Alberto Lerner is a Senior Researcher at the Department of Informatics within the Interfaculty Informatics Department at the University of Fribourg. His research focuses on advancing database systems, storage architectures, and hardware-software co-design. He holds a strong interest in computational storage, network-accelerated query processing, and the integration of modern hardware technologies like CXL into database engines. Lerner's work emphasizes performance optimization and scalable solutions in data-intensive computing environments. His research interests include database architecture, storage co-design, hardware acceleration, and network-driven computing. Recent publications highlight innovations in point cloud data processing, reprogrammable storage devices, and software-defined controllers for NAND flash systems. Lerner's articles reflect a trend toward leveraging modern hardware advancements to enhance database and storage performance. He has contributed to frameworks like BABOL and Data Pipes, advancing declarative control over data movement and network-based graph mining.
Marlon Dumas is a leading researcher in business process management and process mining at the University of Tartu, Estonia. With over 467 publications spanning from 1997 to 2025, his work has significantly advanced methodologies in business process analysis, simulation, and optimization. His research bridges theoretical foundations with practical applications, developing tools and frameworks that enable organizations to analyze and optimize operational processes. Dumas's primary research interests include business process management, process mining, business process simulation, prescriptive process monitoring, and data-aware business processes. He has pioneered methods for modeling resource availability, activity delays, and waiting times in business processes. His work on prescriptive process monitoring addresses critical challenges such as resource constraints, uncertainty in predictions, and causal effect estimation for interventions. Recent publications reveal a strong trend toward integrating artificial intelligence with business process management, particularly exploring the application of large language models to process optimization, monitoring, and redesign tasks. His research demonstrates consistent innovation, with publications appearing in top venues including Information Systems, Data & Knowledge Engineering, and the International Conference on Business Process Management. Dumas has developed several influential tools including SIMOD for automated discovery of business process simulation models, Optimos for simulation-driven process optimization, and Kairos for prescriptive monitoring. His collaborative network is extensive, featuring frequent co-authorship with prominent researchers including Marcello La Rosa, Luciano García-Bañuelos, Fabrizio Maria Maggi, and Wil M. P. van der Aalst. His work on privacy-preserving process mining, particularly regarding differentially private release of event logs, addresses critical challenges in applying process mining techniques while maintaining data privacy and compliance with regulations like GDPR. Dumas's research continues to push boundaries, with recent work exploring the integration of large language models with business process management systems, suggesting an ongoing commitment to advancing the field through innovative applications of emerging technologies.
Professor Chatziantoniou Damianos holds a position at the Athens University of Economics and Business (AUEB) within the Department of Management Science and Technology (DMST), School of Business. He earned a B.Sc. in Applied Mathematics from the National & Kapodistrian University of Athens (1991), M.Sc. from New York University's Courant Institute of Mathematical Sciences, and a Ph.D. from Columbia University. His research focuses on big data systems, business intelligence, query processing, and real-time data analysis, with contributions influencing commercial database systems like Microsoft SQL Server and Oracle. As Director of AUEB's Master’s program in Business Analytics and Big Data, he previously served as an Assistant Professor at Stevens Institute of Technology (1997–1999). His industry collaborations include co-founding Panakea Software and VoiceWeb SA, and consulting roles at Aster Data Systems (now Teradata). He leads big data projects for companies like Cosmote, Piraeus Bank, and HEDNO. His research has been published in top venues including VLDB, ICDE, and SIGMOD, emphasizing practical applications in large-scale analytics and OLAP systems. He advises on strategic partnerships for the MSc program and maintains an active role in academic administration, including steering committees and quality assurance initiatives.
