Statistical Methods for Big Data Professors and Supervisors | ApplySharif

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Jennifer McClure HarawayView profile

Associate Professor

Samford UniversityUnited States

Jennifer McClure Haraway serves as Associate Professor of Religion and Sociology at Samford University's Howard College of Arts and Sciences, holding a joint appointment in the Biblical and Religious Studies and Geography and Sociology department. She concurrently functions as a Faculty Fellow at the university's Congregational Resource Center, where she develops practical ministry resources through partnerships with the Association of Religion Data Archives. Her academic foundation includes a PhD in Sociology (2015) and MA in Sociology (2012) from Pennsylvania State University, complemented by a BA in Sociology and Mathematics (2010) from Mount Vernon Nazarene University. This dual expertise in social science theory and quantitative methods underpins her distinctive research approach. Dr. Haraway's scholarship centers on religious congregations as social networks, examining homophily patterns, resource sharing dynamics, and prosocial behavior mechanisms. She innovatively applies social network analysis to biblical studies—particularly reconstructing Jesus's relational dynamics—and investigates how congregational structures influence community involvement. Her methodological rigor bridges statistical analysis with practical ministry applications, focusing on how faith communities navigate opportunities and challenges through interconnected networks. Analysis of her 2013-2023 publications reveals consistent exploration of congregational interdependence, with recurring themes of network vitality, homophilous clustering, and community impact measurement. Her work demonstrates how relational positioning affects organizational sustainability and how congregational size mediates community engagement—a distinctive contribution merging sociological theory with religious practice. Professionally, she maintains active membership in the American Sociological Association, Association for the Sociology of Religion, Society for the Scientific Study of Religion, and Association of Religion Data Archives. She applies her expertise through congregational consulting on demographic analysis and resource development, particularly supporting churches in understanding community contexts through the Center for Congregational Resources at Samford University. Dr. Haraway attends Holy Trinity-Holy Cross Greek Orthodox Cathedral and pursues rock climbing, cycling, and hiking as personal interests that reflect her engagement with physical and communal challenges.

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Sebastien RochView profile

Professor

University of Wisconsin-MadisonUnited States

Sebastien Roch is a Vilas Distinguished Achievement Professor in the Department of Mathematics at the University of Wisconsin-Madison. He belongs to the Probability Group and Applied Mathematics Group within the Mathematics Department, with affiliate appointments in the Department of Statistics and the Theory of Computing Group in the Department of Computer Sciences. He is also affiliated with the Wisconsin Institute for Discovery and serves on the executive committee of the Institute for Foundations of Data Science (IFDS), a collaboration with the University of Washington, the University of California, Santa Cruz, and the University of Chicago. Professor Roch's research spans the intersection of applied probability, statistics, and theoretical computer science, with a strong emphasis on biological applications, particularly in phylogenetics and phylogenomics. His work focuses on developing mathematical and computational frameworks for understanding evolutionary processes, species tree estimation, sequence alignment, and other problems at the interface of biology and data science. He has made significant contributions to understanding the theoretical foundations of phylogenetic reconstruction methods, particularly regarding statistical consistency, sample complexity, and algorithmic limitations. His research often bridges theoretical insights with practical applications in computational biology. Roch's publication record demonstrates a consistent focus on the mathematical underpinnings of phylogenomics, with recent work addressing species tree rooting methods, limitations of k-mer based approaches, and the impact of evolutionary phenomena like recombination on phylogenetic inference. His work combines rigorous theoretical analysis with practical implications for biological data analysis, spanning both foundational mathematical results and innovative algorithmic approaches. Among his notable honors are being named a Fellow of the Institute of Mathematical Statistics (2022), receiving the Vilas Distinguished Achievement Professorship (2024), and a Van Vleck Research Professor Award (2023). His paper 'Circular Networks from Distorted Metrics' received the Best Paper Award at RECOMB 2018, and his work on concatenation methods in phylogenetics received an Honorable Mention for the Marcus W. Feldman Prize. As an advisor, Roch has successfully guided several PhD students to completion, including Max Hill (2023), Yu Sun (2023), and Shuqi Yu (2023). His research is currently supported by NSF grants DMS-2023239 (TRIPODS Phase II) and DMS-2308495. He is also the author of the book 'Modern Discrete Probability: An Essential Toolkit,' which was published in early 2024. Roch is actively involved in the academic community, serving on the Academic Planning Council of the College of Letters and Science and participating in major conferences and workshops in computational biology and theoretical computer science. In Spring 2025, he will be teaching a new course 'Linear Algebra and Optimization' designed for students interested in AI and data science.

