جست‌وجوی استاد و پژوهشگر

• Engineering Seismology
• Earthquake Engineering
• Probabilistic Seismic Hazard Analysis
• +۱۳ مورد دیگر

Peter Stafford is a Professor of Engineering Seismology in the Department of Civil and Environmental Engineering at Imperial College London, Faculty of Engineering. He is affiliated with the Imperial Centre for Geohazards and the Structures research section. Since joining the faculty in 2007 as a Lecturer, he has advanced to full professor and currently serves as the Department Careers Adviser, supporting student professional development. His research focuses on advanced methodologies in Engineering Seismology and Earthquake Engineering , including probabilistic seismic hazard analysis (PSHA), development of ground-motion models, seismic demand analysis, and structural reliability. He also investigates the dynamic response of bridges, pedestrian- and vehicle-induced vibrations, and the stability of tensegrity structures. His work integrates probabilistic and stochastic methods to improve the accuracy and realism of seismic risk assessments. Recent publications highlight his leadership in national and regional seismic hazard modeling, particularly in New Zealand and the Groningen region of the Netherlands. His research employs Bayesian inference, machine learning, and simulation-based validation to address epistemic uncertainty and improve model robustness. He has contributed significantly to the 2022 New Zealand National Seismic Hazard Model and studies on induced seismicity. His scholarly output is extensive and current, with research spanning from empirical ground-motion modeling to risk-based structural assessment of offshore wind turbines. This indicates a strong, ongoing research program with high relevance to both academic and industrial applications in seismic safety and infrastructure resilience. San Andreas fault hazard validation using precariously balanced rocks Development of GMPEs for induced seismicity in Groningen and Delaware Basin Stochastic and hybrid broadband simulation of ground motions Seismic performance of offshore and bridge structures Application of machine learning to extract physical mechanisms from seismic data Prof. Stafford has no listed scientific awards in the provided text. He advises PhD students and welcomes new research candidates. His industry background in structural and geotechnical consulting enriches his teaching and applied research. He has not received any honorific or emeritus designation, and there is no indication of part-time or retired status, confirming his active role in academia. He is involved in major research initiatives related to seismic risk, ground-motion characterization, and structural dynamics, often collaborating on large-scale hazard modeling projects. His work supports critical infrastructure resilience and informs engineering standards and policy.

Hongpeng Zhouمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Deep Learning
• Interpretable Learning
• +۳ مورد دیگر

Hongpeng Zhou is a Dame Kathleen Ollerenshaw Fellow at the Department of Computer Science, University of Manchester. His research focuses on interpretable machine learning for cancer research, Bayesian deep learning, and bioinformatics. Previously, he was a postdoctoral researcher at the same university, developing ML algorithms for single-cell transcriptomics data. Education: PhD in Bayesian deep learning, Delft University of Technology (2018–2022) MEng in Control Science, Harbin Institute of Technology (2015–2017) BEng in Control Science, Harbin Institute of Technology (2011–2015) Research Interests: Dr. Zhou specializes in creating interpretable ML models for biological and engineering systems. His work bridges deep learning, Bayesian methods, and domain-specific applications like materials science, geotechnical engineering, and cancer genomics. Core themes include neural network compression, physics-informed learning, and unsupervised exploration of complex datasets. Publication Trends: Recent articles demonstrate interdisciplinary collaboration across computer science, bioinformatics, and mechanical engineering. Dominant themes include interpretable ML architectures, physics-informed neural networks, and applications in biomedicine/materials science, with emphasis on uncertainty quantification and real-world system optimization. Awards: Dame Kathleen Ollerenshaw Fellowship Projects & Advising: Leads the TICM R&D project on efficient 3D Gaussian splatting and co-investigates breast cancer prevention initiatives. Accepts PhD students and collaborates with groups like Prof. Magnus Rattray's bioinformatics team.

