C. R. (Bob) Koch is a Professor in the Department of Mechanical Engineering at the University of Alberta's Faculty of Engineering. His research applies control theory to energy systems, including internal combustion engines, fuel cells, and fluid systems. Education: PhD, Stanford University, 1991 MS, Stanford University, 1986 BSc, University of Alberta, 1985 His research focuses on developing control and diagnostic methods using both physics-based and data-driven approaches. Key areas include Model Predictive Control (MPC) combined with Machine Learning for hydrogen and biofuel engines, emissions control, and fluid system optimization. Scientific Awards: None listed in the provided text. Advising and Grants: Dr. Koch leads the Mechanical Engineering Energy Control Lab (MEECL) and is currently recruiting PhD and Postdoctoral students for projects on hydrogen engine control. Current funding sources include: Natural Sciences and Engineering Research Council of Canada (NSERC) Alberta Innovates TEIR Program Alberta Major Innovation Fund Prairies Canada Transition Accelerator Cummins IAV EDI Labs and Teams: The MEECL lab conducts experimental research on heavy-duty transport engines and fuel cells, with state-of-the-art testing facilities for validating advanced control strategies.
Dr. Beth Cudney serves as Professor of Data Analytics and Program Coordinator for Data Analytics at Maryville University's John E. Simon School of Business, leveraging 25 years of international expertise in data analytics and Lean Six Sigma across industry and academic leadership roles. Her academic foundation includes: MBA and MEng in Mechanical Engineering from the University of Hartford Doctorate in Engineering Management from University of Missouri–Rolla (now Missouri University of Science and Technology) Her research critically examines data-driven decision making through statistical analysis, optimization, and multivariate modeling, with emphasis on ethical data interpretation and consumer influence. She pioneers active learning methodologies to translate complex analytics concepts into practical business applications, particularly in quality management systems. Her prolific scholarship encompasses over 100 journal articles, 100+ conference proceedings, and 10 authored books (including the award-winning The Ten Commandments of Lean Six Sigma ), recognized by: 2022 Crosby Medal (ASQ's highest book honor) 2021 Bernard R. Sarchet Award for lifetime achievement in engineering education 2021 Walter E. Masing Book Prize for quality management contributions As program coordinator, she directs curriculum development for data analytics while teaching core courses including Data Analytics 1 & 2, Data Visualization, and Database Principles. She maintains eight ASQ certifications including Lean Six Sigma Master Black Belt status.
Dr. Stephen U. Egarievwe serves as Associate Dean for Research in the School of Engineering and Professor in the Department of Industrial Engineering at Morgan State University. He also directs the Center for Research and Education in Digital Engineering (CREDE) and coordinates the Interdisciplinary Consortium for Research and Educational Access in Science and Engineering (InCREASE). Education: B.Sc. (Honors) in Engineering Physics with Nuclear Engineering option - Obafemi Awolowo University M.Sc. in Physics & Astronomy (Solar Energy) - University of Nigeria M.S. in Nuclear Engineering - University of Tennessee M.S. in Computer Science (Operating Systems and Robotics) - Vanderbilt University M.A. in Physics (Nuclear Detectors) - Fisk University M.S. in Computer Science (Software Engineering and HCI) - Nova Southeastern University Ph.D. in Applied Physics - Alabama A&M University Dr. Egarievwe's research spans three interconnected domains. His nuclear safety work focuses on room-temperature detectors, UAV-based radiation surveys, and nuclear cybersecurity. In STEM education, he advances research capacity at HBCUs and minority-serving institutions through curriculum development and large-scale consortium building. His digital engineering research explores digital twins, simulation, and data virtualization techniques for educational applications. Dr. Egarievwe has published over 150 journal and conference articles including book chapters, with research trends showing increasing integration of nuclear detection technologies with digital engineering approaches and educational applications. Scientific Recognition: Co-winner of the 2009 R&D 100 Award (top 100 scientific/engineering products) SLAC Scholar designation ABET-IDEAL Scholar recognition Reviewer for over 10 world-class technical journals As Partnership Coordinator for the InCREASE consortium, Dr. Egarievwe leads initiatives to increase utilization of national user facilities by minority-serving institutions. He has secured significant grant funding for HBCU research capacity building and serves on external review committees for Savannah River National Laboratory, Brookhaven National Laboratory, and Stony Brook University's diversity programs. His laboratory work through CREDE focuses on developing practical applications of digital engineering for nuclear safety and STEM education.
