Professor Ihab Ilyas is a leading figure in data management and machine learning at the Cheriton School of Computer Science , University of Waterloo , where he holds the Thomson Reuters Research Chair in Data Quality . He is currently on leave from the university, serving as a Distinguished Engineer, Proactive Intelligence at Apple Inc . His research focuses on data cleaning , large-scale data integration , knowledge graphs , and machine learning applications in data quality . He has pioneered systems like HoloClean and Saga , with commercial impacts through co-founded startups Inductiv (acquired by Apple) and Tamr . PhD in Computer Science from Purdue University Co-founder of Inductiv (acquired by Apple) Co-founder of Tamr Research Interests include: Probabilistic and uncertain data management Machine learning for data quality and enrichment Big data systems and information extraction Knowledge graph construction and optimization Scientific Awards and Recognitions include: Fellow of the Royal Society of Canada (2024) C.C. Gotlieb Computer Award (2024) IEEE Fellow (2021) ACM Fellow (2020) Cheriton Faculty Fellowship (2013-2016) Ontario Early Researcher Award
Jianguo Wang is an Assistant Professor in the Department of Computer Science at Purdue University, joining in Spring 2021. His research focuses on database systems for the cloud and large language models, including disaggregated databases and vector databases. He holds a PhD from the University of California, San Diego, and has worked at Zilliz (Milvus) and Amazon Web Services (AWS). Education: PhD in Computer Science (UC San Diego, 2019), MPhil (Hong Kong Polytechnic University), BSc (Zhengzhou University) Research interests include Disaggregated Databases, Vector Databases for Large Language Models, and cloud-native systems. Notable work includes OpenAurora (an open-source Amazon Aurora prototype) and contributions to Milvus. He has received grants like the NSF CAREER Award and honors such as the IEEE TCDE Rising Star Award. Advising a team of students in database systems and teaching courses like CS592 (Disaggregated Database Systems) and CS440 (Large-scale Data Analytics). Serves on program committees for SIGMOD, VLDB, and ICDE.
University of Illinois Urbana-ChampaignUnited States
Aishwarya Ganesan is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign. Her research focuses on distributed systems, storage systems, and fault tolerance mechanisms, with emphasis on high-performance computing and datacenter infrastructure. She leads projects addressing challenges in replicated storage, consensus protocols, and system resilience. Her work explores fault tolerance in disaggregated datacenters, log abstractions for low-latency applications, and novel replication strategies for modern storage systems. She has developed frameworks like LazyLog and IONIA to improve system efficiency and reliability. Her research also extends to automatic reliability testing for cluster management controllers and analyzing distributed storage vulnerabilities. Key contributions include demonstrating how redundancy alone does not guarantee fault tolerance, and proposing consistency-aware durability mechanisms for storage systems. She received the NSF CAREER Award in 2024 for her research on storage-aware fault tolerance. Her work spans 21 peer-reviewed publications, with notable contributions in conferences like SOSP, EuroSys, and FAST. Current projects investigate fault tolerance in emerging memory technologies and system recovery protocols for consensus-based storage.
Shungeng Zhang serves as an Assistant Professor in the Department of Computer & Cyber Sciences at Augusta University's School of Computer and Cyber Sciences. He holds a Ph.D. in Computer Science from Louisiana State University (2021) and a B.E. in Computer Engineering from Huazhong University of Science and Technology (2014). His educational background includes: Ph.D. in Computer Science, Louisiana State University, 2021 B.E. in Computer Engineering, Huazhong University of Science and Technology, 2014 Dr. Zhang's research spans distributed systems, cloud computing, and cybersecurity with a focus on enhancing performance and scalability of web applications and IoT stream processing in cloud environments. He employs sophisticated timeline analysis and fine-grained monitoring to identify transient bottlenecks causing long-tail latency problems, addressing propagation effects in complex dependency chains among application components. His publication record (2017-2022) reveals consistent contributions to cloud systems performance, particularly in n-tier architectures, concurrency control, and latency optimization. Key themes include stream processing synchronization, fanout query performance, adaptive concurrency for SLO compliance, and mitigation of transient resource contention attacks across distributed systems. No scientific awards were mentioned in the provided information. Dr. Zhang actively seeks graduate students with strong computer systems backgrounds for research collaboration. His departmental service includes Faculty Assembly participation (2021-2022) and faculty interviewing duties. Professionally, he serves as a reviewer for ACM SoCC'23, ACM TOIT, SmartCom 2023, The Journal of Supercomputing, and ACM SoCC'22. Teaching responsibilities include AIST 4720 (Enterprise System Architectures), CSCI 1200 (Introduction to Computers and Programming), and graduate courses such as CSCI 8940 (Dissertation Research) and AIST 3310 (Advanced Networking). No dedicated research labs or teams were specified in the available documentation.
