Find Academic Supervisors

• Geodata Analytics
• Applied Geophysics
• Resource Geoscience
• +7 more

Matthew J. Cracknell is a Senior Lecturer in Geodata Analytics at the University of Tasmania's School of Natural Sciences, specializing in Earth Sciences. He holds a PhD in Computational Geophysics (2014) and BSc (Hons) in Geophysics (2009), both from the University of Tasmania. His research integrates geoscience with machine learning to address challenges in mineral exploration, environmental remediation, and sustainable resource management. Key focuses include automated detection of geological features in drillcore imagery, decarbonization of energy systems via ore deposit discovery, and legacy mine waste characterization. Cracknell leads the CODES Research Program 6 (Geophysics and Computational Geosciences) and Module 2 of the AMIRA P1249 project. He has secured significant industry and government funding, including projects with Boliden AB, Anglo American, and the Tasmanian Government. His work emphasizes collaboration with mining partners and agencies like Geoscience Australia and Mineral Resources Tasmania. Teaching roles include developing courses on the mining value chain, climate resilience, and geodata analytics. As Graduate Research Coordinator, he promotes HDR student well-being and supervises over 20 doctoral and masters students. Awards include the 2019 Oz Minerals Explorer Challenge Prize. Key affiliations include the International Association for Mathematical Geosciences, Australian Society of Exploration Geophysicists (Tasmanian Branch Secretary), and Geological Society of Australia.

Ryan Keith ShostedView profile

Professor

• Phonetics
• Phonology
• Historical Linguistics
• +6 more

Prof. Ryan Keith Shosted is a full-time tenured Professor at the University of Illinois at Urbana-Champaign , affiliated with the Department of Linguistics , Spanish and Portuguese , American Indian Studies Program , Beckman Institute , Lemann Center for Brazilian Studies , Center for Latin American and Caribbean Studies , and Center for African Studies . He serves as Director of the Program in Translation and Interpreting Studies and leads the Chin-Woo Kim Phonetics Laboratory . Education: Ph.D. , Linguistics, University of California, Berkeley (2006) M.A. , Linguistics, University of California, Berkeley (2003) B.A. , Linguistics, Brigham Young University (2000) Shosted's research focuses on the intersection of phonetics , phonology , and historical linguistics . He pioneered the application of ultrafast dynamic MRI to study the vocal tract's physiological-acoustic mapping in diverse languages, including Hittite cuneiform , Deseret Alphabet , and endangered languages like Q'anjob'al. His work spans speech production modeling , nasalization mechanisms , and cross-linguistic articulatory analysis . The 15 most recent publications demonstrate his leadership in dynamic speech imaging , phonetic-aerodynamic modeling , and historical sound change analysis . Key trends include advanced MRI techniques for speech study, phonetic universals , and historical writing systems as tools for linguistic reconstruction. Scientific Awards: Campus Award for Excellence in Undergraduate Teaching (2021) Dean's Award for Excellence in Undergraduate Teaching (2021) Arnold O. Beckman Award (2009, 2010) Jacob K. Javits Fellowship (2001-2005) Shosted's grant portfolio includes NSF funding for nasalization research (BCS-1651197, BCS-1121780) and NIH collaboration (1R01DE027989-01A1) on cleft palate speech. He has directed 12 graduate students and taught courses ranging from Hittite language to quantitative phonetic methods . The Chin-Woo Kim Phonetics Laboratory , under his directorship since 2007, expanded in 2010 to include articulatory phonetics facilities with EPG, ultrasound, and MRI analysis capabilities. He continues to lead Beckman Institute collaborations in speech imaging technology.

