Mika Mäntylä is a Professor at the Department of Computer Science, University of Helsinki, affiliated with the Faculty of Science. His research focuses on software engineering, testing, log analysis, and systematic literature reviews. He leads projects such as CRITICAL (cultivating critical reading in the internet era) and a microservice anomaly detection initiative funded by the Academy of Finland. His work spans technical domains like software log anomaly detection, meta-learning applications, and social media credibility evaluation. Notable tools include Credtwi (a browser plugin for credibility assessment) and LogLead (a log analysis system). Key grants include SRC Strategic Research Council funding (2023–2026) and Academy Project funding (2023–2026). Mika’s research bridges technical and societal challenges, addressing software quality, automated anomaly detection, and user-centric credibility evaluation. His publications often emphasize empirical methods, tool development, and interdisciplinary approaches.
James Eagan is an Associate Professor in the Design, Interaction, Visualization & Applications (DIVA) research group at Télécom Paris , affiliated with the Information Processing and Communication Laboratory (LTCI) and the Computer Sciences and Networks (Infres) department. His work bridges Human-Computer Interaction and Information Visualization , focusing on enhancing software expressiveness for users. James explores software malleability through projects like Webstrates (collaborative dynamic media) and Scotty (runtime toolkit overloading). His research integrates qualitative methods with prototype development to address user needs in code adaptation, data visualization, and small-screen interaction. Recent work includes AI explainability frameworks for financial crime detection and studying cognitive biases in XAI-assisted decision-making. He has received the ACM SIGSOFT 2015 Impact Award for his Tarantula debugging tool. His teaching portfolio spans courses in Human-Computer Interaction , Data Visualization , and Web Technologies at Télécom Paris. James actively recruits PhD and Master’s students for high-quality research in these domains. His office is located at 4.D24 , and he encourages students to contact him at james.eagan@telecom-paris.fr .
Theresa Harrer is a Research Fellow at the Hanken School of Economics, specializing in Accounting . Her work bridges finance, sustainability, and academic collaboration.
Daniel Garijo is an Associate Professor at the Artificial Intelligence Department of the Faculty of Computer Science, Universidad Politécnica de Madrid (UPM). He is a researcher at the Ontology Engineering Group and collaborates with the Information Sciences Institute at the University of Southern California. His research focuses on e-Science and the Semantic Web, emphasizing the understandability of research software and computational workflows through provenance tracking and Linked Data exposure . Key areas include FAIR principles, ontology engineering, and automated metadata extraction. Recent publications analyze FAIR software assessment metrics LLM applications for ontology engineering semantic artefact lifecycle optimization carbon emissions transparency RDF-star data integration His work bridges open science with practical standardization efforts in research software. He contributes to the Ontology Engineering Group at UPM, develops open-source tools (RO-Crate, SoMEF), and maintains semantic repositories via Github and ORCID .
Ari Korhonen is a Senior University Lecturer in the Department of Computer Science at Aalto University's School of Science. His academic career spans over two decades with a focus on computing education research and educational technology. Korhonen leads research in algorithm visualization, automatic assessment systems, and learning analytics within computer science education. Education: Doctoral degree in Engineering and Technology, Helsinki University of Technology (2003) Licentiate degree in Engineering and Technology, Helsinki University of Technology (2000) Master's degree in Engineering and Technology, Helsinki University of Technology (1997) Research Interests: Korhonen specializes in computing education research with particular expertise in algorithm visualization and automatic assessment systems . His work explores how visual representations of algorithms impact student learning, with significant contributions to tools like TRAKLA2 for data structures education. He investigates learning analytics approaches to understand programming misconceptions and develop targeted interventions. His recent research focuses on prerequisite skill assessment and differentiated learning pathways in computer science education, examining how fragile knowledge in foundational concepts affects advanced topic mastery. Publication Trends: Analysis of Korhonen's recent publications (2017-2025) reveals a strong focus on algorithm education, particularly Dijkstra's algorithm and data structures. His work increasingly incorporates learning analytics to identify student misconceptions, with growing emphasis on automated assessment systems that provide personalized feedback. The research demonstrates a progression from tool development (TRAKLA2) to sophisticated analysis of student learning patterns and prerequisite knowledge gaps, with recent work exploring JSON-based algorithm animation languages and the cognitive aspects of visualization. Awards and Recognition: Docentship at University of Turku (2012-present) Academic Service and Mentorship: Korhonen has supervised 8 theses and collaborates extensively with researchers across Finland and internationally. He serves on editorial boards for key journals including Computer Science Education and ACM Transactions on Computing Education. His service includes program committee memberships for major conferences like ICER (International Computing Education Research). Korhonen actively contributes to building educator networks, particularly through the Finland-US Network for the Study of Engagement and Learning in Games (FUN) and software engineering teacher networks in Finland. Research Groups: Korhonen leads the LeTech Learning Technology Group at Aalto University, focusing on educational technology development and evaluation. His fingerprint analysis shows strong activity in Data Structures (100%), Early Research in Computer Science (37%), Dijkstra Algorithms (37%), Learning Analytics (37%), and Case Studies (37%). He's deeply involved with the Computing Education Research community in Finland, contributing to national initiatives that strengthen computer science pedagogy across institutions.
