Dr. Abdelhak Bentaleb is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University and founder/director of the IN2GM Lab. His research focuses on optimizing networked multimedia systems using machine learning, with emphasis on video streaming, edge computing, and 5G/6G networks. He holds a PhD from the National University of Singapore (awarded SIGMM and DASH-IF Best Thesis prizes) and completed a postdoctoral fellowship there. Education: PhD in Computer Science, National University of Singapore (2019) Postdoctoral Research Fellowship, National University of Singapore (2019-2022) Research Interests: AI-driven video streaming optimization, low-latency media delivery, network protocols, immersive media technologies, and IoT systems. Current projects explore end-to-end AI-enabled systems for QoE optimization in video delivery using reinforcement learning and deep learning techniques. Awards: SIGMM Award for Outstanding PhD Thesis DASH Industry Forum Best PhD Dissertation Award Multiple DASH-IF Excellence Awards His work includes over 50 publications in top venues (e.g., ACM MMSys, IEEE INFOCOM, USNIX NSDI) and 3 patents. The IN2GM Lab focuses on applied AI/ML solutions for networked systems challenges.
Phillip B. Gibbons is a Professor in both the Computer Science Department and Electrical & Computer Engineering Department at Carnegie Mellon University. He received his Ph.D. in Computer Science from the University of California at Berkeley in 1989 and has held research positions at AT&T Bell Laboratories, Lucent Bell Laboratories, and Intel Research Pittsburgh before joining CMU's faculty. His research spans parallel computing, distributed systems, databases, computer architecture, and machine learning. Gibbons' work bridges theory and systems, with publications in top-tier conferences including SOSP, OSDI, SIGMOD, VLDB, NeurIPS, and many others across computer science and engineering disciplines. His research has been supported by significant funding from NSF, Intel, and other organizations. Gibbons has made substantial contributions to streaming algorithms, parallel computing frameworks, distributed systems security, and large-scale machine learning systems. His work on data stream algorithms with Alon, Matias, and Szegedy has been particularly influential in the field. He has served in numerous leadership roles including Editor-in-Chief of ACM Transactions on Parallel Computing (2012-2018) and on the editorial boards of Journal of the ACM and IEEE Transactions on Cloud Computing. He has also been active on program committees for major conferences in systems, databases, and theory. IEEE Fellow (2014) - For contributions to parallel computing and databases ACM Fellow (2006) - For contributions to parallel computing, databases, and sensor networks Selected for Oral Presentation at NeurIPS '13 (only 20 selected out of 1420 submissions) Co-winner of the best paper award for NSDI '06 Gibbons has advised numerous students and mentored researchers who have gone on to make significant contributions in academia and industry. His research has been supported by major grants including the $15M Intel Science and Technology Center for Cloud Computing (2011-2015) where he served as Co-PI/Co-Director. He currently leads research projects on write-efficient algorithms, big learning systems, and visual cloud systems. His laboratory work focuses on bridging theoretical computer science with practical systems implementation, particularly in the areas of parallel and distributed computing. Current research directions include adapting algorithms for emerging memory technologies and optimizing machine learning systems for large-scale deployment.
