جست‌وجوی استاد و پژوهشگر

Owais Gilaniمشاهده پروفایل

دانشیار

• Spatial Statistics
• Spatiotemporal Statistics
• Air Pollution Modeling
• +۴ مورد دیگر

Owais Gilani is an Associate Professor in the Department of Public Health and Community Medicine at Tufts University School of Medicine. His academic journey includes a PhD in Biostatistics from Yale University and postdoctoral training at the University of Michigan School of Public Health. Prior to joining Tufts, he served as Associate Professor of Statistics at Bucknell University. Dr. Gilani's research program centers on developing spatial and spatiotemporal statistical methods with applications in environmental health. Key areas include: Air pollution modeling and exposure assessment Human mobility pattern analysis Bayesian data fusion techniques Environmental epidemiology Statistical software development for environmental data His work involves interdisciplinary collaborations across public health, clinical medicine, and biological sciences. Analysis of Dr. Gilani's recent publications reveals strong methodological focus on spatiotemporal modeling with applications spanning environmental epidemiology, ecological entomology, and health services research. The majority of his work employs Bayesian approaches and develops novel statistical techniques for analyzing complex environmental and health datasets. Recent outputs demonstrate expanding applications in infectious disease modeling and mobility data analysis. Dr. Gilani maintains an active teaching portfolio including: Principles of Epidemiology & Statistics Biostatistics Data Visualization for Public Health Introduction to Public Health Research Statistical modeling and inference courses He currently leads an NSF-funded project on 'Data Resources and Analytic Tools to Understand Population Scale Human Mobility for Applications in SBE Research' (2025).

• Visual Analytics
• Temporal Data Analysis
• Data Cleansing
• +۳ مورد دیگر

Theresia Gschwandtner is a Researcher at TU Wien's Research Division of Visual Analytics (E193-07). Her work focuses on advancing visual analytics methodologies for temporal data, fraud detection, and uncertainty visualization. She leads the Network Lab and contributes to tools like TimeCleanser for data cleansing and NEVA for fraudulent network identification. Her research emphasizes interactive systems for guidance in data analysis, provenance tracking, and enhancing user-centric visualization frameworks. Key research interests include temporal data preprocessing, multivariate time series analysis, and the integration of automated guidance systems into visual analytics platforms. She has collaborated on projects such as Hermes (economic network exploration) and TBSSvis (temporal blind source separation), which combine algorithmic innovation with intuitive user interfaces. Guidance frameworks and user studies are central to her work, exploring how automated support impacts performance and mental state during complex data analysis tasks. She has advised students on theses addressing data quality, cyclical pattern detection, and lighting design visualization. Notable contributions include the Quantifying Uncertainty in Time Series Processing framework and the LightGuider system for interactive lighting design guidance. Her work bridges theoretical advancements with practical applications in healthcare, finance, and engineering domains.

• Financial Risk Management
• Asset Allocation
• Pricing
• +۱۲ مورد دیگر

Majeed Simaan is an Assistant Professor of Finance and Financial Engineering at the School of Business, Stevens Institute of Technology. He holds a Ph.D. in Finance from Rensselaer Polytechnic Institute (RPI), completed in 2018, and joined Stevens as a tenure-track faculty member thereafter. His academic work focuses on risk management, asset allocation, and pricing, with applications in quantitative and computational finance. Research Interests: His primary research revolves around Financial Risk Management (FRM), with emphasis on asset allocation, portfolio optimization, and financial networks. He integrates machine learning, textual analysis, and network modeling to enhance traditional financial models. His work addresses estimation risk, model interpretability, and the application of reinforcement learning in portfolio construction. He is also a strong advocate for reproducible research and open-source software, particularly using R for empirical finance. The recent publications highlight a strong trend toward integrating machine learning and data science into financial decision-making. Topics include volatility modeling, portfolio efficiency, index tracking, and behavioral aspects of investing such as the 'buy the dip' strategy. The research spans theoretical modeling, empirical validation, and practical implementation, often leveraging public datasets and open tools. Scientific Awards and Recognition: Certified Financial Risk Manager (FRM) from GARP (August 2023) Research funded by the National Science Foundation (NSF) via CRAFT Featured in Bloomberg Markets and other media outlets for research on investor behavior Advising and Grants: Dr. Simaan mentors Ph.D. students through research courses such as FE 960 and MGT 960. His research has been supported by competitive grants, including funding from the NSF. He has served on key institutional committees including the Finance PhD Committee, Research Committee, and Teaching Effectiveness Evaluations Committee, reflecting his active role in academic governance. Labs and Research Teams: While no formal lab is named, his work is associated with computational and data-driven finance research, often involving collaboration on machine learning applications in finance. He maintains an active presence on RPubs and GitHub, sharing reproducible research vignettes on topics like financial networks, volatility modeling, and tactical asset allocation.

