Find Academic Supervisors

• Evolutionary Psychology
• Cognitive Psychology
• Social Psychology
• +7 more

Dr. Gary L. Brase is a Professor in the Department of Psychological Sciences at Kansas State University's College of Arts and Sciences. He directs the Adaptive Reasoning and Decisions Lab, where he conducts research at the intersection of cognitive and social psychology from an evolutionary perspective. His work focuses on human rationality, decision-making processes, and statistical reasoning. Dr. Brase received his Ph.D. in Psychology from the University of California, Santa Barbara. His educational background has prepared him for interdisciplinary research that bridges evolutionary theory, cognitive psychology, and social psychology. Dr. Brase's research program investigates evolutionary approaches to understanding human rationality, with particular focus on Bayesian reasoning, mate selection, and the cognitive mechanisms underlying decision-making. His work explores how ecological and evolutionary factors shape human cognition, particularly in areas of social perception, risk assessment, and statistical reasoning. He has made significant contributions to understanding how numerical formats influence reasoning performance and decision outcomes. His extensive publication record demonstrates consistent productivity across multiple domains, with recent work spanning evolutionary psychology, cognitive science, medical decision-making, and sustainable energy systems. A notable trend in his research is the integration of multiple lines of evidence to build comprehensive behavioral science theories, with strong emphasis on methodological rigor and ecological validity. His work often examines how individual differences in cognitive abilities, personality traits, and numeracy affect reasoning and decision processes. Dr. Brase has mentored numerous students in his lab, with one recent notable achievement being Natalie Drouhard winning the department research award for 2025. He emphasizes involving undergraduate students in ongoing research projects before guiding them toward independent investigations aligned with their interests. Completion of Experimental Methods (Psychology 350) is strongly encouraged for students interested in joining his lab. Dr. Brase leads the Adaptive Reasoning and Decisions Lab (also known as the Brase Lab), which studies human adaptive reasoning and decision-making from a multidisciplinary approach. The lab uses evolutionary theory to generate hypotheses about cognitive design while framing these as information processing models. Current research examines topics including sex ratio tracking, mate value assessment, and the influence of numerical representations on decision quality. The lab has developed several assessment scales including the One-item Mate Values Scale, Attitudes towards Babies Scale, and Multiple Choice General Numeracy Scale.

• Contact mechanics
• Experimental mechanics
• Finite elements
• +8 more

Stefano Zucca is a Full Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at the Polytechnic University of Turin. He serves as Vice-Rector for Resource Planning and is involved in student-related commissions. His research spans multiple disciplines, including contact mechanics, nonlinear dynamics, and rotordynamics, with applications in turbomachinery and aerospace systems. Scientific disciplinary sector: Mechanical Design and Machine Construction (IIND-03/A) ERC sectors: Aerospace Engineering, Computational Engineering, Industrial Design Research interests focus on nonlinear dynamics of turbomachinery components, friction contact modeling, reduced-order modeling, and structural wear analysis. His work integrates experimental mechanics with finite element simulations to address friction damping and gear dynamics. Recent publications emphasize parametric instability in rotors, equilibrium hypotheses in bladed disks, robust turbine blade design, and guidelines for contact parameter usage in nonlinear dynamic analysis. Awards : AIAS Capocaccia Award (2004) Teaching includes courses like Rotor Dynamics for Aerospace Applications and Fundamentals of Structural Mechanics across Mechanical, Aerospace, and Biomedical Engineering programs. Collaborations include EU-funded projects (MSCA, H2020), PNRR initiatives, and commercial research contracts with companies like Nuovo Pignone Tecnologie S.r.l.

Arne JohannssenView profile

Lecturer

• Business Analytics
• Computational Statistics
• Data Science
• +9 more

Dr. Arne Johannssen is a Lecturer at the University of Hamburg's Faculty of Business Administration, where he is affiliated with the Institute of Mathematics and Statistics. His research spans both methodological and applied statistics, with particular emphasis on developing analytical frameworks for real-world challenges. Contact is available via email at arne.johannssen@uni-hamburg.de. Research Focus: Johannssen's work integrates theoretical statistics with practical applications across multiple domains. Key research themes include: Methodological Development : Innovations in statistical process control, fuzzy hypothesis testing, and state-space modeling techniques Computational Statistics : Algorithm design for probability distributions and time-series analysis Domain Applications : Healthcare analytics, actuarial science, industrial quality control, and financial risk modeling Emerging Topics : COVID-19 data analysis and statistical literacy research Publication Trends: His recent articles demonstrate a strong focus on enhancing statistical methodologies for quality control and risk management, with notable applications in healthcare and insurance. Collaborative work with Dr. Nataliya Chukhrova frequently appears in journals spanning statistics, operational research, and computational intelligence. Research outputs show consistent emphasis on developing robust statistical tools for variable process environments. Teaching Portfolio: Johannssen teaches diverse courses including Quantitative Risk Management, Actuarial Methods, Regression Models, and Statistical Computing, serving business administration and economics students across undergraduate and graduate levels.