Dr. Song Jiang is a Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). He holds a PhD from the College of William and Mary (2004) and has held academic positions at institutions such as Wayne State University and Los Alamos National Laboratory. His research focuses on system infrastructure for large language models (LLMs) and big data processing, including GPU/CPU memory systems, file and storage systems, and high-performance computing (HPC) I/O systems. He has received significant funding from the National Science Foundation (NSF) and industry partners like VMware and Tencent. Education: B.S. and M.S. from University of Science and Technology of China (1993, 1996), Ph.D. in Computer Science from College of William and Mary (2004). Postdoctoral research at Los Alamos National Laboratory (2004–2006). Research interests include file and storage systems, data management, big data analytics, and optimizing computing architectures for AI/ML. Key contributions include the LIRS replacement algorithm (adopted in MySQL and NetBSD), CLOCK-Pro page replacement (used in Linux), and swap token algorithms (Linux kernel). Awards include the 2022 ACM SIGMETRICS Test of Time Award and 2009 NSF CAREER Award. His work has led to 15+ patents and impactful industry collaborations with Facebook, Baidu, and others. Advising: Supervised 14+ PhD/Master’s students, including current advisees Chen Zhong and Sujit Maharjan. Active roles in doctoral committees and thesis supervision. Grants: Over $2.5M in NSF funding for projects like 'Software Defined Cache for Index Search' and 'Taming Small Data Writes'. Industry grants include VMware’s $240K project on distributed key-value storage. Labs/Teams: Leads research on persistent memory systems, key-value stores, and LLM infrastructure through UTA’s CSE department and collaborations with industry partners.
Foundation for research and technology-hellasGreece
Kostas Magoutis is Professor and Chair of the Computer Science Department at the University of Crete , and a collaborating researcher at FORTH-ICS . His research focuses on scalable distributed systems, cloud computing, IoT, and quantum-enhanced control. Education and Career: Ph.D. in Computer Science, Harvard University (2003) Research Staff Member, IBM T. J. Watson Research Center (2003-2009) Assistant Professor (2014-2019, tenured 2017) and Associate Professor (2020-2024), University of Crete Professor and Chair, University of Crete (2024-present) Research Interests: His work spans distributed computer systems , cloud computing , scalable data stores and stream-processing engines , Internet of Things , and the emerging area of quantum-enhanced control . Representative projects include the H.F.R.I.-funded QUADS (2025-2028) on quantum-enhanced adaptive systems, STREAMSTORE (2020-2023) on elastic stream processing, SmartCityBus on IoT-driven public transport, and the EU FP7 PaaSage project on model-based cloud lifecycle management. Awards and Honors: Best Paper Awards: USENIX ATC 2002, USENIX BSDCon 2002, IEEE SRDS 2014 (Best Student Paper), IoT 2024 (Runner-up) Grand Challenge Audience Award, ACM DEBS 2022 Best Poster Award, ACM EuroSys 2022 EU Marie Curie IEF Fellow (2009-2011) Alexander S. Onassis Fellow (1994-1995) and J. William Fulbright Scholar (1993-1994) Students and Mentoring: He has supervised or co-supervised more than 30 Ph.D., M.Sc. and undergraduate students, including Antonis Papaioannou (Ph.D. 2021), Efthimios Papageorgiou (current Ph.D.), and numerous M.Sc. graduates now in industry and academia. Labs and Teams: At FORTH-ICS he leads activities within the Distributed Systems and Storage Laboratory, coordinating research on scalable storage, stream processing, and IoT data management. The lab collaborates closely with European and national initiatives, hosting visiting researchers and industry partners.
