Marina Zapater Sancho is a researcher at the Embedded Systems Laboratory (ESL) within the School of Engineering at École Polytechnique Fédérale de Lausanne (EPFL) . She specializes in computer architecture, with a focus on energy-efficient systems, AI accelerators, and memory-centric computing paradigms. Research Interests: Compute-Near-Memory (CnM) : Pioneering architectures like SideDRAM and processing-near-bank designs to reduce energy consumption and latency in DRAM systems. AI Accelerators : Frameworks such as LIONHEART for analog-digital hybrid systems, and Gem5-AcceSys for exploring interconnects in ML accelerators. System Simulation : Contributions to RISC-V full-system simulation validation (gXR5) and component-level calibration methodologies. Her work bridges software and hardware, addressing challenges in heterogeneous systems, thermal management in 2.5D/3D packages, and virtual memory optimization for cache-intensive workloads. Recent trends emphasize energy-proportional computing and edge AI deployment . Grants & Collaborations: Funded by EU H2020 programs and the ACCESS-AI Chip Center (Hong Kong), her research is conducted within the ESL team led by Prof. David Atienza Alonso.
Beyza Eken serves as Assistant Professor in the Department of Software Engineering at Sakarya University's Faculty of Computer and Information Sciences, teaching core courses including Software Project Management, Natural Language Processing, and Graduation Projects while maintaining active research in software engineering and AI applications. Her academic credentials include: Doctorate in Computer Engineering from Istanbul Technical University (2015), thesis: "Software Defect Prediction" Master's in Computer Engineering from Istanbul Technical University (2011-2015), thesis: "Entity Name Recognition in Short Texts" Bachelor's in Computer Engineering from Sakarya University (2007-2011) Dr. Eken's research integrates machine learning with software engineering, specializing in defect prediction models that incorporate personalized developer factors and industrial deployment challenges. Her work bridges natural language processing for Turkish social media analysis with software quality assurance, demonstrating expertise in both theoretical modeling and practical implementation in industrial settings. Recent expansions include neuro-symbolic AI for test oracle generation and MLOps frameworks. Publication trends reveal consistent focus on empirical software engineering from 2018-2021 (defect prediction, community smells, industrial deployment), evolving into cutting-edge domains by 2023-2025 (neuro-symbolic testing, employee feedback analysis, MLOps). Her work shows strong industry-academia collaboration patterns with increasing methodological sophistication. Dr. Eken actively contributes to academic service as reviewer for ACM Transactions on Software Engineering and Methodology (2024) and IEEE Transactions on Software Engineering (2023). She leads research projects including "Developer-specific error prediction modeling" (2020) and the Mevlana exchange project with Ryerson University on data mining for defect prediction (2018), while supervising graduation projects and research area courses that develop student expertise in software engineering practices. Her international research engagement includes participation in the ASTERIx project at Università della Svizzera Italiana's Software Testing and Analysis Research Group (2023), demonstrating ongoing commitment to global collaboration in software engineering advancements.
Anastasios Zafeiropoulos serves as Assistant Professor at Harokopio University of Athens, specializing in Spatial Data Management and Analysis within the Postgraduate Studies Program for “Applied Geography and Spatial Management” (Direction C: Geoinformatics). His academic role encompasses teaching “Spatial Databases” and advancing research at the intersection of geospatial technologies and distributed computing systems. His research program focuses on Spatial Databases, Internet of Things (IoT), Cloud/Edge Computing, and 6G Network Orchestration, with significant extensions into Knowledge Graph applications for Sustainable Development Goals (SDGs) and socio-emotional learning in education. Key innovations include the EduCardia methodology for student competency assessment and frameworks for climate vulnerability analysis using knowledge graphs. Analysis of his 2024-2025 publications reveals three dominant thrusts: (1) AI-driven orchestration of 6G services across the computing continuum using reinforcement learning; (2) Knowledge Graph applications for SDG interlinkage analysis and materials science; (3) EU-funded IoT/Edge Computing project ecosystems. His work consistently bridges theoretical networking concepts with practical sustainability and educational applications. Dr. Zafeiropoulos actively contributes to EU-funded initiatives in IoT and Edge Computing standardization, particularly through AIOTI WG Standardisation. His project portfolio includes NEPHELE multi-cloud ecosystem development and O-RAN slice admission control research, demonstrating strong industry-academia collaboration in next-generation networking. He leads the development of innovative tools including Palindrome.js for distributed system visualization and the EmoSocio open-access emotional intelligence model, reflecting his commitment to translating research into practical educational and environmental solutions.
Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.
Gérard Berry (born December 25, 1948) is a distinguished French computer scientist currently serving as Professor at the Collège de France, holding the permanent chair Algorithmes, machines et langages (Algorithms, Machines, and Languages) since 2012. He previously held the Informatique et sciences numériques chair (2009-2010) and the Technological Innovation Liliane Bettencourt chair (2007-2008) at the same institution. Before joining Collège de France full-time, he served as Director of Research at INRIA Sophia Antipolis (2009-2012) and at École des Mines de Paris (1977-2001). His research spans over 30 years in three main fields: lambda calculus and functional programming, parallel and real-time programming languages, and design automation for synchronous digital circuits. He is particularly renowned for developing the Esterel programming language. His work bridges theoretical computer science with practical industrial applications. Berry's research has evolved to include current work in Hop and HipHop for Web programming, formal verification of compilers, and languages for computer music. His publications demonstrate consistent contributions to programming language theory, formal methods, and their applications in hardware and software systems. Gold Medal of CNRS (2014) Chevalier de l'Ordre de la Légion d'Honneur (2012) Member of French Academy of Sciences (2002) Member of Academia Europaea (1993) Monpetit Prize of Académie des sciences (1990) Berry has advised 17 PhD students and reviewed numerous theses. His industrial experience includes serving as Chief Scientist Officer of Esterel Technologies (2000-2009), where he directed the implementation of the Esterel v7 compiler. He has also held significant leadership roles including President of the Scientific Council of IRCAM and membership on the Scientific Council of the National Education. His teaching at Collège de France has covered topics ranging from the foundations of computation to the societal impact of digital technology, with courses including The Informatics of Time and Events and Proving Programs: Why? When? How? His laboratory work has focused on developing practical applications of theoretical computer science concepts.
Zhiyuan Li is a Professor in the Department of Computer Sciences at Purdue University's College of Engineering. His primary research and teaching focus on program analysis, transformation, and run-time management for high-performance computing and multicore systems, as well as reliable software for networked embedded systems. Professor Li teaches graduate-level courses including CS502: Compiling and Programming Systems and CS591RS1: Research Seminar for First-year Graduate Students. Office: LWSN 3154H Contact: li@cs.purdue.edu Phone: +1 765-494-7822 Professor Li's research spans multiple areas within computer science, with particular emphasis on compiler design, program analysis, and parallel computing. His work addresses fundamental challenges in enabling efficient execution of applications on modern parallel architectures, including multicore processors and large-scale distributed systems. He has made significant contributions to techniques for data dependence analysis, loop parallelization, array privatization, and memory optimization in compilers. His research also extends to reliable software development for embedded and sensor network systems, where resource constraints and reliability requirements present unique challenges. Professor Li's publication record demonstrates consistent contributions to top-tier conferences and journals in computer science, particularly in the areas of parallel computing, compiler optimization, and high-performance numerical methods. His work shows a progression from foundational compiler techniques to applications in scientific computing domains such as computational fluid dynamics for jet engine noise simulation. This interdisciplinary approach connects low-level program analysis with real-world engineering applications requiring petascale computing resources. Principal Investigator for NSF/PetaApps project on jet engine noise simulation Principal Investigator for Intel-sponsored research on data dependence profiling Extensive service on program committees for major conferences including ICS, PPoPP, and LCTES Professor Li has been actively involved in mentoring graduate students through research projects and course instruction. His jet engine noise simulation project specifically mentions training three Ph.D. graduate students and involving undergraduate research assistants. As coordinator for the first-year graduate research seminar, he plays a significant role in guiding new students through the transition to graduate research work in computer science. His laboratory work focuses on developing compiler techniques and runtime systems for parallel and high-performance computing. The research infrastructure includes implementations in GCC for fast data dependence profiling and support for SIMD/SSE instructions, demonstrating practical applications of theoretical compiler techniques.
