Find Academic Supervisors

• Natural Language Processing
• Multimedia Networking
• Software Defined Systems
• +6 more

Jason Quinlan is a Lecturer in the Department of Computer Science at University College Cork (UCC), Ireland. He holds a PhD in Computer Science and has extensive experience in teaching undergraduate modules such as Introduction to Programming (CS1117), Problem-Solving (CS1022), and Team Software Project (CS3305). His current role includes coordinating the CSIT SOLAS support hub for first-year students. Previously, he served as a Senior Post-Doc Researcher focusing on Intelligent Video Delivery at the Edge for 5G networks with the SFI iVID Project and CONNECT institute. Research interests span Natural Language Processing , Video Streaming Optimization , Software Defined Networking , and 5G Infrastructure . He led the development of frameworks like D-LiTE (DASH performance evaluation) and GoDASH (Go-based HAS framework). His work emphasizes QoE/QoS metrics, adaptive streaming algorithms, and network simulation tools. Key achievements include publishing over 30 peer-reviewed articles (e.g., on DASH streaming, 5G dataset analysis), securing €21,744 in research grants, and supervising 2 PhD, 15 Master’s, and numerous undergraduate students. Collaborations span institutions like UC Riverside (USA) and Université Clermont Auvergne (France). He actively contributes to conferences like ACM Multimedia Systems and IEEE LANMAN. Teaching and outreach activities include the MISL Summer of Code initiative mentoring over 60 students since 2018, and completing certifications in Project Management (PRINCE2, Scrum) and Higher Education Teaching. He has reviewed for top journals including Multimedia Systems Journal and IEEE Transactions on Multimedia, and serves on program committees for ACM MM and NetSoft conferences.

Ramesh SitaramanView profile

Professor

• Networking and Distributed Systems
• Theoretical Computer Science
• Internet-scale Systems
• +5 more

Ramesh Sitaraman is a Distinguished Professor and Associate Dean for Educational Programs and Teaching at the Manning College of Information and Computer Sciences (CICS), University of Massachusetts Amherst. He is a world-renowned expert in Internet-scale distributed systems and content delivery networks (CDNs). His contributions include pioneering the first major CDN (Akamai) and foundational work in edge computing and video streaming technologies. He holds part-time roles as Akamai’s Chief Consulting Scientist and directs the Laboratory for Internet-Scale Distributed Systems (LIDS). Education: PhD in Computer Science, Princeton University B.Tech. in Electrical Engineering, Indian Institute of Technology, Madras Research Interests: His work spans distributed systems, networking, energy efficiency, and sustainable computing. Key areas include: CDNs and edge computing architectures Adaptive bitrate algorithms (e.g., BOLA) Energy-aware systems and decarbonization Algorithmic design for scalability Awards and Honors: AAAS Fellow (2023) IEEE William R. Bennett Prize (2023) ACM SIGCOMM Networking Systems Award (2018) ACM and IEEE Fellowships (2019) University Distinguished Teaching Award (2020) Teaching and Leadership: As founding director of the Informatics Program, he bridges computer science with interdisciplinary fields. He has received top teaching awards and advocates for curiosity-driven research and broad undergraduate education. Labs and Initiatives: Directs LIDS and collaborates with the Center for Data Science. Research focuses on sustainable internet infrastructure and next-generation distributed systems.

Jessica ChenView profile

Associate Professor

• Multimedia (online video streaming)
• Data analysis and machine learning
• Formal specification and verification
• +2 more

Dr. Jessica Chen is an Associate Professor at the School of Computer Science, University of Windsor. She holds a BSc from Beijing University and a PhD from Scuola Normale Superiore di Pisa. Her research focuses on multimedia systems, data analysis, formal verification, communication protocols, and software design. Her work includes optimizing adaptive video streaming algorithms, studying network conditions, and formal methods for concurrency. Publications span ACM journals and SIGCOMM conferences. She advises students in co-op programs and contributes to experiential education initiatives. Dr. Chen’s office is located in LT 8100, and she can be reached at xjchen@uwindsor.ca. Her research bridges theoretical computer science with practical applications in streaming and network optimization.

Bing WangView profile

Professor

• Computer Networking
• Quantum Communications
• Network Security
• +4 more

Professor Bing Wang is a faculty member in the Computer Science & Engineering Department at the University of Connecticut. He leads the Computer Networking Research Group, focusing on computer networking, ubiquitous computing, multimedia streaming, network security, and quantum communications. His research integrates machine learning for depression treatment prediction using smartphone data and explores secure quantum key distribution systems. Research interests span quantum networks, adaptive bitrate streaming optimization, wireless network routing strategies, and leveraging mobile sensory data for mental health diagnostics. His work addresses challenges in network security (e.g., BGP hijacking defenses), energy-efficient streaming, and resilient microgrid systems through programmable networks. Publications emphasize cross-disciplinary applications of networking principles, including depression symptom prediction via smartphone sensors and quantum repeater security. Notable contributions include frameworks like C2 for ABR streaming and BGP-Isec for routing security. His group actively develops methodologies for optimizing network performance while balancing resource usage and user experience.

