Walid G. Aref is a Professor of Computer Science at Purdue University since Fall 1999. His research focuses on database systems, spatial and spatio-temporal data management, query processing, and indexing. He has led projects funded by NSF, NIH, and industry partners. Aref is a Fellow of the IEEE and has received awards including the NSF CAREER Award (2001) and VLDB’s Ten-Year Best Paper Award (2016). He serves as Editor-in-Chief of ACM Transactions on Spatial Algorithms and Systems and has authored numerous influential papers. Education: BSc/MSc from Alexandria University (Egypt), PhD from University of Maryland (1993). Research Interests: Extending database functionality for emerging applications like spatial, graph, and sensor databases. Notable contributions include the Chameleon project, AQWA system for big spatial data, and privacy-preserving Casper framework. Selected Projects: NSF-funded work on multi-predicate spatial queries, in-memory graph relational systems, and adaptive spatio-textual processing. Systems like Tornado (spatio-textual streams) and LocationSpark (distributed spatial data) exemplify his contributions. Awards: Multiple best paper awards, IEEE Fellow, and leadership roles in ACM SIGSPATIAL.
Dr. Feng Yan is an Associate Professor at the University of Houston's Computer Science Department, leading the Intelligent Data and Systems Lab (IDS Lab). He previously held an Associate Professor position at the University of Nevada, Reno. His research focuses on bridging Big Data, Machine Learning, and Systems, with interdisciplinary applications in wildfire science, materials engineering, and civil infrastructure. He has received prestigious awards such as the NSF CAREER Award and the Regents' Rising Researcher Award. Education: Ph.D. (2016) and M.S. (2011) in Computer Science from College of William and Mary; B.S. (2008) in Computer Science from Northeastern University. Research experience includes roles at Microsoft Research and HP Labs. Research Interests: Large Language Models (LLM), Distributed Deep Learning, AutoML, Serverless Computing, Federated Learning, and AI-driven domain sciences. His work emphasizes real-world impact through collaborations with industry and national labs. Publications: Over 60+ papers in top-tier venues like NeurIPS, ICLR, KDD, AAAI, SOSP, SC, and VLDB. Key contributions include ZeRO++ (collective communication optimization), Gradient Compression techniques, and Federated Learning frameworks like TiFL and HDFL. Awards: NSF EPSCoR Award ($20M), NSF CAREER Award, FAA BAKFAA Grant, and multiple best paper awards (IEEE CLOUD 2018, CLOUD 2019). Active in program committees for HPDC, ICAC, ICPE, and AAAI. Advising: Supervised over 30+ graduate/undergraduate students, with placements at Microsoft Research, IBM, Oak Ridge National Lab, Facebook, and MathWorks. Runs a vibrant lab with a focus on interdisciplinary AI/Systems research.
Emanuel Sallinger is a Full Professor at TU Wien's Databases and Artificial Intelligence Group and Vice Dean of Academic Affairs for Business Informatics and Data Science. He leads the Knowledge Graph Lab, focusing on scalable knowledge-based systems, reasoning in knowledge graphs, and AI integration. His research spans computational logic, database theory, and blockchain applications. Education: PhD in Computer Science (awarded 'sub auspiciis praesidentis rei publicae'), Master's degrees in Computational Intelligence and Informatics Management, and a Bachelor's in Software and Information Engineering. Research Interests: Knowledge graphs (construction, reasoning, scalability), logic-based systems, AI/ML integration with databases, enterprise architecture modeling, and financial knowledge systems. His work emphasizes practical applications like enterprise modeling, sustainable waste management, and regulatory compliance. Grants & Projects: Lead Vienna Science and Technology Fund (WWTF)-funded Knowledge Graph Lab. Involved in projects like 'Knowledge Graph-driven Tour Management' (sustainability), 'SustainGraph' (waste processing), and 'Enterprise Architecture Knowledge Graphs'. Teaching: Offers courses on Knowledge Graphs, Generative AI, Database Systems, and research methodology. Supervises doctoral and master's students in AI, databases, and knowledge representation. Labs/Teams: Knowledge Graph Lab at TU Wien, collaborating with industry on blockchain-based systems, financial AI, and enterprise architecture frameworks.
