Abraham Silberschatz is the Sidney J. Weinberg Professor of Computer Science at Yale University. He previously served as Vice President of the Information Sciences Research Center at Bell Laboratories and held a chaired professorship at the University of Texas at Austin. His research focuses on database systems, operating systems, and network management. Silberschatz has advised over a dozen PhD students, many now in academia and industry. Education: Ph.D., Computer Science, Stony Brook University (SUNY) Research Interests: His work spans database systems, operating systems, storage systems, and network management. Notable contributions include foundational textbooks like Operating System Concepts and Database System Concepts , which have become industry standards. He has also developed innovative systems like DataPlay and contributed to projects such as NetInventory. Publications: His 15+ years of research include influential papers on database architecture, network routing, and distributed systems. Recent work explores leveraging non-volatile memory technologies in systems design. Awards: ACM Karl V. Karlstrom Outstanding Educator Award (1998) IEEE Taylor L. Booth Education Award (2002) VLDB Test of Time Award (2019) Multiple Bell Laboratories President's Awards for innovation Grants & Patents: Recipient of over two dozen grants and over four dozen patents, including foundational IP in multimedia storage and distributed systems. His team's HadoopDB project merged MapReduce and DBMS technologies. Labs/Teams: Collaborates with Prof. Robert Soulé on projects in database systems and networking, focusing on next-gen memory technologies. Active in mentoring graduate students and postdocs in their research group.
George Kesidis is a Professor in Computer Science and Engineering and Electrical Engineering at Penn State University. His research spans deep learning security, virtual reality optimization, and cloud computing. College of Engineering (Penn State University) Research Focus: Backdoor Attacks, DNN Robustness, Edge Caching Active in NSF and U.S. Navy-funded projects (2022-2026) His work addresses backdoor data poisoning , test-time evasion attacks , and DNN overfitting mitigation . He develops techniques like activation clipping, perturbation analysis, and statistical defense models. Recent projects include edge caching systems for VR and security-driven AI frameworks. Key article trends reveal expertise in adversarial deep learning, immersive media delivery, and cloud resource optimization. Current grants focus on multi-user VR, GPU scheduling, and serverless-cloud hybrid architectures. He collaborates extensively with researchers like David J. Miller and Xinyu Li, particularly on cloud-based adversarial defense mechanisms and VR streaming benchmarks.
Jarno Vanne is a Professor at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences at Tampere University. His research focuses on video coding standards, real-time systems, and hardware acceleration, particularly in the context of FPGA implementations and open-source tools. He leads projects involving VVC (Versatile Video Coding), V-PCC (Volumetric Video Coding), and HEVC (High Efficiency Video Coding), with an emphasis on efficiency, low latency, and machine learning integration. Key research interests include point cloud compression, saliency-guided encoding, parallelization schemes, and real-time video communication protocols. His work often addresses challenges in multi-party video streaming, embedded systems, and encryption mechanisms for privacy protection. He has contributed to open-source projects like the UVG dataset, Kvazaar encoder, and CiThruS simulation frameworks. Recent publications highlight advancements in VVC intra encoding optimizations, machine learning-driven partitioning schemes, and FPGA-accelerated solutions for edge computing. His research bridges theoretical video coding algorithms with practical implementations, aiming to improve compression efficiency while maintaining real-time performance.
