László Lengyel is a Professor at the Budapest University of Technology and Economics (BME), affiliated with the Department of Automation and Applied Informatics . His work bridges theoretical and applied computer science, focusing on industrial automation, IoT systems, and model-driven engineering. Research interests include Model transformations and domain-specific languages IoT device management and multi-domain integration Software obfuscation and cybersecurity Graph algorithms and distributed computing (MapReduce) Real-time data analysis in manufacturing Automotive sensor networks His recent publications reflect expertise in model-driven IoT architectures , granule manufacturing automation , and MapReduce-based graph analysis , with a focus on industrial and automotive applications. He contributes to open-source frameworks like SensorHUB and explores gamification in driver behavior systems.
Dwight Makaroff is a Professor in the Department of Computer Science at the University of Saskatchewan . He leads the DISCUS research group , focusing on distributed systems, networking, and performance analysis. Makaroff holds a Ph.D. from the University of British Columbia (1998), an M.Sc. (1988), and a B.Comm. (1985) from the University of Saskatchewan. Research Interests: Distributed Data Processing & Hadoop Network Support for Multiplayer Games Information-Centric Networking Energy Efficiency in Mobile Devices Multicore Architectures Wireless Network Security Sensor Networks & Data Aggregation Teaching: Courses include Operating Systems Principles , Topics in Parallel & Distributed Systems , and advanced systems courses. He coordinated the ACM ICPC programming contest teams for over a decade. Committees: Graduate Committee Chair (2013-2015) University Council Member (2006-2014) Program Committee roles at IEEE/ACM conferences (IPCCC, CASCON, etc.) Recent Research Highlights: IoT security via blockchain Wearable device communication challenges Caching strategies for information-centric networks
Geoffrey C. Fox serves as Professor and Director of the Digital Science Center at Indiana University Bloomington's Luddy School of Informatics, Computing, and Engineering, with over 1,200 publications spanning physics and computer science. His career encompasses foundational contributions to parallel computing methodology, algorithms, and data-intensive systems. Educational background includes a PhD in Theoretical Physics from Cambridge University. Fox's research focuses on synergies between high-performance computing and data analysis, pioneering decomposition principles for MIMD hypercubes and message-passing efficiency. He developed innovative systems like Twister (enhanced MapReduce for iterative computations) and SPIDAL (Scalable Parallel and Interoperable Data-intensive Application Library), bridging HPC with data-intensive applications through architectures comparable to Apache Spark and Flink. Major recognitions include: ACM-IEEE CS Ken Kennedy Award (2019) for foundational contributions to parallel computing methodology, algorithms, software, and data analysis interfaces with broad applications ACM Fellow (2011) for contributions to high-performance computing software applications and diversity outreach Fox demonstrates exceptional commitment to community engagement through teaching Java and parallel computing courses to Historically Black Colleges and Universities since 1997, creating a Computerworld-recognized 'Big Data Applications' MOOC, and leadership roles in Open Grid Forum and Java Grande Forum standards development. As principal investigator of the FutureGrid cyberinfrastructure testbed, he enabled novel scientific computing approaches while serving as General Chair for multiple conferences. He directs Indiana University's Digital Science Center and leads the SPIDAL project, which supports diverse data-intensive applications across high-performance computing platforms through interoperable library frameworks.
Sir Andy Hopper is a Professor and Head of the Digital Technology Group within the Computer Laboratory at the University of Cambridge. He is a Fellow of Corpus Christi College, Cambridge. His research focuses on creating a world where everything is always connected in a personalized way and developing sentient computing systems that observe the real world to ensure planetary sustainability. His research interests span ubiquitous computing, sentient computing, distributed systems, computer networking, green computing, and provenance systems. He has pioneered work in location-aware systems, energy-efficient computing, and provenance in distributed environments. His vision extends computing to automatically adapt to real-world conditions for sustainable solutions. Recent publications indicate a strong focus on provenance systems, data reliability in cloud environments, and the integration of renewable energy in datacentre computing. His work often involves collaboration with researchers at the University of Cambridge and beyond, addressing challenges in virtualization, MapReduce, and resource accounting. Professor Hopper leads the Digital Technology Group, which is part of the Computer Laboratory. The group conducts cutting-edge research in networking, systems, and sustainable computing, contributing to both theoretical advances and practical applications in the field.
Prof. Alberto S. Cattaneo is a faculty member at the University of Zurich, affiliated with the Department of Mathematics. He holds the rank of Professor and has been actively involved in teaching and research since at least 1998. His research interests span mathematical physics, differential geometry, topology, and interdisciplinary areas like computational biology, genomics, and digital forensics. He has developed courses on topics such as field theory, quantum mechanics, and differential manifolds, reflecting his expertise in theoretical and applied mathematics. Prof. Cattaneo has contributed to numerous publications, including works on distributed genomic analysis, sensor pattern noise (PNU) in forensics, and algorithm optimization for big data frameworks like Hadoop and Spark. His work bridges pure mathematics with applications in bioinformatics and cybersecurity. Though no awards are explicitly mentioned, his extensive publication record and teaching roles highlight his academic standing. He maintains an active presence through courses and research collaborations, with no indication of part-time roles or retirement.
