جست‌وجوی استاد و پژوهشگر

Leo Lahtiمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Machine Learning
• +۱۱ مورد دیگر

Leo Lahti is a Professor in Data Science at the University of Turku, Finland. His research focuses on computational analysis and modeling of complex natural and social systems, with applications in microbiome science, ecological modeling, and computational humanities. He is actively involved in open science initiatives and leads a dynamic research team. University: University of Turku Role: Professor, Data Science Contact: leo.lahti@utu.fi Lahti holds a Doctor of Science (DSc) from Aalto University (2010), where he developed probabilistic machine learning methods for life science data. He conducted postdoctoral research at EBI/Hinxton (UK), Wageningen University (NL), and VIB/KU Leuven (BE), establishing a strong international research profile. His research interests span Data Science, Artificial Intelligence, Machine Learning, Applied Statistics, Probabilistic Models, Microbial Ecology, Computational Humanities, and Open Science . He applies computational and statistical methods to understand complex systems, particularly in microbiome dynamics, ecological resilience, and historical data analysis. His work integrates interdisciplinary approaches, combining data analytics with domain-specific knowledge in biology, medicine, and humanities. The recent publications (2021–2025) reflect a strong focus on gut microbiome analysis , including maternal-infant health, dietary impacts, disease risk (CKD), and methodological advances in differential abundance and phylogenetic diversity. Other themes include ecological modeling (resilience, early warning signals), infectious disease modeling (COVID-19), and open science education (Bioconductor). The keyword trends show deep engagement with microbiology, bioinformatics, and mathematical modeling, with subfields emphasizing reproducibility, host-microbe interactions, and computational methods. Lahti plays a significant role in open science and academic leadership: Vice Chair, Open Science Finland Member, International Science Council Committee on Data (2023–2025) Member, Bioconductor Community Advisory Board Founder, Open Science Working Group, Open Knowledge Finland He is also active in teaching computational and data science, statistical programming, AI, and open science. His research homepage is available at iki.fi/Leo.Lahti, and his full publication list is accessible via ORCID and bibtex.

C. Lockwood Reynoldsمشاهده پروفایل

استاد

• Microeconomics
• Labor Economics
• Public Economics
• +۳ مورد دیگر

C. Lockwood Reynolds is a Professor in the Department of Economics at Kent State University, affiliated with the Ambassador Crawford College of Business and Entrepreneurship. He holds a Ph.D. in Economics from the University of Michigan (2007), an MA in Economics from the same institution (2004), and a BA in Economics (with honors) and Japanese from Williams College (2001). His research focuses on microeconomics, labor economics, public economics, and the economics of education. Key areas include access and completion in higher education, effects of college choice, household resource impacts on educational decisions, and spatial implications of economic policies. He teaches courses such as Principles of Microeconomics, Data Wrangling, and Managerial Economics in the MBA program. Dr. Reynolds has been repeatedly honored with teaching awards, including the College of Business Administration Tenured/Tenure Track Teaching Excellence Award and other university accolades. His recent publications examine topics like equal wage policies in historical contexts, gender disparities in job loss impacts, and urban quality-of-life preferences. He is associated with the Healthy Communities Research Institute (HCRI) and contributes to interdisciplinary initiatives addressing economic and policy challenges.

Scott Jensenمشاهده پروفایل

دانشیار

• Big Data Analytics
• Data Wrangling
• Metadata Management
• +۲ مورد دیگر

Dr. Scott Jensen is an Associate Professor in the School of Information Systems & Technology at San José State University's Lucas College of Business. He earned his Ph.D. in Computer Science from Indiana University Bloomington (2010) and holds master's degrees in Computer Science (DePaul University) and Accountancy (Southern Illinois University). His industry experience includes 15+ years at Ernst & Young, Price Waterhouse, and Arthur Andersen. Research centers on Big Data pedagogy, metadata systems, and distributed computing. Notable projects include NSF-funded workshops on data science literacy and curriculum integration. He developed the 'Data Science for All' seminar series to introduce non-technical students to analytics tools. Publications address Big Data curriculum design, Spark-based data wrangling, and scientific metadata. Recent work explores AI chatbots for education and gamified learning in MIS courses. No awards or student details were provided. Teaches courses in Big Data management, business programming, and data analytics.