Mohamed Hefeeda is a Professor in the School of Computing Science at Simon Fraser University (SFU), Canada. He leads the Network and Multimedia Systems Lab (NMSL) and previously served as Director of the School from 2018 to 2023. His research focuses on multimedia networking, mobile computing, cloud systems, and hyperspectral imaging. He holds an ACM Distinguished Member designation and has received prestigious awards including the NSERC Discovery Accelerator Supplements (2011) and multiple best paper awards at top conferences like ACM MM and IEEE Infocom. Education: Ph.D., Purdue University, 2004 M.Sc., University of Connecticut, 2001 B.Sc., Mansoura University, Egypt, 1994 Research Interests: Design of efficient multimedia systems and protocols for wired/wireless networks Cloud gaming optimization and video encoding techniques Hyperspectral imaging for healthcare and mobile applications AI-driven multimedia systems and mobile computing innovations Grants & Industry Collaborations: Funded by NSERC, CFI, and companies like AMD, Huawei, and CBC Co-founded Video Semantics (acquired by tech firm) Partnered with CBC on peer-assisted content distribution systems Awards Highlights: 2025: ACM Distinguished Member 2019: Best Student Paper Award at ACM MMSys 2015: NSERC Discovery Accelerator Supplements Labs & Leadership: Network and Multimedia Systems Lab (NMSL) at SFU Contributed to creation of Qatar Computing Research Institute (QCRI)
Anthony TUNG Kum Hoe is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he has established himself as a leading researcher in database systems and data mining. He is also affiliated with the NUS Graduate School for Integrative Sciences and Engineering and serves as a SINGA supervisor. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (2001), an M.Sc. in Information Systems & Computer Science from NUS (1998), and a B.Sc. with 2nd Class Upper Honours in Information Systems & Computer Science from NUS (1997). Professor Tung's research spans several interconnected areas within database systems and data mining. His primary focus is on developing efficient methods for indexing and searching complex data structures including time series, trajectories, trees, graphs, and high-dimensional objects. He has pioneered work in visual query processing, keyword search, and ranking systems. His GENIE (Generic Inverted Index) and LAMP (semi-Lazy Mining Paradigm) projects represent significant contributions to big data analytics, particularly in handling the 'variety' aspect of big data by providing unified frameworks for processing diverse data structures while preserving semantic meaning. His research bridges theoretical database concepts with practical applications in visual data mining, collaborative analytics, and just-in-time model construction. His recent publications reveal a clear evolution from traditional database research toward more complex analytics on diverse data types. While maintaining his core expertise in database indexing and query processing, his work has expanded to incorporate machine learning techniques, particularly in areas like nearest neighbor search, anomaly detection, and predictive analytics. There's a noticeable trend toward interdisciplinary applications, with publications spanning computer vision, natural language processing, transportation systems, and social computing. His research group consistently publishes in top-tier venues including SIGMOD, VLDB, ICDE, and KDD, demonstrating both theoretical rigor and practical relevance. 2005 Best Paper Award for 'Indexing DNA Sequences Using q-grams' 2007 Invited panel speaker on 'Advice for a successful database researcher career in Asia' at SIGMOD 2010 Guest Lecturer for VLDB Database School 2012 VLDB 2012 Research PC Co-chairs 2015 10 Years Best Paper Award, DASFAA 2015 Invited to SIGMOD 2008 and SIGKDD 2008 Program Committees Professor Tung has supervised numerous PhD students and research associates throughout his career, including notable researchers like Zhang Zhenjie (recipient of the 2007 President Graduate Fellowship) and Wang Nan (published in SIGMOD'08). His research group has been consistently productive, with students publishing in top conferences including SIGMOD, ICDE, and VLDB. His professional service is extensive, having served as PC Chair for COMAD'06, Research PC Co-chair for VLDB 2012, and on program committees for virtually all major database and data mining conferences over the past two decades. His research has been supported by various grants that have enabled significant contributions to database technology. His GENIE and LAMP projects represent a cohesive research direction focused on developing systematic approaches to big data analytics. GENIE provides a unified platform for storage and retrieval of big data with various structures, while LAMP introduces a novel paradigm for predictive analytics that combines the strengths of lazy and eager learning approaches. These projects have evolved to incorporate GPU acceleration and parallel processing capabilities, reflecting his commitment to addressing real-world scalability challenges in data-intensive applications.
