professor in the Department of Mathematics and Statistics at Texas Tech University, affiliated with the TTU Mathematical Finance Program. His contact details include office location in the Mathematics & Statistics building (Room 104), phone (+1 806 834 2348), and email brent.lindquist@ttu.edu. His research spans computational financial mathematics, porous media flow, neuroscience applications, and quantum electrodynamics. Key contributions include dynamic asset pricing with market microstructure integration, pore-scale flow modeling using 3D micro-tomography, automated neuron morphology identification, and QED computations for electron magnetic moments. Recent work emphasizes ESG factor incorporation into financial models. Analysis of 2023–2025 publications reveals a dominant focus on sustainable finance, particularly ESG-integrated option pricing and portfolio optimization. Methodologies include random forests for market microstructure analysis, skew random walks for volatility modeling, and Lévy processes for Bitcoin dynamics. Cross-cutting themes involve hedonic real estate models with ESG factors and unified asset pricing frameworks bridging classical finance theories.
Joshua Gess is an Associate Professor in the Mechanical, Industrial, and Manufacturing Engineering department at Oregon State University's College of Engineering. He joined Oregon State in 2015 and serves as a co-principal investigator at the Enhanced Heat Transfer Laboratory, where he leads research in thermal management solutions for high-performance microelectronics. His educational background includes: PhD, Mechanical Engineering, Auburn University, 2015 MS, Mechanical Engineering, Auburn University, 2012 B.E., Mechanical Engineering, Vanderbilt University, 2005 Before academia, he worked as a mechanical engineer at SSOE Group (including consulting for Johns Manville) and Northrop Grumman where he focused on military communication equipment. Professor Gess specializes in advancing thermal management solutions for high-performance microelectronic equipment. His research spans multiple scales, examining single and two-phase heat transfer on the macro-scale with passive and active liquid immersion techniques, as well as on the micro and nano scale for complex embedded thermal management solutions. He combines fundamental heat transfer knowledge with novel experimental methods such as two-phase PIV and high-speed image capture to develop reliable and energy-efficient cooling solutions for demanding electronics systems. His publication record demonstrates a clear trajectory toward increasingly sophisticated thermal management solutions, with recent work focusing on additive manufacturing applications for cooling systems, semiconductor thermal management, and nuclear reactor cooling systems. His research has significant implications for data center energy efficiency, where even small improvements in cooling efficiency could save enormous amounts of energy that could be returned to the grid. Gess is deeply committed to mentoring graduate students, emphasizing the practical applications of engineering principles. He attributes his interest in engineering to childhood influences like the movie RoboCop and the TV series MacGyver, and finds the reality of engineering work just as gratifying as he'd imagined. He particularly values the moments when his graduate students "get it" and watching them grow with each new accomplishment. As a person with a disability himself, Gess is passionate about establishing more robust support systems for people with disabilities at Oregon State. He is working with the School of Public Health to start an adaptive sports program, with the goal of building infrastructure that allows anyone to feel welcome and pursue advanced degrees at the university.
Per Christian Hansen is a Professor at the Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark (DTU), where he leads the Section for Scientific Computing. He is a VILLUM Investigator and heads the CUQI (Computational Uncertainty Quantification for Inverse Problems) research initiative, aiming to develop accessible computational platforms for uncertainty quantification in inverse problems. His expertise lies in numerical analysis, numerical linear algebra, iterative reconstruction methods, and computational inverse problems, with applications in tomography, signal analysis, and plasma physics. His research integrates theoretical analysis—such as perturbation and convergence analysis—with the development of robust, adaptive, and efficient computational methods. He has co-authored five books, over 100 scientific papers, and several widely used MATLAB software packages, including IR Tools and Regularization Tools. His recent work (2023–2025) emphasizes uncertainty quantification, Bayesian inversion, and high-dimensional tomography in fusion plasmas, reflecting a strong trend toward probabilistic and robust modeling in inverse problems. He is a SIAM Fellow (2015) for his contributions to computational methods for rank-deficient and discrete ill-posed problems and regularization techniques. His scientific leadership is evident in both theoretical advances and practical software implementations. He actively collaborates across disciplines, particularly in nuclear fusion and medical imaging, and continues to supervise PhD students and publish in top-tier journals such as Inverse Problems , SIAM Journal on Scientific Computing , and Nuclear Fusion . SIAM Fellow (2015) VILLUM Investigator He advises PhD and Master’s students in computational mathematics and inverse problems, and his research is supported by major grants, including the VILLUM Investigator award. He leads the CUQI team, which develops open-source tools for non-experts to apply uncertainty quantification in inverse problems. His lab focuses on creating modeling frameworks that bridge theory, computation, and real-world applications in materials science, imaging, and plasma diagnostics.