Grzegorz Kozieł is a Senior Lecturer at Lublin University of Technology, affiliated with the Faculty of Electrical Engineering and Computer Science and the Department of Computer Science. His research focuses on Information Security, Digital Signal Processing, and Cybersecurity, with additional interests in steganography, database systems, and network security. He has been involved in numerous projects, including the Integrated Development Programme of Lublin University of Technology and initiatives like Moodle ANMC and JCSI Resources. His academic contributions span over 50 publications, emphasizing comparative analyses of technologies such as smart home systems, programming languages, and database frameworks. Notable recent work includes studies on cybersecurity awareness among young learners, watermarking techniques for 3D models, and the application of Fourier transforms in sensor data analysis. Current projects include advancements in digital twin models for visually impaired individuals and participation in LubGame Conference 2025. Dr. Kozieł has received recognition through awards such as the Merit Medal for the City of Lublin (though explicitly awarded to departmental professors, it reflects his collaborative contributions). His teaching and research emphasize practical applications, including WordPress-based student projects and assessments of foreign language learning portals. He actively contributes to the development of IT education curricula, focusing on bridging academic training with industry needs.
Minos Garofalakis is a Professor of Computer Science at the School of Electrical and Computer Engineering (ECE) of the Technical University of Crete (TUC), where he directs the Software Technology and Network Applications Laboratory (SoftNet). He is also the Director of the Information Management Systems Institute (IMSI) at the Athena Research and Innovation Centre in Athens. Previously, he held roles at Yahoo! Research, Intel Research Berkeley, and Bell Laboratories, and was an Adjunct Associate Professor at UC Berkeley. Education: He earned a BSc in Computer Engineering from the University of Patras (1992), followed by MSc (1994) and PhD (1998) in Computer Science from the University of Wisconsin-Madison. Research Interests: His work focuses on Big Data analytics , including database systems, data streams, approximate query processing, probabilistic databases, and secure/private data analytics. Key areas include distributed stream processing, data synopses, and machine learning applications. He has authored over 150 papers and holds 29 patents, with an h-index of 63 and 13,500+ citations. Recent Work Trends: His recent articles emphasize scalable stream analytics (e.g., OmniSketch), privacy-preserving techniques, and distributed event processing. He explores challenges in handling high-velocity data streams, uncertainty in databases, and real-world applications like healthcare analytics. Scientific Awards: ACM Fellow (2018), IEEE Fellow (2017), TUC Excellence Award (2015), and multiple patents from Bell Labs/Yahoo/AT&T. Advising & Grants: He led EU projects such as FERARI, LEADS, and The Human Brain Project. His lab, SoftNet, develops tools for extreme-scale analytics and declarative networking. Current work includes interactive cross-platform analytics (Infore) and AI-driven medical data systems. Labs/Teams: Director of SoftNet Lab and IMSI. Collaborates with industry partners on distributed systems and privacy-preserving technologies.
Richard P. Martin is an Associate Professor and Associate Chair in the Department of Computer Science at Rutgers University's School of Arts and Sciences. His research focuses on wireless networks, sensor systems, and network security applications, with particular emphasis on practical implementations that address real-world challenges in healthcare monitoring, energy efficiency, and network performance. He maintains an active research group working closely with WINLAB (Wireless Information Network Laboratory) and has numerous ongoing collaborations with industry and academic partners. Dr. Martin's research interests span several interconnected domains including wireless communication systems, sensor network design, network security, localization technologies, and energy-efficient computing. His work bridges theoretical computer science with practical engineering applications, often focusing on how wireless technologies can solve problems in healthcare monitoring, particularly medication adherence systems. His research group develops innovative solutions that combine hardware and software approaches to address challenges in wireless sensing, network security, and system optimization. The team's work often involves creating novel protocols and architectures that improve performance while maintaining security and privacy constraints. His recent publication trends show increasing focus on healthcare applications of wireless technology, particularly medication adherence monitoring systems using smart pill bottles and other unobtrusive sensing approaches. He has also maintained a strong research thread in wireless network security, localization systems, and energy-efficient computing. His work often combines machine learning techniques with traditional networking approaches to create more intelligent and adaptive systems. The interdisciplinary nature of his research spans computer science, electrical engineering, and healthcare technology domains. Dr. Martin has advised numerous graduate students who have co-authored publications with him, including Murtadha Aldeer, Mohammadreza Soltaniyeh, and Jorge Ortiz. His research has been supported by various funding sources that enable the development of practical wireless systems and sensor networks. He teaches courses in computer systems and structures, sharing his expertise in networked systems with undergraduate and graduate students. His laboratory work centers around WINLAB, where his team develops and tests wireless and sensor network technologies. The lab focuses on creating practical implementations of theoretical concepts, often building custom hardware and software solutions to address specific challenges in wireless communications and sensing. Current projects emphasize healthcare applications, energy efficiency, and security in wireless systems.