Professor Amr Rizk is the Director of the Networks and Communication Systems (NCS) Lab at the University of Duisburg-Essen, where he has been serving as Professor since April 2021. Previously, he was Assistant Professor at Ulm University (2019-2021) and completed his habilitation at TU Darmstadt in 2019. His academic journey includes research positions at prestigious institutions including University of Massachusetts Amherst, University of Warwick, and TU Darmstadt where he was an Athene Young Investigator. Professor Rizk's research spans multiple aspects of networking and communication systems with a particular focus on network performance analysis, stochastic modeling, and practical implementations. His work bridges theoretical foundations with real-world applications, especially in content delivery, video streaming, and network protocols. He has made significant contributions to network calculus, quality of experience optimization, and novel approaches to congestion control and caching mechanisms. His publication record demonstrates consistent high-impact contributions across top networking conferences and journals. Recent work shows a growing emphasis on programmable data planes, AI/ML applications in networking, and advanced techniques for network measurement and performance prediction. His research group at Duisburg-Essen maintains strong connections with both academic and industrial partners in the networking ecosystem. Best Paper Award at ACM MMSys Conference (2023) Distinguished TPC Member for IEEE INFOCOM (2020, 2022) Best Paper Award at ACM/USENIX Middleware Conference (2017) Athene Young Investigator Award, TU Darmstadt (2017) Professor Rizk serves as Associate Editor for Elsevier Computer Communications and has extensive experience with research funding bodies as a reviewer. His leadership extends to conference organization, including roles as PC Co-Chair for IEEE MIPR (2023) and Steering Committee member for Workshop on Network Calculus (2022). He maintains active participation in numerous top networking conferences as Technical Program Committee member, reflecting his standing within the international networking research community.
Luo Mai is an Assistant Professor at the University of Edinburgh's School of Informatics , with an upcoming promotion to Associate Professor (UK Reader) in August 2025. He leads the Large-Scale Machine Learning Systems Group and co-leads the UK EPSRC Centre for Doctoral Training in Machine Learning Systems and an ARIA Project on Scaling AI Compute by 1000X . PhD in Computer Science (Imperial College London, 2018) MRes in Advanced Computing (Imperial College London, 2012) His research focuses on the intersection of computer systems , machine learning , and data management . Key contributions include award-winning systems like WaferLLM (wafer-scale LLM inference), Tenplex (elastic ML), and ServerlessLLM (serverless LLM serving), published at top venues (OSDI, SOSP, ICML, NeurIPS, JMLR). Recent publications demonstrate trends in GPU-based distributed systems , LLM optimization , and adaptive machine learning . His team has developed groundbreaking open-source projects including TensorLayer , TorchOpt , and ServerlessLLM . Awarded Microsoft Research StarTrack Scholar (2024) , secured ARIA grant (2024) with Imperial College & Cambridge University, and received Google Fellowship during PhD (2012-2016). As an educator, he designed Edinburgh's popular Machine Learning Systems course (150+ students). His group supervises multiple PhD students including Yao Fu (recognized as 2024 Rising Star in ML & Systems) and Leyang Xue .
Robert J. Brunner is a Professor at the University of Illinois with primary appointments in the Gies College of Business (Department of Accountancy) and the School of Information Sciences. He holds affiliate roles across multiple departments including Astronomy, Computer Science, and Statistics, as well as research centers like the Beckman Institute and NCSA. His research focuses on applying statistical/machine learning to solve complex problems in astronomy, finance, and large-scale data science. Education: Ph.D. in Astrophysics from Johns Hopkins University (advisor: Alex Szalay). Postdoctoral work at Caltech on the Digital Sky project. Research Interests: Machine learning applications, computational techniques, data management/visualization, and observational cosmology. His work bridges astrophysical data analysis with modern data science methodologies. Recent work includes developing spatio-temporal neural networks for forecasting, evaluating AI-driven financial analysis tools, and planning for the Vera C. Rubin Observatory. He collaborates internationally on large-scale surveys like the Dark Energy Survey and SDSS. Labs/Teams: Leads data science initiatives at the University of Illinois Research Park. Active in interdisciplinary teams at NCSA and Beckman Institute focusing on algorithm optimization and data-intensive research.