Oscar Romero Moralمشاهده پروفایل

استاد

• Data Management
• Data Science
• Data Governance
• +۷ مورد دیگر

Oscar Romero Moral is a Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Department of Services and Information Systems Engineering at the Barcelona School of Informatics (FIB). He leads research in the inSSIDE, inLab FIB, and DTIM groups, focusing on data management, data science, and big data technologies. His work emphasizes knowledge graphs, data governance, and machine learning integration with data systems. Affiliations: UPC, inSSIDE, inLab FIB, DTIM Group Research Interests: Data Management, Data Engineering, Big Data, Knowledge Graphs, Data Governance, Machine Learning Integration He has authored over 276 academic contributions, including peer-reviewed articles on federated healthcare data systems, GPU-accelerated workflows, and graph-driven data integration. His recent work addresses challenges in heterogeneous computing, automated data governance, and scalable data architectures. Romero has served on the program committees of major conferences like VLDB, ICDE, and EDBT, and led competitive research projects in data systems and analytics. He collaborates extensively with industry partners and academic institutions, driving innovations in distributed data management and edge computing.

Savannah L. Kellyمشاهده پروفایل

دانشیار

• Data Science Education
• Statistical Literacy
• Research Methods
• +۷ مورد دیگر

Savannah L. Kelly is Associate Head and Associate Professor in the Scholar Support and Data Services (SSDS) Department at the University Libraries of the University of Mississippi. She plays a key role in advancing data and statistical literacy among students, faculty, and librarians through research, instruction, and collaboration. Her educational background includes a PhD in Higher Education with an Interdisciplinary Minor in Applied Statistics, a Master of Library and Information Science, and a Bachelor of Science in Psychology with a minor in Philosophy. Dr. Kelly's research focuses on promoting data science and statistical competencies in higher education, interdisciplinary research collaborations, and understanding students’ perceptions of STEM majors and careers. She is passionate about improving quantitative research skills and supporting learners through innovative pedagogical approaches. Her recent publications and presentations highlight a strong trend in statistical education, research data services, information literacy, and academic librarianship. She emphasizes accessible data science training, the use of tools like R and Zotero, and the importance of aligning library services with student needs. IRDL Scholar (2016) IRDL Mentor (2018–2020) ACRL Immersion Program (Teacher Track, 2010; Assessment Track, 2012) Excellence in Assessment Award, Westmont College (2012) People’s Choice Award, EBLIP 2017 Dr. Kelly actively mentors students and collaborates across disciplines. She has led workshops and developed programming such as an R workshop series for graduate students. Her work is supported by professional development grants and institutional programs like IRDL. She is a Certified Instructor for The Carpentries, reflecting her commitment to open, reproducible research practices. She contributes to library-led research through projects on student research challenges, faculty-librarian collaboration, and service loads in academic librarianship. Her work helps shape library instruction, research support services, and data science initiatives at the institutional and national levels.

• Data Stream Mining
• Data Cleaning
• Machine Learning Explainability
• +۷ مورد دیگر

Dr. A.A.A. Qahtan is an Assistant Professor in the Data Intensive Systems research group within the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His academic appointment focuses on advancing research and education in data-intensive computing with particular expertise in data stream mining, data cleaning, and explainability of machine learning techniques. Dr. Qahtan completed his PhD studies at KAUST (King Abdullah University of Science and Technology) under the supervision of Xiangliang Zhang and Soujin Wang. Prior to joining Utrecht University, he worked as a postdoc at QCRI (Qatar Computing Research Institute) where he developed pattern functional dependencies (PFDs) for data cleaning. His research spans several critical areas in data science: Data Stream Mining and Real-time Processing Data Cleaning and Quality Assessment Pattern Recognition and Functional Dependencies Outlier and Anomaly Detection Concept Drift Detection in Streaming Data Fairness in Machine Learning Systems Missing Data Imputation Techniques Dr. Qahtan's publication record demonstrates consistent contributions to top-tier venues including PVLDB, KDD, ICDE, and SIGMOD. His recent work shows a progression from foundational data cleaning techniques to advanced applications in categorical data analysis, fairness in AI, and cryptocurrency market analysis. His research bridges theoretical foundations with practical applications across multiple domains. Dr. Qahtan actively contributes to academic education at Utrecht University, teaching courses including Data Analytics, Data Science and Society, Data Wrangling and Data Analysis, and Databases across multiple academic years from 2019 to 2024.

