Christa Cuchiero is a Professor at the Department of Statistics and Operations Research, Faculty of Business, Economics and Statistics, University of Vienna. Her research spans Mathematical Finance, Stochastic Processes, and Machine Learning applications in finance. She has over 48 publications, including recent work on signature-based models for SPX/VIX options, polynomial McKean-Vlasov SDEs, and infinite-dimensional Wishart processes. Her projects include 'Dynamic Uncertainty Modeling in Finance' and a long-term study on 'Universelle Strukturen in Finanzmathematik' (2020–2028). Research Interests: Signature methods for financial modeling Affine and polynomial processes Machine learning in finance Measure-valued stochastic differential equations Volterra equations and rough path theory Portfolio optimization and risk management Scientific Awards: Bruti-Liberati Visiting Fellowship (2018) Fellow at the Center for Advanced Study (CAS), Norwegian Academy of Science and Letters (2024) ETH Medal for Ph.D. thesis (2012) Recent Publications (2023–2025) focus on signature methods, stochastic portfolio theory, energy markets, and robust calibration techniques. Her work integrates advanced mathematical theory with practical financial applications, emphasizing nonlinear SPDEs, polynomial models, and machine learning frameworks.
Jeff Edwards is a Professor in the Department of Crop, Soil, and Environmental Sciences at the University of Arkansas, serving as Department Head since 2022. Previously, he held similar roles at Oklahoma State University from 2004 to 2022, including the Warth Distinguished Professorship and Small Grains Extension Specialist positions. Ph.D. in Crop Physiology (University of Arkansas, 2004) M.S. in Weed Science (University of Arkansas, 2001) B.S. in Agriculture (Western Kentucky University, 1995) His research focuses on agronomic systems for small grains, particularly winter wheat, emphasizing yield optimization, climate adaptation, and precision agriculture. He has pioneered extension tools for disease and nutrient management using remote sensing technologies. Jeff’s publications span wheat breeding, soil-crop interactions, and agricultural economics, with a focus on scalable solutions for the southern Great Plains. His work bridges field experiments with regional policy through climate risk modeling and resource efficiency studies. 2022 Oklahoma Crop Improvement Association Premier Supporter Award 2021 OSU Career Services Career Champion Award 2013 National Excellence in Extension Award ASA-CSSA-SSSA Early Career Professional Award (2007) As a co-inventor of twelve wheat varieties and a leader in agricultural outreach, Jeff has significantly impacted wheat production practices. His career integrates research, extension, and education to enhance agricultural sustainability and productivity.
Professor Georg Gottwald is a distinguished academic in the School of Mathematics and Statistics at the University of Sydney, where he has been a faculty member since 2002, progressing from Lecturer to his current position as Professor since 2013. He also holds a Visiting Professor position at the University of Surrey in the UK since 2013. His extensive research career spans dynamical systems theory, geophysical fluid dynamics, and the intersection of machine learning with complex systems. Professor Gottwald's research focuses on dynamical systems theory as an abstract formalism for studying systems evolving in time and space. His work has significant applications across diverse fields including climate modeling, biological systems, and complex networks. He is particularly known for developing methods for model reduction of complex dynamical systems, stochastic modeling approaches, and the application of machine learning techniques to dynamical systems. His research aligns with the Faculty of Science Research Strengths in Understanding the Universe, Fundamental Laws of Nature, Complex Systems, Climate and Environmental Change, Data and Decisions, and National Security. His most recent publications demonstrate a strong trajectory toward integrating machine learning with dynamical systems theory, particularly in developing stable generative models, learning dynamical systems with random feature maps, and combining data assimilation with machine learning for forecasting. His work spans pure mathematical theory to practical applications in climate science, finance, and biological systems, showing remarkable breadth while maintaining deep mathematical rigor. Future Fellowship, 'Stochastic methods in mathematical geophysical fluid dynamics', Australian Research Council, 2010-2014 Australian Research Fellowship, 'Stochastic methods in mathematical geophysical fluid dynamics', Australian Research Council, 2010-2015 (declined) Australian Research Fellowship, 'Geometric methods in geophysical fluid dynamics', Australian Research Council, 2004-2009 Professor Gottwald has successfully supervised numerous PhD and Master's students who have gone on to academic and industry positions worldwide. His current research group includes postdocs and PhD students working on machine learning for dynamical systems, stochastic model reduction, physics-informed machine intelligence, and tensor methods for scientific machine learning. He has secured multiple ARC Discovery Project grants and has been involved in significant international collaborative research projects. He is actively involved with the Sydney Dynamics Group, which he co-founded in 2007, fostering collaboration between the University of Sydney and UNSW. Professor Gottwald maintains strong editorial commitments as Associate Editor for Geophysical and Astrophysical Fluid Dynamics, SIAM Journal of Applied Dynamical Systems, and Journal of Computational Dynamics, and serves on the Editorial Advisory Board for Chaos and the Editorial Board for Physical Review E. His professional activities demonstrate leadership in the dynamical systems community through organizing workshops, seminars, and special journal issues.