Francis Y. Yan is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC), holding an affiliate appointment in Electrical & Computer Engineering within the Grainger College of Engineering. He leads the Illinois Networked Systems and AI (NSAI) research group, focusing on building intelligent networked systems that are safe, robust, and performance-optimized through practical machine learning integration. Prior to joining UIUC in January 2025, he served as a Senior Researcher at Microsoft Research Redmond under Victor Bahl. His educational background includes: Ph.D. in Computer Science from Stanford University (2020), advised by Keith Winstein and Philip Levis B.S. in Computer Science (Yao Class) and B.A. in Economics from Tsinghua University (2015) Additional undergraduate studies at MIT Yan's research adopts a holistic approach to practical machine learning for networked systems, emphasizing judicious application rather than indiscriminate use. He builds real-world systems and research platforms to lay ML foundations, devises deployable algorithms using domain insights, and validates performance through extensive empirical evidence. His work consistently addresses operator concerns regarding ML deployment—focusing on safety, robustness, generalization, and efficiency—while strategically combining ML with classical networking and systems techniques. Analysis of his 15 most recent publications (2023-2025) reveals dominant themes in resource allocation for microservices (DeDe, Autothrottle), real-time video optimization (Mowgli, GRACE), and LLM-driven network algorithm design. His work bridges theoretical advances with industrial deployment, evidenced by platforms like Puffer (400,000+ users) and OpenNetLab that have become community standards for validating congestion control algorithms. His research has been recognized with top honors: USENIX NSDI Outstanding Paper Award (2024) for Autothrottle APNet Best Paper Award (2022) IRTF Applied Networking Research Prize (2021) USENIX NSDI Community Award (2020) USENIX ATC Best Paper Award (2018) for Pantheon Yan actively recruits master's and undergraduate researchers for his NSAI group, prioritizing self-motivated students for projects in networked systems and AI. His research is supported by industry collaborations (notably Microsoft) and manifests in deployable platforms like Puffer—which has enabled award-winning research at NSDI and SIGCOMM—and OpenNetLab for real-time communications. His work directly impacts production systems including Microsoft Teams and Bing. He founded and directs the Illinois Networked Systems and AI (NSAI) research group, which operates critical infrastructure including Puffer (a live TV service and research platform) and OpenNetLab. These platforms facilitate community-wide validation of novel algorithms, with Puffer alone supporting multiple best-paper awards at top conferences. Current workstreams span cloud resource management (Teal, Autothrottle, DeDe), low-latency video (Puffer, Tambur, Mowgli), and LLM-augmented systems (Nada, Designing Network Algorithms via LLMs).
Laura Brandolini is a Researcher at the Department of Industrial and Information Engineering, University of Pavia, where she collaborates with the Computer Vision and Multimedia Laboratory. She simultaneously serves in the university's ICT area, administering over 200 central servers including backup systems, virtualized services, and hardware infrastructure. Her educational background includes: Master in Computer Engineering (cum laude), University of Pavia, 1998 PhD in Industrial and Information Engineering, University of Pavia, 2013 Dr. Brandolini's research integrates computational topology with practical imaging applications, specializing in Reeb graph computation for 3D mesh segmentation and medical shape analysis. Her work bridges theoretical computer science with clinical neuroscience, particularly in developing topological descriptors for brain structures like the striatum. This interdisciplinary approach enables novel solutions for surface mesh processing in both industrial and medical contexts. Her 2012 publications reveal a consistent focus on computational geometry applied to medical imaging, demonstrating how Reeb graph theory can simplify complex 3D shape analysis. These works established foundational techniques for efficient mesh segmentation with applications in neuroimaging and computer-aided diagnosis systems. She has received the following recognition: Best Student Paper Award at ICPRAM 2012 Dr. Brandolini maintains active industry engagement through her PMP certification (held since 2004) and PMI-NIC involvement, while her dual roles demonstrate unique synergy between academic research and enterprise IT operations. Her current work combines server infrastructure management with advancing computer vision methodologies.
Saverio Mascolo is a Full Professor at the Polytechnic University of Bari , Department of Electrical and Information Engineering. He leads the Control of Computing and Communication Systems Lab (C3Lab) and contributes to IEEE/ACM Transactions on Networking as an Associate Editor. His research focuses on Future Internet, network congestion control, and real-time communication systems. Laurea in Electronic Engineering, Politecnico di Bari (1991) PhD in Electronic and Automatic Control, Politecnico di Bari (1995) Visiting researcher roles at UCLA (1995, 1999), INRIA (2004), and FTW (2004) Research spans network congestion control , adaptive video streaming , and real-time communication . His lab develops protocols like TCP Westwood+ and contributes to WebRTC standards. Current projects include low-delay protocols for immersive video streaming and autonomous systems control. Recent publications emphasize adaptive threshold mechanisms in congestion control, millimeter-wave radar datasets , and immersive teleoperation systems . Projects integrate nonlinear control , time-delay analysis , and cloud-based multimedia delivery . Scientific recognition includes: Elevated to IEEE Fellow (2018) for congestion control contributions Google Faculty Award (2014) for WebRTC research Cisco Research Award (2013) for video streaming control Best paper awards at MMSYS (2025, 2024, 2016) Grants fund research in cloud-based video platforms (MISE, 2017-2020), WebRTC optimization (MIUR, 2012-2015), and network control algorithms. His lab collaborates with institutions like Uppsala University (since 2001) and industry partners.