Dominik Baumannمشاهده پروفایل

استادیار

• Systems and Control Theory
• Machine Learning
• Communication Networks
• +۳ مورد دیگر

Dominik Baumann is an Assistant Professor at the Department of Electrical Engineering and Automation at Aalto University in Espoo, Finland. His research focuses on the interplay of systems and control theory with machine learning and communication networks, with a current emphasis on causal inference in control systems. Education: Diploma in Electrical Engineering from TU Dresden, Germany (2016) PhD from KTH Stockholm, Sweden (2020), supervised by Sebastian Trimpe (Max Planck Institute) and Karl H. Johansson Postdoctoral positions at RWTH Aachen University (1 year) and Uppsala University with Thomas Schön (1 year) Dr. Baumann's research bridges theoretical foundations with practical applications in robotics, wireless networks, and decision-making systems. His work spans safe reinforcement learning, event-triggered control systems, causal inference in dynamical systems, and ergodicity economics perspectives on long-term decision-making. He applies mathematical rigor to address challenges in resource-constrained environments, particularly focusing on safety guarantees and computational efficiency for real-world implementation. His recent publication record demonstrates a strong trajectory in safe learning-based control, with increasing focus on ergodicity economics in reinforcement learning, multi-agent coordination, and human-robot interaction. The research consistently balances theoretical guarantees with practical implementation constraints, particularly in bandwidth-limited wireless control systems and robotics applications. Scientific Awards: Best Paper Award for 'Feedback control goes wireless: Guaranteed stability over low-power multi-hop networks' at ACM/IEEE International Conference on Cyber-Physical Systems (2019) Dr. Baumann maintains extensive international collaborations, evidenced by numerous seminar invitations worldwide including Oxford University, ETH Zürich, University College London, and institutions across Asia. His research program addresses fundamental challenges in cyber-physical systems with applications in industrial automation, robotics, and the Internet of Things, securing research funding for projects focused on safe learning in control systems and wireless cyber-physical systems. His research group at Aalto University develops both theoretical foundations of learning-based control and practical algorithms for real-world deployment, with active software repositories on GitHub related to predictive triggering, causal structure identification, and ergodic reinforcement learning.

Sarah Depaoliمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Structural Equation Modeling
• Latent Variable Modeling
• +۵ مورد دیگر

Sarah Depaoli is Associate Professor of Quantitative Psychology at the University of California, Merced and Visiting Distinguished Professor at the Methods Center of the Eberhard Karls University of Tübingen . She specializes in Bayesian statistics, structural equation modeling, and mixture modeling with applications in psychological and health-related research. PhD in Quantitative Methods (minor in Mathematical Statistics), University of Wisconsin (2010) MA in Quantitative Psychology, California State University-Sacramento (2007) BA in Psychology, California State University-Sacramento (2003) Her research focuses on: Bayesian estimation of latent variable and growth models Prior distribution selection in small-sample research Measurement invariance testing across populations Development of guidelines for model transparency (WAMBS-checklist) Psychological and health outcome modeling with Bayesian methods The most recent articles demonstrate expertise in: Bayesian longitudinal analysis (CushingQoL interpretation differences) Latent class enumeration in growth mixture models ML vs Bayesian estimation comparisons Health psychological outcomes (PTSD, Quality of Life) Cross-cultural measurement invariance Statistical software implementation (JAGS, Mplus) Scientific awards include: 2015 Association for Psychological Science Rising Star 2013 Hellman Fellow 2011 APA Division 5 Distinguished Dissertation Multiple travel awards from Psychometric Society and SMEP She has advised numerous graduate students and served as Associate Editor for Psychological Methods , Multivariate Behavioral Research , and Journal of Royal Statistical Society, Series A .

Ilse C.F. Ipsenمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Randomized Algorithms
• Probabilistic Numerical Analysis
• +۸ مورد دیگر

Ilse C.F. Ipsen is a Distinguished Professor in the Department of Mathematics at North Carolina State University, with a joint appointment in the Department of Statistics. She is a Fellow of the American Mathematical Society and the Society for Industrial and Applied Mathematics (SIAM), and delivered the Olga Taussky-Todd Lecture at ICIAM 2023. Institution: North Carolina State University School: College of Sciences Department: Department of Mathematics Rank: Professor Email: ipsen@ncsu.edu Her research centers on numerical linear algebra, randomized algorithms, and probabilistic numerical analysis. She has made significant contributions to error analysis in floating-point computation, matrix perturbation theory, and the development of randomized methods for large-scale data problems. Her work bridges theoretical foundations with practical applications in data science, quantum physics, and computational statistics. She is the author of the textbook Numerical Matrix Analysis (SIAM, 2009) and serves as Editor-in-Chief of the SIAM Book Series on Data Science. The recent publications highlight a strong trend toward probabilistic numerical methods, uncertainty quantification, and randomized matrix algorithms. Themes include error analysis for inner products and summation, Bayesian solvers for linear systems, and scalable methods for trace and diagonal estimation. Applications span biobank data analysis, quantum physics, and PageRank computation. Scientific Awards: Fellow of the American Mathematical Society (AMS) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Olga Taussky-Todd Lecture, ICIAM 2023 She has advised multiple graduate and undergraduate students, including J.T. Holodnak, T. Wentworth, and R. Rehman. Her collaborative research has been supported by interdisciplinary grants, particularly in quantum physics and data-intensive computing. She has served on editorial boards of leading journals such as SIAM Review , Acta Numerica , and Numerische Mathematik , and chaired SIAM activity groups, reflecting her leadership in the applied mathematics community. She is actively involved in the development of software and algorithms, including the MATLAB package kappa_SQ for randomized sampling, and has contributed to discussions on communicating applied mathematics through case studies.