Prof. Charalampos Tsoumpas is a Full Professor in Quantification in Molecular Diagnostics & Radionuclide Therapy at the University Medical Centre Groningen (UMCG), affiliated with the Faculty of Medical Sciences. His research focuses on advancing quantitative PET and SPECT imaging, including novel reconstruction algorithms and motion correction techniques. He holds academic roles including team leader in the Crystal Clear Collaboration (CERN), editorial board memberships in journals like European Journal of Nuclear Medicine , and leadership in initiatives such as the Collaborative Computational Project Synergistic Reconstruction in Biomedical Imaging (CCP SyneRBI) . Education includes a BSc in Physics (National University of Athens), MSc in Biomedical Engineering (National Technical University of Athens), and a PhD from Imperial College London. His career includes roles at King’s College London and the University of Leeds, where he led research projects funded by the Leverhulme Trust, EPSRC, and EU grants. Research Highlights: Pioneered methods for parametric image reconstruction, developed open-source STIR software, and contributed to PET-MRI integration. His work on motion correction and scatter correction has influenced commercial systems. Notable achievements include the Rotblat Medal (2017) for impactful research in medical physics. Leadership and Mentorship: Supervised 23 PhD students, fostering open science through collaborative coding and educational resources. Maintained industrial partnerships with companies like GE Healthcare and Bruker. Current projects include the SAFIR PET-MRI preclinical scanner and LAFOV PET/CT advancements. Labs/Teams: Leads the Quantification in Molecular Diagnostics lab at UMCG, collaborates on the SAFIR project (ETH Zurich), and contributes to CCP SyneRBI for synergistic reconstruction frameworks.
Phillip Conrad is a Senior Lecturer with Security of Employment (SLSOE) at the University of California, Santa Barbara (UCSB), holding a joint appointment in the Department of Computer Science (College of Engineering) and the College of Creative Studies (CCS). He specializes in computer science education and curriculum development, with a focus on teaching methodologies and bridging academic-industry gaps. His roles include teaching undergraduate courses in computer science, mentoring students, and facilitating collaboration between CCS and CoE programs. Education: PhD in Computer Science, University of Delaware (2001) MS in Computer Science, West Virginia University (1998) BS in Computer Science, West Virginia Wesleyan College (1985) His research interests include effective teaching practices, undergraduate TA programs, and improving software engineering education through practical assessments. He has conducted industry sabbaticals, such as at AppFolio, to enhance curriculum relevance. Notable awards include the UCSB College of Engineering Outstanding Faculty Award (multiple years) and the UCSB Faculty Senate Distinguished Teaching Award. Recent work focuses on evaluating team software development, individual contributions in projects, and addressing challenges faced by early-career software developers. He actively contributes to conferences like SIGCSE and publishes on pedagogical innovations and educational technology. Awards: UCSB College of Engineering Outstanding Faculty Award (2012–2015) UCSB Faculty Senate Distinguished Teaching Award (2011) Advising and grants include leading initiatives like the Undergraduate TA/Mentor program at UCSB and collaborating on projects such as eVoices and Animal Tlatoque to attract diverse students to computing.