Professor Jinho Choi is a Chair and Professor in Radio Frequency at the School of Electrical and Mechanical Engineering, University of Adelaide, Australia. He holds a B.E. (magna cum laude) from Sogang University, and M.S.E. and Ph.D. degrees from KAIST. His research focuses on advancing wireless communication and sensing technologies, particularly in IoT, 5G/6G, non-terrestrial networks, and cognitive satellite systems. He authored three books and has been recognized with the 1999 EURASIP Best Paper Award, IEEE Fellowship, and inclusion in Stanford's Top 2% Scientists list since 2020. He currently serves as a Senior Editor of IEEE Wireless Communications Letters and editorial roles in multiple journals. Education: B.E. (Electronics Engineering) - Sogang University, Seoul (1989) M.S.E. (Electrical Engineering) - KAIST (1991) Ph.D. (Electrical Engineering) - KAIST (1994) Research Interests: Professor Choi's work addresses connectivity challenges in non-terrestrial networks, leveraging statistical signal processing and machine learning. Current projects include UAV-assisted LEO satellite technologies, cognitive satellite radios, and semantic communication protocols. His research aims to enhance global connectivity and efficiency in terrestrial and satellite networks. Publications: His recent work spans semantic communication, satellite quantum key distribution, federated learning optimization, and coverage diversity in mega constellations. These studies reflect trends in 6G-ready technologies, AI-driven communication systems, and hybrid satellite-terrestrial networks. Awards: 1999 Best Paper Award for Signal Processing (EURASIP) IEEE Fellow (Leadership in technical excellence) World’s Top 2% Scientists (Stanford University, 2020–present) Grants & Supervision: As a senior academic, he oversees grants in wireless innovation and has advised numerous students on advanced communication systems. His lab focuses on next-generation networks, integrating theoretical insights with practical implementations. Labs/Teams: Active in interdisciplinary teams at the University of Adelaide, collaborating on projects funded by industry and government to bridge gaps between academic research and real-world applications.
Dr. Xiaohan Yu is a Lecturer in Artificial Intelligence at Macquarie University's School of Computing, joining in December 2023. Previously, he completed his doctoral studies at Griffith University and served as a Research Fellow at the ARC Research Hub for Driving Farming Productivity. His research focuses on Ultra-Fine-Grained Visual Categorization (Ultra-FGVC), Smart Farming, and Automated Crop Cultivar Identification, with over 70 publications in top-tier venues like ICCV, CVPR, and IEEE Transactions. He holds editorial roles at Pattern Recognition and SN Computer Science , and received the APRS Early Career Award (2022) and ACM MM 2024 Outstanding Area Chair distinction. Education: Completed doctoral studies in Artificial Intelligence at Griffith University, Australia. Research Interests: Ultra-Fine-Grained Visual Categorization (Ultra-FGVC) Smart Farming and Agricultural Robotics Computer Vision Applications in Healthcare (e.g., trachoma detection) Deep Learning, Continual Learning, and Domain Adaptation Key Contributions: Pioneered Ultra-FGVC research, developed frameworks like Mix-ViT and CLE-ViT, and contributed to benchmarking multi-object tracking in farming. His work bridges pattern recognition with real-world applications in agriculture and healthcare. Scientific Awards: Australian Pattern Recognition Society (APRS) Early Career Researcher Award 2022 ACM Multimedia 2024 Outstanding Area Chair Award Advising & Grants: Actively involved in editorial roles (Area Chair for ACM MM, IJCNN) and grant-funded research through ARC hubs. His work is supported by collaborations in agriculture and AI-driven solutions for crop cultivar identification. Labs & Affiliations: Member of Macquarie's Smart Green Cities Research Centre and Frontier AI Research Centre , advancing interdisciplinary AI applications.