Ghaith Hiary is a Professor and Vice Chair for Graduate Recruitment in the Department of Mathematics at The Ohio State University. He holds a PhD from the University of Minnesota (2008). His research focuses on computational and analytic number theory, with emphasis on the Riemann zeta function, L-functions, random matrix theory, and asymptotic analysis. Hiary's work bridges theoretical mathematics and high-performance computation. He develops efficient algorithms (e.g., amortized-complexity methods) to evaluate zeta functions at extreme heights (e.g., near t=10 28 ), implemented in C++/Mathematica. His research uses random matrix models to study zeta-function moments and employs techniques like van der Corput estimates and hybrid methods for explicit bounds. His publications demonstrate consistent focus on zeta-function computations, factorization algorithms, and arithmetic biases. Recent work (2022-2025) explores invariant measures, Lehman's method generalizations, and sign changes in random multiplicative functions, maintaining strong ties to analytic and probabilistic number theory. Hiary shares code and datasets via GitHub, including implementations of T 1/3 algorithms for zeta computations. He collaborates with institutions like the University of Waterloo and maintains numerical databases of zeta zeros.
Arul Shankar is a Professor of Mathematics at the University of Toronto, with his primary appointment at the Mississauga campus (UTM). He holds an office at 215 Huron Street, Room 1023 and can be reached at ashankar@math.utoronto.ca. His research focuses on Number Theory and Arithmetic Statistics, with particular expertise in elliptic curves, Selmer groups, and class groups. Professor Shankar's research interests center on the arithmetic statistics of number fields and elliptic curves. His work has significantly advanced our understanding of the average rank of elliptic curves, the distribution of Selmer groups, and the statistics of class groups. He has developed innovative applications of the geometry of numbers to arithmetic problems, often in collaboration with Manjul Bhargava and other leading number theorists. His research bridges deep theoretical questions with statistical approaches to understand the behavior of arithmetic objects across families. His publications reveal a consistent focus on the statistical behavior of arithmetic invariants, with particular attention to bounding average ranks of elliptic curves and understanding the distribution of class groups. The work demonstrates sophisticated applications of geometry of numbers techniques to problems in arithmetic statistics, with results published in top journals including the Annals of Mathematics, Inventiones Mathematicae, and Journal of the American Mathematical Society. Scientific Awards: 2018 Sloan Research Fellowship from the Alfred P. Sloan Foundation Professor Shankar is currently on sabbatical as a Simons Fellow. His research has established foundational results in arithmetic statistics, particularly regarding the average rank of elliptic curves and the distribution of class groups across families of number fields. His work with Bhargava on bounding the average rank of elliptic curves represented a major breakthrough in the field. His research group focuses on extending geometry of numbers methods to new contexts in arithmetic statistics, with recent work examining coregular vector spaces and applications to class groups. The lab maintains strong collaborations with number theorists at leading institutions worldwide.