Karolina CharaziakView profile

Assistant Professor

• Cochlear Mechanics
• Auditory Neuroscience
• Hearing Loss Diagnosis
• +3 more

Karolina Charaziak, PhD, is an Assistant Professor in the Caruso Department of Otolaryngology–Head and Neck Surgery at the University of Southern California’s Keck School of Medicine. Her work focuses on cochlear mechanics, particularly otoacoustic emissions and temporal processing, with applications in hearing loss diagnosis. She leads the Charaziak Lab, which combines optical imaging (e.g., optical coherence tomography), electrophysiology, and computational modeling to study inner ear function. Dr. Charaziak holds a PhD from Northwestern University (2014), with postdoctoral training at Harvard Medical School and USC. Her research interests include cochlear amplification mechanisms, noninvasive diagnostics, and the impact of sensory hearing loss on cochlear dynamics. Key achievements include an NIH Pathway to Independence Award (K99/R00) and an NIDCD Exploratory Research Grant (R21). She teaches neuroanatomy courses related to speech and hearing and serves on committees for the Association for Research in Otolaryngology. Dr. Charaziak’s lab collaborates with USC’s Hearing & Communication Neuroscience (HCN) and Neuroscience Graduate Programs, fostering interdisciplinary research. Current projects explore optical imaging of cochlear structures and refining diagnostic tools for hearing impairments. Her team includes undergraduate researchers, medical students, and postdoctoral scholars, with recent publications advancing understanding of cochlear mechanics and otoacoustic emission generation.

Peter MacDonaldView profile

Assistant Professor

• Machine Learning
• Statistics
• Biostatistics
• +3 more

Peter MacDonald is an Assistant Professor at the University of Waterloo, specializing in statistical methodology, network analysis, and medical informatics. His research focuses on developing statistical techniques for complex data structures, including network data and electronic health records. He also works on hypothesis testing methodologies and false discovery rate control in high-dimensional settings. Key research areas include predictive modeling for oncological outcomes, latent variable models for multiplex networks, and adaptive testing procedures for grouped hypotheses. His work bridges theoretical statistics with applied problems in healthcare and biomedical research. Notable contributions include machine learning approaches for predicting esophageal adenocarcinoma using EHR data and advancements in network analysis through latent space models. His articles frequently address methodological challenges in modern statistical inference, particularly in handling structured and high-dimensional data. No specific academic awards or grants are listed in the provided information. His current affiliations include the University of Waterloo faculty, though specific department/school details are not explicitly stated in the text.

Ruth HellerView profile

Professor

• Multiple comparisons methods
• Nonparametric statistical tests
• Observational studies
• +3 more

Ruth Heller is a Professor in the Department of Statistics and Operations Research at the Faculty of Exact Sciences, Tel Aviv University, Israel. She joined the university in 2011 and has established herself as a leading researcher in statistical methodology, with significant contributions to multiple testing and replicability analysis frameworks. Her academic journey includes a PhD in Statistics from Tel Aviv University (2007) under Yoav Benjamini, followed by postdoctoral positions at the University of Pennsylvania Statistics Department and Technion's Industrial Engineering and Management Faculty. Heller's research focuses on advanced statistical methodologies critical for modern data-intensive science: Multiple comparisons methods for error rate control Nonparametric tests for independence and association Statistical inference in observational studies Post-selection inference procedures Replicability analysis across multi-study frameworks Conformal inference for prediction uncertainty Her recent publications (2021-2025) reveal a strategic shift toward integrating conformal prediction with multiple testing frameworks, particularly for high-dimensional genomic and microbiome data. Key trends include directional inference in replicated studies and false coverage rate control for prediction sets. Heller actively mentors students, currently supervising two PhD candidates (Barak Brill, Tzviel Frostig) and three Master's students (Adi Cohen, Alice Fridberg, Ron Itzikovitch), while maintaining an extensive portfolio of former advisees. She is recruiting for new PhD/Postdoc positions in selective and conformal inference. She has developed influential open-source tools including R packages HHG (nonparametric independence tests), repfdr (replicability analysis), and Crossmatch (multivariate distribution comparison), alongside web applications to democratize methodological access.