• Mechanical Engineering
• Manufacturing Engineering
• Finite Element Analysis
• +۵ مورد دیگر

Ambarish Kulkarni is an Associate Professor at Swinburne University of Technology's School of Engineering, specializing in mechanical design, virtual/augmented reality (VR/AR), and electric vehicle (EV) technologies. His research focuses on product design, EV drivetrains, battery packaging, medical devices, and bushfire shelter design. He holds a PhD from Swinburne, a Master's from Queensland University of Technology, and a Bachelor's from the Indian Institute of Industrial Engineering. Research interests span finite element analysis, bio-mechanical modeling, and Industry 4.0 applications. Notable projects include Bombardier Tram development, therapeutic sleep systems, and smart manufacturing frameworks. He has supervised over 20 HDR students on topics ranging from predictive maintenance to extended reality in education. Awards include the Teaching Excellence Award (2016) and Premier's Design Awards for medical and safety innovations. He is a Fellow of the Institution of Engineers (Australia) and actively involved in professional bodies like SAE International. Grants funded projects on hydrogen generators, smart manufacturing systems, and graphene supercapacitors. His work bridges academia and industry through collaborations with firms like Bambach Wires and iMOVE Australia.

Michael Lyuمشاهده پروفایل

استاد

• Software Engineering
• Cloud Computing
• AIOps
• +۵ مورد دیگر

Michael Lyu is a Professor at The Chinese University of Hong Kong specializing in software engineering with a focus on cloud reliability, AIOps, and log analysis. His research bridges the gap between theoretical advances and practical industrial applications in large-scale cloud systems. His research interests span Software Engineering , Cloud Computing Reliability , AIOps , and Log Analysis . Dr. Lyu's work addresses critical challenges in modern cloud operations, including failure diagnosis, anomaly detection, and reliability engineering. His recent research has pivoted toward leveraging large language models for software engineering tasks, particularly in code generation and log analysis. His publication portfolio demonstrates consistent contributions to major software engineering conferences (ASE, ICSE, ESEC/FSE) from 2018-2025, with a noticeable increase in LLM-related research since 2023. The trend shows a clear evolution from traditional software engineering topics toward AI-driven approaches for cloud operations. ICSE 2021 Keynote: "Reliability-Driven AIOps for Cloud Resilience" ASE 2023: Maat: Performance Metric Anomaly Anticipation for Cloud Services ASE 2024: LILAC: Log Parsing using LLMs with Adaptive Parsing Cache Dr. Lyu actively mentors students, with numerous co-authored publications showing his advisees as first authors. His work receives significant attention in both academic and industrial software engineering communities, addressing practical problems faced by large-scale cloud service providers. His research group appears focused on developing data-driven approaches for improving cloud system reliability through advanced analytics of logs, traces, and KPIs.

Diego Calvaneseمشاهده پروفایل

استاد مهمان

• Artificial Intelligence for Data Management
• Knowledge Representation and Reasoning
• Virtual Knowledge Graphs
• +۵ مورد دیگر

Diego Calvanese is a Visiting Professor at Umeå University's Department of Computing Science and holds a full professorship at the Free University of Bozen-Bolzano, Italy. His research focuses on AI for data management, including virtual knowledge graphs (VKG), ontology-based data access (OBDA), and formal methods like description logics. He is part-time at Umeå, balancing roles with his primary position in Italy. Calvanese has received prestigious awards including the AAAI Classic Paper Award (2021), EurAI Fellow (2015), and ACM Fellow (2019). He supervises three doctoral students at Umeå and has authored over 350 publications, with an h-index of 71. His work emphasizes data integration, geospatial systems, and ethical AI applications. Key Roles: Associate Programme Chair (IJCAI 2025), Programme Chair (IJCAI-ECAI 2026), Head of AI for Data Management Research Group Research Interests: Knowledge representation, graph data management, explainable AI, and telemonitoring systems like reCOVeryaID. His research group develops tools like Ontop, a VKG system enabling seamless data access across heterogeneous sources. Current projects include geospatial data integration and temporal OBDA frameworks. Calvanese has served on over 150 program committees and editorial boards, including Artificial Intelligence and JAIR. His work bridges technical advancements with societal impacts, addressing AI's role in healthcare, climate, and democracy.

