Elizabeth Race is an Associate Professor in the Department of Psychology at Tufts University's School of Arts and Sciences. She holds a PhD in Neuroscience from Stanford University (2009) and completed postdoctoral training at Boston University's Memory Disorders Research Center. Her Integrative Cognitive Neuroscience Lab investigates the neural mechanisms of learning, memory, and cognitive control using fMRI and EEG techniques. Research focuses on how memory adaptively guides behavior and enables future simulations, examining both healthy adults and clinical populations with amnesia. Recent publications explore temporal dynamics of memory formation, misinformation susceptibility, and neural mechanisms of memory protection. Her work demonstrates consistent innovation in experimental paradigms linking cognitive psychology with neuroscience. She teaches courses including Brain & Behavior, Cognitive Neuroscience, and Statistics at undergraduate and graduate levels. Current research projects examine rhythmic neural synchronization during encoding and ventromedial prefrontal contributions to memory integration. Lab activities include collaborations with the Center for Applied Brain and Cognitive Sciences and research funded by the Grammy Museum Foundation.
Dr. Dawn MacIsaac is an Associate Professor in the Department of Computer Science at the University of New Brunswick’s Faculty of Computer Science, where she has served for over 14 years. She holds a PhD and Master of Science in Engineering from the same institution. Her research focuses on Biomedical Engineering, Software Engineering, Knowledge Engineering, and Signal Processing, particularly in the context of myoelectric control systems and biomedical signal analysis. Dr. MacIsaac has authored 49 peer-reviewed publications and supervised 25 graduate students. Her work emphasizes improving the performance and robustness of myoelectric prosthetics through advanced machine learning techniques, signal processing algorithms, and adaptive control strategies. Notable contributions include developing the Myosim 2.0 EMG simulation tool and pioneering self-supervised learning approaches for pattern recognition in unclear-label environments. She currently serves on the Editorial Board of the Journal of Electromyography and Kinesiology and is a Professional Engineer (PEng) registered with the Association of Professional Engineers and Geologists of New Brunswick (APEGNB). Her research bridges engineering and healthcare, addressing challenges in signal quality assessment, fatigue monitoring, and human-machine interface design. Key themes in her publications include enhancing EMG signal analysis for medical applications, optimizing control systems for prosthetic devices, and advancing methodologies for automated biosignal evaluation. Her interdisciplinary work impacts rehabilitation technology, clinical diagnostics, and biomedical instrumentation.
Emily Kang is a Professor in the Department of Mathematical Sciences at the University of Cincinnati, where she leads the Group on Data Analytics and Decision Sciences (GDADS). Her research develops statistical methodologies for spatial and spatio-temporal data, with applications in environmental science, climate modeling, and remote sensing. Kang specializes in hierarchical Bayesian models, data assimilation techniques, and scalable algorithms for large environmental datasets. Her work includes developing the EcoPro framework for ecological projections using Earth system models and remote sensing data, creating statistical downscaling methods for climate variables, and designing visualization tools for global environmental data on multi-resolution grids. Kang received the American Statistical Association's ENVR Early Investigator Award for contributions to environmental statistics. Current projects focus on neighborhood-scale extreme heat projections, coral reef habitability under climate change, and recursive co-kriging models for multi-fidelity spatial data. Her research advances uncertainty quantification in remote sensing products and climate impact assessments.
Guy Lacroix is an Associate Professor and Chair of the Psychology Department at Carleton University, within the Faculty of Arts and Social Sciences. He holds a Ph.D. from Université de Montréal. His research focuses on cognitive psychology, particularly categorization processes, learning mechanisms, and the intersection of psychology with educational practices. Current research explores topics such as handwritten vs. typed notes' impact on learning, psychological science perception among students, and the 'uncanny valley' phenomenon in digital human likenesses. His academic contributions span experimental psychology, with publications in journals like Journal of Writing Research , Memory & Cognition , and Computers in Human Behavior . He has advised numerous students on projects related to cognitive and educational psychology. Lacroix's work bridges theoretical models with practical applications in education and technology. He is based in the Loeb Building (Office A311) and can be reached at guy.lacroix@carleton.ca.