Amin Mesmoudi serves as Associate Professor in Data Engineering at the University of Poitiers' IUT (Institut Universitaire de Technologie), with dual laboratory affiliations at LIAS-ENSIP (Poitiers campus) and LIAS-ISAE-ENSMA (Chasseneuil campus). His research bridges theoretical database systems with practical large-scale data engineering challenges, particularly in semantic web technologies and machine learning applications. The laboratory maintains physical presences at both ENSIP's Bâtiment B25 in Poitiers and ISAE-ENSMA's Téléport 2 facility in Chasseneuil, facilitating cross-institutional collaboration. Mesmoudi's research program centers on scalable data management systems, with three interconnected pillars: (1) RDF and graph-based query optimization techniques for billion-triple datasets, (2) machine learning integration for spatial query performance and anomaly detection, and (3) explainability frameworks for complex black-box models. His work demonstrates consistent evolution from foundational database systems (2011-2016) toward contemporary AI-driven data engineering, particularly evident in his 2023-2025 publications on temporal dependency preservation and co-selection explainability. The Data Engineering team within LIAS laboratory provides the primary research context for these investigations. Publication analysis reveals strong methodological continuity in addressing scalability bottlenecks across database paradigms. Early work focused on SQL-on-MapReduce benchmarking for astronomy databases (2015-2016), transitioning to specialized RDF processing frameworks (2019-2021), and culminating in current hybrid approaches combining temporal modeling with machine learning (2023-2025). Key technical themes include fragmentation strategies for distributed data, optimizer feedback mechanisms, and graph-based query acceleration - all targeting real-world performance constraints in big data environments. As a core member of LIAS laboratory's Data Engineering team, Mesmoudi contributes to France's national research infrastructure in computer science and automation systems. The laboratory's dual-university structure enables unique cross-pollination between University of Poitiers' academic programs and ISAE-ENSMA's engineering specialization, with Mesmoudi's work exemplifying this synergy through applications spanning astronomy databases to wireless sensor networks.
Peter Haas is a Professor at the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, with an adjunct role in Industrial Engineering. Previously, he spent 30 years as a Principal Research Staff Member at IBM Research and held a consulting professorship in Management Science and Engineering at Stanford University. His research focuses on applying probability and statistics to data management, simulation of complex systems, and machine learning scalability. Education : PhD, Operations Research, Stanford University, 1986 MS, Statistics, Stanford University, 1984 MS, Environmental Engineering, Stanford University, 1979 SB, Engineering and Applied Physics, Harvard University, 1978 Research Interests : Haas’s work spans stochastic systems, probabilistic databases (e.g., MCDB and SimSQL), sampling techniques, and simulation optimization. He pioneered methods for managing uncertain data and scalable machine learning, including compressed linear algebra for declarative systems. His recent focus includes in-database decision support and hybrid simulation metamodeling with neural networks. Key Contributions : He developed the Online Aggregation framework (SIGMOD 1997), which earned a Test-of-Time Award in 2007. His work on matrix factorization and distributed stochastic gradient descent (DSGD) revolutionized large-scale machine learning. He also advanced techniques for estimating distinct-values and correlation discovery in databases. Awards : A six-time recipient of IBM’s Pat Goldberg Memorial Award, he is an ACM and INFORMS Fellow. His honors include the VLDB Best Paper Award (2016), EDBT Best Paper (2018), and recognition in Communications of the ACM. Advising & Grants : He advises four current PhD students and has graduated Matteo Brucato. His IBM career included over 30 patents, including foundational work for DB2’s sampling capabilities and IBM Watson analytics. He leads the DREAM Lab, focusing on data systems for exploration and analytics. Labs/Teams : Directs the Data systems Research for Exploration, Analytics, and Modeling (DREAM) Lab, advancing projects like Splash (health system simulation) and SuDocu (document summarization by example).