Ryan Woods is an Assistant Professor in the University of British Columbia's Faculty of Health Sciences and an Executive Director, Data and Analytics at BC Cancer. He holds a Scientist position in the Department of Cancer Control Research at BC Cancer Research Institute. His academic journey includes a PhD in Population and Public Health from UBC, an MSc in Statistics, and a BSc in Mathematics from the University of Guelph. Dr. Woods' research focuses on leveraging administrative and registry data to address cancer control challenges, particularly in oncology outcomes, cancer screening equity, and health system performance. His work emphasizes international benchmarking, equity gaps in service utilization, and data-driven health policy. Key areas include evaluating disparities in breast cancer screening participation among immigrant populations, analyzing pandemic impacts on cancer diagnosis timing, and optimizing team-based cancer care models. His publications span over two decades, with recent emphasis on pandemic-related healthcare disruptions, multimorbidity trends, and global cancer survival disparities. He has contributed to CONCORD-3 and ICBP initiatives analyzing international cancer outcomes. Dr. Woods also leads BC Cancer's data strategy, integrating biostatistics and epidemiology to advance population health surveillance. His expertise bridges clinical and population-level research, with a focus on translating data insights into actionable strategies for improving cancer care equity and system efficiency. Current projects include spatial access analysis for lung screening programs and modeling pandemic-driven health burden shifts.
Dr. William Claster is an Associate Teaching Professor and Associate Program Director in the Master’s in Information Systems (MSIS) program at Northeastern University’s Arlington, VA campus. He holds a PhD from Ritsumeikan Asia Pacific University, an MA in Mathematics from Temple University, and a BS in Physics from Bard College. His research focuses on machine learning, big data analytics, fintech, natural language processing, and generative AI. He authored Mathematics and Programming for Machine Learning with R: From the Ground Up (CRC Press, 2020) and a Japanese textbook on data science fundamentals. His work includes over 10 peer-reviewed articles, such as studies on social media perception accuracy and medical informatics. Dr. Claster’s academic excellence was recognized with the Ritsumeikan Asia Pacific University Award for Outstanding Research (2014). Professionally, he has consulted for fintech firms like Paidy Finance, Union Bank of Switzerland, and Goldman Sachs. His teaching emphasizes innovative methods, with alumni at Google and Facebook. He leads a comprehensive data science curriculum development effort and actively bridges academia-industry collaboration.
Dimitrios Kallergis is a Lecturer in Net-Centric Systems and Cloud Computing at the University of West Attica's Department of Informatics & Computer Engineering since 2019. He holds expertise in cybersecurity, cloud/fog/edge computing, ad-hoc networking, cyber-physical systems, and IT law. His research bridges theoretical frameworks with practical applications in healthcare, transportation, and environmental systems. Kallergis has contributed to European Union Agency for Cybersecurity (ENISA) projects and serves as a guest editor for scientific journals and conference TPC member. Previously, he served as a Research/Teaching Associate (2000-2017) at the Piraeus University of Applied Sciences and coordinated student placement programs. He is also a visiting lecturer at the University of Piraeus and University of West Macedonia. His work emphasizes secure network protocols, resource allocation in healthcare networks, and IoT security. Key research trends in his publications focus on cybersecurity challenges in emerging technologies like EV charging systems, healthcare-oriented networks, and intelligent transportation systems. He integrates stochastic modeling (e.g., SPN) and policy-driven architectures to address security and efficiency in distributed systems. His advising and professional activities include technical coordination in EU-funded R&D projects and contributions to regulatory frameworks for IT systems. He actively engages in cross-border cloud security performance studies and hybrid cloud approaches for spatial data management.
