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Shiyong LuView profile

Professor

• Big Data Workflows
• Cloud Computing
• Scientific Workflows
• +3 more

Dr. Shiyong Lu is Professor of Computer Science at Wayne State University and Director of the Big Data Research Laboratory. He holds a PhD from State University of New York at Stony Brook and has published over 140 papers in venues including IEEE Transactions on Services Computing and IEEE Transactions on Knowledge and Data Engineering. Research focuses on: Architecture of big data workflow systems Secure execution in cloud environments Optimization algorithms for distributed computing Provenance management for scientific workflows Recent work demonstrates strong emphasis on secure workflow execution, with 80% of last 15 publications addressing trusted computing environments. Leads NSF-funded DATAVIEW project for cloud-based big data analytics. Awarded IEEE TCSVC Outstanding Service Award (2023). Teaches core courses in Database Management Systems and Data Modeling. Supervised 17 PhD graduates, 10 of whom hold faculty positions. Editorial board member for International Journal of Big Data and International Journal of Big Data Intelligence.

Vic MattaView profile

Professor

• Consumer Behavior
• Pedagogy
• Technology Adoption
• +3 more

Vic Matta is a Professor of Analytics & Information Systems at Ohio University's College of Business. He holds a Ph.D. in Industrial Systems (2008) and an M.S. from Ohio University. His research focuses on consumer behavior, pedagogy, user motivation, and technology adoption. He teaches courses in business analytics, information systems, and strategic IT use, and has received multiple teaching awards including the COB Faculty Excellence in Professional Activity & Service (2020) and Senior Teaching Fellow at the Bruning Academy. His professional experience includes IT project management, systems administration, and database administration. Dr. Matta's research spans educational technology innovations, such as plagiarism prevention in Excel assignments and automated grading systems. He has published extensively on topics like team formation algorithms, digital detox behaviors, and RFID adoption in supply chains. He serves as Industry Editor and Co-Editor for the Journal of IT & Case Application Research. His work emphasizes practical applications of technology in education and industry, with a focus on improving learning outcomes and organizational effectiveness.

Samuel Clowes HunekeView profile

Assistant Professor

• Modern European History
• History of Sexuality
• Legal History
• +4 more

Samuel Clowes Huneke is Assistant Professor of History at George Mason University, specializing in modern European history with particular focus on Germany. His research intersects queer theory, legal history, and Cold War studies, examining how state structures interact with marginalized communities. PhD from Stanford University (2019) MSc in Applicable Mathematics (LSE, 2012) BA in German and Mathematics (Amherst College, 2011) Major publications include States of Liberation (2022), analyzing gay experiences in Cold War Germany, and A Queer Theory of the State (2023), exploring queer theory's engagement with state structures. His co-edited volume Reimagining Citizenship (2025) addresses postwar European identity politics. Recent work examines both GDR and Weimar-era queer histories, with a forthcoming monograph I Will Not Abandon You (2026) on Nazi-era queer women. He directs the East German Poster Database project and contributes to public discourse through media outlets like CNN, Washington Post, and Boston Review. David Barclay Book Prize (2023) Charles E. Smith Award (2022) Waterloo Centre Finalist Active in digital humanities initiatives and Cold War oral history projects, Huneke bridges mathematical precision with historical analysis. His teaching includes courses on Nazi Germany, Cold War Europe, and global sexuality history.

Chaitanya Krishna SambharaView profile

Assistant Professor

• Business Value of IT
• IT-Enabled Process Innovation
• Enterprise Systems Risks
• +3 more

Chaitanya 'Chai' Sambhara is an Assistant Professor in the Information Systems and Operations Management department at the University of Texas at Arlington's College of Business. He researches how enterprise systems and complementary capabilities improve business processes and mitigate risks. His work integrates information systems, accounting, and operations perspectives. Education includes: Ph.D. in Information Systems, Georgia State University (2015) M.S. in Computer Science, Georgia State University (2009) B.Tech in Electronics Engineering, Biju Patnaik University (2005) Research examines IT-enabled business process innovation, enterprise system configurations, blockchain applications, and cybersecurity risks. Current projects investigate paradoxical effects of IT use on internal controls and information risk management. Publications demonstrate consistent focus on enterprise system risks, reverse auction challenges, and blockchain implications. Recent work explores unintended consequences of IT implementation and configuration strategies. Awards include: Best Conference Paper Nominee, ICIS (2018, 2016) Teaching Excellence Award, Georgia State University (2013) BNSF Spirit Award (2008) Best Speaker Award, India (2001) Teaching covers Business Programming, Database Management, IT Strategy, and Blockchain Technology. Service includes editorial roles for IS conferences and journals.