• Mathematics Education
• Geoscience Applications
• Remote Sensing
• +4 more

Linda Bailey Hayden is a distinguished Professor and Associate Dean of Mathematics and Computer Science at Elizabeth City State University (ECSU), where she has made significant contributions to mathematics education and geoscience applications since 1980. Originally from Portsmouth, Virginia, she overcame segregation barriers to pursue her academic career, earning degrees from Virginia State University, University of Cincinnati, Old Dominion University, and American University. At ECSU, she founded the Center of Excellence in Remote Sensing Education and Research to increase minority participation in environmental science. Dr. Hayden's research interests span mathematics education, applications of mathematics in geoscience, remote sensing technologies, and environmental science. Her work uniquely bridges theoretical mathematics with practical applications in climate science, ice sheet monitoring, water quality assessment, and Antarctic research. She has pioneered educational approaches that connect mathematics with real-world environmental challenges, particularly through NASA-related programs and the GLOBE initiative. Her publication record reveals a strong focus on mentorship, educational outreach, and interdisciplinary research connecting mathematics with environmental science. The most recent publications demonstrate her ongoing commitment to developing innovative approaches for engaging students in scientific research, particularly through hackathons, remote sensing applications, and climate change education. Her work consistently emphasizes increasing participation of underrepresented groups in STEM fields. Presidential Award for Excellence in Science, Mathematics, and Engineering Mentoring (2003) Emerald Honors for Educational Leadership from US Black Engineer magazine Mathematically Gifted & Black Black History Month 2017 Honoree An Antarctic ice shelf was named after her institution by the Advisory Committee on Antarctic Names Dr. Hayden has dedicated her career to mentoring minorities and women in STEM fields through various programs including the Nurturing ECSU Research Talent (NERT) initiative, the Home-Institution Support Program, and numerous NASA-funded educational projects. Her grant history reflects strong partnerships with federal agencies to offer interdisciplinary training in remote sensing and environmental science. She has established multiple educational partnerships focused on enhancing undergraduate involvement in research, particularly for underrepresented students. As founder of the Center of Excellence in Remote Sensing Education and Research at ECSU, she has built a significant infrastructure for geoscience research and education, developing cyberinfrastructure for remote sensing of ice sheets and creating numerous educational programs from middle school through undergraduate levels. Her work has connected ECSU with major research institutions and federal agencies, establishing the university as a hub for remote sensing education in minority-serving institutions.

Joris M. MooijView profile

Professor

• Causality
• Causal Modeling
• Causal Discovery
• +5 more

Joris M. Mooij is a Professor of Mathematical Statistics at the Korteweg-De Vries Institute of the University of Amsterdam, Netherlands. His research focuses on causality, spanning causal modeling, discovery, and inference with applications in biology, medicine, fairness, and business analytics. He combines mathematical modeling with statistical and algorithmic approaches in his work. Dr. Mooij received his PhD with honors from Radboud University Nijmegen in 2007, focusing on approximate inference in graphical models. After postdoctoral work at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, he obtained an NWO VENI grant in 2011 for further postdoctoral research at Radboud University. He became Assistant Professor at the University of Amsterdam's Informatics Institute in 2013, was promoted to Associate Professor in 2017, and became a full Professor of Mathematical Statistics in 2020. Dr. Mooij's research centers on causal inference, with particular expertise in structural causal models, cyclic causal systems, and causal discovery algorithms. His work addresses fundamental questions about when causal relationships can be identified from observational data and how to develop robust causal discovery methods that work in complex real-world settings with latent variables, cycles, and selection bias. He has made significant contributions to understanding the limitations of existing causal discovery approaches and developing new methods that overcome these limitations. His research group organizes the Amsterdam Causality Meeting series and develops theoretical frameworks for causal modeling that encompass both acyclic and cyclic systems. Dr. Mooij has collaborated extensively on applications of causal methods in biological systems, including protein signaling networks and gene expression data. The group's recent work explores performative predictions, causal domain adaptation, and robust causal discovery methods that account for selection bias and missing data. Dr. Mooij has received numerous awards for his research, including: Best paper award at UAI for "Establishing Markov equivalence in cyclic directed graphs" IEEE Geoscience and Remote Sensing Society 2011 Letters Prize Paper Award ICML Test of Time Honorable Mention Best student paper award at UAI 2010 He has secured competitive research funding through an NWO VENI grant, NWO VIDI grant, and an ERC Starting Grant, which supported the establishment of his research group consisting of 3 PhD students and 3 postdocs focused entirely on causality. Dr. Mooij has supervised several PhD students, including Tineke Blom, whose work on "Causality and Independence in Perfectly Adapted Dynamical Systems" significantly influenced his thinking about causality in complex systems. He has co-taught the MasterMath course on Causality and published lecture notes titled "A Mathematical Introduction to Causality." His research continues to push the boundaries of causal inference methodology and its applications across diverse scientific domains.