Paolo Trunfio is a Professor of Computer Engineering at the University of Calabria, Italy, and co-founder of DtoK Lab S.r.l., an academic spin-off focused on data analysis and distributed systems. He holds a Ph.D. and is affiliated with the DIMES Department, specializing in big data, cloud computing, and high-performance computing (HPC). His research emphasizes scalable data analysis frameworks, edge-cloud continuum solutions, and machine learning applications for social media and disaster monitoring. Trunfio serves as an Associate Editor for ACM Computing Surveys and Journal of Big Data , and is on the editorial boards of several journals including Future Generation Computer Systems . He has authored four influential books, including Programming Big Data Applications (2024) and Data Analysis in the Cloud (2015). His work spans distributed systems, IoT-based smart objects, and exascale computing. Notable projects include the EU-funded eFlows4HPC and ASPIDE initiatives, which focus on HPC workflows and exascale programming models. Trunfio’s publications (over 200 papers) address topics like social media analytics, energy-efficient P2P networks, and parallel data mining. He leads research in urgent computing for disaster response, edge-cloud integration for urban mobility, and AI-driven data analysis. His contributions to cloud frameworks (e.g., JS4Cloud, ParSoDA) and HPC libraries (e.g., DCEx) highlight his expertise in bridging theory and practice in distributed computing ecosystems.
Changcheng Huang is a Professor at Carleton University's Department of Systems and Computer Engineering, part of the Faculty of Engineering and Design. He holds a Ph.D. from Carleton University and is licensed as P.Eng. His research focuses on Machine Learning, Network Architecture, and Optical Networks, emphasizing resource optimization and protocol design. Dr. Huang leads the Advanced Optical Network Laboratory (AONL), funded by CFI and OIT, which explores optical network technologies and interworking with electronic networks. His lab includes state-of-the-art equipment like Nortel switches and photonic switches. Recently, he advised PhD students Qiao Lu, Khoa Nguyen, and others, and completed postdoc Eslam G. AbdAllah. RA positions are available at both master's and PhD levels. His work spans publications in journals like IEEE Transactions and conferences such as Globecom and ICC. Research areas include intelligent network control mechanisms, wireless networks, and network protocol implementation. Education: Ph.D. (Carleton University). Research interests also include modeling/simulation techniques and reliability mechanisms for optical networks. He teaches courses like SYSC 5108 (Deep Learning) and SYSC 4602 (Computer Communications). Grants funded by CFI and OIT support his lab's optical networking projects. Over 150+ publications highlight his contributions to virtual network embedding, edge computing, and optical data center networks. Lab facilities include OMM photonic switches, Nortel routers, and Dell servers. Collaborative projects involve industry and academic partnerships, advancing interworking technologies between optical and electronic networks. His work bridges theoretical research with practical implementations, addressing challenges in network scalability, energy efficiency, and reliability.
Sheheeda Mariam Manakkadu is an Associate Professor in the Department of Computer Science at Southern Illinois University Carbondale. She teaches graduate courses in Data Structures, Object-Oriented Programming, Data Mining, Text Mining, and Cloud Architecture, along with undergraduate courses in Operating Systems and Data Analytics. Ph.D., Computer Engineering M.E., Biomedical Engineering Her research spans robotics, data analytics, and parallel computing. Key areas include adaptive control of robotic manipulators, big data processing via MapReduce, IoT resource allocation, and computational bioinformatics for protein networks. Recent publications focus on neuro-sliding mode control, cloud architecture, and scalable recommender systems. She actively participates in academic service as a committee member for graduate courses and the IEEE Erie Section. Her work integrates machine learning, optimization algorithms, and distributed systems across diverse domains.
Xiaojun Ruan is an Associate Professor in the Department of Computer Science at California State University, East Bay. He holds a Ph.D. in Computer Science from Auburn University (2011) and a B.E. in Computer Science and Technology from Shandong University (2005). His primary research focuses on energy-efficient systems, cloud computing optimization, storage systems, and security-aware resource management. He has extensive experience in thermal modeling, parallel I/O performance, and distributed deep learning frameworks. Dr. Ruan’s work emphasizes balancing energy efficiency, reliability, and performance in storage and cloud environments. Notable projects include DuoFS (hybrid storage system), energy-aware VM allocation strategies, and securing cloud infrastructure against co-residence attacks. His research bridges hardware-software co-design principles with practical system optimizations. His publications span topics from NVMe SSD performance optimization to text augmentation for spam detection, reflecting a blend of storage systems and machine learning applications. He has actively contributed to improving Shuffle I/O in big data processing, thermal management in clusters, and secure virtualization techniques. Dr. Ruan collaborates on interdisciplinary projects involving distributed computing, cybersecurity, and real-time systems. His lab focuses on deploying energy-efficient solutions while maintaining robust reliability, evidenced by over 50 peer-reviewed articles and ongoing contributions to academic conferences.