Peter Popovمشاهده پروفایل

پژوهشگر

• Software Reliability
• System Dependability
• Software Fault-Tolerance
• +۵ مورد دیگر

Peter Popov is a Reader at the Centre for Software Reliability (CSR) , City St George's, University of London , where he has been employed since 1997. He specializes in software dependability , fault tolerance , and stochastic modeling of critical infrastructures. Before his current position, he was an Associate Professor at the Bulgarian Academy of Sciences (1990-1997) and a Research Fellow at City St George's. Peter's academic journey began with a PhD in Computer Science from the Kiev National University of Technologies and Design (1989), following his BEng in Computer Engineering from the National Technical University of Ukraine (KPI, 1982). He has worked as a visiting scientist at renowned institutions including the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign , LAAS-CNRS in Toulouse, and Duke University . His research interests span Software reliability assessment System dependability Software fault-tolerance Performance evaluation Interdependencies of critical infrastructures He has contributed extensively to projects such as ReSIST , IRRIIS , DISPO , and AFTER , focusing on the dependability of composite systems and critical infrastructure resilience. Key publication trends reveal expertise in Stochastic modeling of autonomous vehicle safety Software diversity for fault tolerance Interdependency analysis in critical systems Bayesian reliability assessment Performance evaluation of distributed protocols Security implications in cyber-physical systems Peter has supervised several PhD students , including those researching autonomous vehicle resilience , safety assurance with ML components , and adaptable web services . His professional activities include serving on program committees for ISSRE , SAFECOMP , and EDCC conferences, as well as editorial contributions to CEUR Workshop Proceedings . He is proficient in Bulgarian , English , and Russian , with peer-review capabilities in all three languages.

Anders Nielsenمشاهده پروفایل

استاد

• Biostatistics
• Stock Assessment
• Fish Population Dynamics
• +۵ مورد دیگر

Anders Nielsen is a Professor at the National Institute of Aquatic Resources (DTU Aqua), Technical University of Denmark, working within the Section for Marine Living Resources. His research focuses on the intersection of biostatistics, fisheries science, and marine ecology, with particular emphasis on developing statistically sound approaches for fish stock assessment and management. His research interests span biostatistics with a focus on modeling dynamical systems and state-space models, fish stock assessment methodologies, fish movement patterns, and statistical software development. Nielsen specializes in improving stock assessment models through better statistical frameworks, optimized sampling designs, and incorporation of spatial structure. His work on automatic differentiation in statistical tools has contributed significantly to computational approaches in fisheries science. Analysis of Nielsen's recent publications reveals strong trends in environmental DNA (eDNA) applications for fisheries monitoring, advanced statistical modeling for stock assessments, and integration of climate variables into fish population dynamics. His work bridges theoretical statistical development with practical fisheries management applications, particularly in North Sea and Baltic Sea ecosystems. The recurring themes include uncertainty quantification, retrospective analysis of assessment models, and development of robust frameworks for sustainable fisheries management. Nielsen actively participates in numerous research projects including MAKE-IT (sustainable mackerel management), FISHMAP (fish distribution and fisheries management), REVIVAL (herring stock assessment), CanMac (genetic approaches to mackerel assessment), and LenSAM (length-based assessment models). He serves as project coordinator for CanMac and LenSAM, demonstrating leadership in developing innovative assessment methodologies. His collaborative network spans multiple institutions and countries, reflecting the international nature of fisheries science. As a member of the International Council for the Exploration of the Sea (ICES), Nielsen contributes to international fisheries science and advice. His work directly supports UN Sustainable Development Goals related to sustainable marine resource management. While specific details about laboratory facilities aren't provided in the available information, his research clearly involves advanced computational modeling and analysis of fisheries data, likely utilizing high-performance computing resources at DTU.