• Statistics
• Data Science
• Machine Learning
• +۴ مورد دیگر

Ursula Laa is an Assistant Professor at the Institute of Statistics , University of Natural Resources and Life Sciences, Vienna (BOKU) since 2020. Previously, she was a Postdoctoral Researcher at the Department of Econometrics & Business Statistics and School of Physics & Astronomy , Monash University (2017–2020), and earned her doctorate in particle physics at the University of Grenoble-Alpes , France (2014–2017). Her academic journey began with physics studies at the University of Vienna (2007–2014). Research Interests: She specializes in statistics, with a focus on data visualization, machine learning, computational statistics, and environmental statistics. Her work bridges high-dimensional data analysis with applications in particle physics and environmental studies, particularly in climate change and hydrology. Scientific Contributions: Ursula has actively contributed to projects involving climate change impacts on hydrological systems interactive visualization tools (lionfish, cubble, tourr) statistical modeling for environmental and particle physics data Her recent publications emphasize tour algorithms, projection pursuit, and spatiotemporal indexing, reflecting a blend of data science and domain-specific applications. Awards & Recognition: She has been a keynote speaker at DAGStat 2025 active participant in international conferences (EGU, useR!, Joint Statistical Meetings)

Benjamin S. Baumerمشاهده پروفایل

استاد

• Data Science
• Sports Analytics
• Network Science
• +۵ مورد دیگر

Benjamin S. Baumer is a Professor in the Statistical & Data Sciences program at Smith College , where he has been teaching since 2012. He previously served as the first full-time Statistical Analyst for the New York Mets from 2004 to 2012, pioneering data-driven approaches in baseball operations. Education: Ph.D. in Mathematics, The Graduate Center of the City University of New York (2012) M.A. in Mathematics, University of California, San Diego B.A. in Mathematics, Wesleyan University His research spans multiple domains: Sports Analytics (sabermetrics, player evaluation, game simulation) Data Science Education (curriculum development, reproducible analysis tools) Network Science (graph theory, complex systems) Statistical Computing (R programming, tidyverse workflows) Recent publications highlight: Advancements in reproducible analysis (infer package, 'Fertile' framework) Educational innovations in team-based data science and ethics integration Tools for election data (fec16 package) and collaborative learning (Slack applications) Scientific Recognition: 2016 Contemporary Baseball Analysis Award (SABR) 2019 Waller Education Award (ASA) 2019 Significant Contributor Award (ASA Section on Statistics in Sports) Accredited Professional Statistician™ since 2014 (American Statistical Association) He co-organizes the annual ASA Five College DataFest and contributes to expanding data science pathways for community college students.

Jeannette Anastasia Colyvasمشاهده پروفایل

دانشیار

• Institutional Theory
• Organizational Innovation
• Science Policy
• +۵ مورد دیگر

Jeannette Anastasia Colyvas is an Associate Professor at Northwestern University, holding positions in the School of Education and Social Policy, Department of Sociology (courtesy), and MORS at the Kellogg School of Management. She is also an Executive Board Member of the Northwestern Institute on Complex Systems (NICO), a Faculty Affiliate at the Institute for Policy Research, and former Director of Undergraduate Programs in her school. Her research focuses on organizational dynamics, institutional theory, innovation ecosystems, and public policy interfaces with science. Educational Background: PhD in Sociology from Stanford University (2007), alongside dual MA degrees in Sociology and East Asian Studies. Also holds a Master's in Management Science and Engineering from Stanford (implied through context). Research Interests: Explores how organizations emerge, persist, and transform across sectors, emphasizing institutionalization processes, innovation networks, gender disparities in commercialization, and the governance of academic-industry partnerships. Her work critiques metrics-driven academic practices and examines racialized systems within higher education funding structures. Grant Contributions: Principal Investigator (PI) on multiple funded projects including NSF grants analyzing racialized grantmaking and career impacts of proprietary science. Collaborates internationally on sociotechnical innovation systems and co-leads an EU-funded study on knowledge production co-evolution with European researchers. Award Highlights: Multiple teaching accolades including 2017, 2010, and 2007 Teacher of the Year awards, along with visiting fellowships at the American Bar Foundation (2018, 2022) and a runner-up best paper award (2015). Recognized for bridging interdisciplinary scholarship in science policy and organizational sociology. Labs/Teams: Affiliated with NICO’s complex systems research and the Kellogg-MORS innovation networks. Co-edited a special issue on agency and actorhood in institutional theory (2019). Works closely with collaborators like Vincent Yung and Eleanor Anderson on computational sociology projects.