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Gail RosenView profile

Professor

Drexel UniversityUnited States

Gail Rosen is a Professor in the Department of Electrical and Computer Engineering at Drexel University, where she leads the Ecological and Evolutionary Signal Processing and Informatics (EESI) Lab and chairs the University Research Computing Facility . She also directs Drexel's Center for Biological Discovery from Big Data and serves on the editorial board of the BMC Microbiome journal. Education: PhD, MS, and BS in Electrical and Computer Engineering from Georgia Institute of Technology (2006, 2002, 1999). Her research spans machine learning , metagenomics , microbiome analysis , and evolutionary computation . Recent work focuses on deep learning for genomic data, DNA language models, scalable gene embedding methods, and microbiome risk prediction. She pioneered embedding techniques for 16S rRNA sequences and developed tools like theseus for microbial community analysis. Active in diversity initiatives, she co-organizes the AIME Scholarship Program targeting underrepresented groups in engineering. Her accolades include the NSF CAREER award , multiple Drexel internal grants, and the Stein Family Fellowship . Labs and teams: Ecological & Evolutionary Signal Processing & Informatics Lab Drexel's Center for Biological Discovery from Big Data

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Marielle StoelingaView profile

Professor

University of TwenteNetherlands

Prof. Marielle Stoelinga is a Professor at the University of Twente, Netherlands, working within the Electrical Engineering, Mathematics and Computer Science faculty in the Formal Methods and Tools department. She leads significant research initiatives in formal methods with applications to safety, security, and reliability engineering. Her research spans predictive maintenance , fault tree analysis , attack tree modeling , and the integration of safety and security through formal methods. She focuses on applying big data analytics to predict system failures, with particular emphasis on critical infrastructure including railway systems, satellite missions, and nuclear reactors. Her work bridges theoretical formal methods with practical applications in asset management. Analysis of her recent publications reveals a strong trend toward integrating safety and security analysis through attack-fault-defense trees, developing formal frameworks for risk assessment, and applying model checking techniques to real-world maintenance problems. Her research increasingly addresses the human and organizational aspects of predictive maintenance systems while maintaining rigorous formal foundations. 5 million euros research grant from Dutch National Organization for Scientific Research (NWO) for PrimaVera project Prof. Stoelinga leads the PrimaVera research project ( Predictive maintenance for Very effective asset management ), which takes a holistic approach to predictive maintenance spanning sensor systems, data science, maintenance optimization, and human factors. Her research group actively contributes to formal methods applications in critical infrastructure sectors including energy, transportation, and aerospace.

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Renáta Benda ProkeinováView profile

Associate Professor

Slovak University of Agriculture in NitraSlovakia

Renáta Benda Prokeinová serves as Associate Professor and Head of the Department of Statistics and Operations Research at the Faculty of Economics and Management, Slovak University of Agriculture. She maintains an active role in academic governance, currently serving on the Scientific Council of the Faculty of Economics and Management and the Study Programme Committee for Applied Economy and Data Analysis. Her educational background includes a Doctoral degree in Economics and Business Management with focus on Management in Agriculture (2008) and an Engineering degree (2005). Her research centers on consumer behavior modeling, particularly related to food consumption patterns in Slovakia, sustainable consumption practices, and econometric analysis of demand systems. Dr. Benda Prokeinová's scholarly work demonstrates consistent focus on quantifying sustainable consumption through statistical modeling and analyzing food purchasing behaviors. Her recent publication examines factors influencing organic food purchases among Slovak consumers, highlighting the interplay between quality perceptions and promotional activities. She actively supervises numerous graduate students across various research topics related to consumer behavior and economic modeling, with 68 theses supervised to date. Her grant portfolio includes significant projects such as 'Datamining analysis of public procurement in the public sector' as principal investigator and 'Big Data Mining & Analytics for Academic Operational Excellence' as coordinator. Dr. Benda Prokeinová teaches advanced courses including Data Mining, Econometric Modelling, Forecasting Methods, and Support Systems for Statistical Analyses, contributing to both undergraduate and postgraduate education at the university.