Thomas Lectka is the Jean and Norman Scowe Professor in the Department of Chemistry at Johns Hopkins University, where he has been a faculty member since 1994. His research focuses on synthetic and physical organic chemistry, particularly in the area of organofluorine chemistry. PhD, Cornell University Postdoctoral Fellow, Heidelberg (Alexander von Humboldt Fellow) Postdoctoral Fellow, Harvard University (NIH Fellow) Dr. Lectka's research is centered on developing novel synthetic methods, especially for fluorination, and understanding the physical organic principles underlying reactivity. His work spans radical fluorination , catalytic asymmetric synthesis , and the design of fluorinated bioactive molecules . Using a combination of experimental and computational techniques, his lab investigates C-F bond formation , reaction mechanisms , and the biological applications of fluorinated compounds. His recent work, as reflected in publications from 2010 to 2024, shows a consistent trajectory in advancing fluorination methodologies, with increasing emphasis on site-selectivity , enantiocontrol , and biomedical relevance . Themes include the development of new reagents, mechanistic studies, and the synthesis of fluorinated natural product analogs and peptidomimetics. Dr. Lectka has received numerous honors and awards, including: ACS Arthur C. Cope Scholar (2024) ACS Maryland Chemist of the Year (2017) John Simon Guggenheim Memorial Fellowship Dreyfus Teacher-Scholar Award Sloan Fellowship NSF CAREER Award NIH First Award Eli Lilly Grantee Award He actively mentors graduate and undergraduate students in his research group, contributing to education and training in organic chemistry. His lab, The Lectka Group , is supported by grants from the NIH and NSF, enabling cutting-edge research in synthetic methodology and physical organic studies. The group fosters a collaborative environment focused on innovation in fluorine chemistry. The Lectka Group is an active research laboratory at Johns Hopkins University dedicated to pushing the boundaries of synthetic organic chemistry through the exploration of fluorine's unique properties. Current projects include site-selective radical fluorination and the synthesis of unusual fluorinated species, aiming to provide new tools for drug discovery and materials science.
Gautam Kamath is an Assistant Professor at the University of Waterloo's Cheriton School of Computer Science, a Faculty Member at the Vector Institute, and a Canada CIFAR AI Chair. He leads The Salon, a research group focused on statistics, algorithms, machine learning, and optimization. His work bridges theoretical foundations with practical applications in data privacy and robustness, contributing to both academic research and real-world deployments that impact millions of users. Dr. Kamath earned his PhD and SM degrees in Electrical Engineering and Computer Science at MIT, where he was advised by Costis Daskalakis. Prior to MIT, he graduated from Cornell University in May 2012 with a degree in Computer Science and Electrical and Computer Engineering, where he worked with Bobby Kleinberg. His academic journey reflects a strong foundation in both theoretical computer science and practical applications. His research focuses on developing solutions for trustworthy and reliable machine learning and statistics, with particular emphasis on data privacy and robustness. He addresses fundamental problems in these areas, revitalizing statistical toolkits for the modern data era where privacy preservation is paramount. His work spans theoretical foundations of differential privacy to practical applications that have been deployed at scale, including contributions to systems that protect the sensitive information of hundreds of millions of individuals. Analysis of his recent publications reveals a strong trend toward addressing the interplay between privacy, robustness, and machine learning performance. His work increasingly examines practical deployment challenges while maintaining theoretical rigor, with growing emphasis on diffusion models, generative AI, and the legal implications of AI systems. There's a clear trajectory from foundational privacy theory toward addressing real-world implementation challenges across diverse application domains. Canada CIFAR AI Chair Ontario Early Researcher Award 2024 Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies STOC Best Student Presentation Award ICML 2024 Best Paper Award Microsoft Research Fellow at the Simons Institute for the Theory of Computing Dr. Kamath actively mentors students, with notable successes including Valentio Iverson winning the Germain-Erdős Undergraduate Award and Chris Trevisan receiving the CRA Outstanding Undergraduate Researcher Award. His service to the research community is extensive, serving as Editor-in-Chief of TMLR, on the Executive Committee of the Learning Theory Alliance, and on steering committees for major conferences including ICML, COLT, and ALT. He has organized numerous workshops focused on privacy-preserving machine learning and differential privacy. Through The Salon research group, Dr. Kamath fosters a collaborative environment where postdoctoral fellows, graduate students, and undergraduates work together on cutting-edge problems at the intersection of statistics, algorithms, machine learning, and optimization. The group maintains strong connections with industry partners and participates in major research initiatives, including the Vector Institute's privacy and security research efforts. Looking forward, Dr. Kamath will be moving to the Computer Science department at NYU's Courant Institute of Mathematical Sciences in September 2026, where he plans to expand his research program.