Zhaoxia Yuمشاهده پروفایل

استاد

• Statistical Methods
• Neuroscience
• Genetic Epidemiology
• +۵ مورد دیگر

Zhaoxia Yu is a Professor in the Department of Statistics at the University of California, Irvine. Her research focuses on statistical methodology and applications in neuroscience, genetic epidemiology, and high-dimensional data analysis. University: University of California, Irvine Department: Department of Statistics Her scientific work spans mixed-effects models for neuroscience, Bayesian nonparametric analysis of calcium imaging data, applications in connectome genetics, and rigorous statistical inference for multi-subject single-cell omics. She has also contributed to methodological advancements in clustered data analysis, ridge penalization, and spike inference. Recent publications demonstrate expertise in applying statistical models to neuroscience (e.g., cortical plasticity, ketamine effects), genetic interactions (e.g., GWAS, gene-environment), and medical imaging. Her teaching includes advanced statistics courses like Statistics 200A and mixed-effects model tutorials.

Sabrina Giordanoمشاهده پروفایل

دانشیار

• Categorical Data Analysis
• Longitudinal Data Analysis
• Copula Functions
• +۷ مورد دیگر

Sabrina Giordano is Associate Professor of Statistics at the University of Calabria , where she teaches Statistics and Data Science courses in both Italian and English. She holds a PhD in Methodological Statistics from the University of Milano-Bicocca and has held visiting positions at RWTH Aachen University, University of Florida, and University of Plymouth. Her research focuses on Statistical modeling of categorical and ordinal data Latent variable models and copula functions Longitudinal analysis and hidden Markov chains Applications in finance, health, and environmental statistics Her methodological contributions are implemented in the hmmm R-package and supported by grants like the PRIN2022 project "SMILE: Statistical Modelling and Inference to Live the Environment". She serves as Associate Editor for Biometrical Journal and Statistical Methods & Applications Director of the Post-Graduate Master in Artificial Intelligence & Data Science Local coordinator for the COST Fin-AI research group Her recent collaborative work explores Dynamic response styles in longitudinal data Fairness-aware classification algorithms Copula-based dependence structures Applications to financial vulnerability and risk perception

William Martinمشاهده پروفایل

پژوهشگر

• Urban Design
• Data Science for Social Impact
• Transdisciplinary Design
• +۳ مورد دیگر

William Martin is a PhD Researcher and Teaching Fellow at the Carnegie Mellon University School of Design, focusing on transdisciplinary strategies to create inclusive, equitable, and resilient futures. He works across physical and digital environments, utilizing tools like web applications, geospatial mapping, and documentary films to evolve communities. B.A. in International Studies in Economics from Middlebury College M.Arch and M.S. in Conservation from the University of Michigan Co-founding principal of Studiobvio (Denver-based architecture and urban research firm) Founder of the Architecture Lobby advocating for equitable labor practices His research spans urban design, data science, and community empowerment, with projects presented at institutions like MoMA, Yale School of Architecture, and the Architectural League of New York. He bridges economics, geography, and design to develop cooperative modes of practice. William’s publications demonstrate expertise in robotics, control systems, and facial recognition. His work includes algorithm development for bipedal locomotion on challenging terrain, optimal policy learning for compliant gaits, and component-based SVM approaches for biometric systems. He collaborates internationally through the Architecture Lobby and has worked at award-winning firms such as WXY (New York) and RNL (Denver). His career integrates design, activism, and technology to reimagine built environments.

Tudor-Bogdan Airimitoaieمشاهده پروفایل

دانشیار

• Active noise and vibration control
• Adaptive regulation techniques
• Flat dynamic systems
• +۵ مورد دیگر