Kylie L. Anglin is an Assistant Professor in the Department of Educational Psychology at the University of Connecticut, specializing in Research Methods, Measurement, and Evaluation (RMME). She holds a Ph.D. in Education Policy from the University of Virginia (2021), an MPP in Leadership and Public Policy (2018), and a BA in Political Science (2013) from Southwestern University. Her research focuses on developing natural language processing (NLP) techniques to monitor program implementation in impact evaluations, enhancing causal validity and replicability in education research. Dr. Anglin teaches graduate courses in research methods, data science, and text analytics. Her work has been published in journals such as the Journal of Research on Educational Effectiveness , Prevention Science , and AERA Open . She has received the NAEd/Spencer Dissertation Fellowship and participated in the IES Pre-doctoral Training Program. Her research emphasizes scalable methods for analyzing policy variation, improving intervention adherence, and leveraging large language models for education evaluation. She actively mentors students interested in education policy or data science, and her contact details are available at kylie.anglin@uconn.edu .
Dr. Jun Yan is a Professor in the Department of Statistics at the University of Connecticut. His research spans network analytics, spatial extremes, survival analysis, and statistical computing with applications in public health, finance, and environmental science. His core research interests include: network modeling and analysis, spatial statistics for climate extremes, survival analysis methodologies, statistical computing frameworks, and applications in interdisciplinary domains including sports analytics. Dr. Yan has developed significant statistical methodologies for network analysis, climate change detection, financial modeling, and health analytics. His recent publications demonstrate innovation in modeling complex network structures, analyzing climate extremes, developing computational approaches for massive datasets, and creating specialized statistical methods for health and finance applications. He maintains active collaborations across disciplines and contributes to open-source statistical software. Honors include: Guggenheim Fellowship, multiple Fromm Foundation commissions, and Barlow Endowment recognition.
Tracy Camp is a Professor and Founding Department Head of Computer Science at the Colorado School of Mines. She leads the Toilers research group, focusing on ad hoc networks and wireless sensor systems for geosystems. With over 20 NSF grants and $20M in funding, her work has produced 12 software tools used globally. She holds ACM and IEEE Fellowships, a Fulbright Scholarship, and the Mines Outstanding Faculty Award. Education: B.S. Mathematics, Kalamazoo College (1987) M.S. Computer Science, Michigan State University (1989) Ph.D. Computer Science, The College of William & Mary (1993) Research Interests: Her work bridges machine learning and geosystems, including dam integrity monitoring via seismic data, UAV communication protocols, and secure encrypted traffic classification. She emphasizes interdisciplinary approaches for real-world challenges like disaster response and environmental safety. Awards: ACM Fellow (2017) IEEE Fellow (2015) NSF CAREER Award (2007) Fulbright Scholar (2006) Grants & Impact: Over 80 refereed publications and 12 invited articles, cited ~7,000 times. Her grants include initiatives to broaden participation in computing, such as the S-STEM scholarship program. Software tools developed under her grants have been adopted by 3,000+ researchers in 86 countries. Labs & Teams: Directs the Toilers group, advancing ad hoc network evaluation and geophysical monitoring. Collaborates on projects like the ADMIRE dam monitoring system and the DREAM master’s program for underrepresented students.