Denny Yu is an Associate Professor at the Edwardson School of Industrial Engineering, Purdue University. His work bridges human factors, neuroergonomics, and healthcare safety through advanced sensor systems and AI. Primary Affiliation : Edwardson School of Industrial Engineering, Purdue University Research Themes : Surgical ergonomics, autonomous vehicle human factors, cognitive workload assessment, multimodal physiological sensing Dr. Yu's research focuses on neuroergonomics and human-robot interaction , particularly in surgical and transportation contexts. His team develops sensor-based systems for workload monitoring, including: EEG-eye tracking fusion for situation awareness Wearable exoskeletons for surgical posture support Computer vision tools for lifting task risk analysis Smart infusion pump usability frameworks AI-driven surgical coaching systems Recent publications emphasize deep learning applications in soft tissue deformation estimation and real-time adaptive systems for robotic surgery augmentation. His work spans both occupational health (veterinary surgeons, airport workers) and medical device innovation domains.
David Roueche serves as the Gottlieb Associate Professor of Structural Engineering within the Department of Civil and Environmental Engineering at Auburn University's Samuel Ginn College of Engineering. His research focuses on structural performance under extreme wind events, forensic engineering methodologies, and improving building resilience against hurricanes and tornadoes through interdisciplinary approaches. Dr. Roueche's academic foundation includes advanced degrees from the University of Florida, with complementary physics training: Ph.D. in Structural Engineering, University of Florida M.S. in Civil Engineering, University of Florida B.S. in Civil Engineering, University of Florida B.S. in Engineering Physics, Jacksonville University His primary research explores extreme wind loads on low-rise buildings , post-disaster field investigations , and performance-based wind engineering , with specialized expertise in light wood-frame structures and surge/flood modeling. He integrates engineering analysis with social science through survivor interviews to reconstruct tornado events and identify vulnerabilities in residential construction, particularly for mobile and manufactured housing in the Southeastern United States. Analysis of his recent publications reveals a dominant focus on post-disaster assessment frameworks, field data collection protocols, and performance-based evaluation methods for wind-affected structures. His work increasingly emphasizes interdisciplinary collaboration—combining engineering, social science, and geospatial technologies—to develop comprehensive disaster response systems and improve building codes. Key trends include standardization of forensic engineering practices through organizations like StEER and application of computational modeling to predict structural failures. Dr. Roueche's significant recognitions include: Ginn Faculty Achievement Fellow designation NSF CAREER Award (2020) for advancing post-windstorm assessment methodologies He directs substantial research funding including a $500,000 USDA grant for timber-steel composite research and leads the Auburn Mass Timber Collaborative—an interdisciplinary initiative uniting forestry, architecture, and engineering faculty. Through the Structural Engineering Emergency Response (StEER) network, he coordinates Field Assessment Structural Teams for disasters like Hurricane Ian and the 2022 Arabi tornado, developing standardized protocols adopted nationally for post-disaster evaluations. His mentoring extends to doctoral students in civil engineering, with recent success in securing competitive fellowships for advisees. As a core member of StEER, Dr. Roueche develops and implements field assessment protocols used in rapid disaster response. He leads FAST teams deploying UAVs, LiDAR, and ground surveys to document structural performance after hurricanes and tornadoes, with datasets informing FEMA guidelines and building code revisions. His work with the Auburn Mass Timber Collaborative advances sustainable construction methods through experimental testing of innovative structural systems.