Dr. Alvaro Miyazawa is a Lecturer in the Department of Computer Science at the University of York. He holds a PhD in Computer Science from the University of York (2012) and an MSc from the University of São Paulo, Brazil (2008). His research focuses on formal methods for robotics, including robotics modelling, verification, and tool development. He has held roles such as Deputy Chair of BOE (Paper Checking for On-Campus) and has contributed to projects like RoboTest, RoboCalc, and hiJaC. His work emphasizes diagrammatic notations (e.g., RoboChart), hybrid languages, and formal verification techniques for robotic systems. Miyazawa has developed tools like RoboTool and RoboSim, integrating formal methods with practical robotic applications. He has extensive experience in safety-critical systems, including work on Safety-Critical Java (SCJ-Circus) and probabilistic modelling with PRISM. Research interests span robotics semantics, domain-specific languages, state-based notations (Z, B, VDM), and process algebras. His publications address model-based engineering, architectural design, and verification frameworks for robotics. Miyazawa’s career includes roles as a Research Associate across multiple projects, culminating in his current academic position.
Mika Saari is a Lecturer at the Computing Sciences Department within the Faculty of Information Technology and Communication Sciences at Tampere University. His research focuses on Internet of Things (IoT), software engineering, artificial intelligence (AI), and their applications in education and prototyping. He has supervised numerous master’s theses and contributed to over 47 research outputs, including peer-reviewed articles, conference contributions, and books. His work emphasizes IoT sensor networks, AI tool utilization in programming education, and collaborative prototyping processes between academia and industry. Education: Doctoral thesis (2024) titled ' Software Hardware Combination for IoT Sensor Data Gathering and Prototyping: Architecture model, Framework, and Process model '. Awards: Holder of 2 prizes (specific details not explicitly mentioned). Research interests include IoT system design, AI-driven software development, and the integration of generative AI tools in education. His recent articles explore topics like autonomous software development via multi-agent systems, digital twin applications, and the effective use of ChatGPT for documentation. Saari also investigates energy-saving IoT solutions and the challenges of visualizing building data. Publications span journals, conferences, and book chapters, with a focus on practical applications in smart systems, robotics, and educational technology. He collaborates actively with industry partners and has contributed to frameworks for IoT data gathering and prototyping processes.
Nouh Alhindawi is an Associate Teaching Professor at Arizona State University's School of Computing and Augmented Intelligence. His research focuses on Software Engineering, Natural Language Processing, and Machine Learning applications in education. He actively teaches courses like Software Enterprise, Programming Languages, and Software Factory Capstone projects. Key research interests include improving software testing quality, analyzing code complexity, and leveraging AI for education technology. Notable work includes studies on student performance prediction, IoT security, and Arabic text emotion classification. His teaching portfolio spans both undergraduate and graduate levels, emphasizing practical software development methodologies. Prominent publications address challenges in localization techniques, network security, and deep learning applications across healthcare and education sectors.
Pia Niemelä is a Lecturer in Computing Sciences at the University of Tampere. She holds a Doctor of Science (Technology) in Information Technology (2018), a Master of Arts in Educational Science (2018), and a Master of Science (Technology) in Engineering Physics (1995). Her research focuses on computational thinking education, peer review systems in programming courses, and the integration of AI tools like ChatGPT into computer science pedagogy. She has pioneered work on autograding systems, flipped classroom models, and learning analytics with privacy-aware MLOps frameworks. Her educational background bridges engineering and pedagogy, enabling interdisciplinary research in areas such as version control pedagogy (Git), functional programming literacy (JavaScript), and cross-cultural curriculum development in Nordic/Baltic regions. Key contributions include analyzing student motivation in algorithm competitions and designing privacy-preserving learning analytics systems. Her work emphasizes practical educational innovations: from using GitHub commits to assess teamwork skills to creating MOOCs for math teachers integrating computational tools. Recent research highlights include AI chatbots as tutoring systems and the impact of autograders on both instructor workload and student feedback quality. While no formal awards are listed, her consistent publication record from 2012 to 2024 demonstrates sustained academic engagement. No advising/grant details are provided in the source materials.