Dr. Mark Hoggard is an ARC DECRA Research Fellow at the Research School of Earth Sciences, The Australian National University (ANU). His research focuses on geodynamics, sea-level modelling, nuclear test monitoring, and critical mineral systems. He holds a PhD from ANU and has studied at institutions including Cambridge University, Harvard, and Columbia. Hoggard’s work integrates geophysical data with numerical modelling to explore Earth’s dynamic processes, including mantle convection, glacial isostatic adjustment, and lithospheric evolution. Affiliations: Research School of Earth Sciences (ANU), Geoscience Australia (collaborative projects). Education: PhD (ANU), MA (Cambridge), BSc (ANU). Research Interests: Dynamic topography and its influence on sea-level records. Mantle structure and its relationship to mineral systems. Glacial cycles and ice sheet dynamics. Seismic monitoring of underground nuclear tests. Recent Article Trends: Recent work emphasizes geodynamic corrections to Pliocene sea-level estimates, mantle rheology influences on ice sheet models, and statistical methods for distinguishing seismic events from explosions. Key themes include linking deep Earth processes to surface observations and advancing methods for critical mineral exploration. Grants & Projects: Leads projects such as CoastRI GIA Modelling (2024–2027) and Next Generation Sea-Level Modelling (2022–2025). Collaborates with institutions like Los Alamos National Laboratory on nuclear test detection algorithms. Labs/Teams: Part of ANU’s Geodynamics group and the Exploring for the Future program at Geoscience Australia, focusing on Australia’s crustal structure and mineral potential.
Jonas Bergström is a Professor in the Department of Mathematics at Stockholm University specializing in Algebra, Geometry, Topology, and Combinatorics. His research focuses on arithmetic geometry, moduli spaces, Siegel modular forms, and number theory, with extensive collaborations across international institutions including KTH Royal Institute of Technology. His research interests span algebraic geometry, topology, combinatorics, and number theory, with particular emphasis on moduli spaces of curves, abelian varieties, Siegel modular forms, and arithmetic geometry. Bergström's work bridges theoretical mathematics with computational approaches, often developing algorithms for complex mathematical structures. His research group actively explores commutative and homological algebra, complex and real algebraic geometry, arithmetic geometry, homotopy theory, and Ramsey theory. The most recent publications reveal a strong focus on cohomology of moduli spaces, Siegel modular forms, abelian varieties over finite fields, and L-functions. His work demonstrates a consistent pattern of combining algebraic geometry with number theory, particularly investigating arithmetic properties of algebraic varieties and developing computational methods for modular forms. The research shows increasing emphasis on algorithmic approaches and connections to theoretical physics through moduli space cohomology. Bergström has supervised several PhD students including Sjoerd de Vries (current), Stefano Marseglia, and Olof Bergvall (with Prof. Carel Faber). He currently mentors postdoctoral researchers Séverin Philip and Thomas Wennink, while former postdocs include Angelina Zheng, Valentijn Karemaker, Oliver Leigh, and Alex Samuel Bamunoba. His research is supported through collaborations with major mathematical networks including the Nordic number theory network and joint seminars with KTH. He is affiliated with the Algebra and Geometry Seminar (KTH and SU) and maintains active research connections through multiple collaborative projects, including joint work with Gerard van der Geer and Carel Faber on Hecke operators and Siegel modular forms. Bergström also contributes to open mathematical research through GitHub repositories containing computational results on cohomology of moduli spaces.