Vinh Nguyen is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University, where he directs the Michigan Tech Center for AI and coordinates the NIST-PREP program. His research focuses on advanced manufacturing through Industry 4.0, human-robot-machine interaction, and physics-based/data-driven modeling. He has developed solutions for machining, additive manufacturing, metal forming, and robotic assembly to promote smart and sustainable manufacturing. Prior to joining Michigan Tech in 2022, he was a National Research Council Postdoctoral Fellow at NIST (2020–2022). Dr. Nguyen earned his PhD (2020), MS in Mechanical Engineering (2017), and MS in Electrical & Computer Engineering (2017) from Georgia Institute of Technology. He received dual bachelor’s degrees in Electrical and Mechanical Engineering from Rensselaer Polytechnic Institute (2014). His research portfolio spans Advanced Manufacturing Industry 4.0 and 5.0 Human-Robot Interaction Physics-Based/Data-Driven Modeling Industrial Automation based on his lab’s interdisciplinary focus on human-centric, resilient solutions. His recent publications address trends in Machine Learning for Manufacturing Autonomous Vehicle Sensors Hybrid Additive/Subtractive Manufacturing Augmented/Mixed Reality Interfaces Industrial Robot Diagnostics Material-Specific Machining with keywords spanning Robotics, Data Science, and Industrial Engineering.
James Shackleford serves as Associate Professor and Interim Associate Dean for Enrollment Management and Graduate Education in the Department of Electrical and Computer Engineering at Drexel University. His research bridges medical image processing, high performance computing, and emerging neuromorphic architectures with significant contributions to radiation therapy applications. Education: PhD in Electrical Engineering, Drexel University, 2011 MS in Electrical Engineering, Drexel University BS in Electrical Engineering, Drexel University Research Focus: Professor Shackleford's work centers on GPU-accelerated medical image registration (forming the core of the open-source Plastimatch software), real-time tumor motion management for radiation therapy, and digital spiking neuromorphic systems . His research integrates computer vision, machine learning, and embedded systems to solve clinical imaging challenges. Publication Trends: Recent work (2020-2024) reveals dual research trajectories: (1) advancing deformable image registration through CycleGAN-based domain adaptation for CT auto-segmentation in radiation oncology, and (2) pioneering neuromorphic computing with configurable hardware architectures, dataflow-based compilers, and resource-aware neural network mapping. These streams converge on high-performance solutions for medical imaging and efficient neural processing.
Pr. Jean-François Lalande is a Professor at CentraleSupélec, affiliated with Inria's PIRAT and CIDRE teams. His research focuses on the security of IT infrastructures, Android applications, and C embedded software, including access control policies, intrusion detection tools, and software code analysis. He has led projects like PEPR DefMal (2022–2028) and ANR LYRICS (2011–2015) for privacy-preserving cryptographic protocols in NFC systems. Roles: Conference Chair (EICC 2025, EICC 2020), Workshop Organizer (IWSMR, 3SL, COLSEC, SHPS). Editorial: Guest Editor for journals like Information Technology and Future Generation Computer Systems . His work spans malware analysis, privacy in mobile systems, and security validation. He has served on technical program committees for international conferences (IEEE, ACM) and national events like SSTIC. His students include PhD graduates such as Romain Brisse and Tomas Miranda Concepcion.
Prof. Wilfried Kubinger serves as the Head of the Department of Electronic Engineering at the University of Applied Sciences Technikum Wien, Austria. He holds a PhD in Technical Sciences from the Technical University of Vienna (1999) and has extensive experience in research and industry. His academic roles include leading the 'Automation & Sensor Technology' competence field and managing the 'Automation & Robotics' research area. **Research Focus:** His work centers on embedded systems, machine vision, autonomous robotics, and real-time control systems. Notable projects include obstacle detection for autonomous vehicles, stereo vision algorithms, and agricultural robotics applications. He actively contributes to IEEE and OVE engineering associations. **Professional Journey:** Prior to academia, he worked at Siemens Austria (2000–2003) as a software developer and project manager, and at AIT Austrian Institute of Technology (2003–2010) managing research projects in autonomous systems. He participated in DARPA Grand Challenge/Urban Challenge as a principal scientist for vision-based obstacle detection. **Publications:** His research spans embedded vision systems, FPGA implementations, and autonomous vehicle technologies. Recent work emphasizes agricultural robotics and Industry 4.0 applications. He also leads R&D project acquisition and implementation for Technikum Wien.