Scott Morris is the Nambury S. Raju Endowed Chair in Psychology and a Professor at Illinois Institute of Technology's Lewis College of Science and Letters. He directs the Industrial/Organizational Psychology Program and leads the Personnel Selection & Analytics Lab. Dr. Morris holds a Ph.D. in Industrial-Organizational Psychology from the University of Akron (1994) and a B.A. in Psychology from the University of Northern Iowa (1987). His research examines psychometric methods for fair personnel selection systems, with emphasis on: Advanced statistical models for employment testing Adverse impact analysis and workplace equity Meta-analysis methodology and validity generalization Computer adaptive testing and item response theory Bias in subjective hiring practices and interviews His publications demonstrate consistent focus on quantitative methods for employment decisions, with recent works concentrating on meta-analytic techniques, adverse impact analytics, and adaptive testing systems. Earlier research established foundations in effect size estimation and differential item functioning. Honors include: Fellow, Society for Industrial and Organizational Psychology Fellow, American Psychological Association Dr. Morris serves as Associate Editor for the Journal of Applied Psychology and develops open-source psychometric software. His lab investigates selection system design, AI in hiring reactions, and multidimensional testing applications.
Barry Rowlingson is a Research Fellow at Lancaster University Medical School , affiliated with the Chicas Research Group and DSI-Health . He specializes in spatial statistics applied to disease epidemiology and geospatial software development . Teaches Geospatial Data module for MRes in Global Health Developed online course on Spatial Statistics in R with DataCamp Focuses on open-source geospatial tools for public health Research Interests: His work bridges spatial statistical methodology and infectious disease modeling , with applications in antimicrobial resistance mapping , wastewater-based pandemic surveillance , and health inequality analysis . Recent projects include modeling ESBL-producing bacteria in Malawi and developing spatio-temporal frameworks for COVID-19 wastewater monitoring . Scientific Contributions: Over 15 years, he has developed critical R packages like stpp for spatio-temporal analysis and rgdal for geospatial data abstraction. His 2023 publications address health workforce disparities and disease transmission dynamics using advanced statistical methods. Collaborations: Works with multidisciplinary teams across Lancaster Medical School , DataCamp , and SAVSNet Agile research projects. Supervises PhD student Charlotte Appleton in biostatistics.
Joshua Warren is a Professor in the Department of Biostatistics at the Yale School of Public Health. He holds appointments in Climate Change and Health, Public Health Modeling, the Yale Superfund Research Center, and the Yale-BI Biomedical Data Science Fellowship. His academic journey includes a PhD in Statistics from North Carolina State University (2011), an MS from the same institution (2009), and a postdoctoral fellowship at UNC Chapel Hill (2014) before joining Yale. Warren's research focuses on developing hierarchical Bayesian methods for spatial and spatiotemporal data, assessing environmental exposures' impact on human health, and characterizing infectious disease spread. His work frequently involves introducing spatial and spatiotemporal models in Bayesian settings to study associations between environmental exposures like air pollution and health outcomes including preterm birth, low birth weight, and congenital anomalies. He also applies these methods in collaborative settings including epidemiology, geography, nutrition, and glaucoma research. His 15 most recent publications demonstrate extensive work at the intersection of environmental health, spatial statistics, and public health. The research spans air pollution health effects, tuberculosis transmission modeling, heat wave impacts on birth outcomes, and statistical methodology development. His work shows particular strength in applying Bayesian approaches to complex spatial and temporal data in public health contexts. Coauthor, Kenneth Rothman Epidemiology Prize Paper (2018) First Author, Best Paper in Biometrics (2012) Warren serves as Associate Editor for Statistics in Biosciences (2015-present) and previously for the Journal of the American Statistical Association (2018-2020). He has reviewed for the Health Effects Institute (2019), Swiss National Science Foundation (2017), and NIH study sections (2016). His collaborative network includes researchers like Nicole Deziel, Daniel Weinberger, Ted Cohen, and Xiaomei Ma. His laboratory focuses on statistical methods development with applications to public health problems, particularly those involving spatial and temporal data structures. Warren's work bridges theoretical statistics with practical public health applications, making significant contributions to how we understand environmental health risks and infectious disease dynamics.