• Computer Networks
• Internet Video Distribution
• Network Synthesis
• +6 more

Sanjay Rao is a Professor in the School of Electrical and Computer Engineering at Purdue University, leading the Internet Systems Laboratory. His research focuses on network synthesis/design/verification and Internet video distribution. He holds a B.Tech from IIT Madras and a Ph.D from Carnegie Mellon University. He has held visiting roles at Google, AT&T Research, and Princeton University. Research & Awards: His work includes groundbreaking contributions like End System Multicast (winner of the ACM SIGMETRICS Test of Time Award), Oboe (ABR auto-tuning), and Veritas (causal video streaming analysis). He received the NSF CAREER Award (2010) and is an ACM Distinguished Member (2021). Teaching: Courses include Computer Networking (ECE 463), Object-Oriented Programming (ECE 39595), and Computer Network Systems (ECE 595). Service: Chair, ACM Sigcomm Doctoral Dissertation Award Committee (2022); Associate Editor, IEEE/ACM Transactions on Networking (2016–2020). Labs & Teams: Directs the Internet Systems Lab (ISL) with active projects on video streaming, network resilience, and intent-based design. Graduate students collaborate on these initiatives.

Keith WinsteinView profile

Associate Professor

• Networked Systems
• Computer Networks
• Data Compression
• +6 more

Keith Winstein is an Associate Professor of Computer Science at Stanford University, with a courtesy appointment in Electrical Engineering. His research focuses on creating innovative networked systems, particularly in communication, compression, and computing. Notable projects include Mosh (an interactive remote shell for mobile clients), Puffer (a video-streaming platform), Lepton (a compression tool), Mahimahi (network emulators), and the gg framework for distributed computing. He has received prestigious awards such as the SIGCOMM Rising Star Award, Sloan Research Fellowship, and NSF CAREER Award. Winstein's academic journey includes undergraduate and graduate studies at MIT. Before academia, he worked at The Wall Street Journal as a reporter and at Ksplice (now part of Oracle), where he held roles in product management and business development. His research spans network protocols, video streaming optimization, cloud computing, and machine learning applications in networking. His work emphasizes practical systems that bridge theoretical concepts with real-world implementation. Recent projects explore computation-centric networking, in-network performance enhancements, and low-latency video streaming. He advocates for reproducible experiments through tools like Mahimahi and has contributed to open-source software widely used in academia and industry. Key Projects: Mosh, Puffer, Lepton, Mahimahi, gg Awards: SIGCOMM Rising Star Award, Sloan Fellowship, NSF CAREER Expertise: Networked Systems, Compression Algorithms, Distributed Computing

Francis YanView profile

Assistant Professor

• Networked Systems
• Computer Networks
• Machine Learning for Systems
• +3 more

Francis Yan is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Siebel School of Computing and Data Science and the Electrical & Computer Engineering department. He leads the Illinois Networked Systems and AI (NSAI) Lab, focusing on integrating practical machine learning into networked systems. Previously, he was a Senior Researcher at Microsoft Research Redmond, working on systems and networking research. His work emphasizes deployable ML solutions, validated through large-scale platforms like Puffer and Pantheon. Education: Ph.D. in Computer Science (Stanford University, 2020); B.S. in Computer Science (Tsinghua University, 2015) and B.A. in Economics (Tsinghua University, 2015). Research Interests: Networked systems, machine learning for systems and networking, intelligent video systems, and cloud resource management. His lab develops frameworks like DeDe, Mowgli, and Autothrottle to enhance system performance and robustness. Awards: USENIX NSDI Outstanding Paper Award (2024), Applied Networking Research Prize (2021), and USENIX ATC Best Paper Award (2018). He serves on program committees for NSDI, SIGCOMM, and ACM workshops. Grants & Service: Co-chair of SIGCOMM 2024 Artifact Evaluation. Active in open-source projects and research platforms supporting reproducible networking experiments. Labs/Teams: Directs the NSAI Lab, collaborating on projects such as Puffer (400k-user video service), Teal (WAN optimization), and Genet (curriculum learning for networking).

• Computer Vision
• Networking Protocols
• Machine Learning
• +3 more

Maria Ito is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science, focusing on interdisciplinary research at the intersection of computer vision, networking protocols, and adaptive media streaming. Her work emphasizes video stabilization techniques, network performance optimization, and machine learning-driven solutions for real-time media delivery systems. Her research interests span video processing, deep learning applications in computer vision, and the design of efficient adaptive streaming protocols. Notable contributions include frameworks for distortion-free video stabilization and performance evaluations of QUIC vs. TCP protocols. Publications highlight trends in combining control theory with adaptive streaming systems and developing benchmark datasets for deep learning models. While no specific awards or grants are listed, her work demonstrates significant contributions to both theoretical and applied aspects of media streaming technologies. Advising and lab affiliations are not explicitly detailed in the provided materials.