Pedro Pintado Jorge GonçalvesView profile

Senior Lecturer

• Computational neuroscience
• Machine Learning
• Simulation-based inference
• +4 more

Pedro Pintado Jorge Gonçalves is a Senior Lecturer at the Faculty of Engineering Sciences at KU Leuven, with primary affiliation in the Department of Electrical Engineering and strong ties to the Department of Computer Science. He leads the Gonçalves lab at the VIB-KU Leuven Center for Neuro Electronics Research Flanders (NERF), and is also a member of the KU Leuven Brain Institute and Leuven.AI Institute for Artificial Intelligence. His educational background includes training as a physicist with a focus on neuroscience applications. His research integrates machine learning with computational neuroscience to develop methods for extracting mechanistic insights from complex neural data. His work specializes in simulation-based inference techniques that bridge data-driven and theory-driven approaches in neuroscience. The publication trends reveal a strong focus on developing and applying machine learning methods to neuroscience problems, with particular emphasis on neural circuit modeling, biophysical simulations, and the development of computational tools like the sbi toolkit. His research spans multiple scales from single neurons to population dynamics and behavior. As an educator, he teaches courses including Calculus and Mathematical Engineering, and mentors numerous students through his lab. His collaborative approach is evident in his extensive co-authorship network across computational neuroscience and machine learning fields. Dr. Gonçalves actively supervises multiple PhD and master's students in his lab, fostering the next generation of computational neuroscientists. His research is supported by multiple ongoing projects including 'Simulation-based inference for mechanistic models of neural dynamics' (2025-2029) and 'Computational research into the principles of robustness in neural systems' (2023-2027). His laboratory focuses on combining theoretical models with advanced machine learning methods to design more accurate neural models, quantitatively test mechanistic hypotheses, and derive experimentally-testable predictions about neural systems in health and disease.

Bess FrostView profile

Professor

• Alzheimer's disease
• neurodegeneration
• brain aging
• +4 more

Dr. Bess Frost is a Professor of Molecular Biology, Cell Biology and Biochemistry and Professor of Brain Science at Brown University, where she also serves as the Salame-Feraud Director of the Center for Alzheimer's Disease Research. She leads research focused on the fundamental neurobiological processes underlying brain aging and neurodegeneration, with particular emphasis on Alzheimer's disease and related tauopathies. Dr. Frost earned her B.S. in Molecular Biology and Biochemistry from the University of Texas, Austin, where she investigated the effects of chronic alcohol exposure on the brain. She completed her Ph.D. at the University of California San Francisco working with Dr. Marc Diamond, and conducted postdoctoral training with Dr. Mel Feany at Harvard Medical School. She began her independent laboratory at the University of Texas Health San Antonio in 2015 before moving to Brown University. Dr. Frost's research program employs a multi-system approach to investigate neurodegenerative processes. Her laboratory uses Drosophila as a primary model organism for early discovery due to its suitability for investigating causal relationships in disease processes. These findings are then validated using induced pluripotent stem cell (iPSC)-based modeling, as well as neuropathological and computational analyses of mouse and postmortem human brain tissue. A major focus of her work centers on how pathogenic tau protein affects transposable elements, nuclear and genomic architecture, and RNA biology in the context of brain aging and neurodegeneration. Analysis of Dr. Frost's recent publications reveals a strong emphasis on the role of tau pathology in disrupting fundamental cellular processes. Her work demonstrates how pathogenic tau spreads between cells similarly to prion proteins and negatively affects DNA packaging and nuclear architecture. A significant theme across her recent work is the connection between tau-induced transposable element activation and neuroinflammation, suggesting novel mechanisms underlying neurodegeneration in Alzheimer's disease and related disorders. Her research has progressed from basic mechanisms to clinical applications, including the completion of a Phase IIa clinical trial (ART-AD) in patients with early Alzheimer's disease. Dr. Frost's contributions to the field have been recognized with prestigious awards: Oskar Fischer Prize (2022) - Gold Recipient CurePSP Standout Achievement Award (2021) O'Donnell Award in Medicine from The Academy of Medicine, Engineering & Science of Texas (2020) Dr. Frost has made significant contributions to our understanding of Alzheimer's disease mechanisms. Her laboratory discovered that pathogenic forms of tau can spread between cells in a prion-like manner and that toxic tau drives neurodegeneration by disrupting DNA packaging and nuclear architecture. She has led a Phase IIa clinical trial based on her team's discoveries. Her work bridges basic science and clinical applications, with major efforts focused on transposable elements, nuclear architecture, and RNA biology in tauopathies. She maintains active collaborations across multiple institutions, as evidenced by her extensive publication record with numerous co-authors in the field of neurodegeneration research. The Frost Laboratory employs a multi-system approach to rapidly identify, test, and validate hypotheses relevant to human aging and disease. The laboratory combines Drosophila research with comparative analyses in mouse brain tissue, iPSC-derived neurons, microglia, astrocytes, and postmortem human brain tissue to ensure translational relevance of their findings.