Ellen Rathje is a Professor and Janet S. Cockrell Centennial Chair in Engineering at the University of Texas at Austin's Department of Civil, Architectural, and Environmental Engineering within the Cockrell School of Engineering. Her expertise spans geotechnical engineering with a focus on earthquake engineering, seismic response of earth structures, and liquefaction evaluation. She leads research initiatives such as DesignSafe cyberinfrastructure and TexNet Seismological Network, advancing geohazard risk assessment and computational modeling. Education: Ph.D. (1997), M.S. (1994), and B.S. (1993) in Civil Engineering from University of California, Berkeley and Cornell University. Research Interests: Dr. Rathje investigates earthquake-induced ground failures, including lateral spreading, liquefaction, and slope instabilities. Her work integrates machine learning, finite element analysis, and geospatial data to enhance predictive models for infrastructure resilience. Recent projects address induced seismicity in energy-producing regions, site amplification in Central and Eastern North America, and tailings dam failure mechanisms. Awards: Recipient of the 2022 Ralph B. Peck Award, 2018 William B. Joyner Lecture Award, and 2016 ASCE Fellow distinction. Her work has advanced open science through DesignSafe's cyberinfrastructure, supporting natural hazards research collaboration. Technical Contributions: Developed hybrid finite-element/material point methods for granular collapse modeling, probabilistic frameworks for regional landslide assessments, and neural network-based site amplification models. Active in post-earthquake reconnaissance through GEER (Geotechnical Extreme Events Reconnaissance) and TexNet operations.
Erik Schlogl is a Professor in the School of Mathematical and Physical Sciences at the University of Technology Sydney (UTS), Australia, with a focus on Financial Mathematics and Quantitative Finance. He previously held academic appointments at UTS Business School and the School of Finance and Economics at UTS, as well as the School of Mathematics at UNSW Australia and the University of Bonn, Germany. He received a Doctorate in Economics from the University of Bonn, Germany (1992–1997), specializing in term structure models and the pricing of fixed income derivatives. His research spans quantitative finance, including model calibration, interest rate term structure modeling, credit risk, and the integration of multiple sources of risk. He has published in journals such as Finance and Stochastics , Quantitative Finance , Risk , and Journal of Economic Dynamics and Control . Erik's recent work includes a 2021 publication in Risks on parameter learning and change detection using a particle filter incorporating genetic algorithms. This aligns with his expertise in computational financial engineering and practical implementation of quantitative finance models. Scientific Awards & Recognition : Highly Commended in the Gerald Durrell Award for Endangered Wildlife (2002) by the Natural History Museum/BBC Wildlife Photographer of the Year competition. He is available for media inquiries and supervises Masters/PhD students in quantitative finance research. Erik also co-organizes the Quantitative Methods in Finance (QMF) conference and chairs the Sydney Financial Mathematics Workshop (SFMW).
Ji Hyung Lee is a Professor of Economics at the University of Illinois Urbana Champaign, with a courtesy appointment in the Department of Finance at Gies College of Business. His research focuses on econometric theory, time series analysis, financial econometrics, and machine learning applications in economics. He holds a Ph.D. in Economics from Yale University (2013) and a B.A. in Economics from Seoul National University (2005). His research interests include developing robust econometric methods for high-dimensional data, quantile regression techniques, and applications to macroeconomic policy and financial risk analysis. Notable contributions include work on predictive quantile regression, nonparametric density estimation, and modeling household inflation expectations. His recent articles explore topics such as machine-learning approaches to growth risk, quantile impulse responses for value-at-risk dynamics, and parameter-free methods for density estimation. Lee’s work emphasizes methodological innovation and practical relevance in policy contexts. He has held positions at multiple institutions and maintains affiliations with the Midwest Econometrics Group. His research has been published in top journals like Journal of Econometrics and Econometric Theory .
Dr. Mei-Ling Ting Lee is a Professor in the Department of Epidemiology and Biostatistics at the University of Maryland, College Park. She specializes in developing statistical models, notably the first hitting-time based threshold regression (TR) for analyzing time-to-event survival data, which has been extended to machine learning applications. As the Founding Editor and Editor-in-Chief of the Lifetime Data Analysis journal, she has significantly contributed to the field of time-to-event data methodologies. Her research encompasses genomic data analysis, statistical distribution theory, nonparametric methods, and applications in epidemiology. Dr. Lee has authored over 150 peer-reviewed articles and a seminal monograph, Analysis of Microarray Gene Expression Data (2004), widely used in genomic research. She has also co-edited influential books, including Lifetime Data Models in Reliability and Survival Analysis (1995) and Risk Assessment and Evaluation of Prediction (2013). Education: BS in Mathematics (National Taiwan University), MS (National Tsing Hua University), MA and PhD in Mathematics/Statistics (University of Pittsburgh). Her work focuses on integrating statistical theory with practical applications, including clinical trials, genomic studies, and public health research. She leads initiatives in statistical software development (e.g., R packages like clusrank ) and advocates for rigorous methodological standards in survival analysis.