Tudor-Bogdan Airimitoaie is an Associate Professor in the Automatic Control group at IMS-lab, CNRS UMR 5218, University of Bordeaux. He leads research in adaptive control systems and flat dynamic systems within the Engineering and Digital Sciences Department. His work spans theoretical developments and practical applications in aerospace, automotive, and energy systems, with strong industry collaborations including Stellantis and Beyond the Sea. System's Engineering Diploma, University Politehnica of Bucharest, Romania (2008) PhD in Automatic Control, University of Grenoble and University Politehnica of Bucharest (2011-2012) Dr. Airimitoaie's research focuses on adaptive/learning algorithms and flat dynamic systems. His work addresses fundamental challenges in active noise and vibration control, with applications spanning wind energy systems, predictive maintenance, and convertible UAVs (combinations of VTOLs and conventional aircraft). He develops robust control algorithms that ensure safety and performance in complex dynamic environments, with particular emphasis on systems requiring fault tolerance and adaptive capabilities. His research bridges theoretical control principles with practical engineering implementations, often involving experimental validation on physical platforms. His recent publications demonstrate a strong trend toward integrating machine learning with traditional control theory, particularly in predictive maintenance applications. There's significant focus on accelerating adaptation algorithms through dynamic gain techniques and advancing fractional-order control methodologies. His work consistently addresses real-world challenges in aerospace (kite-based maritime transportation, convertible aircraft) and automotive systems (powertrain control), with increasing emphasis on sustainable energy solutions and safety-critical applications. Robert-Houdin award at the French seminar on Experimental Vibratory Analysis (2014) Best PhD award from the GdR MACS Best PhD in Automatic Control by the EEA Club (April 2013) Dr. Airimitoaie has supervised numerous PhD students working on convertible aircraft, tethered kite systems, and predictive maintenance applications. He has secured significant research funding as Principal Investigator for the ANR-funded MICA project (2016-2019) on fault detection for convertible UAVs, and as Co-investigator for the KiWin project (2023-) with Beyond the Sea on kite-based maritime transportation. His industry collaboration with Stellantis (2018-2022) on powertrain modeling and control demonstrates his ability to translate theoretical advances into practical automotive solutions. He also serves on editorial boards including IEEE Control Systems Letters starting in 2025. He is a key member of the CRONE and FFTG research teams within the Automatic Control group at IMS-lab. His laboratory work focuses on experimental validation of control algorithms using platforms including convertible aircraft demonstrators and tethered kite systems. The research environment supports both theoretical development and practical implementation of advanced control strategies, with strong connections to the broader control systems community through conference organization and technical committee memberships.

Jean-Pierre da Costaمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Precision Agriculture
• +۳ مورد دیگر

Jean-Pierre da Costa is a Professor at Université de Bordeaux, affiliated with the Institut Polytechnique de Bordeaux and the IMS (Integration of Material to System) Laboratory. His research focuses on signal and image processing with applications in precision agriculture and plant disease detection, particularly within the MOTIVE team of the Signal and Image Processing research group. Dr. da Costa's research spans computer vision, deep learning, and proximal sensing technologies applied to agricultural challenges. He specializes in developing automated systems for crop monitoring, disease diagnosis, and precision farming operations. His work bridges theoretical image processing with practical agricultural solutions, with significant contributions to grapevine disease detection (particularly Flavescence Dorée and downy mildew), mechanical weeding systems, and vineyard monitoring technologies. The research combines algorithm development with real-world field validation, often involving multi-year experimental studies across different grape varieties and crop types. His publication record shows a consistent trajectory toward increasingly sophisticated AI-driven agricultural solutions. Early work focused on fundamental image processing techniques, while recent publications demonstrate integrated systems combining proximal sensing, deep learning, and real-time decision making. The research spans both fundamental materials science (pyrocarbon nanostructure analysis) and practical agricultural applications, showing remarkable interdisciplinary breadth. A common thread is the development of robust, field-deployable technologies that operate effectively in challenging outdoor conditions. Dr. da Costa collaborates extensively with agricultural researchers, viticulturists, and industry partners including STMicroelectronics, Stellantis, and various French research institutions. His work often involves multi-year projects with practical implementations, such as the BIPBIP project (Bloc-outil et Imagerie de Précision pour le Binage Intra-rang Précoce) funded by ANR. The MOTIVE team he leads develops complete solutions from image acquisition through to actionable agricultural insights, with several publications describing systems that operate in real-time on low-power embedded platforms suitable for field deployment.