Christopher R. Bollinger is a Gatton Endowed Professor and University Research Professor in the Department of Economics at the University of Kentucky’s Gatton College of Business and Economics. He holds a PhD from the University of Wisconsin (1993) and has served in multiple leadership roles, including Director of the Center for Business and Economic Research (2012–2018) and current Executive Director of the Kentucky Research Data Center. His research focuses on data quality, measurement error, and policy evaluation in labor, urban, and applied microeconomics. He has authored over 35 peer-reviewed articles and contributed to numerous policy projects with state and federal agencies. Education: B.A., Economics (Michigan State University, 1988); M.S. and Ph.D., Economics (University of Wisconsin-Madison, 1990 and 1993). Research interests include the impact of survey data quality on economic models, poverty research, and urban development policy. Key areas: measurement error in earnings and program participation, labor market dynamics, and immigration effects. Award-winning teacher and recipient of the Robertson Faculty Leadership Research Award (2010). Recent grants include National Science Foundation-funded projects on earnings volatility and Kentucky state economic forecasts. Active in professional organizations, including editorial roles for the Journal of Econometric Methods and Southern Economic Journal .
Dr. Ian R. H. Telford is a researcher at the University of New England, affiliated with the School of Environmental and Rural Science. He is actively engaged in herbarium curation, teaching, and botanical research with a strong focus on Australian flora. Research Interests: His primary research areas include plant systematics, taxonomy, and biogeography, particularly within the families Cucurbitaceae, Phyllanthaceae, Rutaceae, Lamiaceae, and Asteraceae. He is dedicated to understanding species limits and evolutionary relationships in Australian endemic plants, especially in northeastern New South Wales, where biodiversity remains under-documented. He also prepares scientific illustrations for his studies. Publication Trends: His recent publications (2005–2012) reflect a consistent focus on taxonomic revisions, species delimitation, and molecular phylogenetics. Collaborative international research is a hallmark of his work, particularly on Cucurbitaceae and Phyllanthaceae. Many studies combine morphological and molecular data to resolve complex species boundaries and biogeographic patterns. Scientific Awards: Carrick Teaching Excellence Award (2006) Teaching and Community Engagement: Dr. Telford trains undergraduate and postgraduate students in field collection and herbarium techniques. He leads a team of volunteers in specimen preparation and data entry, contributes to school outreach programs, and provides plant identification services to naturalist groups. His educational impact is recognized through a national teaching award. Labs and Teams: He leads a herbarium curation team at UNE, overseeing best practices in collection management and data integrity. This team includes trained volunteers and student assistants, contributing to both research and public engagement during open days and exhibitions.
Matthew Brake is an Associate Professor of Mechanical Engineering at Rice University's George R. Brown School of Engineering, where he has been a faculty member since 2016. He leads the Tribomechadynamics Lab, which focuses on the confluence of structural dynamics, contact mechanics, and tribology to predict the response of assembled structures during the design stage and optimize interfacial components. Dr. Brake completed his entire academic training at Carnegie Mellon University, earning B.S. (2002), M.S. (2004), and Ph.D. (2007) degrees in Mechanical Engineering. Prior to joining Rice, he worked for nine years at Sandia National Laboratories. Ph.D., Mechanical Engineering, Carnegie Mellon University, 2007 M.S., Mechanical Engineering, Carnegie Mellon University, 2004 B.S., Mechanical Engineering, Carnegie Mellon University, 2002 His research spans multiple disciplines within mechanical engineering, with a particular focus on understanding interfaces across length scales from nano to macro. His work bridges theoretical foundations with practical applications in aerospace, defense, and automotive industries, addressing constitutive modeling for impact dynamics, joint mechanics, and the application of additive manufacturing for system-level assemblies. Dr. Brake has established himself as a leader in his field through significant contributions to nonlinear dynamics and joint mechanics, evident in his book 'The Mechanics of Jointed Structures' published by Springer and his founding of the Nonlinear Mechanics and Dynamics (NOMAD) Institute. Scientific Awards and Honors Future Energy Leaders Program, CERAWeek (2020) Favorite Teacher Honor, Will Rice College, Rice University (2019) The 2018 C. D. Mote Jr., Early Career Award The 2012 Presidential Early Career Award for Scientists and Engineers (PECASE) (awarded in 2014) ASME Fellow (2019) As a Fellow of the American Society of Mechanical Engineers since 2019, Dr. Brake has held several leadership positions including Executive Director of the ASME Research Committee on Mechanics of Jointed Structures, Vice Chair of the SEM Technical Division for Nonlinear Structures and Systems, and Vice Paper Solicitation Chair for the STLE Contact Mechanics Committee. He has also been a visiting academic at the University of Oxford and taught as an adjunct professor at the University of New Mexico. His Tribomechadynamics Lab hosts both graduate and undergraduate researchers as well as the Nonlinear Dynamics of Coupled Structures and Interfaces (ND-CSI) Summer Research Program, fostering the next generation of mechanical engineers and researchers in this specialized field.