Ziawasch Abedjanمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Database Systems
• +۵ مورد دیگر

Ziawasch Abedjan is a full professor of computer science and chair of the Data Integration and Data Preparation (D2IP Lab) Group at Technische Universität Berlin, part of the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds a PhD from the Hasso Plattner Institute (2014) and was previously a junior professor at TU Berlin (2016–2020), with postdoctoral work at MIT (2014–2016). His research focuses on scalable methods for processing large heterogeneous datasets, emphasizing automated data preparation, extraction, and cleaning for data science workflows. Education: PhD in Computer Science, Hasso Plattner Institute (2014) MSc in Computer Science, Hasso Plattner Institute (2010) BSc in Computer Science, Hasso Plattner Institute (2008) Research Interests: Dr. Abedjan’s work bridges database systems and machine learning, addressing challenges in data integration, automated data cleaning, and scalable data science tools. His team develops systems like Blend for unified data discovery and MATE for multi-attribute table extraction. Recent projects include exploring the environmental impact of AutoML and advancing catalog enrichment techniques. Awards & Recognition: First Prize, GI Data Science Challenge (BTW 2023) SIGMOD Reproducibility Award (2019) Best Dissertation Award (2013/2014) Teaching & Service: Teaches foundational courses in data structures and databases. Serves on committees for major conferences (SIGMOD, VLDB) and chairs roles such as Reproducibility Chair for BTW (2023/2025). Active in editorial roles for journals including ACM JDIQ and IEEE Data Engineering Bulletin. Labs & Teams: Leads the D2IP Lab, collaborating with universities and industry. Current projects include TDC2 NFDI4DS. Offers thesis topics in data science lifecycle optimization and ML system design.

• Business Analytics
• Data Science
• Data Engineering
• +۳ مورد دیگر

Thi Xuan Diem PHAM is a Lecturer in the Department of Business Information Systems at La Trobe University, Melbourne, Australia. She holds a PhD from La Trobe University (completed April 2024). Her research and teaching focus on business analytics, data engineering, and Python programming. She is affiliated with the Orcid identifier 0000-0003-3273-209X. Teaching Activities: DATA ENGINEERING & PYTHON (Feb 2025) DATA ANALYTICS CONCEPTS (Mar 2024) DESIGNING BUSINESS ANALYTICS SOLUTIONS (Feb 2024) DATA WRANGLING AND R (Mar 2022) No scientific awards or research outputs are listed in the provided data.

• Artificial Intelligence
• Machine Learning
• Energy Systems Optimization
• +۴ مورد دیگر

Dr. Harsha Kumara Moraliyage is a Lecturer in Business Information Systems at La Trobe University, specializing in AI and machine learning applications. He holds a PhD in Artificial Intelligence and Data Science from La Trobe and has over a decade of software development experience. His research focuses on AI-driven solutions for energy systems optimization, cybersecurity, healthcare informatics, and sustainable development. He leads the award-winning La Trobe Energy Analytics Platform (LEAP), contributing to the university's net-zero emissions target by 2029. Education: PhD in Artificial Intelligence and Data Science (La Trobe University), certifications in Deep Learning, TensorFlow, and AI from Coursera. Research Interests: Adversarial machine learning, natural language processing, time series forecasting, energy analytics, MLOps, AutoML, and AI ethics. Current projects include developing smart energy platforms, cybersecurity frameworks for energy systems, and empathetic AI tools for healthcare. Teaching: Teaches Master of Business Analytics courses such as Data Wrangling (BUS5DWR), Principles of Business Analytics (BUS5PB), and Artificial Intelligence and Hyperautomation (BUS5PR1). Focuses on designing curriculum to enhance AI literacy. Grants & Labs: Leads the LEAP project, a smart energy analytics platform, and collaborates on AI-driven solutions for net-zero carbon emissions. Active in interdisciplinary teams involving energy infrastructure, robotics, and telehealth. Future Work: Expanding research in generative AI for sustainable cities, explainable AI frameworks, and ethical AI integration in education and healthcare sectors.

Gemma Moranمشاهده پروفایل

استادیار

• Bayesian Statistics
• Machine Learning
• Deep Generative Models
• +۴ مورد دیگر

Gemma Moran is an Assistant Professor in the Statistics Department at Rutgers University. Her office is located at Hill Center 475, 110 Frelinghuysen Road, Piscataway, NJ 08854. She teaches courses including MSDS597 Data Wrangling with Python, MSDS534 Statistical Learning for Data Science, and STAT588 Data Mining. Dr. Moran received her PhD in Statistics from the University of Pennsylvania, where she was advised by Edward George and Veronika Rockova. Prior to joining Rutgers, she was a postdoctoral researcher at the Columbia Data Science Institute working with David Blei. Dr. Moran's research focuses on developing flexible Bayesian models for analyzing high-dimensional data. Her recent work includes developing identifiable and interpretable deep generative models, particularly variational autoencoders, and creating improved tools for Bayesian model criticism. Her methodological contributions bridge statistics, machine learning, and data science, with applications spanning computer vision, biomedical imaging, and materials science. Analysis of Dr. Moran's publication record reveals a strong emphasis on Bayesian methodology, particularly in variable selection (spike-and-slab approaches), deep generative modeling, and model criticism. Her work consistently addresses the challenge of maintaining statistical rigor while developing practical machine learning solutions, with a growing focus on model interpretability and identifiability in complex models. Dr. Moran actively mentors students at multiple levels. Prospective PhD students are encouraged to apply to the Rutgers Statistics PhD program and mention her name. For Masters of Data Science students, she supervises capstone projects requiring independent revisions of group work using different statistical methods or datasets. She also encourages students interested in research to take her MSDS-534 Statistical Learning for Data Science course as preparation for research projects.