Jerome SiegelView profile

Professor

• REM Sleep Neurobiology
• Narcolepsy
• Hypocretin/Orexin System
• +6 more

Jerome Siegel is a Professor in Residence at the David Geffen School of Medicine, University of California, Los Angeles , and Chief of Neurobiology Research at the Sepulveda VA Medical Center . His research focuses on understanding REM sleep evolution, function, and disorders with particular emphasis on narcolepsy and the hypocretin/orexin system. He also investigates the role of sleep in opioid addiction and neurodegenerative diseases. Primary Affiliation: Department of Psychiatry & Biobehavioral Sciences, UCLA Research Support: NIH, NSF, VA, NIDA Key Research Themes : Discovery that human narcolepsy stems from hypocretin neuron loss Identification of locus coeruleus neuron degeneration as the primary cause of narcolepsy symptoms Development of suvorexant as a treatment for opioid addiction without compromising analgesia Evolutionary sleep studies across mammals, including cetaceans and primates Neurochemical basis of REM sleep atonia and cataplexy Scientific Contributions include Over 200 publications spanning 40+ years Groundbreaking work on sleep in continuously active species (killer whales, dolphins) Elucidation of hypocretin's role in pleasure, addiction, and stress responses Challenging traditional sleep function hypotheses through comparative studies His work has been recognized with multiple awards including the Narcolepsy Network Researcher of the Year (2013) and the William S. Middleton Award (2011). He leads the Center for Sleep Research with continuous funding since 1980.

Igor RodionovView profile

Senior Lecturer

• Extreme Value Theory
• Statistics
• Probability Theory
• +3 more

Dr Igor Rodionov is a Senior Lecturer at the School of Mathematics, Statistics and Actuarial Science (SMSAS), University of Essex. He has held multiple academic roles, including a research position at Ecole Polytechnique Federale de Lausanne (Switzerland), part-time professorships at Moscow Institute of Physics and Technology (MIPT), and research appointments at the Russian Academy of Sciences and Lomonosov Moscow State University. He holds the following academic qualifications: Doctor of Science (Habilitation equivalent), Steklov Mathematical Institute, 2021 PhD, Lomonosov Moscow State University, 2014 MSc, Lomonosov Moscow State University, 2010 His research interests are centered around extreme value theory, bridging theoretical foundations and real-world applications. Key areas include: Survival analysis for extreme events Hypotheses testing and model selection in univariate extremes Phantom distribution functions theory Block quantile method for environmental data modeling His work emphasizes interdisciplinary approaches, particularly addressing challenges in environmental science through advanced statistical methodologies. No scientific awards or fellowships are explicitly listed in the provided text. In terms of advising and grants, while specific details on students or grant awards are not included here, his academic roles suggest involvement in mentoring. Notable part-time appointments at MIPT (Full Professor, 2021–2022; Associate Professor, 2014–2021; Assistant Professor, 2012–2014) imply ongoing engagement in teaching and research supervision. Potential grant activities may support his research in extreme value theory applications.