• Cloud Computing
• Edge Computing
• Task Scheduling
• +5 more

Dr. Zhijun Wang is an Associate Professor of Research in the Department of Computer Science and Engineering at The University of Texas at Arlington. His work focuses on cloud and edge computing, resource management, task scheduling, and network traffic control. He holds a PhD in Computer Science from UTA (2005), an MS in Electrical Engineering from Penn State (2001), and a BS in Physics from Huazhong University of Science & Technology (1992). His research explores internet traffic control mechanisms , cloud resource allocation , and microservices architecture . Recent projects include NSF-funded work on tail latency guarantees for microservices ($600k grant, 2022-2026) and industry collaborations with Alibaba on datacenter transport protocols ($148k grant, 2018-2021). Publications highlight innovations like CurTail (tail latency scheduling) and Tailguard (data-intensive task scheduling). His work often combines price-aware protocols with distributed scheduling algorithms . Teaching focuses on foundational topics: discrete math, computer networks, and cloud computing. Grants: NSF (2022), Alibaba (2018) Key Patents: Tail Latency Scheduling (2023), Database Target Enforcement (2024) Recent Publications: 15+ papers since 2020 in top-tier venues

Chenjuan GuoView profile

Associate Professor

• Machine Learning
• Data Engineering
• Spatio-temporal Data Analysis
• +4 more

Chenjuan Guo is an Associate Professor at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. She is affiliated with the Data Engineering, Science and Systems group and the AI for the People initiative, and is part of the Daisy - Center for Data-intensive Systems. Her research focuses on machine learning, data engineering, spatio-temporal data analysis, and time series forecasting. Key projects include the Villum Foundation-funded 'Explainable AI for Complex Microbial Community Interactions and Predictions' (2021-2024) and the Astra project on time series analytics in spatial networks (2018-2021). Her research interests span representation learning, autoencoders, path representation, outlier detection, trajectory data analysis, and time series modeling. She has supervised 3 PhD students and contributed to over 60 publications, with a recent emphasis on transformer-based forecasting, neural architecture search, and continuous learning frameworks for spatio-temporal data. Her work bridges theoretical advancements with practical applications in environmental science, cloud computing, and urban mobility systems. Key achievements include developing frameworks like AutoCTS++ for automated time series forecasting and LightGTS for lightweight models. She actively collaborates internationally, contributing to conferences like ECML PKDD and CVPR. Her research is supported by grants from the Villum Foundation and other institutions.

Jennifer GerkeView profile

Researcher

• Organic Chemistry
• Microbial Secondary Metabolism
• Fungal Development
• +3 more

Dr. Jennifer Gerke is a Researcher at the Institute of Organic Chemistry within the Faculty of Natural Sciences at Leibniz University Hannover. Her work focuses on microbial secondary metabolism, fungal development, and synthetic biology. She leads research in the Synthetic Biology Group and contributes to interdisciplinary projects in organic chemistry and molecular biology. Her research explores mechanisms of fungal adaptation, including secondary metabolite regulation, transcription factor networks, and plant-microbe interactions. Recent studies highlight novel biosynthetic pathways, pathogen virulence mechanisms, and applications in biotechnology such as fragrance production in yeast. Key contributions include discoveries in tropolone biosynthesis, velvet domain protein functions, and the role of Hülle cells in fungal survival. Her work bridges fundamental research with applied biotechnology, addressing topics from antibiotic discovery to metabolic engineering.

Gea RahmanView profile

Lecturer

• Data Science
• Machine Learning
• Ensemble Learning
• +7 more

Dr. Gea Rahman is a Lecturer in Computing at Charles Sturt University (CSU), specializing in data science and machine learning. He holds a PhD in Computer Science from CSU, and MSc/BSc degrees from Rajshahi University, Bangladesh, where he was awarded a Gold Medal for academic excellence. With over 20 years of teaching experience, he previously served as Professor and Programme Director at Bangladesh Agricultural University. Education: PhD in Computer Science (Data Science/ML), Charles Sturt University (2011-2015) MSc in Computer Science & Engineering, Rajshahi University (2002-2003) BSc (Hons) in Computer Science & Technology, Rajshahi University (1998-2002) Research Interests: Data science applications in agriculture, healthcare, and environmental monitoring Machine learning techniques including ensemble/deep learning, transfer learning, and incremental learning Data preprocessing methods (missing value imputation, outlier analysis) AI ethics and healthcare consent strategies Awards: Global Research Impact Recognition Award 2020 Best Researcher Award Gold Medal for Academic Excellence (2003) Advising & Grants: Principal supervisor for multiple postgraduate students Recipient of grants including: Ai-Enabled Segmentation of Brain MRI (2024) Adaptive Federated Learning Framework (2024) Unusual Behaviour Detection in Aged Care (2023) He is part of the Data Mining Research Group (DaMRG) and actively contributes to conferences/journals as a reviewer and editorial board member.