Antonio Carzaniga is a Full Professor and founding member of the Faculty of Informatics at Università della Svizzera italiana (USI), where he has been active since 2004. Previously, he served as an Assistant Research Professor at the University of Colorado at Boulder from 2001 to 2007. He holds a Ph.D. in Computer Science and a Bachelor’s degree in Electronic Engineering from Politecnico di Milano. Full Professor, Faculty of Informatics, Università della Svizzera italiana (2004–Present) Assistant Research Professor, Department of Computer Science, University of Colorado at Boulder (2001–2007) Ph.D. in Computer Science, Politecnico di Milano Bachelor’s in Electronic Engineering, Politecnico di Milano His research spans distributed systems and software engineering, with a strong focus on content-based addressing networks, publish/subscribe systems, middleware, software fault tolerance, and verification. He has pioneered work in information-centric networking and developed the Siena project, a scalable publish/subscribe service. His recent work extends into programmable networks, GPU-accelerated matching, and performance annotations for cloud systems. The 15 most recent publications highlight a consistent trajectory in scalable, high-performance networking and adaptive software systems. Key themes include content-based communication, packet subscriptions, information-centric networking, and leveraging redundancy for fault tolerance and testing. His work bridges theoretical foundations with practical implementations, often involving system-level software and performance evaluation. Best Paper Award, ACM SIGCOMM Workshop on Information-Centric Networking (ICN'13) Carzaniga has advised multiple graduate students, including Michele Papalini, Koorosh Khazaei, and Daniele Rogora, and has collaborated on funded research projects in distributed systems and networking. He has contributed to software development through projects like the Siena Fast Forwarding engine and the Synthetic Workload Generator. His service includes organizing workshops and contributing to major conferences in software engineering and computer systems. He leads research initiatives such as Siena and Content-Based Networking, focusing on scalable, decentralized communication infrastructures. His lab has developed key tools for evaluating publish/subscribe performance and implementing high-speed forwarding algorithms.
Lieven Desmet is an Associate Professor at the Department of Computer Science , part of the Faculty of Engineering Science at KU Leuven . His work focuses on cybersecurity, software security, and privacy engineering, with a strong emphasis on machine learning applications and web security.
Dr. Olaf Hartwig is a Senior Scientist at the Albert Einstein Institute (AEI) in both Potsdam and Hannover. His research focuses on precision interferometry and fundamental interactions, specifically for the Laser Interferometer Space Antenna (LISA) project. He holds a PhD in Physics from the University of Hannover (2021) and has held postdoctoral positions at SYRTE - Observatoire de Paris and AEI. His work bridges instrumental modeling, data processing, and noise reduction for space-based gravitational wave detection. Education: BSc and MSc in Physics (University of Hannover), PhD in Physics (University of Hannover via AEI Potsdam) Current Roles: Split post-doctoral position between AEI Potsdam (global fit for LISA) and AEI Hannover (Performance and Operations team) Research Interests revolve around space-based gravitational wave detectors, with emphasis on: Instrumental Modeling - Refining noise models, addressing data gaps, and mitigating glitches in LISA data Data Processing - Developing simulations, performance models, and software tools like PyTDI Detector Optimization - Clock synchronization, light-travel time estimation, and onboard optical delay compensation Publication Trends (15 most recent) show a focus on LISA instrumentation, with key topics including time-delay interferometry (TDI), stochastic gravitational wave background reconstruction, instrumental noise characterization, and intersatellite ranging. His work frequently integrates GPU acceleration, Python-based toolchains, and end-to-end simulation pipelines.
Diјana Stoјić is a Lecturer at the Department of Computer and Software Engineering , Faculty of Technical Sciences Čačak , affiliated with the University of Kragujevac . She holds a Master's degree in Computer Engineering and is currently pursuing her PhD in Electrical and Computer Engineering at the same faculty. Her academic role involves teaching subjects such as Programming Languages, Human-Computer Interaction, and Visual Programming. Born: March 7, 1988, in Foča, Bosnia and Herzegovina Education: Primary (Vuk Karadžić School, Čačak), Technical School in Čačak (Computer Engineering), BSc and MSc from the Technical Faculty in Čačak Her research interests span Computer Engineering , Internet of Things (IoT) , Machine Learning , and Human-Computer Interaction . Recent publications highlight her work in artificial neural networks , augmented reality applications , and image classification for agricultural and medical purposes. She has contributed to projects like the TR32043 project on low-power systems and the DeSKoll project on German-Serbian language resources. Notable scientific awards include: Award for Best First-Year Student in Electrical Engineering (Technical Faculty in Čačak) Scholarship from the Ministry of Education, Science, and Technological Development (2013-2017) Her grant involvement includes participation in the TR32043 project (2011-2019) and the DeSKoll project (2024-2025). She has also collaborated on lab experiments and remote engineering education initiatives during the pandemic.