Darren ScottView profile

Professor

• Transportation Geography
• Geographic Information Science (GIScience)
• Sustainable Transportation
• +13 more

Dr. Darren Scott is a Professor in the School of Earth, Environment & Society at McMaster University, specializing in Geographic Information Science (GIScience) and teaching courses such as GIS Programming and Data Processing Using Python. He previously served as an Assistant Professor at the University of Louisville’s Department of Geography and Geosciences (1999–2002) and held a Visiting Research Professor position at the Swiss Federal Institute of Technology (Zurich) in 2008. His academic background includes a BA (Honors and Co-op) in Geography from Saint Mary’s University (1991), an MA from the University of Western Ontario (1994), and a PhD from McMaster University (2000). Dr. Scott’s research focuses on transportation systems, aging populations, GIScience innovations, and sustainable infrastructure. He has pioneered work in route choice modeling, transportation demand analysis, and the impact of demographic changes on mobility. In 2008, he established TransLAB, a research lab within his school, which explores advanced transportation topics using GPS and geospatial tools. His projects have been funded by major agencies like NSF, SSHRC, and NSERC, addressing issues ranging from electric vehicle adoption to flood resilience in emergency services. His teaching emphasizes Python programming and advanced GIS applications, reflecting his expertise in geospatial technologies. He has contributed to policy debates on urban sustainability, particularly regarding Canada’s Greenbelt legislation and telework trends during the pandemic. Despite no explicitly mentioned scientific awards, his extensive grant history and scholarly output highlight his impactful contributions to the field. Dr. Scott’s advising and grant activities center on interdisciplinary studies, including the McMaster Monitoring My Mobility Study (MacM3), which tracks mobility patterns of older adults. His work bridges transportation engineering, urban planning, and public health, often leveraging big data and GIS tools to analyze real-world scenarios in cities like Hamilton and Calgary.

Sanjay SrinivasanView profile

Professor

• Petroleum Reservoir Characterization
• Reservoir Recovery Processes
• Integration of Seismic Data
• +7 more

Sanjay Srinivasan is a Professor of Petroleum and Natural Gas Engineering and the John and Willie Leone Family Chair in the Department of Energy and Mineral Engineering at Penn State University. He serves as Director of the EMS Energy Institute and leads the Penn State Initiative for Geostatistics and GeoModeling Applications. His research focuses on petroleum reservoir characterization, CO2 sequestration, and integration of seismic data in reservoir models through advanced geostatistical and machine learning methods. Ph.D., Petroleum Engineering, Stanford University M.S., Petroleum Engineering, University of Southern California B. Tech, Petroleum Engineering, Indian School of Mines Srinivasan’s work addresses reservoir recovery processes, unconventional reservoirs, and subsurface energy security. His methodologies include probabilistic modeling, data assimilation, and AI-driven workflows for fracture network mapping and porous media generation. Key applications span Gulf of Mexico deepwater plays and geological carbon storage. Recent publications highlight trends in: Reinforcement learning for geostatistical workflows and well optimization Physics-informed GANs for 3D porous media modeling Probabilistic integration of geomechanical and geostatistical inferences Machine learning approaches for seismic fracture identification CO2 sequestration in heterogeneous reservoirs Scientific awards include Distinguished Member (SPE, 2022), SPE Faculty Pipeline Award (2012), Cox Visiting Fellowship (Stanford, 2010), and SPE Southwest Region Reservoir Description Award (2009).

Marie-Colette van LieshoutView profile

Professor

• Stochastic Geometry
• Spatial Statistics
• Image Analysis
• +6 more

Marie-Colette van Lieshout is a Professor of Spatial Stochastics at the Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, and a Scientific Staff Member in the Stochastics group at Centrum Wiskunde & Informatica (CWI), Amsterdam. She has been active in research since 1997 and is a leading expert in stochastic geometry, spatial statistics, and image analysis. Her educational and professional background includes positions at the University of Warwick and the Free University Amsterdam. She is currently engaged in advanced research on point processes, random fields, and tessellation models, with applications in seismic hazard, fire risk, and machine learning. Her research interests include: Stochastic Geometry Spatial Statistics Image Analysis Point Process Modeling Seismic Risk Assessment Machine Learning for Spatial Data Her recent publications (2023–2025) focus on spatial intensity estimation, marked point processes, and data-driven risk modeling, showing a strong integration of classical spatial statistics with modern computational and machine learning techniques. Key themes include adaptive kernel smoothing, infill asymptotics, and applications in environmental and public safety domains. She has received significant recognition, including: Elected Fellow, International Statistical Institute (ISI) She has been awarded multiple research grants from NWO and other agencies, including the KLEIN grant for fire risk management and the DeepNL grant for seismicity prediction in Groningen. She has supervised or collaborated with researchers such as C. Lu, Z. Baki, and R. Markwitz. She is also active in academic service, serving on editorial boards (e.g., Methodology and Computing in Applied Probability), advisory boards (InHolland University), and councils of learned societies (Bernoulli Society, KWG). She leads and participates in research clusters such as STAR and contributes to outreach and education through courses and public lectures on earthquake modeling and spatial statistics.