Jan Bulla is a Professor in the Department of Mathematics at the University of Bergen, Norway. His contact information includes email jan.bulla@uib.no and phone number +47 55582875, with his office located at Allégaten 41, 5007 Bergen. His research spans multiple disciplines with a focus on statistical methodology development and applications in diverse fields. Bulla's research interests center around Hidden Markov Models , Time Series Analysis , and Statistical Modeling , with significant applications in Tinnitus Research , Financial Mathematics , and Biostatistics . His work demonstrates expertise in developing advanced statistical techniques for analyzing complex data structures, particularly in longitudinal settings. He has made notable contributions to regime-switching models, hidden semi-Markov models, and computational approaches for parameter estimation. Analysis of Bulla's recent publications (2020-2024) reveals a clear research trajectory with three major strands: (1) methodological advances in hidden Markov models and related statistical techniques, (2) extensive research on tinnitus including clinical, physiological, and genetic aspects, and (3) applications of statistical models in financial mathematics. His work shows increasing interdisciplinary collaboration, particularly with medical researchers studying tinnitus, while maintaining strong methodological contributions to statistics. Bulla has been consistently productive with publications spanning from 2005 to the present, demonstrating sustained research activity. His most recent work focuses on computational improvements for statistical models and continued investigation into tinnitus mechanisms and correlates.
Graham R. Hendra is a Lecturer and Mech 2 Coordinator in the Department of Mechanical Engineering at the University of British Columbia's Faculty of Applied Science, where he serves as a licensed Professional Engineer (P.Eng.). His academic responsibilities include program coordination and instruction in core mechanical engineering disciplines. His educational qualifications are: Bachelor of Applied Science (B.A.Sc.) from the University of Waterloo Doctor of Philosophy (Ph.D.) from the University of British Columbia Dr. Hendra's research and teaching expertise spans thermodynamics, fluid dynamics, solid mechanics, and programming, with specialized focus on turbulent reacting flows and combustion modeling. His scholarly work demonstrates advanced computational approaches to fluid mechanics problems, particularly in subfilter modeling techniques and conditional state frameworks for reactive flows. This interdisciplinary research bridges theoretical combustion principles with practical computational fluid dynamics applications. His publication record reveals concentrated expertise in computational combustion science, with both 2019 publications addressing sophisticated modeling challenges in turbulent flows. The research output emphasizes methodological innovation in large eddy simulation techniques and theoretical frameworks for predicting complex combustion phenomena, indicating a sustained research trajectory in high-fidelity flow simulation. Dr. Hendra has received significant recognition for his educational contributions: Junior Faculty Teaching Award (2023) His teaching portfolio includes Mech 260 (Solid Mechanics), Mech 375 (Heat Transfer), and Mech 497 (Introduction to Research Skills and Data Analysis), reflecting comprehensive coverage of fundamental mechanical engineering principles. As Mech 2 Coordinator, he shapes curriculum development and academic programming for undergraduate mechanical engineering students. No laboratory affiliations or research team memberships were documented in available sources.