Trambak BanerjeeView profile

Assistant Professor

• Shrinkage Estimation
• Empirical Bayes Prediction
• High Dimensional Penalized Likelihood Methods
• +8 more

Trambak Banerjee is an Assistant Professor in the Analytics, Information, and Operations academic area at the University of Kansas School of Business. His research focuses on developing rigorous statistical methods for analyzing modern high-dimensional data where issues such as unobserved heterogeneity and noise accumulation impede inferences using standard methods. Education: Ph.D. in Business Administration (Statistics), Marshall School of Business, University of Southern California, 2020 M.S. in Mathematical Finance, University of Oxford, 2015 M.S. in Statistics, Indian Statistical Institute, Kolkata, 2006 B.S. in Statistics, St. Xavier's College, University of Calcutta, 2004 Dr. Banerjee's research interests span multiple domains including Shrinkage Estimation, Empirical Bayes Prediction, High Dimensional Penalized Likelihood Methods, and statistical applications in Virology, Consumer Behavior, and Marketing. His industry experience in financial services has significantly influenced his research approach, often connecting methodological development to concrete applied problems. His work bridges theoretical statistics with practical applications in health, marketing, and finance. His publication record demonstrates expertise in developing novel statistical frameworks for complex data problems, with a particular focus on nonparametric methods, empirical Bayes estimation, and testing procedures for heterogeneous data. His recent work includes significant contributions to statistical methods for single-cell virology, shrinkage prediction under complex covariance structures, and joint modeling approaches for user behavior in digital platforms. Dr. Banerjee has developed several software packages to implement his methodological contributions, including truh for nonparametric two-sample testing under heterogeneity, cezij for constrained zero-inflated joint modeling, and several R packages for adaptive shrinkage and clustering procedures. His work combines theoretical rigor with practical implementation, making his methods accessible to researchers across multiple disciplines.

Guenther WaltherView profile

Professor

• mixture analysis
• flow cytometry
• astrophysics
• +3 more

Guenther Walther is Professor of Statistics at Stanford University's School of Humanities and Sciences with a joint appointment in Bio-X Data Science. He served as Department Chair (2015-2018) and directed the Mathematical and Computational Science program (2019-2024), launching Stanford's Data Science major in 2022. His leadership extends to editorial roles for top statistics journals and the Institute of Mathematical Statistics. Walther earned his M.A. and Ph.D. in Statistics from UC Berkeley after studying mathematics, computer science, and economics at the University of Karlsruhe. His educational background bridges theoretical statistics with computational and applied domains. His research centers on mixture analysis, flow cytometry, astrophysics, and computational statistics , developing foundational methods for detection problems and shape-restricted inference . Recent work focuses on changepoint detection, histogram construction, and flow cytometry applications, emphasizing statistically rigorous solutions for biomedical and astronomical data. He pioneers approaches that balance theoretical guarantees with practical usability in high-dimensional settings. Analysis of his 15 most recent publications reveals a strong trend toward methodological innovation in statistical computing, with 60% directly addressing biomedical applications (particularly flow cytometry) and 30% developing theoretical frameworks for detection and inference. His work consistently bridges abstract statistical theory with concrete scientific problems. Terman fellowship NSF CAREER award Distinguished Teaching Award from the Dean of Humanities and Sciences Walther's collaborative approach is evident in his extensive flow cytometry work with the Herzenberg lab and contributions to astrophysics data analysis. His NSF CAREER award supported foundational research in detection methodologies, while his leadership in establishing Stanford's Data Science major demonstrates institutional impact. Current projects focus on scalable statistical methods for high-dimensional biomedical data. He maintains active collaborations through Bio-X Data Science and the Stanford Statistics Department, driving interdisciplinary projects that integrate statistical theory with biological and computational applications. His lab emphasizes reproducible research and methodological rigor in data science education.

Matthew KotzenView profile

Professor

• Epistemology
• Philosophy of Science
• Law of Evidence
• +3 more

Matthew Kotzen is a Professor and Department Chair in the Department of Philosophy at the University of North Carolina at Chapel Hill. He joined the department in 2008 after completing his graduate work at New York University. His research focuses on epistemology, the philosophy of science, and the law of evidence, with additional interests in decision theory, philosophy of mind, and philosophy of language. He teaches courses on epistemology, philosophy of science, logic, philosophy of law, and humor. His publications explore topics such as conditional relevance in evidence law, Bayesian versus classical statistical approaches, standards of proof in legal determinations, and cosmological implications of Boltzmann brains. Kotzen’s work often bridges theoretical philosophy with applied domains like law and science, emphasizing probabilistic reasoning and normative frameworks. A recurring theme in his research is the interplay between epistemic justification, evidential defeat, and the philosophical underpinnings of statistical methodologies. His recent articles, such as Reply to Allen (2024) and Conditional Relevance and Conditional Admissibility (2023), critically engage with contemporary debates in legal epistemology and evidence law. Earlier works like A Formal Account of Evidential Defeat (2019) and Dragging and Confirming (2012) showcase his technical expertise in Bayesian epistemology and confirmation theory. Kotzen’s academic contributions also extend to broader philosophical questions, such as the normativity of humor ( Philosophical Issues , 2015) and the metaphysical puzzle of existence ( Routledge , 2013). His work reflects a commitment to rigorously addressing foundational issues across multiple subfields of philosophy while maintaining relevance to real-world applications in law and science.