Sebastian Diehl is a Professor at Umeå University's Department of Ecology, Environment and Geoscience. His research focuses on mathematical modeling of aquatic ecosystem dynamics, with particular emphasis on consumer-resource interactions and climate change impacts on northern lake ecosystems. Current affiliations: Umeå University (Department of Ecology, Environment and Geoscience) Previous affiliations: Ludwig-Maximilians-Universität München (Professor of Aquatic Ecology) Academic background: Biology undergrad from University of Göttingen, PhD in Animal Ecology from Umeå University (1994) Research interests center around aquatic ecosystem dynamics in response to environmental change, including: Climate change effects on lake ecosystems (temperature and terrestrial nutrient inputs) Mathematical modeling of consumer-resource interactions Benthic-pelagic coupling in nutrient and energy flows Phytoplankton-zooplankton dynamics under environmental stressors Trophic interactions in stream and lake systems Stoichiometric constraints on ecosystem function His recent publications (2025-2020) demonstrate an integrated approach combining mathematical modeling with experimental validation through field studies and lab experiments, focusing particularly on: Climate-induced regime shifts in lake ecosystems Stoichiometric mismatch in warming systems Functional response modeling Vertical niche partitioning in plankton Resource acquisition trade-offs Browning impacts on primary producers Administrative roles include: Final examiner for PhD education Chair of Umeå Marine Science Centre advisory council Steering group member of Integrated Science Lab (IceLab)
Karthik Srinivasan is an Assistant Professor in the Analytics, Information, and Operations Academic Area at the University of Kansas School of Business. He holds a Ph.D. in Management Information Systems from the University of Arizona, an M.Mgt. in Business Analytics from the Indian Institute of Science, and a B.E. from Mumbai University. His research focuses on interpretable machine learning, explanatory modeling, text mining for business applications, and healthcare information systems. He develops methods to enhance transparency in AI systems and applies data science to healthcare, finance, and retail contexts. Recent work includes predictive modeling for incomplete data, graph-based retail analytics, and analyzing pandemic impacts on stock markets and public health. His publications span journals like MIS Quarterly, Decision Support Systems, and Nature Digital Medicine. He also contributes open-source tools like TextRegress and MoreThanSentiments for advanced text analysis. Teaching responsibilities include undergraduate and graduate-level data management courses. His research emphasizes practical applications in business and public health, leveraging interdisciplinary approaches to address real-world challenges.
Dr. Andrew McCarren is an Associate Professor and Head of the School of Computing at Dublin City University (DCU). He holds a PhD and BSc from DCU and is a funded investigator in the Insight Centre for Data Analytics. His research focuses on applying data analytics to Fintech, Agriculture, Health, and Sports Performance. As a former industry professional with 20+ years experience in Agri, Engineering, and Pharmaceuticals, he bridges academic and industrial collaboration. Professional Affiliations: Fellow of Royal Statistical Society and Advance HE Key Roles: PI on SFI/EI projects, Visiting Professor at Princess Nourah bint Abdulrahman University Research spans software engineering (microservices architecture), health informatics (exercise interventions), and agri-tech (automated food processing). Over 100 publications across data science, sports analytics, and engineering.
Dr. Cheryl Barnes is an Assistant Professor in Marine Fisheries at Oregon State University, affiliated with the Coastal Oregon Marine Experiment Station and the Department of Fisheries, Wildlife, and Conservation Sciences. She leads the Integrated Marine Fisheries Lab focusing on management-relevant research of groundfish populations. Education includes: PhD in Fisheries from University of Alaska Fairbanks MS in Marine Science from Moss Landing Marine Laboratories BS in Biology from San Diego State University Her research investigates population and community dynamics of North Pacific groundfish, emphasizing biogeographic effects on life history traits, food web interactions, and climate change impacts. She develops scientific products to inform stock assessments and ecosystem-based fisheries management through field sampling, laboratory research, and statistical modeling. Research employs collaborative approaches with agency scientists, resource managers, and fishery stakeholders. Current projects examine spatial ecology, climate vulnerability, and statistical tool development for fisheries management. Dr. Barnes mentors graduate students including Madison Bargas (MS) studying black rockfish life history and Peri Gerson (MS) modeling prey availability. She serves as Oregon's representative on the Pacific Fishery Management Council's Scientific and Statistical Committee.