Christian Timmerer is a Professor at the Institute of Information Technology, Alpen-Adria-Universität Klagenfurt. His research focuses on adaptive video streaming , energy efficiency , MPEG standardization , and quality of experience (QoE) , with significant contributions to HTTP Adaptive Streaming (HAS), multi-codec optimization, and immersive media systems. Email: christian.timmerer@aau.at Office Hours: Monday 3:00-4:00 PM (by appointment) Projects: CD-Labor ATHENA, GAIA, SPIRIT His research integrates machine learning and generative AI to enhance video encoding, super-resolution, and voice dubbing, while prioritizing sustainability through energy-aware algorithms and open-source tools like GREEM and VEED. Current work emphasizes latency reduction and dynamic bitrate adaptation in live streaming environments. Recent publications address VVC optimization , multi-resolution encoding , and perceptual quality modeling , reflecting interdisciplinary efforts in networking , computer vision , and human-computer interaction . Awards include leading funded projects on adaptive streaming and green video systems.
Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.
Prof. Dr. Matteo Große-Kampmann is a faculty member at Hochschule Rhein-Waal, serving as Professor of Distributed Systems within the Faculty of Communication and Environment. His research and teaching are centered on building secure, resilient, and reliable digital systems, with a strong emphasis on integrating information security from the earliest stages of system design. He is based at the Kamp-Lintfort Campus and actively leads research in the Cloud Resilience Lab. His research interests span a wide range of cybersecurity domains, including information security awareness, healthcare IT security, mobile and 5G/6G network security, threat modeling, and privacy in smart devices. He advocates for a proactive, design-first approach to security, particularly in increasingly interconnected environments. His work combines technical depth with human factors, examining both system-level vulnerabilities and user behavior in cyber risk contexts. The recent publications reflect a strong focus on applied cybersecurity research, with trends in mobile network penetration testing, privacy in wearables, governmental cybersecurity communication, and security in healthcare and childcare technologies. His work frequently appears in top-tier venues such as DSN, PETS, ESORICS, and ACSAC, often in collaboration with students and international researchers. His scientific contributions have been recognized with awards including an Honorable Mention Award at the International Conference on Mobile and Ubiquitous Multimedia (2024) and a Best Paper Candidate at the ACM Web Conference 2022. He also contributes to the academic community as a reviewer and technical program committee member for major security conferences including NDSS, PETS, ESORICS, and ACSAC. Prof. Große-Kampmann actively supervises bachelor's and master's theses, encouraging students to explore topics such as post-Darknet marketplaces, AI in cybersecurity education, and flood of information challenges. He emphasizes ownership, preparedness, and learning through failure, fostering independent research skills. He collaborates with students and industry partners on practical projects, particularly in the areas of penetration testing and security analysis. He is involved in several research initiatives, most notably the Cloud Resilience Lab , where he and his team investigate real-world security and privacy issues in modern digital systems. His work bridges academic research with practical applications, often receiving media attention, such as coverage in Wired , EFF , and Die Zeit for his study on childcare app security.
Riccardo Lancellotti is an Associate Professor at the Department of Engineering 'Enzo Ferrari' of the University of Modena and Reggio Emilia. His research focuses on Edge/Fog/Cloud Computing, Cyber Security, and Resource Management in distributed systems. He has extensive contributions in optimizing infrastructure performance, load balancing, and energy efficiency in cloud and fog environments. His work often combines theoretical models with practical simulations, addressing challenges like stale information in edge systems and heterogeneous resource allocation in smart cities. Key research areas include: Fog/Edge computing infrastructure design and optimization Cloud resource provisioning and SLA compliance Security for Industry 4.0 and automotive systems Genetic algorithms for service placement Scalable VM clustering and resource allocation Publications highlight trends in cloud/fog integration, robust game theory for microservices, and distributed load balancing under dynamic conditions. His work emphasizes practical applications, such as pharmaceutical distribution routing and smart city sensor management. No awards are explicitly listed, but his extensive publication record reflects recognition in the field.