Ivan Viola is an Associate Professor at the Institute of Computer Graphics and Algorithms, part of the Faculty of Informatics at TU Wien, Austria. He holds a leave of absence until December 2024 while also being affiliated with King Abdullah University of Science and Technology (KAUST) as an Associate Professor funded by the Vienna Research Groups program. His research focuses on visualization techniques in medicine, biological sciences, and earth sciences, with a specialty in illustrative visualization and DNA-nanotechnology applications. Viola has contributed over 100 scientific works and serves as a reviewer and panelist for major conferences in computer graphics and visualization. Education: M.Sc. (2002) and Ph.D. (2005) in Computer Graphics from TU Wien. Postdoctoral research at the University of Bergen (2006-2011), where he became Full Professor before returning to TU Wien. Research Interests: Whole-cell visualization Molecular modeling Interactive 3D environments Biomedical visualization Data-driven colormap techniques Awards: IEEE VIS 2017 Best Paper Honorable Mention, 'Best Overall Concept' for CellView, and multiple visualization awards. Active in EuroVis and IEEE VIS organizing roles. Grants & Supervision: Leads the Visualization Group at TU Wien, supervising student projects and master’s theses. Involved in grants like the Vienna Research Groups program. Labs/Teams: Visualization Group at TU Wien, collaborating on projects like CellView and Molecumentary.
Zhan Ma is a Professor and PhD Advisor at the School of Electronic Science and Engineering, Nanjing University. He leads research in Neural Video Communication, Smart Cameras, and Computational Vision Models. His work focuses on end-to-end learning for compression, networking, and hardware-software co-design. Dr. Ma holds a PhD from New York University's Tandon School of Engineering (2010), and prior to his current role, he served as Senior Staff Researcher at Huawei (2013-2015) and Senior Researcher at Samsung (2011-2013). Research highlights include pioneering work in point cloud compression (adopted into IEEE standards) and dual-camera systems for high-resolution video acquisition. His algorithms are deployed in WeChat/WeChat Video for rate-quality optimization and in ISO standards for video complexity indicators. Recent work emphasizes machine learning-driven approaches for image/video compression and adaptive streaming frameworks. Honors include the 2023 IEEE CAS Society Outstanding Young Author Award and multiple best paper awards at IEEE WACV, BMSB, and other venues. He leads the Vision Lab at Nanjing University and collaborates with industry partners on practical implementations of his research.
Jonathan Ragan-Kelley is the Esther and Harold E. Edgerton Assistant Professor of Electrical Engineering & Computer Science at MIT and an Assistant Professor of EECS at UC Berkeley. He leads the Visual Computing group at CSAIL, focusing on high-efficiency visual computing, compilers, and architectures for image processing, machine learning, and 3D rendering. His research bridges systems, compilers, and hardware design, emphasizing scalable solutions for computational challenges. Education: PhD in Computer Science from MIT (2014), postdoc at Stanford University, and visiting researcher at Google. He co-created the Halide language and has developed multiple domain-specific languages (DSLs) and compiler systems. Research interests include compiler optimization, scheduling languages (e.g., Exo), and efficient computing frameworks. He has received awards such as the NSF CAREER Award and ACM SIGGRAPH’s Significant New Researcher Award. Awards: ACM SIGGRAPH Award, NSF CAREER, Intel Outstanding Researcher Award Key Contributions: Halide compiler framework, Exo scheduling language, machine learning acceleration techniques Labs/Teams: Visual Computing at MIT CSAIL
Andrew D. Ker is Professor and Associate Professor of Computer Science at the University of Oxford's Department of Computer Science, and Tutorial Fellow in Computer Science at University College, Oxford since 2009. He received his BA in Mathematics & Computer Science (1994-1997) and DPhil in Computer Science (1997-2000) from Oxford, followed by academic appointments including Junior Research Fellow (2000-2003), Special Supernumerary Fellow (2003-2009), and Royal Society University Research Fellow (2003-2011). His research focuses on information hiding, particularly steganography (covert communication in digital media) and steganalysis (detection of hidden data), with additional interests in digital media forensics and programming language semantics. His foundational work includes the mathematical formalization of the 'square root law of steganographic capacity'. Recent publications explore practical implementations in social media platforms, GPU-accelerated steganalysis, and linguistic steganography techniques. He has received multiple scientific awards including Best Paper Awards at ACM Workshops on Information Hiding & Multimedia Security, SPIE conferences, and the International Workshop on Digital Watermarking. As Associate Editor for IEEE Transactions on Information Forensics and Security, he maintains active involvement in the academic community. Professor Ker has supervised numerous graduate students in computer security and steganography research, including doctoral candidates and master's students. He leads research within Cyber Security Oxford and has developed four major lecture series: Lambda Calculus and Types, Discrete Mathematics, Computer Security, and Advanced Security: Information Hiding. He remains active in teaching despite administrative responsibilities as Tutorial Fellow.