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Julia NevárezView profile

Professor

Kean UniversityUnited States

Dr. Julia Nevárez is a Professor in the Department of Political, Social and Cultural Sciences at Kean University, where she maintains an active research and teaching profile. Her office is located in J103H, and she can be contacted via email or phone. She approaches cities as 'laboratories of human behavior' through a sociological lens, with interdisciplinary expertise spanning urban development, public space, and climate change. Her current book project examines smart city development , exploring how big data and algorithms influence sustainability policies and quality of life, while advancing the concept of digital urbanity —the simultaneous engagement with digital platforms in physical urban environments. She serves as a reviewer for the Journal of Urban Affairs and Hong Kong Research Council grants, and holds leadership roles including: Executive Board Member of the Urban Affairs Association Past President of the Environmental Design Research Association Past President of Phi Kappa Phi (Kean Chapter) Member of the European Urban Research Association Dr. Nevárez teaches diverse courses including: Undergraduate : Urban Sociology, Qualitative Methods, Social Research Statistics, Senior Seminar Graduate : Sociological Analysis, Thesis Supervision, Qualitative Research Practicum

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Chong Ho Alex YuView profile

Professor

Hawaii Pacific UniversityUnited States

Dr. Chong Ho "Alex" Yu is a Professor and Program Director of Data Science and Artificial Intelligence at Hawaii Pacific University, where he is affiliated with the College of Natural and Computational Sciences and the Department of Mathematics. With dual Ph.D.s in Measurement, Statistics and Methodological Studies and Philosophy from Arizona State University, Dr. Yu brings a unique interdisciplinary perspective to his work at the intersection of data science, statistics, and philosophical inquiry. Dr. Yu's educational background includes: Ph.D. in Measurement, Statistics and Methodological Studies, Arizona State University Ph.D. in Philosophy, Arizona State University M.A. in Theology, Azusa Pacific University M.Ed. in Educational Psychology, University of Oklahoma MHR in Human Relations, University of Oklahoma B.S. in Mass Communication, Bemidji State University B.A. in Visual Arts, Bemidji State University Graduate Certificate in Public Health, University of Arizona Graduate Certificate in Liberal Studies, Hamline University Certificate in History and Philosophy of Science, Arizona State University Dr. Yu's research interests span multiple domains with a focus on data science and machine learning methodologies. He is particularly interested in alternate and emerging research methods including exploratory data analysis, data visualization, and item response theory. His work also examines philosophical aspects of research methodologies such as causal inferences, the nature of latent constructs, and abductive reasoning. Additionally, he conducts cross-cultural comparisons in STEM education using international assessments like PISA, PIAAC, and TIMSS. As Vice President for Statistics Education and President-elect of the Southern California Chapter of the American Statistical Association (SCASA), he actively disseminates knowledge through a weekly newsletter and manages the statistics and data science newsroom on SCASA's website. Dr. Yu's extensive publication record demonstrates his expertise across multiple disciplines. His recent work shows a clear trend toward applying data science and machine learning techniques to diverse fields including healthcare, education, and psychology. He has made significant contributions to understanding the relationship between well-being and academic performance using large-scale assessment data, as well as developing applications of machine learning in educational science. His work often bridges theoretical statistical concepts with practical applications, reflecting his commitment to making data science accessible and relevant across disciplines. Dr. Yu has received numerous professional accolades including: Three-time winner of the SAS faculty scholarship (2016, 2017 SAS Global Forum and 2017 Western Users of SAS Software Conference) Distinguished SAS Educator Award in 2021 Vice President for Statistics Education of the Southern California Chapter of the American Statistical Association President-elect of the Southern California Chapter of the American Statistical Association As an educator, Dr. Yu is committed to staying current with industry developments to provide students with cutting-edge skills. He regularly attends conferences and workshops run by leading data analytics companies including SAS Institute, Salesforce (Tableau), and Amazon Web Services. His dedication to connecting academic knowledge with industry needs has resulted in a curriculum that prepares students for real-world data science challenges. Dr. Yu teaches courses including Statistics, Pre-Calculus, Probability, Applied Statistics, Data Science and Machine Learning, Data Visualization, Data Ethics, and Text Mining and Unstructured Data. Dr. Yu maintains active research collaborations across multiple institutions and disciplines. His work often involves interdisciplinary teams examining topics at the intersection of data science, healthcare, and education. Through his leadership as Program Director of Data Science, he has developed initiatives that connect students with industry professionals and real-world data challenges.