Qiang Zhu is a Professor in the Department of Computer and Information Science at the University of Michigan-Dearborn, holding the William E. Stirton Professorship (2017–2024). He founded the Data Science/Management Research Laboratory and is affiliated with the Michigan Institute for Data Science (MIDAS). His research spans data science, data management, and machine learning. Ph.D., University of Waterloo M.S., McMaster University M.Eng., Southeast University B.S., Southeast University Research focuses on advanced data indexing, query optimization, and AI-driven data management, with applications in genomics, network systems, and education. His work integrates machine learning with database systems for scalable solutions. Recent publications include topics in federated learning fairness, digital twin middleware, project-based CS education, and genome data indexing. Scientific contributions recognized through awards like the Wilkes Award (2008), ACM Distinguished Scientist (2013), and Springer Nature Editor of Distinction (2025). 2013–2018: Department Chair NSF, IBM, and Ford grants Over 250 conference committee roles He directs the Data Science/Management Research Lab, focusing on collaborative projects in genome analytics and smart computing infrastructures.
Azza Abouzied is Associate Professor of Computer Science at New York University Abu Dhabi and Global Network Associate Professor at the Tandon School of Engineering. She serves as Vice Provost for Faculty Advancement and Engagement at NYUAD starting September 2024. Her research bridges database systems and human-computer interaction, focusing on intuitive tools for data querying and decision-making in uncertain, collaborative environments. PhD, Yale University (2013) MPhil, Yale University MSc, Dalhousie University BSc, Dalhousie University Her research centers on human-data interaction, designing systems that make data accessible to non-experts. She combines techniques from UI design, machine learning, and databases to build tools that simplify complex data tasks. Her earlier work focused on example-driven querying and synthetic data generation, while her recent work explores in-database prescriptive analytics and decision support in domains like disinformation mitigation and epidemic planning. Her publications span database and HCI venues, with a recurring theme of enhancing usability without sacrificing scalability. She co-founded Hadapt, a Big Data analytics platform, and has led interdisciplinary research through the Human-Data Interaction Lab and the Center for Interacting Urban Networks. Her teaching includes foundational courses such as Database Systems, Operating Systems, and Data, as well as the critical thinking course Techruption. VLDB Test of Time Award (2019) Best Paper Award in Database Systems Honorable Mention in HCI Publications Azza mentors undergraduate capstone students and advises prospective PhDs, research assistants, and postdocs. She is actively involved in academic leadership, having chaired NYUAD’s faculty council in 2024 and co-chaired the SIGMOD 2025 program. Her work emphasizes empowering users to critically engage with data and AI, both in research and education.
Professor Foto N. Afrati is a Distinguished Faculty Member at the National Technical University of Athens, specifically within the School of Electrical and Computing Engineering and the Division of Communication, Electronic and Information Engineering. She has held this position since 1993, following previous academic ranks at the same university as Associate Professor (1989-1993), Assistant Professor (1985-1989), Lecturer (1982-1985), and Research Fellow (1980-1982). She completed her PhD in Electrical Engineering at Imperial College of the University of London in March 1980, with a dissertation focused on Error Correcting Codes by Algorithms. Her academic journey also included a Diploma from Imperial College (March 1980) and an earlier Diploma in Electrical and Mechanical Engineering from the National Technical University of Athens (June 1976). Professor Afrati's research interests span several critical areas in computer science: Parallel and distributed computation Processing of very large data (including MapReduce) Data and web mining Database Systems Information integration Query optimization Computation and complexity of algorithms Approximation algorithms Her most recent publications demonstrate expertise in MapReduce environments, query optimization with views, and data exchange frameworks. These works are published in prestigious venues like EDBT, VLDB, PODS, and ICDT, with specific focus areas including adaptive sampling techniques, data source integrity, and algorithm complexity in database environments. Professor Afrati has received significant recognition in her field, including Fellow of the Association for Computing Machinery (ACM) Best Paper Award at the International Conference on Database Theory (ICDT) 2009 She has advised numerous PhD students throughout her career, including Theodoros Mitakos, Ezz Hattab, Nikos Kiourtis, and Angelos Vasilakopoulos. Her current PhD students include Victor Kyritsis and Nikos Stassinopoulos. Professor Afrati maintains strong professional networks through her various visiting positions at institutions such as Google, Stanford University, IBM Research Center, University of Helsinki, University of Paris, DIMACS, and others. She has served as associate editor and reviewer for major academic journals and conferences including IEEE TKDE, ACM Transactions of Database Systems (TODS), Journal of ACM (JACM), and Theoretical Computer Science (TCS). Her extensive work in research projects spans both national and international initiatives, with funding from sources including the European Union's Thalis project, ESPRIT working groups, HCM networks, and Greek General Secretariat of Research and Technology grants.