• Numerical and analytical modeling of subsurface multi-component, multiphase flows
• Environmental and ecological implications of coastal groundwater flow
• Aquifer parameter estimation
• +3 more

Dr. Li Hailong is a Chair Professor at the School of Environmental Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen. He holds a PhD in Hydrogeology from the University of Hong Kong (2003), an MSc (1991) and BSc (1988) in Applied Mathematics from Fudan University. His academic journey includes professorships at China University of Geosciences-Beijing (2009-2020) and Anshan Normal University (1999-2009). Recipient of 2010 NSFC Outstanding Young Scientist Grant 2022 Dayu Water Conservancy Science and Technology Award 2008 Chutian Professorship (Hubei Province's highest academic honor) Dr. Li's research focuses on multi-component, multi-phase subsurface flows in coastal zones and their ecological/environmental effects. His work spans aquifer parameter estimation, ecohydrology, marine groundwater discharge, and computational fluid dynamics. Recent projects involve submarine groundwater discharge (SGD) quantification in Bohai Sea and Jiaozhou Bay using radium/radon isotopes. His publications in top journals like Nature Geoscience , Geochimica et Cosmochimica Acta , and Water Resources Research have been cited 7,072 times (H-index 49). He has led 19 research projects, including 6 NSFC grants and 973 Program subprojects. Dr. Li serves on editorial boards of Advances in Water Resources and Water Science and Engineering , and was Associate Editor for Hydrogeology Journal (2012-2015).

Robin CrockettView profile

Researcher

• Document Forensics
• Academic Integrity
• Contract Cheating
• +9 more

Dr Robin Crockett is the University Academic Integrity Lead at the University of Northampton, based in the Academic Registry. He is a mathematician-ethicist actively engaged in research and professional development in academic integrity, document forensics, and the detection of contract cheating and AI-generated text. He is a member of the European Network for Academic Integrity (ENAI), co-founder of the Midlands Integrity Group (UK), and has advised UK policymakers on legislation to ban essay mills. He holds Chartered Scientist and Chartered Mathematician status. MPhil, The Management of Electricity Supplies via Storage as Hydrogen, Cranfield University Master, Energy Conservation and the Environment, Cranfield University PhD, Electrostatic Damage to Semiconductor Devices, University of Southampton Master, Natural & Electrical Sciences, University of Cambridge Bachelor, Natural & Electrical Sciences, University of Cambridge Dr Crockett's research centers on document forensics and academic integrity, with core interests in Fourier theory, time-series analysis, and stylometry for identifying contract cheating. His work increasingly addresses the challenges posed by generative artificial intelligence in education. He applies mathematical and statistical methods to analyze linguistic cues, writing styles, and embedded information in student submissions. His recent publications highlight a strong trend toward understanding and mitigating academic misconduct in the AI era. Topics include AI-text detection uncertainties, forensic stylometry, and policy development for generative AI misuse. Earlier work includes environmental research on radon remediation and signal processing applications in telecommunications. Chartered Scientist Chartered Mathematician Dr Crockett has supervised PhD students, including Believe Nwamae in Computing. He has secured internal research funding, such as the Small Grants Scheme for Early Career Researchers at the University of Northampton for a project on AI-synthesized text detection. He has been an Academic Visitor at Loughborough University and served on the Turnitin Advisory Board, indicating active collaboration and external engagement. He frequently presents at academic events and contributes to policy discussions. He is affiliated with research networks including the European Network for Academic Integrity (ENAI) and the European Geosciences Union (as a former Scientific Officer). His work is supported by institutional and collaborative projects focused on advancing machine discernment of academic misconduct.