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Luvai F. MotiwallaView profile

Professor

University of Massachusetts LowellUnited States

Luvai F. Motiwalla serves as Professor and Chair of the Operations and Information Systems Department at the Manning School of Business, University of Massachusetts Lowell. He previously held the department chair position from 2008-2011 and 2015-2018, demonstrating sustained leadership in academic administration. His academic credentials include: Ph.D. in Business Administration (1989), University of Arizona MS in Management Information Systems (1986), University of Arizona BBA in Accounting (1983), Penn State University Accounting Certification (1979), University of Bombay Professor Motiwalla's research centers on IT adoption dynamics , information privacy frameworks , and mobile analytics systems , with recent work emphasizing design science approaches to enterprise system implementation. His scholarly contributions span theoretical modeling of user behavior and practical development of analytics platforms, particularly in mobile banking and health IT contexts. The Journal of Information Systems and Decision Sciences have featured multiple publications reflecting his methodological rigor. Analysis of his 15 most recent publications reveals a clear trajectory toward mobile technology applications (70% of recent work), with increasing focus on privacy-preserving analytics (40%) and FinTech solutions (25%). His 2023-2024 output shows heightened industry collaboration, particularly in banking and healthcare sectors, while maintaining strong theoretical foundations in information systems success models. His scholarly recognition includes: Best Paper Award 2023 (New England Association of Information Systems) Best Student Paper Award 2022 (New England Association of Information Systems) Professor Motiwalla has secured over $2.5M in competitive research funding, including NIH grants for health data privacy systems and NSF awards for educational technology transformation. His current projects focus on physician selection analytics and serverless microservices for FinTech, indicating ongoing innovation in applied research. While specific student advising details aren't public, his 40+ journal articles suggest extensive mentorship of doctoral candidates. He actively collaborates with interdisciplinary teams including health informaticians at UMass Medical School and banking technology specialists, with recent work involving Indian fintech startups and healthcare analytics firms through the Wilson Center for Entrepreneurship.

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Eberechukwu OnukwughaView profile

Professor

University of Maryland, BaltimoreUnited States

Eberechukwu Onukwugha is a Professor in the Department of Pharmaceutical Health Services Research at the University of Maryland School of Pharmacy, where she also serves as the Executive Director of the Pharmaceutical Research Computing center and Program Director of the PhD program in PHSR. She holds a PhD in Economics from Virginia Tech and completed a postdoctoral fellowship in pharmacoeconomics at the University of Maryland. University: University of Maryland School of Pharmacy School: School of Pharmacy Department: Department of Pharmaceutical Health Services Research Academic Rank: Professor Her research focuses on pharmacoeconomics, health disparities, medical decision-making, and cost-effectiveness analysis, particularly in oncology and cardiovascular disease. She employs advanced statistical and econometric methods to evaluate healthcare interventions and disparities in treatment access and outcomes. The most recent articles demonstrate a strong trend in health economics, observational study methodology, and disparities research, particularly in cancer care. Her work frequently uses large datasets like SEER-Medicare to assess treatment patterns, costs, and survival outcomes. Key themes include the economic burden of chronic diseases, pharmacogenetics, and the impact of social determinants on health outcomes. Contributed Research Award: Best New Investigator Podium Presentation, ISPOR 2006 Contributed Research Award: Best Podium Presentation, ISPOR 2009 Best Student Podium Presentation, ISPOR 2013 Lee B. Lusted Student Prize, Society for Medical Decision Making, 2013 Foreword: Big Data and Health Disparities Research, 2017 Dr. Onukwugha has successfully mentored PhD students including Candice Yong and Jinani Jayasekera, and has secured significant research funding as Principal Investigator from the American Cancer Society, AHRQ, NIH, and pharmaceutical partners. Her editorial roles at PharmacoEconomics and Ethnicity & Disease reflect her leadership in the field. She is actively involved in professional service, including advisory roles and program leadership within the School of Pharmacy. She leads the Pharmaceutical Research Computing center, which supports data-intensive health services research, and her work bridges economics, epidemiology, and clinical practice to inform policy and improve patient care.