Clément Pit-Claudel is an Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), leading the SYSTEMF lab focused on programming languages, formal methods, and systems engineering. His work bridges mathematical formalisms with practical system development to achieve full assurance in critical software and hardware. PhD in Computer Science from MIT (2016) William A. Martin Memorial Thesis Award recipient Former Senior Applied Scientist at Amazon AWS Teaching accolades including the Frederick C. Hennie III Teaching Award Research spans three axes: extensible proof-producing compilers for performance-critical systems, verified hardware compilation with cycle-accurate semantics, and interactive theorem prover tooling for democratizing verification technology. Key projects include Kôika for hardware verification, Alectryon for Coq proof visualization, and Fiat for correct-by-construction program synthesis. Recent publications address JavaScript regex verification (ICFP 2024), cryptographic server integration (PLDI 2024), and hardware simulation optimization (ASPLOS 2021). Articles demonstrate expertise in functional-to-imperative translation, domain-specific compiler extensions, and hardware-software co-verification. Scientific contributions recognized through: Distinguished artifact award (SLE 2020) MIT William A. Martin Thesis Award Frederick C. Hennie III Teaching Award Teaching philosophy emphasizes hands-on lab instruction , oral assessment , and automated tooling . Courses taught include Software Construction (undergraduate) and Interactive Theorem Proving (graduate) at EPFL. Research service includes program committee roles at Dafny, POPL, and SPLASH conferences.
Tali Moreshet is a Research Assistant Professor in the Department of Electrical & Computer Engineering at Boston University. Her primary appointment is as a Master Lecturer, reflecting her dual focus on teaching and research. She is affiliated with the College of Engineering and holds a PhD from Brown University (2006). Her research interests include computer architecture, energy-efficient computing, hardware-software co-design, near-data processing, and embedded systems. Dr. Moreshet has received notable awards including the Senior Member distinction from ACM, the ECE Department Teaching Award (2017), a Best Paper Award at SAMOS XIV (2014), and an NSF BRIGE Award (2009). She teaches core courses such as Introduction to Logic Design (EC 311), Advanced Data Structures (EC 504), and Computer Architecture (EC 513). Her work emphasizes energy efficiency in embedded systems and transactional memory implementations. Recent research explores hardware acceleration for garbage collection and near-memory processing architectures. She also investigates voltage noise mitigation and concurrency control mechanisms in embedded multi-core systems. Moreshet has contributed to collaborative NSF projects on durable data structures for non-volatile memory and energy-efficient speculation in NUMA architectures. Her publications span 20+ years, with a focus on embedded systems, transactional memory, and parallel computing optimizations.
Jürgen Pfeffer is a Professor of Computational Social Science & Big Data at the Technical University of Munich's School of Social Sciences and Technology, with an additional appointment as Adjunct Professor at Carnegie Mellon University's Institute for Software Research. His interdisciplinary work bridges computer science and social science with a focus on analyzing large-scale socio-technical systems. His research expertise spans computational social science, network analysis, and big data methodologies. Pfeffer's work examines methodological, algorithmic, and theoretical challenges in analyzing dynamic social systems, with current projects focusing on modeling and detecting negative dynamics from social media, particularly online firestorms and hate speech against politically active women. His research combines network science approaches with computational methods to understand complex social phenomena. Pfeffer's publication record demonstrates significant contributions to the field since his 2010 doctorate, with high-impact papers in journals like Science and EPJ Data Science. His work on social media analysis, particularly the influential 2014 Science paper 'Social Media for Large Studies of Behavior' co-authored with Derek Ruths, has shaped methodological approaches in the field. His research shows consistent evolution from foundational network analysis to contemporary applications in political discourse, hate speech detection, and multi-layer network analysis. Hennig, M., Brandes, U., Pfeffer, J., & Mergel, I. (2012). Studying Social Networks. A Guide to Empirical Research Ruths, D., & Pfeffer, J. (2014). Social Media for Large Studies of Behavior Pfeffer, J., Morstatter, F., & Mayer, K. (2018). Tampering with Twitter's Sample API As an advisor and collaborator, Pfeffer has worked extensively with researchers including Raji Ghawi, Mirco Schönfeld, Momin Malik, and Kathleen Carley. His work demonstrates strong connections between theoretical network science and practical applications in social media analysis. His current research continues to address pressing issues in online discourse, with recent work focusing on hate speech classification, lexical change in negative word-of-mouth, and polarization dynamics in social media environments. Pfeffer leads the Pfeffer Lab, which focuses on developing methodological approaches for analyzing complex social systems through computational methods. His work has implications for understanding political legitimacy, social influence, and community dynamics in both online and offline contexts.