Dr. Serdar Arslan is a Lecturer at the Department of Computer Engineering at Cankaya University. He holds a PhD in Computer Engineering from Middle East Technical University (METU), with a thesis on multidimensional data indexing. His academic background includes a Master's (2005) and Bachelor's (2001) in Computer Engineering from METU and Hacettepe University, respectively. His research focuses on database systems, machine learning, multimedia data indexing, and forecasting models. Education: Bachelor of Engineering, Computer Engineering, Hacettepe University (2001) Master of Science, Computer Engineering, METU (2005) Doctor of Philosophy, Computer Engineering, METU (2018) Research Interests: Machine Learning applications in healthcare forecasting and financial markets Advanced indexing techniques for multimedia databases (e.g., MM-FOOD structure) Natural language processing for stance detection in political discourse Hybrid forecasting models combining LSTM and Prophet algorithms Domain-specific NLP for product name extraction in Turkish text Publications: His recent work emphasizes machine learning-driven solutions for complex systems, including pandemic modeling, cryptocurrency analysis, and conflict discourse analysis. His earlier contributions focused on multimedia indexing and image retrieval systems using MPEG-7 standards. The 2025 paper on OSINT architecture frameworks highlights his expanding focus on cybersecurity and system design. Labs/Teams: While no specific lab is mentioned, his GitHub repositories (e.g., Forecasting, NLP projects) suggest active involvement in collaborative research projects related to his domains.
Anca Muscholl is a Professor at the University of Bordeaux and holds the Hans Fischer Senior Fellowship at the Technical University of Munich (TUM-IAS). She leads the Formal Methods group at the Bordeaux Laboratory for Computer Science Research (LABRI). Her research focuses on foundational aspects of formal verification, automata theory, logics, concurrent systems, and database foundations. She has held academic positions at the University of Paris 7 and has been recognized with prestigious awards, including the Silver Medal from CNRS (2010) and membership in the Institut Universitaire de France (2007–2012). Education: Master’s from Technical University of Munich (TUM), PhD from University of Stuttgart (1994), and habilitation at the same institution. She has contributed to editorial roles for journals like Information Processing Letters and Discrete Mathematics & Theoretical Computer Science , and serves on the council of the European Association for Theoretical Computer Science (EATCS). Her work emphasizes automated controller synthesis for distributed systems and formal methods in concurrency. Notable achievements include advancements in distributed synthesis, temporal logic, and verification of reactive systems. She actively participates in organizing major conferences like ICALP and steering committees for theoretical computer science initiatives. Awards: Silver Medal (CNRS 2010), Junior Member of IUF (2007–2012), Best Paper Awards (PODS 2006, ETAPS 2001). Professional Roles: Editor for TheoretiCS , member of EATCS council, and leader of the Formal Methods group at LABRI. Research Themes: Formal verification, automata theory, concurrency, distributed systems, database logics.
Jamshed Khan is an Assistant Research Scientist at the University of Maryland (College Park) in the Department of Computer Science. He completed his PhD in December 2024 at the same institution under the advisement of Dr. Rob Patro. His research bridges theory and systems, focusing on resource-efficient computational solutions for genomic data challenges. Research interests span several interconnected domains: Parallel Algorithms : Developing multi-core and distributed computing techniques External-Memory Systems : Creating algorithms optimized for SSD/disk storage hierarchies Large-Scale Data Structures : Designing indexes for genomic sequences and graphs Algorithm Engineering : Implementing practical solutions for computational biology His work consistently targets scalability challenges in genomic analysis pipelines. Publication trends demonstrate extensive contributions to: Parallel construction of genomic indexes (de Bruijn graphs, suffix arrays) Memory-efficient algorithms for sequence search and alignment Scalable systems for processing single-cell and bulk sequencing data Work is frequently published in premier venues including SPAA, ISMB, and Bioinformatics. Recognitions include: Ian Lawson Van Toch Memorial Award (ISMB/ECCB 2021) Outstanding Research Assistant Award (UMD 2021-2022) Excellence in Teaching Assistance Award (2018-2019) Seeks postdoctoral positions in high-performance parallel computing and large-scale data management, continuing work at the theory-systems intersection.