Joel E. Cohen is the Abby Rockefeller Mauzé Professor at The Rockefeller University, where he leads the Laboratory of Populations. With over five decades of research experience, Cohen has pioneered innovative mathematical approaches to study biological populations and variability. His work bridges mathematics, biology, and environmental science, fundamentally changing how scientists understand population dynamics and the significance of biological variability. Dr. Cohen's research focuses on developing new mathematical tools to address population problems in demography, epidemiology, and ecology. He has made seminal contributions to the understanding of heavy-tailed distributions that describe extreme events like hurricanes and disease outbreaks, challenging traditional statistical approaches. His laboratory has conducted groundbreaking research on the spatial distribution of human populations in relation to geophysical factors, with unexpected practical applications ranging from soap formulation to semiconductor manufacturing. Cohen has also developed mathematical models for Chagas disease control in rural Argentina and created algorithms to predict international migration patterns. Analysis of Cohen's recent publications reveals a sustained focus on Taylor's law of fluctuation scaling, population dynamics, and ecological statistics. His work consistently demonstrates how abstract mathematical concepts can transform our understanding of biological systems, from cellular processes to global population trends. The research spans theoretical mathematics to practical applications in disease control, conservation biology, and environmental management. Olivia Schieffelin Nordberg Prize for excellence in writing in the population sciences (March 1997) Gheorghe Lazar Prize of Romanian Academy (December 2000) As director of the Laboratory of Populations, Cohen has led research on human population growth, infectious diseases, food webs, and international migration. His methods for assessing the uncertainty of population projections have been applied in court cases for predicting future claimants of asbestos-related diseases. Cohen's laboratory has collaborated with the United Nations Population Division on migration studies and developed mathematical models that account for more than half of the variability in annual migration numbers among 229 countries. Current research directions include understanding how demographic, economic, and cultural changes interact with Earth's physical, chemical, and biological environments. The Laboratory of Populations employs a multidisciplinary approach that combines mathematical modeling, statistical analysis, and field studies to address complex population issues. Their work exemplifies how basic quantitative research on populations frequently yields unexpected practical applications, demonstrating the profound connections between theoretical mathematics and real-world challenges in public health, environmental science, and resource management.
Dr. Saptarshi Sengupta is an Assistant Professor in the Department of Computer Science at San José State University (SJSU), leading the Machine Intelligence and Complex Systems (MICoSys) Lab. He advises the ACM student club at SJSU and holds a 'Alien of Extraordinary Ability' visa (Einstein Visa) from USCIS. His work focuses on resilient cyber-physical systems, risk analysis, and deep learning applications in healthcare and industrial systems. Education: Ph.D. in Electrical Engineering, Vanderbilt University M.S. in Electrical Engineering, Vanderbilt University B.Tech. in Electronics & Communication Engineering, West Bengal University of Technology Research Interests: Cyber-Physical Systems Security Healthcare AI for Cancer and Chronic Disease Prediction Battery Prognostics and Energy Systems Machine Learning for Complex Systems Analysis Key Achievements: Dr. T.M.A. Pai Gold Medal Award for Healthcare AI contributions Recipient of multiple best paper awards at international conferences Author of over 30 peer-reviewed publications Labs & Teams: Leads the MICoSys Lab, developing AI solutions for healthcare diagnostics, industrial prognostics, and smart infrastructure systems. Collaborations include interdisciplinary projects with biomedical and engineering domains.
Jovica Milanovic is a Professor in the School of Electrical and Electronic Engineering at the University of Manchester, where he leads the Electrical Energy and Power Systems research group. He holds a Dipl.Ing. and MSc in Electrical Engineering from the University of Belgrade, a PhD from the University of Newcastle, and a DSc from the University of Manchester. With over 600 publications, his research focuses on probabilistic modeling of power systems, renewable integration, and power quality. Research Expertise: Probabilistic assessment of uncertain power systems Machine learning applications for grid stability Demand side management in low-carbon networks Global power quality monitoring frameworks Awards & Leadership: FIEEE, FIET, and IEEE PES Distinguished Lecturer Editor-in-Chief of IEEE Transactions on Power Systems Chair of multiple international conferences including PowerTech 2017 Recipient of the Verriest Medal for contributions to power systems
John D. Albertson is a Professor in the School of Civil and Environmental Engineering at Cornell University, where he has been since 2015. Previously, he served as Department Chair of Civil and Environmental Engineering at Duke University. He holds visiting appointments at the University of Cork, University of Cagliari, University of Padova, and EURAC (Italy). His research focuses on mass, energy, and momentum exchange between land and atmosphere, with applications in hydrometeorology, air quality, and model-data fusion. B.S. in Civil Engineering (SUNY Buffalo, 1985) M.B.A. in Finance (University of Hartford, 1989) M.E.S. in Hydrology (Yale University, 1993) Ph.D. in Hydrology (UC Davis, 1996) Research interests include fluid dynamics, environmental fluid mechanics, remote sensing, and sustainable energy systems. His work integrates computational methods with field measurements to advance environmental science and engineering solutions. Awards: Croll Fellow Professor (2015), Natural Resource Institute Award (2000), NASA New Investigator Award (1999) Teaching includes courses on hydrology, environmental transport processes, and smart cities. His lab (Albertson Lab) develops innovative approaches for environmental monitoring and sustainable systems.
