جست‌وجوی استاد و پژوهشگر

Jian Taoمشاهده پروفایل

استادیار

• Digital twin
• Machine learning
• Numerical modeling
• +۶ مورد دیگر

Dr. Jian Tao serves as Assistant Professor in the Section of Visual Computing & Computational Media at Texas A&M University's College of Performance, Visualization & Fine Arts, with courtesy appointments in Electrical & Computer Engineering, Nuclear Engineering, and Multidisciplinary Engineering departments. He directs the Digital Twin Lab and acts as Assistant Director for Project Development at the Texas A&M Institute of Data Science. His educational background includes: Ph.D. in Physics (Computational Astrophysics) from Washington University in St. Louis (2008) B.S. in Space Physics from University of Science and Technology of China (2000) Dr. Tao's research integrates digital twin technologies with machine learning, numerical modeling, and distributed computing to advance data analytics and visualization systems. His work focuses on practical implementations in high performance computing environments, particularly for disaster management simulations and autonomous vehicle systems, while emphasizing workflow optimization and remote sensing applications. He leads Department of Commerce-funded digital twin development for Disaster City and advises the Texas A&M 12th Unmanned Team in the SAE/GM AutoDrive Challenge. As an NVIDIA DLI University Ambassador and SPEC CPU 2017 contributor, he drives computational benchmarking initiatives while co-chairing the IEEE Public Safety Technology Initiative Testbed Committee. Dr. Tao's leadership extends to the Digital Twin Lab where he oversees cross-disciplinary projects connecting visualization technologies with engineering applications, including the annual Texas A&M Digital Twin Workshop.

• Wireless and Mobile Networks
• Transport Layer Protocols
• Internet of Things
• +۳ مورد دیگر

Ilknur Aydin serves as a Full Professor in the Computer Systems Department at Farmingdale State College, State University of New York. Holding a Ph.D. and M.S. in Computer Science from the University of Delaware alongside a B.S. in Computer Engineering from Marmara University, she brings extensive industry experience from her software engineering role in Istanbul and prior academic appointments at SUNY Plattsburgh. Education: PhD in Computer Science, University of Delaware, 2010 MSc in Computer Science, University of Delaware, 2002 BSc in Computer Engineering, Marmara University, 1999 Research Focus: Dr. Aydin pioneers work in wireless/mobile networks with specialized expertise in transport layer protocols (SCTP, multihoming, congestion control) and network coding. Her development of the publicly available SCTP module for QualNet simulator underpins experimental IoT research using Arduino/Raspberry Pi platforms. She actively leverages national cyberinfrastructure testbeds like CloudLab and FABRIC while championing computer science education reform and diversity initiatives through the Women in Computing club. Academic Leadership: As long-time faculty co-advisor for FSC's Women in Computing club, she mentors undergraduate and high school students in IoT projects while teaching core courses spanning programming (Java/C++/C#), networking, database systems, and software engineering. Her outreach specifically targets underrepresented groups in computing fields. Research Infrastructure: Current projects utilize distributed testbeds including CloudLab and FABRIC for large-scale networking experimentation, reflecting her commitment to accessible cyberinfrastructure in academic research.

Babatunde Olubandoمشاهده پروفایل

استاد مدعو

• Industrial Engineering
• Systems Engineering
• Software Engineering
• +۴ مورد دیگر

Dr. Babatunde Olubando serves as an Adjunct Faculty member and Technical Support Specialist in the Department of Industrial and Systems Engineering at Morgan State University's Clarence M. Mitchell, Jr. School of Engineering. Based in Mitchell Engineering Building (Room 215) and Schaefer Engineering Building (Room 310), he leverages dual expertise in academic instruction and technical systems administration to support engineering education and research initiatives. His educational credentials include: Doctor of Engineering in Industrial Engineering, Software and Systems Engineering (Morgan State University, 2011) Master of Engineering in Industrial Engineering (Morgan State University, 2006) Bachelor of Science in Industrial Engineering, Manufacturing (Morgan State University, 2004) Dr. Olubando's research integrates industrial engineering principles with practical technology solutions, focusing on supply chain testbeds, RFID middleware, and web-enabled systems. His work emphasizes accessible tools for manufacturing education and small business applications, combining data analysis with software engineering to solve real-world operational challenges. Fluency in Yoruba further supports cross-cultural research collaboration. Recent publications demonstrate a clear shift toward applied technology development, with 2023 works concentrating on low-cost RFID systems and university-industry supply chain testbeds. This builds upon his foundational 2009 research in sociological modeling frameworks, reflecting an evolving trajectory from theoretical systems modeling to tangible engineering implementations. Professional recognition includes: Certified Six Sigma Green Belt (CSSGB) His technical capabilities span object-oriented analysis, web design (HTML/CSS/WordPress), and social media systems, enabling effective translation of complex engineering concepts into educational and operational tools. Current work focuses on expanding web-enabled supply chain applications through university partnerships.

Stephan Doerfelمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Professor Stephan Doerfel holds a professorship for Data Science at Kiel University of Applied Sciences since 2021, where he is assigned to the Department of Media and serves as head of the Institute for Data Science (IfDS). He teaches Mathematics and Multivariate Statistics, Machine and Deep Learning modules in the Master's Program Data Science (MADS). His primary research interests span Data Science, Machine Learning, and Deep Learning, with significant contributions to recommender systems, social bookmarking systems (particularly BibSonomy), formal concept analysis, and publication analysis. His recent work shows a shift toward applied industrial problems, especially in injection molding quality prediction and agricultural technology. Analysis of his publication trends reveals an evolution from foundational work in formal concept analysis and social tagging systems (2010-2016) to more applied machine learning research in manufacturing and agriculture (2020-2024). His work consistently bridges theoretical computer science with practical applications across diverse domains. Professor Doerfel has been actively involved in numerous academic service roles including peer reviewing for major journals and serving on program committees for prestigious conferences including ECML/PKDD, WebSci, and ICFCA. His extensive conference participation spans over a decade with consistent contributions to the machine learning and data science community. He has supervised various research projects including AVAPS (focusing on injection molding data), PUMA (Academic Publication Management), and Info 2.0 (Informational self-determination in Web 2.0). As a core contributor to the BibSonomy open-source project, he has helped create a scholarly social bookmarking system that serves as a testbed for recommendation algorithms and user behavior studies.