Prof. Dr. Michael Merz holds the Chair of Mathematics and Statistics in Economics at the University of Hamburg's Faculty of Business Administration. He specializes in actuarial science, risk management, and quantitative finance. His research focuses on stochastic claims reserving, solvency frameworks (Basel II/Solvency II), credibility theory, and risk-adjusted performance measurement. Education: Completed his Diplom in Mathematics (1.1 grade) at the University of Tübingen (2001) with a focus on stochastic processes and mathematical statistics. Earned his PhD (summa cum laude) in 2004 at Tübingen with a thesis on credibility models using orthogonal projections. Previously studied in Valencia, Spain (1998-1999). Research Interests: Actuarial valuation, stochastic reserving methods, risk theory, filter/control theory applications in finance, and capital allocation strategies. Key areas include Solvency II compliance, pricing models for insurance risks, and quantitative risk management frameworks. Publications: Authored/co-authored influential works like Financial Modelling, Actuarial Valuation and Solvency in Insurance (Springer, 2013) and Stochastic Claims Reserving Methods in Insurance (Wiley, 2008). Recent articles address claims uncertainty quantification, multivariate reserving models, and cost-of-capital approaches. Awards: SCOR Prize for Actuarial Science (2004), Promotionspreis der Universität Tübingen (2005), and academic distinctions in physics competitions during high school. Professional Roles: Program director for the Wirtschaftsmathematik program, speaker of the Institute for Statistics and Econometrics, and adjunct faculty at the University of Basel's Actuarial Science program. Served as actuary at Basler Versicherung (2004-2006) and held junior professorships at University of Tübingen (2006-2009).
Kallol Sett is an Associate Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo (SUNY), within the School of Engineering and Applied Sciences. His research focuses on risk and reliability analysis of civil infrastructure under extreme events, with expertise in uncertainty quantification, multi-hazard resilience, and geomechanics. He leads the Risk and Reliability Research Group, which develops computational tools integrating physics-based and data-driven modeling, stochastic calculus, and high-performance computing. Education includes a PhD from the University of California, Davis (2007), an MS from the University of Houston (2003), and a BE from Jadavpur University (1997). His work is funded by NASA, NSF, USDOT, NIST, and industry partners. Key research themes include probabilistic geotechnical site characterization, stochastic simulation of seismic ground motion, and life-cycle cost-benefit analysis of infrastructure systems. Advising includes mentoring over 10 PhD and MS students, with notable alumni now in academia and industry roles such as Assistant Professors at Embry-Riddle Aeronautical University and Tianjin University. His lab’s recent studies address real-time decision support systems for hurricane-impacted infrastructure, resilience deficit indices, and multi-hazard financial risk assessment of integrated infrastructure systems.
Cory Simon serves as Associate Professor in the Department of Chemical, Biological, and Environmental Engineering within Oregon State University's College of Engineering. His research integrates machine learning, optimization, and chemical engineering to advance materials discovery and environmental sensing systems. His academic foundation includes a Ph.D. in Chemical Engineering from the University of California, Berkeley and a B.S. in Chemical Engineering from The University of Akron. Simon's work centers on Bayesian methodologies for scientific challenges, featuring: Bayesian optimization for adaptive materials synthesis Statistical inversion of physical systems with uncertainty quantification Computational design of nanoporous sensor arrays Stochastic algorithms for robotic environmental monitoring Recent publications demonstrate accelerating focus on multi-fidelity optimization for molecular design and atmospheric water harvesting, bridging chemical engineering with computational science through data-driven approaches. Leading The Simon Ensemble research group, Simon champions a versatile 'buffet-style' research philosophy—drawing from mathematics, statistical mechanics, and machine learning to address interdisciplinary problems across chemistry, materials science, and environmental engineering.