Stephen PankavichView profile

Professor

• Partial Differential Equations
• Kinetic Theory
• Mathematical Biology
• +4 more

Stephen Pankavich is a Professor and Department Head in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. He holds a PhD in Mathematical Sciences from Carnegie Mellon University, with research focused on partial differential equations, kinetic theory, and mathematical biology. His work bridges theoretical analysis and computational methods, addressing challenges in plasma dynamics, epidemiological modeling, and multiscale systems. Education: PhD, Mathematical Sciences, Carnegie Mellon University (2005) MS, Mathematical Sciences, Carnegie Mellon University (2001) BS, Mathematical Sciences, Carnegie Mellon University (2000) Research interests include the analytical and numerical study of collisionless plasmas, HIV dynamics, and epidemiological models. He has received awards such as the W.M. Keck Mentorship Award and the Colorado School of Mines Alumni Teaching Award. His articles explore topics like plasma decay rates, HIV therapy models, and particle-tracking algorithms. He has advised over 20 graduate and undergraduate students, contributing to impactful research in applied mathematics and computational science.

Kevin PughView profile

Professor

• Transformative Experience Theory
• Motivation
• Learning Transfer
• +5 more

Kevin Pugh is a Professor in the School of Psychological Sciences at the University of Northern Colorado, affiliated with the College of Education and Behavioral Sciences. Specializing in transformative experience theory, motivation, and cognition, his work bridges educational psychology with real-world learning applications. Ph.D. in Educational Psychology (Learning & Cognition), Michigan State University (2002) B.S. in Psychology, Brigham Young University (1994) Assistant Professor, University of Toledo (post-Ph.D.) Joining UNC in 2006 Dr. Pugh's research investigates how learning transforms students' worldviews through Deweyan aesthetics and motivational frameworks. His transformative experience theory emphasizes real-world application of knowledge, studied across science education, problem-based learning, and culturally relevant pedagogy. Recent publications analyze mineral misconceptions, instructor-student connections, and gender dynamics in geology education. He teaches learning theory, cognition, and motivation courses using transformative, authentic pedagogies. The Calvin Project, which uses comic characters to explore motivation theory, exemplifies his innovative approach. Dr. Pugh has secured National Science Foundation grants and serves on the editorial board of the Journal of Experimental Education. Principal investigator for NSF-funded projects Editorial board member, Journal of Experimental Education School of Psychological Sciences faculty Contact: kevin.pugh@unco.edu | Office: McKee Hall 63 | Phone: 970-351-2989

William NewmanView profile

Professor

• Geophysics
• Planetary Science
• Astrophysics
• +5 more

William Newman is a Professor in the Department of Earth, Planetary, and Space Sciences at the University of California, Los Angeles (UCLA), currently on sabbatical at the Institute for Advanced Study in Princeton. His primary academic home resides within UCLA's geoscience and planetary science division. His educational credentials include: B.Sc. (Hon.) in Physics from the University of Alberta, Canada (1971) M.Sc. in Physics from the University of Alberta, Canada (1972) M.S. in Astronomy and Space Science from Cornell University (1975) Ph.D. in Astronomy and Space Science from Cornell University (1979) Professor Newman applies theoretical physics and applied mathematics to solve critical real-world problems across multiple disciplines. His research spans statistical techniques for climate change assessment, earthquake hazard modeling, solar system evolution (including collision risks from trans-Jovian bodies), astrophysical jet dynamics, and pattern emergence in complex systems. This interdisciplinary work bridges geophysics, planetary science, and astrophysics through rigorous mathematical frameworks. His publication record (2024-2016) reveals three dominant research thrusts: (1) Semiconductor electron emission physics (GaAs nanotips, photoemission sources), (2) Solar system dynamics and celestial mechanics (N-body simulations, impact hazards), and (3) Complex systems analysis (earthquake patterns, statistical record-breaking events). These intersect physics, earth sciences, and computational mathematics through shared methodologies in statistical modeling and nonlinear dynamics. At UCLA, Newman developed innovative courses including a natural disasters undergraduate GE course (satisfying diversity requirements) and graduate-level planetary atmospheres and continuum mechanics curricula. His academic contributions include over 100 refereed papers and graduate textbooks published by Princeton and Cambridge University Presses, focusing on mathematical methods for geophysics and space physics.