Kostas Magoutis is Professor and Chair of the Computer Science Department at the University of Crete and collaborating researcher with FORTH-ICS. His research focuses on distributed systems, scalable data processing, IoT, and cloud computing, with projects including GreenInCities for urban regeneration and STREAMSTORE for stateful stream processing systems. Research interests include: Distributed computer systems architecture Elastic stream processing platforms Quantum-enhanced computing applications Multi-cloud application lifecycle management Recent publications demonstrate strong focus on federated data systems, quantum computing applications, and IoT-enhanced infrastructure, with consistent output in high-impact conferences and journals. Awards and distinctions: Multiple best paper awards from USENIX conferences Grand Challenge Audience Award at DEBS 2022 Marie Curie Fellowship and IBM Research awards Advises over 20 PhD and MSc students in distributed systems research. Leads multiple EU-funded projects and serves on program committees for top conferences including SOSP, EuroSys, and IEEE BigData. Directs research groups in distributed systems and cloud computing at FORTH-ICS.
Swiss Federal Institute of Technology in LausanneSwitzerland
Manos Athanassoulis is an Associate Professor in the Department of Computer Science at the College of Arts and Sciences, Boston University. He is the Founder and Director of the BU Data-intensive Systems and Computing (DiSC) lab and a member of the BU MiDAS group. His research focuses on data systems, particularly cloud data management, hybrid transactional/analytical workloads, and integration with emerging hardware such as non-volatile memory and heterogeneous computing. His educational background includes a PhD from EPFL (2014), an MSc in Computer Systems Technology, and a BSc in Informatics and Telecommunications from the University of Athens, Greece. Prior to BU, he was a Postdoctoral Researcher and Research Associate at Harvard University, supported by a SNSF Postdoc Mobility Fellowship. His research interests span data systems, database architectures, LSM trees, indexing, storage systems, and performance optimization. He explores how novel hardware can be leveraged to improve data management efficiency and scalability, especially in cloud environments. His recent publications (2021–2025) predominantly focus on LSM trees, covering topics such as compaction policies, Bloom filter tuning, DPU offloading, adversarial resilience, and sustainable caching. Earlier works include foundational contributions on access methods (RUM Conjecture) and optimal key-value stores (Monkey). The trend shows a consistent focus on data system efficiency, adaptability, and robustness under varying workloads and hardware constraints. Scientific Awards: NSF CAREER Award (2022) Facebook Faculty Research Award (2020) NSF CRII Award (2019) Best of VLDB 2017 and Best of SIGMOD 2017 SIGMOD Most Reproducible Paper Award (2017) Multiple ACM SIGMOD Distinguished PC Member recognitions (2018–2025) VLDB 2023 Best Demo Award RedHat Collaboratory Research Incubation Awards (multiple, 2021–2023) SNSF Postdoc Mobility Fellowship (2015–16) IBM PhD Fellowship (2011–12) Dr. Athanassoulis has advised numerous students and collaborators, evident from his co-authorship on works with researchers such as Niv Dayan, Stratos Idreos, and A. Ailamaki. His grants include major awards from NSF, Facebook, and RedHat, supporting research in robust data systems, hardware-software co-design, and learned cost models. He has also been recognized for teaching excellence at Harvard University. He leads the DiSC lab at Boston University, which focuses on data-intensive computing and systems research. The lab explores next-generation data architectures, particularly in cloud and hardware-aware environments. Collaborations with the BU MiDAS group enhance interdisciplinary research in data science and AI.
University of Illinois Urbana-ChampaignUnited States
Ram Alagappan is an Assistant Professor at the University of Illinois Urbana-Champaign's Siebel School of Computing and Data Science, Department of Computer Science. He co-leads the Distributed And Storage Systems Laboratory (DASSL) and focuses on improving reliability and performance in computer systems. PhD from University of Wisconsin-Madison (2019) Postdoctoral researcher at VMware Research (2020-2022) Research interests span storage systems, distributed systems, and operating systems, with emphasis on: Crash consistency and reliability Consensus protocols and fault tolerance Non-volatile memory optimization Datacenter storage abstractions Recent publications highlight advancements in shared log abstractions (LazyLog, SOSP 2024), replication for KV stores (IONIA, FAST 2024), and fault tolerance in disaggregated datacenters (SplitFT, EuroSys 2024). His work also includes foundational studies on crash vulnerabilities and consistency models. Scientific recognitions include: Best Paper Awards at SOSP 2024, FAST 2020, FAST 2018, FAST 2017 NSF CAREER Award (2023) NetApp Faculty Fellowship (2024) Teaching honors: Listed as Excellent/Outstanding Teacher at UIUC (CS598 Storage Systems, 2022-2023) Ranked 1st in student evaluations for CS739 Distributed Systems at UW-Madison (2020) Grants and service: NSF CAREER Award, IBM-IL Discovery Grant Program Committee roles at OSDI, SOSP, EuroSys, HotStorage Labs & Teams: Co-leader of DASSL research group at UIUC.