Konstantinos Nikitopoulos is a Professor at the University of Surrey , UK, specializing in Wireless Communications and Signal Processing . His research focuses on MIMO Systems , Open-RAN , and Non-Linear Processing for next-generation wireless networks. His recent work explores Analogue Processing for Tbps Wireless Systems and Neuromorphic Computing in MU-MIMO detection. He has developed frameworks like MIMO-SoftiPHY and SACCESS for software-based radio acceleration and power-efficient network design. Key Publications : Power-Efficient RIC, NL-COMM, NeuroMIMO Collaborators : Rahim Tafazolli, George Katsaros, Marcin Filo His research impacts 6G Network Development through innovations in Beamforming , Channel Estimation , and Software-Defined Radios .
Martin Aumüller is a Lecturer in Theoretical Computer Science Algorithms at the IT University of Copenhagen . He serves as Head of Education and Master of Software Design , focusing on algorithm engineering, differential privacy, and similarity search. Research interests include: Algorithm engineering for high-dimensional data Locality-sensitive hashing and nearest neighbor search Privacy-preserving machine learning Fairness in approximate search algorithms Benchmarking and evaluation of similarity search tools Publications trends highlight his work on approximate nearest neighbor search , privacy-preserving techniques , clustering algorithms , and scalable outlier detection in high-dimensional spaces. His recent projects (2024-2025) focus on fairness, differential privacy, and efficient indexing. Grants and projects : DIREC (2020-2025): Digital Research Centre Denmark (Innovation Fund Denmark) DIREC: Bias and Benefit of Approximate Nearest Neighbor Search (2022-2025): Principal Investigator (Innovation Fund Denmark) BARC (2017-2024): Basic Algorithms Research Copenhagen (Villum Fonden) SSS (2014-2019): Scalable Similarity Search (European Commission)
Boro Jakimovski is an Assistant Professor at the Institute of Informatics, Faculty of Natural Sciences and Mathematics, Ss. Cyril and Methodius University in Skopje. He specializes in Grid computing, High-performance computing, and Parallel and distributed processing, with significant contributions to international projects like the European Grid Initiative and SEE-GRID series. PhD in Informatics (2010) - Thesis: Modelling and verification of Grid workflows MSc in Informatics (2004) - Thesis: Communications in Grids BSc in Informatics (2000) - Graduated top of class His research focuses on optimizing computational grids through adaptive workflows and genetic algorithms. He teaches courses such as Distributed Operating Systems and Software Construction, while previously tutoring foundational informatics topics including Algorithms and automata, Data structures, and Parallel processing. Publications span 18 international conferences and 1 journal article, with a notable awarded paper at MII 2003 . Key projects include FP7 coordination for Pan-European infrastructure and leadership in SEE-GRID initiatives as Grid Infrastructure Manager for Macedonia. Scientific Awards : Awarded paper at MII 2003 International Conference Grants : Participated in 15+ international projects (FP6, FP7, TEMPUS, UNESCO, DAAD, INTERREG III) International Engagement : Lectured at 20+ workshops, visited 10+ universities/summer schools Specializations include Grid infrastructure development, having completed professional visits to institutions like CERN (Switzerland), Humboldt University (Germany), and Software Technology Research Laboratory (UK). He chairs Macedonia's coordination in European Grid Initiative projects and contributes to curriculum development across SEE countries.