• Computer Graphics
• 3D Visualization
• User Interfaces
• +۳ مورد دیگر

Tamara Munzner is a researcher in computer science with a focus on visualization and interactive 3D graphics. She holds a B.S. in Computer Science from Stanford University and pursued a PhD at Stanford's Computer Science Department. She was part of the senior technical staff at the Geometry Center, University of Minnesota, where she contributed to projects like Geomview, WebOOGL, and the Outside In animation. Her work integrates visualization with the World Wide Web, emphasizing user interfaces and interactive systems. Notable achievements include developing the Triangle Tiling exhibit with the Science Museum of Minnesota and contributions to the Siggraph Electronic Theater. Her research spans scientific visualization, web integration of 3D graphics, and collaborative visualization tools. Education: B.S. in Computer Science, Stanford University, 1991 PhD candidate in Computer Science, Stanford University (as of 1995) Research Interests: Munzner's work emphasizes interactive systems, 3D graphics visualization, and the integration of web technologies with visualization tools. She pioneered projects like Cyberview and WebOOGL, which enabled distributed 3D visualization via the web. Her focus on user interfaces and accessibility has shaped modern approaches to visualization design. Professional Contributions: Co-author of Geomview, a widely used 3D visualization system Animator/director of the Outside In mathematical animation Key contributor to the Triangle Tiling exhibit

John Shepherdمشاهده پروفایل

مدرس ارشد

• Data Management
• Information Retrieval
• Web Search
• +۴ مورد دیگر

Dr. John Shepherd is a Senior Lecturer in the School of Computer Science and Engineering (CSE) at UNSW, Sydney, Australia. His research focuses on data/information/knowledge management, including data modeling, query processing, multimedia retrieval, web search, and text mining. Current projects include RAM (Research Area Mapper) for research data management, and educational tools like ORE (Online Role-Play in Education), CMap (Curriculum Mapping), and WebCMS (Course Management System). His work spans interdisciplinary areas such as database systems, information retrieval, and educational technology. Notable contributions include the TEXUS framework for PDF table extraction and understanding, and collaborative projects with industry partners. Dr. Shepherd’s publications (e.g., on popularity forecasting, automated table understanding) reflect his expertise in machine learning, data systems, and applied research. He actively engages in curriculum development and educational innovation, leveraging technology to enhance learning experiences.

• Database Systems
• Data Engineering
• Data Governance
• +۴ مورد دیگر

Sergi Nadal Francesch is a Professor at the Universitat Politècnica de Catalunya (UPC) , affiliated with the College of Informatics of Barcelona (FIB) and the Department of Service and Information Systems Engineering . He leads research within the inSSIDE and DTIM groups, focusing on database technologies, data governance, and knowledge graph applications. Research areas: Database Systems, Data Engineering, Knowledge Graphs, Data Governance, and Automated Data Lifecycle Management Email: sergi.nadal@upc.edu His recent work explores automated data pipelines , scalable data discovery , and AI-driven medical diagnostics . He contributes to knowledge graph lifecycle development and graph-driven federated data systems , with projects funded by Horizon Europe and Spanish national programs. Scientific Awards : UPC Award for Quality in University Teaching (2021), Best Doctoral Thesis in SISTEDES (2021), EDBT 2025 Best Demo Paper

• Business Analytics
• Computer Information Systems
• Predictive Analytics
• +۵ مورد دیگر

Richard V. McCarthy is a Professor of Business Analytics and Information Systems and Associate Dean for the School of Business at Quinnipiac University. His work bridges academia and industry through predictive analytics, data management, and virtual team pedagogy. BS, Central Connecticut State University MBA, Western New England College DBA, Nova Southeastern University Dr. McCarthy's research focuses on healthcare analytics, financial fraud detection, and data science pedagogy. He applies machine learning to prevent post-surgical falls, combat money laundering, and enhance educational strategies. His publications emphasize predictive modeling techniques, including decision trees, neural networks, and regression analysis, across healthcare, finance, and information systems domains. Computer Educator of the Year, International Organization for Computer Information Systems (2019)

• Table Representation Learning (TRL)
• Generative Models for Tabular Data
• Data Retrieval
• +۳ مورد دیگر