Dr. Jun Wu is an Assistant Professor in the School of Molecular Sciences and School of Earth and Space Exploration at Arizona State University (ASU). His research focuses on mineralogy, crystallography, and high-pressure geoscience using transmission electron microscopy (TEM). He developed the HPTEM (high-pressure TEM) technique, enabling in-situ analysis of Earth's interior under extreme conditions. Dr. Wu collaborates on the Keck Foundation-funded project exploring Earth’s water origins and investigates nano-material synthesis and deep earthquakes. He holds a Ph.D. in Mineralogy from Johns Hopkins University and postdoctoral training under Prof. Peter Buseck at ASU. Education : Ph.D., Mineralogy, Johns Hopkins University. Research Interests : Dr. Wu’s work bridges geoscience and materials science through advanced TEM applications. His HPTEM technique uses carbon nanostructures to simulate Earth’s high-pressure environments, advancing understanding of planetary formation and deep Earth processes. He also explores nanomaterials synthesis and unresolved geological phenomena like deep earthquakes. Grants : Active funding from the Keck Foundation for origins-of-water research. Collaborative projects integrate experimental and computational approaches in Earth sciences. Labs/Teams : Engaged in cross-disciplinary teams at ASU’s School of Molecular Sciences and Earth and Space Exploration, leveraging cutting-edge microscopy facilities for high-pressure experiments.
Dr. George Yu is an Associate Professor of Computer Science and Information Systems at Youngstown State University (YSU), Ohio. He holds a Ph.D. in Computer Science from Southern Illinois University (2013), an M.S. in Pure Mathematics from Shandong University (2008), and a B.S. in Information and Computation Science from Northeastern University (2005). As the Campus Champion of NSF XSEDE at YSU, he facilitates access to national supercomputing resources and organizes workshops on High-Performance Computing (HPC). His research focuses on Database Systems , Approximate Query Processing (AQP) , Big Data Analytics , Cloud Computing , and Bioinformatics . Notable contributions include the AQPrius framework for error-aware AQP and collaborative work on genomic data analysis in plants like Aspergillus niger and tomato. Dr. Yu has received multiple recognitions, including the 2020 Distinguished Professor in Scholarship at YSU and the 2019 Best Paper Award at the International Conference on Software Engineering and Data Engineering. He serves as an associate editor for journals like Transactions on Large-Scale Data and Knowledge-Centered Systems and actively contributes to conference program committees. His educational initiatives include developing YSU's Data Analytics (DATX) Certificate Program and teaching courses on databases, cloud computing, and blockchain. He leads the YSU Data Lab, which operates high-performance research clouds like Sarah Cloud and YSU STEM Cloud, and mentors students in NSF-funded research projects.
Matthias Paul Lanzinger is an Assistant Professor at the Technische Universität Wien's Faculty of Informatics, Department of Database and Artificial Intelligence. His research focuses on algorithms, graph neural networks, hypergraph decomposition techniques, parameterized complexity, and computational logic. He leads projects like 'DeConquer' (Vienna Science Fund) and 'HyperTrac', exploring efficient query processing and hypergraph-based algorithms. Research interests include theoretical computer science, database systems, and applying logical frameworks to solve complex computational problems. Recent work emphasizes hypertree decompositions, fuzzy Datalog, and graph motif analysis via the Weisfeiler-Leman test. He co-edited the 2024 Datalog-2.0 workshop proceedings and has supervised students on topics like column-store performance and graph query languages. His publications span venues like ACM Transactions on Database Systems, ICLR, and IJCAI, highlighting contributions to algorithmic efficiency, database theory, and logical reasoning systems. Active in academic service, he teaches courses on database systems, scientific research, and advanced topics in informatics.