Mostafa BAMHA is an Associate Professor (Maître de Conférences) at the University of Orleans, affiliated with the LIFO Laboratory (Laboratoire d'Informatique Fondamentale d'Orléans). He leads research in parallel and distributed computing, focusing on MapReduce optimization , data skew handling , and scalable graph processing . Member of the PRV team (Parallelism, Virtual Reality, System Verification) Active in projects: HPIAF (High Performance computing for AI in Finance) INEx (Cloud Computing experiments) Girafon (Graph & BigData processing) Research Trends His publications from 2018–2024 show focus on: MapReduce optimizations for join operations and LSH similarity joins Graph processing challenges in Pregel with high-degree vertices Skew-insensitive algorithms across distributed architectures Academic Contributions Co-author in 15+ peer-reviewed publications (2000–2024) including International Journal of Parallel Programming , DEXA , and HLPP conferences. Key collaborators: Sébastien Rivault , Mohamad Al Hajj Hassan , Sophie Robert .
Dr. Ana Milanova is a Professor in the Department of Computer Science at Rensselaer Polytechnic Institute, where she has been since 2003. Her research focuses on programming languages, compilers, and software engineering, with emphasis on static program analysis, security, and applications in Android app taint analysis, secure cryptographic protocols, and machine learning library verification. Research Interests: Her work addresses challenges in secure software development, privacy-preserving techniques, and static analysis methodologies. Recent projects include federated learning frameworks, Python-based static analysis tools, and secure computation protocols. She has contributed to tools like Submitty for automated programming assignment grading and frameworks for secure MapReduce applications. Scientific Awards: NSF CAREER Award, Google Faculty Research Award Grants: SaTC: CORE grants for secure computation and multi-party optimization Labs/Teams: Leads research in secure computation, federated learning, and static analysis tool development. Collaborates on open-source platforms for educational grading systems (Submitty).
Ioannis Milis is a Professor at the Department of Informatics, Athens University of Economics and Business (AUEB), part of the School of Information Sciences and Technology. He holds a BS in Electrical Engineering from Democritus University of Thrace (1983) and a PhD in Computer Science from AUEB (1989). His research focuses on algorithms, computational complexity, and optimization for computer/communication networks, combinatorial optimization, graph theory, and game theory. He has conducted postdoctoral research at LRI (1992-94), INRIA-Sophia Antipolis (1994-95), and NTUA as a Marie Curie fellow (1995-96). His teaching includes courses on Algorithms, Advanced Algorithms, and Topics in Algorithms at both undergraduate and graduate levels. He co-authored a textbook on Distributed Systems with Java (2005). His conference involvement includes organizing the Athens Colloquium on Algorithms and Complexity (ACAC) since 2006, the Euro-Par 2012 conference, and the ISCO 2012 symposium. His research has addressed scheduling algorithms, energy-efficient computing, and combinatorial optimization problems in networks.
Pradip K Srimani is a Professor at the School of Computing , Clemson University , with research focus on Parallel and Distributed Computing , Self-Stabilizing Systems , and Graph Theory Applications . He is an IEEE Life Fellow and ACM Distinguished Scientist with over 250 publications. Research Interests Self-stabilizing algorithms for network graphs Ontology-based biomedical information retrieval High-performance computing resource management Biologically inspired distributed algorithms Network topology computation Recent publications demonstrate expertise in token circulation protocols, genome assembly frameworks, and semantic similarity measurement. He has served on program committees for APDCM , GPC-2018 , and IEEE BigMM conferences, while maintaining editorial roles at International Journal of High Performance Computing and Journal of Big Data . Scientific Awards IEEE Life Fellow ACM Distinguished Scientist
Kyoung-Don (KD) Kang is a Professor in the School of Computing at Binghamton University, State University of New York. He specializes in real-time embedded systems, cyber-physical systems, and IoT security. His research focuses on enhancing real-time data services' timeliness and power efficiency, supported by NSF grants and industry collaborations. Education: BS, MS (Kyungpook National University); MS, PhD (University of Virginia). Research Interests: Real-time embedded systems, cyber-physical systems, IoT, security, edge computing, and wireless sensor networks. His work emphasizes control-theoretic approaches, feedback mechanisms, and adaptive algorithms for real-time systems. Grants & Projects: NSF-funded projects include 'Enhancing Timeliness of Real-Time Data Services' and 'QoS-Aware Data Management.' He leads the Real-Time Embedded Systems Laboratory, developing frameworks like Chronos and RTMR. Awards: Best Paper nominations (DCOSS'19), NSF grants, and patents in surveillance systems. Over 16 MS students and 9 PhD students have graduated under his mentorship, many joining top tech firms. Labs & Teams: Director of the Real-Time Embedded Systems Lab, focusing on edge computing, real-time stream processing, and secure IoT applications. Collaborates with industry partners like Samsung and Intel.