Minh Hue Nguyen is a Senior Lecturer in EAL/TESOL Teacher Education at Monash University's School of Curriculum, Teaching and Inclusive Education within the Faculty of Education. She holds a PhD in Education (TESOL focused) from Monash University and has taught at Vietnam National University, Deakin University, and the University of Melbourne. Her educational background includes: PhD in Education (TESOL focused), Monash University (2015) MA in Applied Linguistics, Victoria University of Wellington (2008) BA in English Language Teaching, Vietnam National University (2003) Nguyen's research centers on teachers' professional learning, curriculum, and pedagogy in TESOL and EAL contexts, with specific focus on emotional experiences, identity development, mentoring, agency, and collaboration. She explores sociocultural contexts of teacher development through activity theory and sociocultural perspectives, examining how institutions support teachers' learning journeys from preservice to in-service stages. Recent work investigates professional learning for teacher educators and implementation of the Victorian EAL Curriculum. Her 15 most recent publications (2024-2025) reveal a strong thematic focus on language teacher agency, identity negotiation, and multilingual pedagogies. The research increasingly examines emotional dimensions of teaching, cross-cultural identity tensions, and collaborative models between EAL and content teachers, with significant contributions to understanding how teachers navigate complex educational contexts while developing professional identities. Her scientific recognition includes: Penny McKay Award Special Commendation Monash Education Research Community's Publication Award ATEA/Kay Martinez Award for Best Paper Monash Dean of Education’s ECR Project Award Advancing Women's Research Success Grant Vietnamese Government Merit-based Scholarships Nguyen actively supervises PhD research in TESOL teacher professional learning, teacher identity, and curriculum development, though is currently unavailable for new PhD students until 2027. She contributes to editorial boards for Teaching and Teacher Education, Second Language Teacher Education, and System journals, and serves on the Australian Teacher Education Association. Her work supports UN Sustainable Development Goal 4 (Quality Education) through inclusive pedagogical frameworks. She leads research projects including 'Elucidating practices to assist EAL learners to acquire specialised science vocabulary' and 'Establishing an online community-of-practice model for learner agency during work placements,' demonstrating commitment to practical educational innovations.
Biondo Biondi is the Barney and Estelle Morris Professor of Geophysics at Stanford University, affiliated with the School of Earth Sciences. He leads the Stanford Exploration Project and holds roles such as Chair of the Geophysics Department (2019–2022) and Director of the Stanford Earth Imaging Project (1998–Present). His research focuses on seismic imaging algorithms, computational geophysics, and fiber-optic sensing technologies. He earned his Ph.D. (1990), M.S. (1987) in Geophysics from Stanford, and M.Sc. in Electrical Engineering from Politecnico di Milano (1984). Dr. Biondi's research emphasizes improving seismic data imaging through advanced computational methods. He pioneered urban seismic monitoring using preexisting telecommunication fibers, enabling cost-effective subsurface analysis. His work integrates machine learning and high-performance computing to address challenges in reservoir imaging, CO2 monitoring, and infrastructure health. Key research areas include distributed acoustic sensing (DAS), ambient noise tomography, and inverse theory applications. He has authored over 180 publications and received awards like the SEG Honorable Mention (2019, 2016, 2009) and the Distinguished Instructor Short Course (2007). His teaching includes courses like 3-D Seismic Imaging and Reflection Seismology, and he advises graduate students in geophysics and computational science. Collaborations span industry (e.g., Schlumberger, Saudi Aramco) and global institutions. Biondi’s administrative contributions include co-directing the Stanford Earth Sciences Algorithms and Architectures Initiative and serving on editorial boards like the SIAM Journal on Imaging Sciences. His lab’s innovations bridge geophysics with emerging technologies, advancing both academia and industry applications in energy, environment, and urban infrastructure.