Dr. Fabiola Iannarilli is a wildlife biologist and quantitative ecologist specializing in animal behavior, conservation ecology, and biodiversity monitoring. As a Marie Skłodowska-Curie Postdoctoral Fellow at the Max Planck Institute of Animal Behavior's Department of Migration , she leads cross-institutional research initiatives focused on understanding human-driven ecological changes and advancing camera trap data methodologies. Education: PhD in Conservation Sciences (University of Minnesota, 2020), MA in Ecobiology (Sapienza - University of Rome, 2012), BA in Biology (Sapienza - University of Rome, 2010) Her research examines how wildlife responds to habitat loss, domestic species interactions, and human presence across spatio-temporal scales. She promotes standardized monitoring programs and open data sharing through projects like WildEuro and Snapshot Europe , leveraging camera traps and AI tools to improve ecological inference. Recent projects include: WildEuro: Quantifying the 'landscape of fear' in Europe via camera trap networks. Snapshot Europe: Systematic continent-wide mammal surveys. Big_Picture: Overcoming data-sharing barriers with Biodiversa+ funding. She has developed tools like MLWIC2 for machine learning-based animal identification and published extensively on topics including observer bias, activity pattern modeling, and biodiversity tracking. Scientific Awards: Marie Skłodowska-Curie Postdoctoral Fellowship Collaborations: Active in Yale Center for Biodiversity and Global Change (2020-2023), Norwegian Institute for Nature Research (2014-2015), and Fondazione Ethoikos (2013-2014). Her work emphasizes legal and institutional barriers in data sharing, statistical best practices, and global conservation strategies.
George Amvrosiadis is a Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, with a courtesy appointment in Computer Science. He is a core member of the Parallel Data Lab and spends part of his time at Amazon S3 as an Amazon Scholar. Education: 2016 - Ph.D., Computer Science, University of Toronto 2009 - BA, Computer Science, University of Ioannina Research Interests: His work focuses on distributed systems , operating systems , data analysis , cloud computing , and storage technologies . He explores high performance computing (HPC), zoned storage , systems security , and storage solutions for machine learning . Scientific Trends: Articles highlight innovations in storage systems, including zoned storage , HPC data services , and machine learning infrastructure . Research spans distributed systems , I/O optimization , and data integrity in large-scale environments. Scientific Awards: DeltaFS project received the R&D 100 Award from R&D World Magazine Teaching & Service: He co-teaches graduate courses on storage and cloud systems, serves on program committees for top conferences (SOSP, OSDI, FAST), and mentors students in systems research and infrastructure projects.
Timothy R. Konold is a Professor at the University of Virginia's School of Education and Human Development, serving as Director of the Quantitative Analytics in Education and Research, Statistics, and Evaluation programs. He holds affiliations with the Center for the Advanced Study of Teaching and Learning (CASTL), the Virginia Education Science Training (VEST) program, and the Youth Violence Project (YVP). Ph.D., University of Delaware, 1995 M.A., University of Delaware, 1993 M.S., Shippensburg University of Pennsylvania, 1990 Dr. Konold specializes in latent variable and multilevel modeling, with a focus on measurement error and complex educational systems. His recent substantive research examines school climate and threat assessment teams' impact on student outcomes, emphasizing equity across diverse populations. Key journals include Educational and Psychological Measurement , Multivariate Behavioral Research , and Structural Equation Modeling . His six most recent publications (2022-2025) address methodological challenges in clustered data aggregation, Bayesian inference pitfalls, measurement invariance, and innovative applications of structural equation modeling. These works emphasize improving statistical rigor in educational research. Curry Memorial Professor of Education Dr. Konold has taught graduate-level quantitative methods courses for 29 years and served as senior psychometric consultant for the Chartered Financial Analyst (CFA) program for 25 years. His work includes over 100 peer-reviewed articles, book chapters, and technical reports. He contributes to initiatives like the Authoritative School Climate Survey and collaborates with labs such as the Youth Violence Project.
Matt Higham is an Assistant Professor at St. Lawrence University in the Math, Computer Science, and Statistics Department . His research focuses on Spatial Statistics with ecological applications, particularly Spatial Prediction Models that account for imperfect detection of animals in surveys. Education: PhD in Statistics from Oregon State University (2019) B.S. in Statistics and Botany from Miami University (2014) Research interests involve developing statistical methodologies for ecological applications, including spatial sampling , spatio-temporal modeling , and imperfect detection adjustment in wildlife surveys. He contributes to R software packages like spmodel and sptotal for spatial data analysis. Recent publications demonstrate trends in spatial statistics (4/7 articles), ecological modeling (5/7 articles), and statistical software development (2/7 articles). Key subfields include spatial prediction , block kriging , finite population estimation , and big spatial data handling . Teaching activities include courses in Introduction to Statistics , Applied Regression Modeling , Foundations of Data Science , and Data Visualization . Personal interests include racket sports, jogging, gaming, hiking, and backpacking.