Dianne Cookمشاهده پروفایل

استاد

• Data Science
• Data Visualization
• Exploratory Data Analysis
• +۵ مورد دیگر

Prof. Dianne Cook is a Professor of Business Analytics at Monash University's Department of Econometrics and Business Statistics, affiliated with Impact Labs. A Fellow of the American Statistical Association and Editor of the Journal of Computational and Graphical Statistics, her research focuses on data visualization, exploratory data analysis, and statistical graphics. Her work bridges statistical inference and visualization, leveraging crowd-sourcing and eye-tracking to validate visual discoveries. Major contributions include software tools like ggobi and cranvas, and methodologies like the 'tour' for high-dimensional data exploration. Current projects include spatio-temporal bushfire visualization and Melbourne pedestrian data analysis. Key collaborations span physics data visualization and multidimensional analysis. Awards include ASA Fellowship. Projects include Accessibility Enhancements for the R Journal and Multivariate Spatio-Temporal Visualization for bushfire risk. Teaching and advising emphasize open-source software and reproducible research. She leads RiskLab projects on econometrics and explores applications in climate change, education, and health. Her work contributes to UN SDGs like Sustainable Cities (Goal 11) and Quality Education (Goal 4).

• Computer Science
• Web Data Management
• Artificial Intelligence
• +۵ مورد دیگر

Pierre Senellart is a Professor in the Computer Science Department at the École Normale Supérieure (ENS), part of Université PSL. He holds academic appointments at CNRS, Inria Paris, and ENS, and leads the Valda team, an Inria project-team. He is deputy director of DI ENS and chairs the PRAIRIE Paris School of AI initiative. His roles include president of section 6 (Computer Science) of the National Committee for Scientific Research and vice-president of the Gilles Kahn PhD Award. Education: ENS alumnus with a PhD (2007) and Habilitation (2012) from Université Paris-Sud and Université Pierre et Marie Curie, respectively. His research focuses on Web data management, including crawling, information extraction, and uncertainty management. He has published extensively in top venues like SIGMOD, VLDB, and the Journal of the ACM. Awards include membership in the Institut Universitaire de France (2020–2025) and the ACM HyperText 2014 Best Paper Award. He has led major projects such as PRAIRIE (600k€) and Dissemin (124k€). Teaching includes courses on data wrangling, databases, and AI. Administrative roles span managing entrance exams, SWERC programming competitions, and steering committees for data management initiatives.

Iñaki Úcarمشاهده پروفایل

استادیار

• Machine Learning
• Computational Social Science
• High-Performance Computing
• +۳ مورد دیگر

Iñaki Úcar is an Assistant Professor at the Department of Statistics, University Carlos III de Madrid, and a Research Fellow at the university's Big Data Institute. He directs the Master in Computational Social Science program and is an advocate for open-source software, particularly contributing to R packages like simmer , units , and quantities . His expertise spans statistical software, high-performance computing, simulation, data visualization, and network analysis. He holds an MEng in Telecommunications Engineering and an MScEng in Communications from Universidad Pública de Navarra (2011-2013), followed by an MScEng and PhD in Telematic Engineering from University Carlos III de Madrid (2014-2018). His research focuses on ML interpretability, computational social science applications (e.g., misinformation, inequality), and network dynamics. Recent work includes analyzing gender gaps in academic productivity during the pandemic and developing polynomial-based neural network frameworks. His articles highlight interdisciplinary contributions to R tooling, neural network theory, and socio-technical systems analysis. He actively organizes the Statistics Reading Club and NICDA workshops. Despite no listed formal advisees, his open-source contributions and collaborative projects reflect strong academic engagement.