Prof. Mathias Drton holds the Chair of Mathematical Statistics at the Technical University of Munich (TUM), within the Department of Mathematics and School of Computation, Information and Technology. His research focuses on graphical models, algebraic statistics, causal inference, and multivariate data analysis. He has authored numerous publications in top-tier journals and conferences, including work on conditional independence, sparse factor analysis, and causal discovery in linear models. Drton has supervised a large number of theses, mentoring students in areas like high-dimensional statistics, graphical models, and causal inference. He is actively involved in teaching advanced courses such as 'Graphical Models in Statistics' and 'Fundamentals of Mathematical Statistics.' His academic contributions span theoretical developments in statistical methodology and computational tools, including R packages like SEMID and symRC . Drton collaborates internationally, contributing to projects like the TUM-ICL Mathematical Sciences Hub and Exzellenzcluster MCQST. His work bridges algebraic methods with statistical challenges, addressing identifiability in latent variable models and robust graphical modeling under non-Gaussian assumptions. Recent research emphasizes causal structure learning under partial homoscedasticity, distribution-free independence tests, and multi-domain causal representation learning. Drton’s lab actively explores applications in genomics, epidemiology, and machine learning, leveraging both theoretical rigor and practical computational methods.
Jessica R. Lamb is an Assistant Professor and McKnight Land-Grant Professor at the University of Minnesota, Department of Chemistry. Her research spans catalysis, physical organic chemistry, and polymer chemistry. Developing switchable N-heterocyclic carbene organocatalysts Designing sustainable non-isocyanate polyurethanes for high-temperature applications Combining polymerization mechanisms for novel materials The Lamb group emphasizes interdisciplinary training and mechanistic investigations, with a focus on sustainable synthesis. Recent publications highlight structure-property relationships in polymers and NHC-CDI adducts. Dr. Lamb is recognized for her contributions via the McKnight Land-Grant Professorship. Her lab receives funding from NSF, ACS PRF, and 3M NTFA, among others. The Lamb Research Group actively mentors graduate students and prioritizes diversity, equity, and inclusion in STEM. They are currently accepting new graduate research students.
Rima Alaifari is currently an Assistant Professor for Applied Mathematics at ETH Zürich , where she works on applied analysis, inverse problems, and scientific machine learning. Her research emphasizes stability analysis and regularization of inverse problems, applied harmonic analysis, phase retrieval, and operator learning. She is an associated member of the ETH AI Center and will transition to a full professorship at RWTH Aachen University in 2025 as Chair of Analysis and its Applications. Education : PhD in Mathematics (2010–2014, Vrije Universiteit Brussel); MSc in Applied and Industrial Mathematics (2005–2010, Johannes Kepler University) Research Focus : Stability estimates for inverse problems, phase retrieval in wavelet/Gabor transforms, operator learning with neural networks, and deep learning robustness. Article Trends : Her recent work bridges harmonic analysis with machine learning, focusing on phase retrieval stability, adversarial perturbations in imaging, and mathematically grounded neural operator frameworks like ReNO and CNO. Advising : She has supervised PhD students like Tandri Gauksson and Matthias Wellershoff. Former postdoctoral researchers include Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner).
John Shaw is the Harry C. Dudley Professor of Structural and Economic Geology and a Professor of Environmental Science and Engineering at Harvard University's School of Engineering and Applied Sciences. He also serves as Vice Provost for Research at Harvard, overseeing institutional research strategy and initiatives. His primary research focuses on structural geology, geophysics, and earthquake hazards, particularly in active fault systems, mountain belt tectonics, and subsurface energy development. Prof. Shaw leads the Structural Geology & Earth Resources Program, an industry-academic consortium that integrates geophysical data (3D seismic surveys, remote sensing) with advanced numerical modeling to address geological and environmental challenges. Education and professional experience: Joined Harvard Faculty in 1997. His research portfolio includes collaborations with the Southern California Earthquake Center (SCEC) and the development of critical infrastructure like the SCEC Unified Community Velocity Model (UCVM). His work emphasizes practical applications such as fault stability assessments and carbon sequestration impact studies. Research interests span: 1) active fault characterization for seismic hazard mitigation, 2) tectonic evolution of mountain belts, 3) numerical modeling of fault dynamics, and 4) geomechanical impacts of subsurface energy projects. His structural modeling innovations have advanced understanding of thrust fault systems and fault-bend folding mechanisms. Professional contributions include leadership roles in interdisciplinary consortia and development of open-source geophysical software frameworks. Administrative duties at Harvard's Office of the Vice Provost for Research (VPR) focus on advancing institutional research capacity and fostering cross-disciplinary collaborations.