Matteo Spadaمشاهده پروفایل

پژوهشگر

• Risk Assessment
• Resilience Assessment
• Multi-Criteria Decision Analysis
• +۴ مورد دیگر

Dr. Matteo Spada is a Senior Researcher in Risk and Resilience at the Zurich University of Applied Sciences (ZHAW) School of Engineering, where he focuses on Technology Assessment. His research spans multiple institutions including the Paul Scherrer Institute, Swiss Seismological Service, and Istituto Nazionale di Geofisica e Vulcanologia in Italy. His educational background includes a Ph.D. in Earth Sciences from ETH Zurich (2006-2011) and an MSc in Physics from the University of Bologna (1999-2005). Dr. Spada's research focuses on risk and resilience assessment of critical infrastructure systems, with particular expertise in energy systems, natural hazards, and multi-criteria decision analysis. His work integrates quantitative methods with practical applications to inform policy and decision-making processes related to infrastructure safety and security. He has developed innovative approaches for probabilistic risk assessment, uncertainty quantification, and decision support systems that have been applied across multiple sectors including energy, water resources, and transportation. His recent work has particularly emphasized the energy transition, analyzing comparative accident risks across different energy technologies and developing frameworks for assessing resilience of electricity supply systems. Through his research, he has contributed to understanding the societal implications of energy infrastructure failures and developing methodologies for more robust risk assessment. Dr. Spada is an active member of several professional networks including the European Safety and Reliability Association (ESRA), the Institute for Operations Research and the Management Sciences (INFORMS), and the Special Activity Group - Sustainable Concrete Structures of the International Federation for Structural Concrete. He leads several research projects including 'Innovative Spatio-Temporal multi-criteria decision analysis Interface to support informed decision-Making for real-world applications' and contributes to initiatives focused on urban energy transition, resilient supply chains, and building decarbonization. His work often involves interdisciplinary collaboration with engineers, economists, and policy experts to address complex infrastructure challenges. Dr. Spada maintains an active research laboratory focused on risk and resilience assessment, where his team develops and applies advanced analytical methods to real-world infrastructure challenges. The lab utilizes a combination of statistical modeling, simulation techniques, and decision analysis tools to support evidence-based policy making.

• Statistics on Manifolds
• Bayesian Theory
• Deep Learning
• +۶ مورد دیگر

Lizhen Lin is a Professor of Statistics in the Department of Mathematics at the University of Maryland. Her research bridges statistical theory, Bayesian methods, and machine learning through theoretical and applied work in statistics on manifolds, deep learning, and network analysis. University: University of Maryland Department: Mathematics Email: lizhen01@umd.edu Office: Kirwan 1107 Her research focuses on: Foundations of deep neural networks via statistical theory Bayesian modeling for infinite-dimensional and high-dimensional data Geometry and statistics for manifold-valued data Network analysis with covariates and topological structures Robust optimization and inference on non-Euclidean spaces Recent publications emphasize: Bayesian community detection and stochastic blockmodels Variational inference and posterior contraction Manifold-adaptive deep generative models High-dimensional change point detection Applications to microbiome networks and DNA topology Teaching includes courses on linear models (STAT 741) and statistical foundations of deep learning (STAT 818).

John Fiebergمشاهده پروفایل

استاد

• Statistical Ecology
• Wildlife Telemetry
• Habitat Selection Modeling
• +۳ مورد دیگر

John Fieberg is a Professor in the Department of Fisheries, Wildlife, and Conservation Biology at the University of Minnesota, where he applies statistical and mathematical models to ecological problems with emphasis on wildlife telemetry data, survival analysis, and habitat selection modeling. His educational credentials include: Ph.D. in Biomathematics from North Carolina State University MS in Biostatistics from the University of North Carolina-Chapel Hill BA in Mathematics from Westminster College Fieberg's research bridges sophisticated modeling using MCMC techniques for complex ecological data and practical statistical guidance for biologists. He focuses on tailoring models to address challenges like imperfect detection in wildlife surveys, autocorrelation in movement data, and functional responses in habitat selection, while advocating for robust yet accessible methods that deliver 99% of analytical value for 99% of applications. His recent publications (2020-2024) reveal concentrated advancements in animal movement ecology, habitat selection methodology, and statistical validation frameworks. Key trends include innovative goodness-of-fit testing via lineup protocols, integrated step-selection analyses accounting for individual variability, and critical examinations of detection scale in multi-sensor wildlife monitoring systems, complemented by two foundational textbooks on ecological statistics. He directs the Fieberg Lab, which synergizes research with teaching through courses like Biometry (FW4001) and Statistics for Ecologists (FW8051), incorporating real-world ecological examples to demonstrate statistical concepts and mentoring future quantitative ecologists for natural resource management careers.