Willy Zwaenepoel is a Professor and Dean of the Faculty of Engineering at the University of Sydney. He holds a B.S. from the University of Gent and M.S./Ph.D. from Stanford University. Previously, he served as Dean of the School of Computer and Communication Sciences at EPFL and was a faculty member at Rice University. His expertise spans operating systems, distributed systems, and high-performance computing. Education: B.S., University of Ghent, Belgium (1979) M.S., Stanford University (1980) Ph.D., Stanford University (1984) Research Interests: Dr. Zwaenepoel focuses on distributed systems, operating systems, and their applications in database replication, virtual machine performance, and software update mechanisms. His work includes foundational contributions to distributed shared memory (e.g., Treadmarks) and startups like iMimic Networking. Awards: ACM Fellow (2000) IEEE Fellow (1998) Fellow of the Australian Academy of Technical Sciences and Engineering (2020) Recipient of the IEEE Tsutomu Kanai Award (2007) Key Contributions: His research addresses challenges in distributed systems performance, such as latency reduction in key-value stores and efficient graph processing. Current projects explore I/O optimization in virtualized environments and causal consistency for geo-replicated systems. Students/Advising: Advises Ph.D. students and postdocs, including William in database replication. His mentorship led to the Rice University Teaching Award (2000).
Evimaria Terzi is a Professor and Department Vice Chair at Boston University (BU), affiliated with the Data Management Lab@BU. Her research focuses on algorithmic data mining with applications in network analysis, recommendation systems, ranking, and clustering. She holds a PhD from the University of Helsinki and has held prior roles at IBM Almaden Research Center (2007–2009) and the Helsinki Institute for Information Technology (HIIT) before 2007. Her work spans theoretical and applied domains, including team formation algorithms, fairness in AI, and large language model evaluation. Notable contributions include studies on LSM tree optimization, counterfactual explanations for auditing fairness, and the dynamics of memorization in LLMs. Her recent publications emphasize flexibility in database systems and ethical AI practices. Evimaria’s research has been recognized through her contributions to conferences like WSDM and KDD, where she has served in organizing roles. The themes of her work consistently bridge algorithmic innovation with real-world applications in social networks, healthcare, and collaborative systems.
Prof. Korbinian Schneeberger is a full Professor of Computational Genetics and Genome Plasticity at the Ludwig Maximilian University of Munich , embedded within the Graduate School of Life Science Munich (LSM) . He leads a multidisciplinary team of bioinformaticians, biologists, and biotechnologists, all driven by a shared curiosity in genomic technologies and plant genome evolution. Contact: k.schneeberger@lmu.de . Research Focus: Genome plasticity and mutational dynamics across plant species Development and refinement of next-generation sequencing and assembly pipelines Comparative genomics, pan-genome construction, and structural variation Epigenetic regulation and transposon biology in plant genomes Meiotic recombination and crossover patterning in holocentric plants Application of single-cell and single-nucleus technologies to dissect gamete-level variation His laboratory develops widely-used bioinformatics tools—including SHOREmap , findGSE , SyRI , and plotsr —that enable the community to assemble, compare, and interpret plant genomes at unprecedented resolution. Recent work advances understanding of centromere evolution, adaptation to extreme soils, layer-specific somatic mutation patterns in fruit trees, and large-scale Arabidopsis population genomics. Scientific Output & Impact: Since 2015, Prof. Schneeberger has published more than 60 peer-reviewed articles, many appearing in top-tier journals such as Nature Genetics , Nature Plants , and Genome Biology . His 2025 studies already tackle the mutational landscape of Arabidopsis centromeres, scalable eQTL mapping in gametes, and the phased pan-genome of tetraploid potato, underscoring a trajectory at the forefront of plant genomic science. Funding & Collaborations: Research in the Schneeberger Lab is supported by multiple national and international grants, providing resources for high-throughput sequencing, computational infrastructure, and interdisciplinary training. The group actively collaborates with leading plant research centers worldwide, sharing data and tools to accelerate discoveries in crop improvement and evolutionary biology. Team & Environment: The lab operates as a vibrant, international environment with state-of-the-art wet-lab and computational facilities. Trainees and staff benefit from the rich ecosystem of LSM, including structured doctoral programs, career mentoring, and access to cutting-edge core facilities.