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.
Charles Reiss is an Assistant Professor in the Computer Science Department at the University of Virginia, focusing on teaching and research in computer systems and education. He holds a PhD from UC Berkeley (2016) and has a strong background in computer architecture and distributed systems. His research interests include computer science education methodologies, cloud computing, and systems optimization. Education: B.S. Computer Science (Georgia Tech, 2008), M.S. Computer Science (UC Berkeley, 2011), Ph.D. Computer Science (UC Berkeley, 2016). Teaching: Reiss has taught courses such as CS 3130 (Computer Systems and Organization), CS 3330 (Computer Architecture), and CS 4630 (Defense Against the Dark Arts). His courses emphasize hands-on learning with tools like HCLRS, an educational hardware description language. Awards: Winner of the SoCC 2021 Test of Time Award for work on Google cloud trace analysis. Contributions: Developed educational software like HCLRS and CPU cache exercises. Active in curriculum design for computer systems and security education.
Ana Edelmira Pasarella Sanchez is a Professor in the Department of Computer Sciences at the Faculty of Mathematics and Statistics, Universitat Politècnica de Catalunya (UPC). She is a member of the ALBCOM research group, focusing on algorithms, bioinformatics, complexity, and formal methods. Her work bridges theoretical computer science with practical data systems. PhD in Computer Science, Universitat Politècnica de Catalunya Her research centers on logic programming, knowledge representation, and graph databases. She explores how formal methods can enhance data processing, particularly through dynamic pipelines and trust-aware access control. Her work integrates Datalog, semantic reasoning, and big data frameworks to improve scalability and correctness in knowledge systems. She has contributed to foundational semantics of logic programs and their applications in security and data integration. Her recent publications highlight a trend toward efficient, adaptive data processing systems, especially for graph analytics and knowledge graphs. She compares paradigms like MapReduce and pipelining, advocating for dynamic, functional approaches to big data. Her work increasingly addresses real-world challenges in federated knowledge graphs and access control. SACMAT 2017 Best Paper Award Pasarella has been involved in multiple competitive R&D+i projects, such as 'Modelos y Técnicas para el Procesamiento de Información a Gran Escala' and 'Modelos y métodos basados en grafos para la computación en gran escala,' indicating sustained funding and collaborative leadership. She advises on research direction within her group and mentors through collaborative publications. She has served on the scientific committee of the Latin American Informatics Conference (CLEI), contributing to the broader academic community. She is part of the ALBCOM research group and collaborates extensively with researchers like Fernando Orejas, Maria-Esther Vidal, and Elvira Pino, working on logic-based frameworks for data and security systems.
İbrahim Rıza Hallaç is an Assistant Professor at RAFET KAYIŞ FACULTY OF ENGINEERING, Department of Computer Engineering. He holds a PhD (2021), Master's (2014), and Bachelor's (2011) in Computer Engineering from Firat University, with doctoral research on social media analysis using deep learning methods under advisor Galip Aydın. His research focuses on: Artificial Intelligence and language models Machine learning for distributed systems Big data analytics and cloud computing Natural language processing applications Social media sentiment analysis Radar-based object detection systems His publications (2015-2024) demonstrate consistent focus on machine learning applications across diverse domains including social network analysis, distributed computing, Turkish NLP, and sensor data processing. Recent works emphasize radar-based multi-target detection (2024), credit scoring models (2023), and wind speed estimation (2023), reflecting expertise in scaling AI solutions for real-world problems. He has led four national R&D projects: Deep Learning-Based Face Recognition Authorization System (2017-2019) DEĞİRMEN Big Data Analysis Platform for Turkish Armed Forces (2017-2021) KOSGEB Innovation Program for Face Recognition Systems (2017-2019) Cloud-Based Social Media Reputation Analysis System (2015-2017) No students, awards, or contact emails are documented in available records.
Dario Colazzo is a Professor at Université Paris-Dauphine, affiliated with the LAMSADE laboratory. His research focuses on database systems and programming languages, particularly in cloud databases, type systems for semi-structured data, and query processing optimizations. He has contributed extensively to XML and XQuery research, including parallel query execution and static analysis techniques. His teaching includes courses on programming (Java), UML, database systems, and semantic web technologies at both undergraduate and graduate levels (in French). Key research interests span XML/XQuery optimization, JSON processing, distributed systems, and type systems for safe and efficient data handling. His work emphasizes scalable cloud-based solutions and efficient query execution models. Publications highlight advancements in parallel query processing (e.g., PAXQuery), type-based optimizations for XML updates, and semantic graph analytics. He has collaborated with institutions like EDBT, WWW, and VLDB conferences.