Madelon Hulsebos is a faculty member at Centrum Wiskunde & Informatica (CWI), leading the Table Representation Learning (TRL) Lab and part of the Database Architectures group. She is also an ELLIS faculty member with the Amsterdam unit. Her research focuses on TRL, generative models for tabular data, and end-to-end systems for data analysis. She holds a PhD from the University of Amsterdam (2019) and conducted postdoctoral research at UC Berkeley (2020–2024). Her work has been supported by grants like the NWO AiNed Fellowship and the Accenture-BIDS Fellowship. She organizes TRL workshops at NeurIPS, ACL, and SIGMOD, and her research spans over 1.7M tables from GitHub (GitTables), schema analysis (SchemaPile), and dataset search systems. Key achievements include founding the TRL workshop series and leading the $1.5M DataLibra project to democratize structured data insights. Education: PhD (2019) and postdoc at UC Berkeley. Grants: NWO AiNed ($1M+), NWO/AiNed DataLibra ($1.5M total). Affiliations: CWI, ELLIS Amsterdam, and collaborations with MIT, Sigma Computing, and industry sponsors. Research Interests: TRL, generative AI for tables, data retrieval systems, and scalable data science. Recent Work: SIGMOD 2024 presentations, TRL@NeurIPS 2024 workshop leadership, and the SchemaPile corpus. Community Roles: ELLIS society member, co-organizer of DEEM workshop, and TRL Discord/Bluesky communities.

• Geospatial Data Management
• Database Systems
• Cluster Computing
• +۵ مورد دیگر

Jia Yu is a researcher affiliated with Arizona State University , Tempe, AZ, USA. Their work focuses on geospatial data management, database systems, and cluster computing frameworks like Apache Spark. They have collaborated extensively with Mohamed Sarwat and other researchers on projects such as GeoSpark , GeoSparkViz , and GeoSparkSim , contributing to scalable spatial data processing and visualization systems. Key research areas include Learned indexing mechanisms (e.g., GLIN) Microscopic traffic simulation Parallel and distributed data processing Interactive geospatial dashboards Column correlation exploitation for database efficiency Integration of visualization with backend data systems Recent publications (2014-2024) demonstrate expertise in geospatial analytics, database indexing, software testing, and Apache Spark-based systems. Notable projects include Turbocharging Visualization Dashboards , HERMIT Indexing , and Spindra Knowledge Graph Management . Work emphasizes both theoretical innovation and practical implementation for handling massive-scale spatial data.

Matt Germonprezمشاهده پروفایل

استاد

• Open Source Software
• Information Systems
• Field Research
• +۲ مورد دیگر

Matt Germonprez serves as Professor and Mutual of Omaha Distinguished Chair of Information Science & Technology at the University of Nebraska Omaha. He holds appointments in the College of Information Science & Technology and the Department of Information Systems and Quantitative Analysis. His academic credentials include: BS in Biology from Iowa State University MS in Information Systems from Colorado State University PhD in Information Systems from University of Colorado - Boulder Germonprez's research examines organizational engagement with open communities through qualitative field studies. His work investigates open source project health analytics, collaborative work dynamics, and sociotechnical systems in distributed environments. His research has secured funding from prominent organizations including the Alfred P. Sloan Foundation, Ford Foundation, and National Science Foundation. Analysis of publication trends reveals three primary research trajectories: (1) community health metrics and analytics, (2) open source supply chain complexity, and (3) sociotechnical systems design. Recent work increasingly focuses on operationalizing theoretical frameworks into practical tools for community management. His contributions have been recognized through: Mutual of Omaha Distinguished Chair appointment Multiple NSF and Sloan Foundation grants Germonprez co-founded the Linux Foundation's CHAOSS project focused on community health analytics. He maintains active roles in university governance while contributing to international open source initiatives.

Brian Maitnerمشاهده پروفایل

استادیار

• Global Change Ecology
• Macroecology
• Community Ecology
• +۳ مورد دیگر

Brian Maitner is an Assistant Professor in the Department of Integrative Biology at the University of South Florida, affiliated with the College of Arts and Sciences. His research focuses on biodiversity conservation, global change ecology, and the application of computational tools to ecological questions. He teaches courses such as Community Ecology and Statistical Ecology. Maitner’s lab emphasizes Open Science practices, including developing R packages like BIEN, TNRS, and Wallace 2.0, and collaborates globally on projects addressing biodiversity patterns, conservation strategies, and ecosystem resilience. Education: Ph.D., University of Arizona. Research Interests: Quantifying biodiversity patterns at local to global scales Climate change impacts on plant communities and conservation strategies Improving trait-based ecological methodologies Analyzing global change drivers as ecological experiments (e.g., fires, species introductions) Advocating for Open Science and collaborative data-sharing Labs/Teams: The Maitner Lab focuses on biodiversity science, conservation, and Open Science. Current projects include assessing plant conservation gaps, modeling species responses to climate change, and developing computational tools for ecological research. Advising: Seeking motivated Masters students interested in data science, field ecology, or both. Applications are encouraged via email.