Dr. habil. Simon János PhD is an Associate Professor at the University of Szeged's Faculty of Engineering, Institute of Technology. Born on July 27, 1980, he maintains his office at 6724 Szeged, Moszkvai krt. 9. Room F9, with contact number +36-62-546-575. His educational background includes IT engineering and electrical engineering from Technical College of Subotica (1999-2005), Certified Computer Engineering from University of Novi Sad (2005-2008), PhD in Engineering from University of Osijek (2008-2014), and habilitation from Óbuda University Doctoral School of Security Sciences (2020). English (intermediate, complex) Serbian (advanced, complex) Dr. Simon's research focuses on design and programming of Internet of Things environments, hardware and software development of mobile robots and wireless sensor networks, and analysis of Industry 4.0 case studies. His teaching portfolio includes Computer Modeling, Simulation courses, Microcontrollers, Graphical Programming at BSc level, and Real-time systems, Autonomous and intelligent robots at MSc level. He serves as Associate Editor for Analecta Technica Szegedinensia and is a member of the Higher Education Management Education Methodology Association (FIOM). His international experience includes CEEPUS mobility to Timisoara, Erasmus mobility to multiple Romanian cities, and participation in IoTTech Expo Global in London.
Pedram Johari serves as a Principal Research Scientist at Northeastern University's Institute for Wireless Internet of Things, working under Prof. Tommaso Melodia in the Department of Electrical and Computer Engineering within the College of Engineering. His research focuses on next-generation wireless communications with particular emphasis on medical applications and nanoscale networks. Dr. Johari earned his Ph.D. in Electrical Engineering from the University at Buffalo, State University of New York in 2018 under the supervision of Prof. Josep M. Jornet. Prior to joining Northeastern, he served as CTO of an IoT-tech startup in New York (2018-2019) and held academic positions at University at Buffalo as Adjunct Instructor and Research Assistant Professor (by courtesy appointment). His research spans four interconnected domains: Intra-body Communications and Networking focusing on nanoscale electromagnetic and optical communications within biological tissues; Internet of Medical Things developing wireless systems for healthcare applications; Low Power Wireless IoT creating energy-efficient communication protocols; and Vehicular Communications advancing cooperative driving systems. His work bridges theoretical modeling with practical implementation, often incorporating AI techniques for network optimization. Analysis of his recent publications reveals a strong trend toward digital twin technology for wireless networks, AI-enabled communication systems, and medical applications of nanoscale communications. His research increasingly integrates machine learning with traditional communication theory, particularly in Open RAN systems and seizure prediction technologies, demonstrating cross-disciplinary impact across engineering, computer science, and biomedical fields. Best Paper Award at IEEE Global Communications Conference (GLOBECOM) Best Short Paper Award at IEEE Vehicular Networking Conference (VNC) Best Presentation Award at IEEE Conf. on Computer Communications Workshops (INFOCOM WKSHPS) Dr. Johari actively contributes to academic service as a reviewer for numerous prestigious journals including IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, and IEEE Internet of Things Journal. He has served on technical program committees for major conferences including IEEE/ACM CHASE and IEEE SECON. His teaching experience includes courses in programming, circuit analysis, and digital principles at University at Buffalo. As a key member of the Wireless Networks and Embedded Systems Lab at Northeastern University, Dr. Johari contributes to the Colosseum wireless network emulator project - recognized as the world's largest wireless network emulator. His work with the Institute for Wireless Internet of Things involves collaboration with industry partners to translate research into practical wireless communication solutions.