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Dirk Van den PoelView profile

Professor

Ghent UniversityBelgium

Dirk Van den Poel is a Professor of Data Analytics/Big Data at Ghent University, Belgium, specializing in statistical computing, social media analytics, and predictive modeling. He has co-founded pioneering academic programs including the first predictive analytics master’s program globally, the Master of Science in Statistical Data Analysis, and the Master of Science in Business Engineering/Data Analytics. Teaches courses: Statistical Computing, Big Data, Social Media Analytics Research focus: Analytical CRM, ensemble classification methods, big data 110+ ISI-indexed publications in journals like IEEE Transactions on Power Systems and Decision Support Systems His recent work explores topics such as rumor detection in social media, predictive maintenance using sensor data, and crisis response through service adaptation. He leverages machine learning and deep learning for applications in agriculture, healthcare, and energy sectors. Current projects involve topic modeling (BERTopic), credit scoring with interpretable models, and spatiotemporal crime prediction using mobile phone data. Dirk’s methodology integrates stream processing frameworks , reinforcement learning , and ensemble techniques .

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Fiona CrawfordView profile

Lecturer

University of GlasgowUnited Kingdom

Dr Fiona Crawford is a Lecturer in Transport Planning at the University of Glasgow's Department of Urban Studies & Social Policy. She joined in November 2022, previously working at the University of the West of England (UWE) in the Centre for Transport and Society. She holds a PhD in Transport Studies from the University of Leeds, alongside master’s degrees in Social Research Methods and Data Science and Analytics. Her research focuses on emerging data methods for travel behavior analysis, sustainable transport modes, intrapersonal travel variability, and transport equity, with an emphasis on gender biases and societal trends like remote work and online shopping. Her grants include the £1.2m OptiWaSP project (2022–2025) and a Clean Air Discovery & Innovation project (2021). She currently supervises Thi Phuong Linh Le and welcomes PhD applications in travel behavior data, intrapersonal variability, air quality, and socio-transport connections. She teaches the MSc Urban Transport program and convenes the Transport Planning Methods course. Publications highlight innovative uses of sensor data, cyclist/pedestrian safety around automated vehicles, and pandemic-driven travel changes. She contributes to the Urban Big Data Centre, managing datasets like Nextbike mobility and Glasgow OD matrices. As a Chartered Statistician, her work bridges quantitative methods with urban policy, emphasizing data-driven solutions for equitable transport systems.

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Qingmin MengView profile

Associate Professor

Mississippi State UniversityUnited States

Dr. Qingmin Meng is an Associate Professor in the Department of Geosciences at Mississippi State University, specializing in human-environment interactions through quantitative geospatial analysis. His work integrates mathematics and statistics to model landscape dynamics, natural resources, and human activities using vector, raster, and time-series datasets. Dr. Meng's educational background includes: PhD in Forestry and Natural Resources, University of Georgia (2006) MS in Statistics, University of Georgia (2005) PhD in Geography, Peking University, China (2001) MS in Geography, Lanzhou University, China (1997) BS in Geography, Shandong Normal University, China (1994) His research focuses on geospatial big data exploration , with emphasis on remote sensing data integration, ecological system assessment, and environmental-social interactions in Gulf coastal ecosystems, hydraulic fracturing impacts, and West Nile Virus dynamics. Key methodologies include GIS, remote sensing, and geospatial modeling for analyzing complex systems. Recent publications reveal a strong trend toward applied environmental justice research , particularly in urban flood vulnerability, wildfire risk disparities, and health inequities exacerbated by climate change. His work increasingly utilizes machine learning and high-resolution spatial analytics to address climate adaptation challenges. Notable recognitions include: CHANS Fellowship (2010) from the International Network of Research on Coupled Human and Natural Systems (NSF/MSU) UCGIS/ESRI Junior Faculty Award (2010) Dr. Meng mentors graduate students in geospatial science, with current advisees including PhD candidates Sadia Shammi, Khalid Hossain, and Tianyu Li. His research is supported by grants focused on environmental hazard assessment and geospatial technology development, though specific funding sources aren't detailed in available materials. His work leverages Mississippi State University's geospatial infrastructure for analyzing Gulf coastal ecosystems and hydraulic fracturing impacts, with growing emphasis on urban environmental justice and climate resilience applications.