Abani PatraView profile

Professor

• Computational Sciences
• Data Driven Modeling
• Computer Science
• +5 more

Abani Patra is a Professor of Computer Science, Mathematics, Mechanical Engineering, and Civil and Environmental Engineering at Tufts University. He also serves as the Center Director for Data Science at the Tufts Institute for Artificial Intelligence (TIAI). His research focuses on computational sciences and data-driven modeling, with applications spanning environmental systems, biomedical imaging, and geophysical hazards. He has directed major initiatives at the National Science Foundation (NSF) and U.S. Department of Energy (DOE), and previously founded the Institute for Computational and Data Sciences at the University at Buffalo. Education: PhD in Mathematics, University of Texas, 1995 MS in Mechanical Engineering, University of Missouri, 1990 BSc in Engineering, Birla Institute of Technology & Science, India Research Interests: Large-scale computational modeling and uncertainty quantification Data-driven approaches for geophysical hazards (e.g., debris flows, volcanic eruptions) Biomedical imaging and metabolic analysis Open science platforms for glaciology and volcanology Key Projects: Developed the Ghub platform for open cryosphere research Launched VICTOR, a cyberinfrastructure for volcanology Advanced AI-driven techniques for postfire debris flow prediction Grants & Leadership: Directed NSF and DOE programs in computational science PI for NSF Cyberinfrastructure grants Former director of the Institute for Computational and Data Sciences

• Applied Mathematics
• Mechanics of Materials
• Building and Construction
• +11 more

Diego Guillermo Manzanal Milano is an Associate Professor in the Department of Continuum Mechanics and Structural Theory at the Technical University of Madrid. His current institutional affiliations include: Computational Mechanics Research Group (member since October 2020; Adjunto de Dirección General since September 2020) University Research Institute for Intelligent and Sustainable Civil Infrastructures (CIVILis) (member since November 2024) His research spans highly interdisciplinary domains at the intersection of engineering and computational sciences: Core mechanics: Computational Mechanics, Mechanics of Materials, Structural Theory Civil infrastructure systems: Building and Construction, Construction & Building Technology, Geotechnical Engineering Materials science: Multidisciplinary and Miscellaneous Materials Science applications Cross-cutting domains: Applied Mathematics, Computer Science Applications, Energy & Fuels, and Geological Engineering Emerging areas: Sustainable Civil Infrastructures, Interdisciplinary Computer Science in Engineering No scientific awards were documented in the provided information. Details regarding student supervision, research grants, or advising activities were not specified in the source text.

Brandon ShuckView profile

Assistant Professor

• Solid-Earth Geophysics
• Tectonic Processes
• Active-Source Seismic Imaging
• +5 more

Brandon Shuck is an Assistant Professor in the Department of Geology & Geophysics at Louisiana State University (LSU), part of the College of Science. His research focuses on solid-earth geophysics, particularly tectonic processes within plate boundaries and mature tectonic settings. He uses active-source seismic reflection and wide-angle ocean-bottom seismometer data to image subsurface lithospheric architecture and integrate interdisciplinary datasets including bathymetry, borehole logs, and geodynamic models. Education: Ph.D. in Geological Sciences (2021) from the University of Texas at Austin; B.S. in Geology (Petroleum Emphasis) and Mathematics (2015) from Western Colorado University. Previously served as a Postdoctoral Fellow at Lamont-Doherty Earth Observatory, Columbia University (2021–2024). Research Interests: Tectonic evolution of the lithosphere at plate boundaries, feedbacks between lithospheric stress and fluid-rock interactions, geologic hazards related to subduction zones, and mantle dynamics during continental breakup. Active projects include studies of subduction initiation in the Tyrrhenian Sea, megathrust fault segmentation in Cascadia, and water content in the Cocos Plate offshore Mexico. Current opportunities include an NSF-funded PhD position (starting Fall 2025) investigating mantle dynamics during early Atlantic seafloor spreading, involving seismic data acquisition on the R/V Langseth. His group emphasizes collaborative, interdisciplinary approaches combining seismic imaging, field geology, and modeling. Awards: None explicitly listed, though his work has been supported through NSF funding and institutional grants. Lab/Team: Directs the LSU Solid-Earth Geophysics Research Group, which integrates geophysical data analysis with field observations to address tectonic questions at multiple scales.