Aws Albarghouthi is affiliated with the University of Wisconsin-Madison, USA. He is an active researcher with significant contributions to program synthesis, formal verification, and machine learning. Key roles: Author, Session Chair, Committee Member in conferences like PLDI, POPL, VMCAI, SPLASH, and ICFP. Research spans quantum computing, differential privacy, and static analysis. Research Trends include: Quantum Circuit Compilation and Optimization Probabilistic Verification of Fairness and Privacy Synthesis of Datalog and MapReduce Programs Neural-Augmented Static Analysis Bias Detection in Data Security Robustness in Machine Learning
Professor Tova Milo is the Chair for Information Management at the School of Computer Science, Tel Aviv University, leading the prominent Databases Lab (DB Group). Her research spans databases, big data management, crowd-based data sourcing, and business process querying, with significant contributions to data integration and semi-structured data systems. Her research interests focus on innovative approaches to data management challenges through machine learning integration, crowd computing, and business process analysis. Key projects include Business Process Querying (BPQ) for analyzing BPEL specifications, MoDaS for crowd-based data management, and PROX for data provenance summarization. Her work bridges theoretical foundations with practical applications in fraud detection, recommendation systems, and data cleaning. Analysis of her recent publications reveals strong trends in human-in-the-loop data management systems, with growing emphasis on crowd integration for data cleaning and knowledge acquisition. Her research increasingly combines traditional database theory with machine learning techniques for scalable big data processing, while maintaining focus on business process modeling and provenance tracking. ACM PODS Alberto O. Mendelzon Test-of-Time Award (2010) ERC Advanced Investigators grant MoDaS (2011) The Weizmann Prize for Exact Sciences (2017) VLDB Women in Database Research award (2017) IEEE TCDE Impact award (2022) ISF Breakthrough Research Grant (2022) Doctorate Honoris Causa, University of Zurich (2023) ACM Fellow Member of Academia Europaea Professor Milo has advised over 30 graduate students including PhD candidates like Yael Amsterdamer and Ohad Greenshpan, and numerous MSc students working on projects including MoDaS, BPQ, and EDOS. Her research has been supported by major grants including the ERC Advanced Investigators grant and ISF Breakthrough Research Grant. She actively collaborates with industry partners including IBM and Microsoft, particularly in business process management standards. She directs Tel Aviv University's Databases Lab, which maintains the DB Group with multiple research streams including business process querying (BPQ), crowd-based data management (MoDaS), and self-adaptive data dissemination (EDOS/COLT). The lab operates from the Schreiber Building (M-20) and maintains strong international collaborations, particularly with European institutions through the ERC-funded MoDaS project.
Reza Bosagh Zadeh is an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning, deep learning, and their applications in video classification, healthcare analytics, and distributed algorithms. He specializes in developing scalable computational methods for real-time data processing and has contributed to advancements in neural networks and optimization techniques. Reza's work spans theoretical and applied domains, with notable contributions to TensorFlow frameworks, video summarization systems, and medical imaging analysis. His research often integrates interdisciplinary approaches, leveraging both academic and industrial collaborations. Notable projects include developing machine learning models for glaucoma detection and creating efficient algorithms for large-scale data processing in environments like Apache Spark. His publications emphasize real-time video stream analysis, distributed computing architectures, and practical implementations of deep learning. Reza holds a strong presence in both academic and tech sectors, with contributions to platforms like Twitter's Who-to-Follow system and innovations in edge computing for video surveillance.