Professor Shenghua Gao is an Associate Professor at the School of Computing and Data Science of the University of Hong Kong (HKU), concurrently serving as Assistant Director for Shanghai Initiatives. He holds a PhD from Nanyang Technological University. His research focuses on integrating machine learning, spatio-temporal data analysis, and database systems to address challenges in mobility prediction, traffic management, and geospatial representation learning. He has contributed significantly to trajectory modeling, indexing frameworks for multi-dimensional data, and the application of large language models (LLMs) in spatio-temporal contexts. Key research interests include: Spatio-Temporal Data Science: Developing frameworks for efficient processing and analysis of point cloud, trajectory, and traffic data. Machine Learning for Databases: Innovating indexing algorithms (e.g., BMTree, MAST) and query optimization techniques leveraging ML. Trajectory and Mobility Prediction: Creating personalized models for next-location prediction and transfer learning across regions. Geographic AI (GeoAI): Enhancing road network representation and urban function inference using physics-guided and foundation models. Recent work highlights include the ST-LLM+ framework for traffic prediction, the MAST system for point cloud analytics, and the exploration of City Foundation Models for urban challenges. His publications span top venues in databases (SIGMOD, VLDB) and AI/data science (ICML, NeurIPS). While no awards are explicitly mentioned, his prolific output and leadership roles indicate significant academic contributions. He is actively involved in teaching and supervising research in the School’s undergraduate and postgraduate programs, including MSc(AI) and MPhil/PhD tracks.
Tong Zhang is a Professor in the Electrical, Computer and Systems Engineering Department at Rensselaer Polytechnic Institute (RPI). He joined RPI in 2002 as an assistant professor, advancing to associate professor in 2008 and full professor in 2013. His research focuses on computer systems, particularly memory and data storage across software and hardware stacks, with interdisciplinary applications in computer architecture, VLSI signal processing, and error correction coding. He holds a B.S. and M.S. from Xian Jiaotong University (China) and a Ph.D. from the University of Minnesota. His work emphasizes energy-efficient storage solutions, transparent compression, and hardware-software co-design. Notable contributions include innovations in SSD arrays, computational storage drives, and database systems. He is an IEEE Fellow and maintains affiliations with RPI's Computer Science programs. Research interests span memory systems optimization, storage architectures, and emerging technologies like CXL-based AI acceleration. His publications highlight advancements in reducing energy consumption, improving data deduplication, and enhancing B+-tree performance on modern storage hardware. Awards: IEEE Fellow (2023) Grants/Advising: Extensive grant-funded research in storage systems; no student advisees explicitly listed. Labs/Teams: Engaged in interdisciplinary collaborations within RPI's computational storage and memory research groups.
Rong Zhang is a Professor at East China Normal University's School of Computer Science and Software Engineering in Shanghai, China. With a PhD from Fudan University's Department of Computer Science and Engineering (2007), Dr. Zhang has established himself as a leading researcher in database systems, particularly in transaction processing, performance evaluation, and distributed database technologies. His extensive collaboration network includes prominent researchers like Aoying Zhou, Weining Qian, and Xiaofeng He. Dr. Zhang's research primarily focuses on database performance evaluation, transaction processing systems, workload generation, and isolation level verification. His work addresses critical challenges in modern database systems including generating realistic test databases, benchmarking transactional performance, verifying isolation levels in distributed environments, and optimizing query processing. His research has significant practical applications for database vendors and users who need reliable performance metrics and verification tools. Analysis of Dr. Zhang's recent publications (2022-2025) reveals a strong focus on database benchmarking and verification. His work on the Leopard test suite for isolation level verification represents a major contribution to ensuring database correctness. The Mirage and Touchstone projects demonstrate innovative approaches to generating enormous, query-aware databases for comprehensive performance evaluation. His research increasingly incorporates machine learning techniques, as seen in SPQO for query plan reuse and functionality-aware database tuning. Dr. Zhang has mentored numerous researchers who appear as co-authors on his papers, including Xiaotong Wang, Siyang Weng, Yuming Li, and Qingshuai Wang. His research has been supported by projects focused on database performance evaluation, transaction processing, and distributed systems. While specific grant details aren't provided in the source material, his extensive publication record across top database venues suggests sustained research funding. Dr. Zhang appears to lead or be a key member of a research group focused on database systems at East China Normal University. This group has developed several significant tools and frameworks including Leopard for isolation verification, Mirage for database generation, and Lauca for workload duplication. Their work bridges theoretical database concepts with practical system implementation, producing tools that have become influential in database research and development.