Carson LeungView profile

Professor

• Big Data Science
• Data Mining
• Machine Learning
• +5 more

Dr. Carson Kai-Sang Leung is a Full Professor in Computer Science at the University of Manitoba's Faculty of Science. He founded and directs the Database & Data Mining Lab. His research focuses on big data science, data mining, machine learning, health informatics, and visual analytics. He holds SMIEEE and SMACM fellowships, reflecting his contributions to the field. Education: B.Sc., M.Sc., and Ph.D. from the University of British Columbia (UBC). Research interests include human-centered exploratory data mining, image databases, and scalable algorithms. He emphasizes user-driven constraints in mining processes and has developed techniques like the segment support map and OSSM for optimized frequency counting. His work on subimage queries in large image databases addresses real-world challenges in visual data retrieval. Publications span data mining, healthcare analytics, and transportation systems. His lab collaborates on projects like visual analytics for motor vehicle accidents and environmental data science for smart cities. He is affiliated with institutions such as the Institute of Industrial Mathematical Sciences (IIMS) and TRLabs. Key awards: Senior Member of IEEE (SMIEEE) and Senior Member of ACM (SMACM).

Michael A. BenderView profile

Professor

• Algorithms
• Data Structures
• Cache and I/O-Efficient Computing
• +7 more

Michael A. Bender is the John L. Hennessy Chaired Professor of Computer Science at Stony Brook University. His work spans both theoretical and applied domains in algorithms, data structures, and storage systems. He co-founded Tokutek, Inc., a database company acquired by Percona in 2015, and has held visiting positions at MIT and King's College London. Education: PhD in Computer Science from Harvard University (1998), DEA and Magistère in Computer Science from École Normale Supérieure de Lyon (1993), BA in Applied Mathematics from Harvard (1992). His research focuses on cache-oblivious algorithms , I/O-efficient computing , parallel systems , and scheduling . His work addresses scalability in large-scale data management and computational geometry, with applications in bioinformatics and robotics. The 15 most recent publications highlight advancements in list labeling , hash table design , graph algorithms , and memory optimization . These papers reflect his commitment to solving practical problems with rigorous algorithmic approaches. Awards include: PODS Best Paper Award (2024) ASPLOS Distinguished Paper Award (2023) USENIX FAST Best Paper Award (2016) Chancellor's Award for Excellence in Teaching (2015) R&D 100 Award (2006) He has led the Stony Brook Computer Science Honors Program and advised graduate students in algorithmic research. His grants portfolio includes 39 funded projects, emphasizing algorithm engineering and systems optimization.

Eleni EliaView profile

Senior Lecturer

• Biostatistics
• Artificial Intelligence
• Advanced Computational Methods
• +3 more

Eleni Elia is a Senior Lecturer in Statistics at Oxford Brookes University's School of Engineering, Computing and Mathematics, leveraging expertise from research positions at Harvard University, Boston Children's Hospital, and the University of Leicester to develop AI-driven healthcare solutions that improve patient outcomes through advanced computational methods and biostatistics. Her academic foundation includes: BSc (Hons) in Mathematics MSc in Statistics PhD in Statistics Dr. Elia's research bridges biostatistics and artificial intelligence with clinical applications, focusing on prognosis research and healthcare data analysis . She pioneers computational approaches to transform patient care, particularly in pediatric cardiology and epidemic surveillance, using real-world data to address critical medical challenges through interdisciplinary collaboration. Analysis of her 14 publications (2015-2024) reveals consistent application of statistical modeling to medical domains, with growing emphasis on AI integration in cardiology, biomarker analysis, and epidemic prediction. Her work demonstrates methodological rigor while addressing urgent healthcare needs through pediatric applications and refugee camp disease surveillance. Her scientific recognition includes: Enterprise Champion Award 2022/23 for 'Gamifying entrepreneurship education for mathematics students' Executive Coaching Award 23/24 as a 'Research Leader' Mardia Prize from the Royal Statistical Society for refugee-focused statistical workshops Dr. Elia actively mentors PhD candidates and leads significant research initiatives including 'Infectious disease surveillance in refugee camps', 'Biomechanics interactive database development', and 'Epidemic surveillance systems', while securing substantial university funding for healthcare AI innovation and entrepreneurship programs. She contributes to cutting-edge research through the Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute and Visual Artificial Intelligence Laboratory (VAIL), driving interdisciplinary projects that connect statistical theory with life-saving medical applications.