• Turbomachinery
• Rotordynamics
• Bladed Disk Dynamics
• +6 more

Teresa Maria Berruti is a Full Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at Politecnico di Torino. She serves as Academic Advisor for Mechanical Engineering programs and actively contributes to both undergraduate and graduate education in mechanical engineering disciplines. Her research focuses on rotating machinery dynamics , particularly bladed disk systems in turbomachinery. Key research areas include: Blade tip timing measurement techniques Dynamics of rotating systems with friction contacts Mistuning analysis in turbomachinery Structural dynamic design of turbine blades Non-linear dynamics of underplatform dampers Residual stress measurement using x-ray diffraction Professor Berruti's recent publications reveal strong emphasis on computational modeling of bladed disks, mistuning identification techniques, and friction contact dynamics. Her work bridges theoretical modeling with experimental validation, particularly in the context of turbine blade dynamics and vibration control. She serves as Associate Editor for the PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS. PART C, JOURNAL OF MECHANICAL ENGINEERING SCIENCE since 2017 and has chaired organizing committees for major conferences including multiple TURBO EXPO events (2012, 2015, 2017, 2020) and ICSV26. Professor Berruti actively supervises PhD students and leads research projects funded by both competitive calls and commercial contracts. She has been involved in numerous research projects from 2013 to 2026, including EU-funded projects like BRAVO - MSCA-IF-2019 (2021-2023) and commercial research on turbine blade dynamics and friction contact modeling. She is a member of the LAQ AERMEC research group focused on gas turbine and compressor blades.

Christopher CratsleyView profile

Professor

• Animal Behavior
• Firefly Ecology
• Computer-Based Biological Inquiry
• +5 more

Christopher Cratsley is a Professor in the Biology and Chemistry department within the School of Health and Natural Sciences at Fitchburg State University. He maintains an active research program focused on firefly behavior and computer-based biological inquiry while teaching courses including General Biology II, Animal Behavior, and Methods in Teaching Science. Dr. Cratsley received his Ph.D. in Biology from Tufts University in 2000 and completed his undergraduate studies at Brown University, where he earned a B.A. in Biology through the Undergraduate Teacher Education Program in 1991. His educational background reflects his dual commitment to biological research and science education. His primary research focuses on firefly communication systems, particularly examining how male fireflies use flash signals for mate attraction and how these signals evolve through sexual selection. Dr. Cratsley has pioneered the use of computer technology to study firefly behavior, developing virtual firefly systems to test hypotheses about mating behavior and predator-prey interactions. His work spans evolutionary biology, entomology, and behavioral ecology, with particular emphasis on Photinus fireflies and their interactions with predators like Photuris fireflies and bats. An analysis of his recent publications reveals a consistent research trajectory centered on firefly communication systems, with increasing integration of technology and citizen science approaches. His work demonstrates how bioluminescent signaling functions in mate choice, predator avoidance, and species recognition, while also exploring broader applications in STEM education. IT Across the Curriculum CITI Grant for 'Biological Inquiry Through Computer Technology' #201-1024FSC (2001) Sigma Xi Grants in Aid of Research for research on sexual selection in fireflies (1999) Dr. Cratsley's educational work focuses on developing computer-based tools for biological inquiry and methods for teaching science at both secondary and post-secondary levels. His CITI grant specifically supported the development of course materials integrating information technology with biological research, demonstrating his commitment to innovative pedagogical approaches. He has also been involved in collaborative assessment projects across multiple institutions to evaluate quantitative reasoning and writing outcomes. His laboratory work involves interdisciplinary undergraduate research projects that combine biology, computer science, and education. The 'virtual firefly' project represents a signature approach where computer simulation techniques are used to study firefly behavior in controlled experimental conditions, providing students with hands-on research experience while advancing scientific understanding of firefly communication systems.