Panagiotis Tampakis is an Associate Professor at the Department of Mathematics and Computer Science, University of Southern Denmark. His research focuses on Big Data Analytics, Mobility Data Analysis, and Predictive Modeling with applications in fields such as maritime surveillance, sports analytics, and healthcare informatics. He is actively involved in developing scalable algorithms for trajectory clustering, spatiotemporal data mining, and distributed systems. His work emphasizes practical solutions through platforms like i4sea for maritime activity monitoring and Pythia for distributed trajectory prediction. He has collaborated internationally on mobility data projects and earned the SSTD 2021 Best Paper Award for innovative spatial-keyword indexing techniques. Tampakis also supervises student projects integrating machine learning with real-world problems in finance, sports, and journalism. Key research themes include predictive analytics for traffic patterns, outlier detection in traffic flows, and analyzing passing sequences in football to predict goal-scoring opportunities. His publications span trajectory clustering, spatial-keyword query optimization, and explainable AI in journalism. Awards: SSTD 2021 Best Paper Award Key Projects: i4sea maritime platform, Pythia trajectory prediction framework, RoadRunner big data processing framework Collaborations: Active in global mobility data communities, with contributions to BMDA initiatives
Dr. Saida Elmi is an Assistant Professor Adjunct at Yale University School of Medicine and an Assistant Professor at the University of New Haven. She holds a Ph.D. in Computer Science from the National School of Mechanics and Aerotechnics (2017), followed by postdoctoral research at Korea University of Technology and Education (2017–2018) and National University of Singapore (2018–2021). Her research focuses on AI applications in healthcare, transportation systems, and spatial data mining. She developed the Automated Test of Embodied Cognition (ATEC), an AI-driven system using motion capture and machine learning for mental disease detection. Key contributions include predicting taxi fares, travel times, and energy consumption in transportation networks using deep learning architectures like RNNs and CNNs, and designing group-oriented recommendation systems for points-of-interest (POIs) using graph convolution networks and attention mechanisms. Education : Ph.D., Computer Science, National School of Mechanics and Aerotechnics (2017) Postdoctoral Research, Korea University of Technology and Education (2017–2018) Postdoctoral Research, National University of Singapore (2018–2021) Her research interests span machine learning, spatial data mining, and AI ethics. She has pioneered frameworks for spatio-temporal data analysis, including a transfer learning approach for travel time prediction in data-scarce regions. Her work on energy consumption modeling and POI prediction has been published in top-tier conferences like WWW, MOBIQUITOUS, and ECIR. Dr. Elmi’s interdisciplinary projects integrate AI with healthcare and urban systems, emphasizing automated cognitive assessment and smart city technologies. Her current efforts focus on refining action recognition algorithms for clinical applications and optimizing recommendation systems for group mobility decisions.
Minos Garofalakis is a Professor at the School of Electrical and Computer Engineering , Technical University of Crete, and serves as Director of the Information Management Systems Institute at the Athena Research & Innovation Center in Athens. He has held senior research roles at Bell Labs, Intel Research, Yahoo! Research, and academic positions at UC Berkeley as an Adjunct Associate Professor. Currently, he is a Senior Research Consultant at Huawei Edinburgh Research Center and co-founder of Agora Labs , focusing on medical data privacy. Research Interests His work centers on Big Data Analytics , encompassing private data analytics , machine learning , federated analytics , and blockchain systems . He has pioneered advancements in data stream management , query optimization , and approximation algorithms , with applications in distributed systems and privacy-preserving technologies. Scientific Awards ACM Fellow (2018) IEEE Fellow (2017) Excellence in Research Award, Technical University of Crete (2015) FP7 Marie-Curie International Reintegration Fellowship (2010-2014) Bell Labs President’s Gold Award (2004) Central Bell Labs Teamwork Award (2003) Patents and Citations Garofalakis holds 29 issued US patents (36 filed) with applications in data management and analytics. His research has garnered over 16,000 citations on Google Scholar and an h-index of 69.