Yifan WangView profile

Assistant Professor

• Database Systems
• High-Dimensional Data
• Vector Databases
• +2 more

Yifan Wang is Assistant Professor of Computer Science at University of Hawaii at Manoa, specializing in database systems and information retrieval. His research develops efficient algorithms for high-dimensional data processing. Educational background: PhD in Computer Science from University of Florida (2024) Master's in Computer Science from University of Florida BS from Huazhong University of Science and Technology Research innovations include: Vector database indexing techniques Machine learning-enhanced query optimization Scalable similarity search algorithms Publications focus on improving efficiency of high-dimensional data operations, with applications in large-scale information retrieval. Wang serves on program committees for major database conferences including VLDB and ICDE.

• Digital Forensics
• Criminal Investigation
• Computer Science
• +4 more

Matthew James Sorell is a Senior Lecturer at the University of Adelaide since October 2002. He holds a Doctor's Degree from George Mason University (1998), supervised by Geoff Orsak, focusing on 'Robust Importance Sampling'. His research spans Digital Forensics, Criminal Investigation, and Computer Science, with emphasis on image/video analysis, surveillance technologies, and data provenance. Education: PhD in Computer Sciences, George Mason University, 1998 Research Interests: His work focuses on digital evidence analysis, forensic science applications, and the development of methodologies for authenticating digital media. Key areas include photo-sensor fingerprinting, mobile forensics (e.g., Snapchat surveillance protocols), and health data provenance analysis (Apple Health databases). He has contributed to advancing video motion detection, smart camera systems, and forensic photography audit logs. Publications: His recent work emphasizes digital forensics in health data and social media, with notable contributions to Forensic Science International Digital Investigation and IEEE conferences. Earlier research includes biologically-inspired video enhancement and JPEG quantization analysis. Professional Roles: From 2017-2022, he worked as a Digital Investigations Consultant (part-time). He has edited conference proceedings (e.g., e-Forensics 2009) and contributed to interdisciplinary studies like food advertising patterns on Australian TV. Labs/Teams: Collaborates with teams in digital crime forensics, surveillance systems, and multimedia forensics, reflected in his conference organizing roles and journal editorships.

Maribeth PriceView profile

Professor

• Geographic Information Systems
• Remote Sensing
• Spatial Analysis
• +3 more

Maribeth Price serves as Dean of Graduate Education and Professor at South Dakota Mines, with faculty tenure since 1995. Previously chaired Geology and Geological Engineering (2006-2011). Holds M.S./Ph.D. from Kharkov State University (Ukraine) and D.Sc. from Donetsk Physics & Technology Institute. Research expertise spans geospatial technologies , planetary remote sensing, and spatial database design. Authored instructional texts on GIS applications and taught professional workshops for over 1,300 participants. Planetary science contributions include Magellan Mission analysis of Venusian tectonics. Curates Maps at Museum of Geology and promotes online pedagogy as Quality Matters certified reviewer. Interdisciplinary collaborations include hydrogeology, forestry, carbon sequestration, and lightning pattern analysis.

• Cyber Physical Systems
• Risk/Reliability Analysis
• Deep Learning
• +3 more

Dr. Saptarshi Sengupta is an Assistant Professor in the Department of Computer Science at San José State University (SJSU), leading the Machine Intelligence and Complex Systems (MICoSys) Lab. He advises the ACM student club at SJSU and holds a 'Alien of Extraordinary Ability' visa (Einstein Visa) from USCIS. His work focuses on resilient cyber-physical systems, risk analysis, and deep learning applications in healthcare and industrial systems. Education: Ph.D. in Electrical Engineering, Vanderbilt University M.S. in Electrical Engineering, Vanderbilt University B.Tech. in Electronics & Communication Engineering, West Bengal University of Technology Research Interests: Cyber-Physical Systems Security Healthcare AI for Cancer and Chronic Disease Prediction Battery Prognostics and Energy Systems Machine Learning for Complex Systems Analysis Key Achievements: Dr. T.M.A. Pai Gold Medal Award for Healthcare AI contributions Recipient of multiple best paper awards at international conferences Author of over 30 peer-reviewed publications Labs & Teams: Leads the MICoSys Lab, developing AI solutions for healthcare diagnostics, industrial prognostics, and smart infrastructure systems. Collaborations include interdisciplinary projects with biomedical and engineering domains.