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Philseok LeeView profile

Associate Professor

George Mason UniversityUnited States

Dr. Philseok Lee is an Associate Professor of Industrial and Organizational Psychology at George Mason University (GMU), where he teaches courses such as personnel selection, psychometrics, and multivariate statistics. He holds editorial roles at journals like Human Resource Development Quarterly and Organizational Research Methods . His research focuses on applying AI, machine learning, and psychometric modeling to improve personnel assessments, addressing challenges like faking in selection processes and cross-cultural measurement issues. Lee’s lab recently won the 2024 SIOP Machine Learning Competition for advancing AI-driven assessment methods. Education: PhD in Industrial and Organizational Psychology from the University of South Florida (2016). Research interests include: - Generative AI and LLMs in test development - Big data applications in work settings - Modern psychometric modeling - Non-cognitive assessment design Key grants include a US Navy contract for machine learning in personnel selection and a Virginia Department of Veterans Services project on workplace turnover. His lab actively explores technology-based assessments, such as game-based evaluations and NLP-driven scale development. Awards: 1st Place in 2024 SIOP Machine Learning Competition; Editor Commendation for a 2023 paper on AI in personality test development. Advising: Open to doctoral students pursuing academic careers. Current and former advisees include M. Son, Z. Jia, and S. Zhou. Collaborations focus on advancing fair and accurate assessment tools. Lab: Paid Lab , researching innovative approaches to personnel evaluation through AI and psychometrics.

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Chau-Wai WongView profile

Associate Professor

North Carolina State UniversityUnited States

Chau-Wai Wong is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University, with affiliations to the Forensic Sciences Cluster and Secure Computing Institute. He previously served as a Data Scientist at Origin Wireless, Research Assistant at University of Maryland, and Research Associate at Hong Kong Polytechnic University. His work bridges machine learning with applications in multimedia forensics, signal processing, and computational social science. Ph.D. , Electrical Engineering, University of Maryland (2017) M.Phil. , Electronic and Information Engineering, Hong Kong Polytechnic University (2010) B.Eng. , Electronic and Information Engineering, Hong Kong Polytechnic University (2008) Research spans federated learning (security vulnerabilities, communication efficiency), physically unclonable features (PUF-based authentication), generative models (GANs for hardware modeling), and computational social science (AI chatbots for disaster communication, TikTok behavioral analysis). Recent work explores neural tangent kernels and multi-LLM agent collaboration . Key publications focus on decentralized AI systems (ICML'25), deepfake detection (under review), and social media health analytics (Telematics and Informatics'25). His federated learning research has appeared at ICLR, IEEE T-NNLS, and USENIX Security. NSF CAREER Award IEEE Signal Processing Cup Organizer (2016) Technical Program Committee Chair (IH&MMSec'25) Area Chair (ICME'21–'24) Senior Member, IEEE Advises students in AI security , physiological sensing , and social science automation . Collaborates with teams at University of Maryland, Hong Kong Polytechnic University, and industry partners. Current projects include identity-privacy protection for smart health and UAV-assisted network optimization (IEEE DySPAN'25).

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Christos KastrisiosView profile

Research Professor

University of New HampshireUnited States

Christos Kastrisios is a Research Professor at the Center for Coastal and Ocean Mapping (CCOM) within the University of New Hampshire. His work focuses on advancing nautical cartography, hydrographic data analysis, and automated charting workflows. He leads research in bathymetric data quality, interpolation uncertainty, and maritime navigation safety systems. Education: Ph.D. in Cartography, National Technical University of Athens M.P.S. in Geospatial Information Sciences, University of Maryland B.Sc. in Naval Science and Art, College of Naval Architecture, Piraeus Research Interests: Cartography, Hydrography, Nautical Chart Automation, Data Quality Visualization, and Maritime Spatial Analysis. His projects emphasize integrating geospatial standards (e.g., IHO S-122) and machine learning to enhance chart accuracy and safety. Publications Trends: Recent work addresses automated charting workflows, bathymetric uncertainty quantification, and vessel trajectory analysis. Key themes include optimizing data visualization for ECDIS systems and improving interoperability between survey data and navigation charts. Grants & Laboratories: Active in CCOM’s ocean engineering lab, contributing to projects like the Spanish EEZ mapping initiative. Collaborates on open-source tools for hydrographic surveying and marine data integration.

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