F. Donelson (Don) Smith is a Research Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill . He holds a Ph.D. in Computer Science (1978) from UNC-Chapel Hill, with prior degrees in Chemistry (1962) and Industrial Management (1964) from the University of Tennessee. Key research areas: Computer networking, multimedia systems, distributed systems Collaboration with: Kevin Jeffay , Jasleen Kaur , and the DiRT Lab Notable contributions include: Co-inventor of U.S. Patents 7,444,720 (2008) and 5,892,754 (1999) Key roles at IBM (1965-1997) in network architecture , protocol development , and multimedia networking Active in professional service: Program Chair, TriComm '91 Co-chair, ACM CSCW '94
Alan J. Smith is a Professor in the Electrical Engineering and Computer Sciences department at the University of California, Berkeley, within the College of Engineering. With a distinguished career spanning several decades, he has made significant contributions to computer architecture, system performance analysis, and memory systems. Dr. Smith received his S. B. from MIT and a Ph.D. from Stanford University. His educational background provided the foundation for his extensive research in computer systems. Professor Smith's research focuses on computer architecture and engineering, particularly in system performance analysis, I/O systems, cache memories, and memory systems. His work has profoundly influenced how computer systems are designed and evaluated, with particular emphasis on optimizing storage systems, cache performance, and energy efficiency in computing platforms. His research has bridged theoretical analysis with practical implementation, resulting in numerous influential publications and real-world applications. His publication record shows a consistent focus on computer system performance, evolving from early work on cache memory and paging algorithms to more recent research on multimedia workloads, energy management, and storage systems. The breadth of his work spans fundamental computer architecture principles to applied system design, with particular emphasis on measurement-based analysis and optimization techniques. Harry Goode Award of the IEEE Computer Society (2006) IEEE Reynold B. Johnson Information Storage Systems Award (2008) A. A. Michelson Award of the Computer Measurement Group (2003) Fellow of the American Association for the Advancement of Science (2001) Fellow of the ACM (2000) Fellow of the IEEE (1988) Throughout his career, Professor Smith has actively contributed to the academic community through editorial roles, conference organization, and professional society leadership. He has served as Subject Area Editor for the Journal of Parallel and Distributed Computing since 1989, chaired ACM SIGARCH and SIGOPS, and participated in numerous technical committees. His research has been supported by various grants that enabled extensive experimental work and system development. Professor Smith has been instrumental in developing benchmarking methodologies and performance analysis techniques that have become standard practices in computer system evaluation. His work on cache memory systems, in particular his influential 1982 Computing Surveys paper "Cache Memories," has shaped the field for decades.
Professor Petri Vuorimaa is a faculty member at the Department of Computer Science, Aalto University. His research spans web technologies, digital television, and human-computer interaction. Research Interests: Web Engineering and Development Edge Computing and Serverless Architectures Smart Home Systems and Semantic Interoperability Blockchain Applications in E-commerce Publication Trends: Recent work focuses on serverless edge computing, resumability in web applications, disappearing frameworks, and blockchain-based reputation systems. Earlier research includes declarative languages, XML frameworks for digital TV, and mobile computing.
Zoran Jančić is a lecturer at Algebra University and head of its E-learning Department . With over 20 years of experience in digital education projects, he specializes in e-learning system implementation, content development, and project management across educational, public, and corporate sectors. Graduated from Faculty of Electrical Engineering and Computing, Zagreb Adobe Certified Expert (ACE) for Adobe Captivate Moodle Educator Certificate (MEC) based on DigCompEdu framework His research interests focus on e-learning systems , digital education infrastructure , and learning management systems (LMS) . Key projects include: Developing 1500+ school hours of digital content for Croatian academic network CARNET Implementing national e-learning systems for the State School of Administration Creating certification platforms for Croatian tradesmen used across four countries His recent publications analyze corporate LMS investments , non-formal learning recognition , and storage solutions for small businesses , reflecting his interdisciplinary work at the intersection of educational technology and enterprise IT systems. Notable contributions include digital career tools and multimedia training programs .
