Professor Ferrante Neri is a faculty member at the University of Surrey, holding the positions of Professor of Machine Learning and Artificial Intelligence and Associate Dean (International) for the Faculty of Engineering and Physical Sciences (FEPS). He is affiliated with the Nature Inspired Computing and Engineering Research Group, Surrey Institute for People-Centred AI (PAI), and the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on optimization, explainable AI, and machine learning, with contributions to memetic computing and differential evolution. Since 2010, he has chaired the IEEE Task Force on Memetic Computing. He advises PhD students in topics like dynamic multi-objective optimization and AI-driven applications. His teaching expertise includes mathematical foundations for computer science. He has supervised students such as Aisha E S E Saeid and Pengjin Wu. Notable research areas include evolutionary algorithms, neural architecture search, and applications in robotics and environmental monitoring. Labs and teams include the Nature Inspired Computing group, which explores AI-driven solutions for complex problems. His work bridges theoretical advancements and practical applications in fields like autonomous systems and deep learning.
Professor Titus Sebastiaan van Erp is affiliated with the Department of Chemistry at the Norwegian University of Science and Technology (NTNU), where he has worked since 2016. His research focuses on advancing molecular simulation techniques to study complex biological and industrial processes without approximations, particularly through path sampling methods for rare events. 2016 – Present: Professor, NTNU 2012 – 2016: Associate Professor, NTNU 2006: Centre-of-Excellence Fellow, Leuven 2004: Marie Curie Fellow His research develops innovative methodologies like RETIS and REPPTIS to enhance simulation accuracy and expand accessible time/system scales. He has supervised students in DNA denaturation, electron transfer reactions, and protein folding studies. Recent publications analyze NaCl dissociation, ABL-imatinib kinetics, and permeation mechanisms. His work involves Python-based PyRETIS software development and collaborations across computational chemistry, biophysics, and materials science. 2025: NaCl Dissociation via Predictive Power Path Sampling 2025: RETIS/REPPTIS for Biomolecular Kinetics 2024: PyRETIS 3 for Boundary-Free Rare Events Scientific recognitions include: Centre-of-Excellence Fellowship (2006) Marie Curie Fellowship (2004) He has advised multiple students in masters theses on molecular simulation, including projects on DNA unwinding, electron transfer, and protein folding. His lab integrates algorithm development with applications in chemical reactions, biomolecular systems, and nanoscale materials.
Dr Mahir Arzoky is a Lecturer in the Department of Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. He holds a PhD from Brunel University London (2015) and has extensive research experience in artificial intelligence and software engineering. His research focuses on: Artificial Intelligence and Intelligent Data Analysis Search Based Software Engineering (SBSE) Clustering algorithms and heuristic search methods Software refactoring and quality assessment Data mining applications in healthcare and education Analysis of his 15 most recent publications (2018-2022) reveals strong interdisciplinary work bridging computer science with healthcare (diabetes patient modeling, medical imaging) and education (chatbot design, algorithm visualization). His technical focus centers on clustering optimization, refactoring impact analysis, and explainable AI, with frequent use of empirical validation methods. Key collaborations include researchers like Stephen Swift, Steve Counsell, and Giuseppe Destefanis. Dr Arzoky has secured significant research funding through EPSRC grants including: AQUATIC project (EP/M024083/1): Assessing Test Suite Quality in Industrial Code FIAR-NET (EP/N011627/1): Fault Analyses in Industry and Academic Research Network His professional network includes active collaborations across computer science, healthcare informatics, and educational technology domains, with recent work extending into transformer models for healthcare SQL conversion and graph partitioning for software modularization.
Dr. Akbar Siami Namin is a Professor in the Department of Computer Science at Texas Tech University's Whitacre College of Engineering . He leads the AdVanced Empirical Software Testing & Analysis (AVESTA) research group and contributes to cybersecurity, software engineering, and program analysis. Ph.D., Computer Science, University of Western Ontario (2008) M.S., Lakehead University/University of Western Ontario (2004) Research Interests : Dr. Namin specializes in Natural Language Processing , Software and Cyber Security , Machine Learning , Time Series Analysis , Modeling Human Factors , and Program Analysis . His work bridges security testing , mutation analysis , and empirical software engineering . Publications : His research spans sonification of security threats , keystroke dynamics , statistical fault localization , and mutation testing , with recent works published at CHI , ICMLA , and CyberWorlds (best paper award 2015). Scientific Awards : Best Paper Award at CyberWorlds 2015; 'Most Influential Professor' recognition by Computer Science undergraduates (2012). Students & Grants : Supervised numerous Ph.D. and Master's students, including Alaa Darabseh and Xiaozhen Xue. Secured over $1M in NSF grants for projects like CyberCorps Capacity Building , Security Sonification , and Cybersecurity Education for Community Colleges .
David A. Egolf is an Associate Professor in the Department of Physics at Georgetown University, specializing in computational physics with a focus on systems maintained far-from-equilibrium. His research spans fluid dynamics, granular materials, biophysics, and statistical mechanics, employing nonlinear dynamics and large-scale computation to understand complex phenomena. His educational background includes undergraduate and doctoral studies at Duke University, where he earned his PhD in Physics with a thesis on Characterizations of Extensively Chaotic States and Transitions . Prior to Georgetown, he held postdoctoral positions at Cornell University's Cornell Theory Center and Los Alamos National Laboratory's Center for Nonlinear Studies. Egolf's research interests center on spatiotemporal chaos and nonequilibrium systems. He investigates how localized events determine the evolution of complex systems, with applications ranging from fluid convection to fibrillating heart tissue. His work reveals that seemingly chaotic systems often contain predictable elements around specific critical events, providing pathways to develop a statistical mechanics for nonequilibrium phenomena. His publication record shows consistent contributions to understanding dynamical systems, with recent work focusing on granular jamming transitions, biopolymer networks, and QCD calculations. The research demonstrates recurring themes of identifying fundamental building blocks within chaotic systems and establishing connections between nonequilibrium behavior and equilibrium statistical mechanics. Alfred P. Sloan Research Fellow Software of the Year Award (1984) for AtariLab Science Series Egolf has successfully mentored numerous undergraduate researchers at Georgetown, supervising over a dozen senior theses with several students receiving departmental awards and honors. His research has been supported by major funding agencies including the National Science Foundation, Research Corporation, NASA, and the Alfred P. Sloan Foundation. He maintains an active collaboration with experimental physicist Jeffrey Urbach, combining theoretical and experimental approaches to study driven granular systems and biophysics. His laboratory work focuses on computational modeling of nonequilibrium systems, utilizing large computer clusters to simulate complex phenomena across multiple scales. Current projects include studying granular systems driven by both shaking and shearing to introduce multiple time-scales, and investigating biopolymer networks relevant to cellular mechanisms.
Lorenzo Strigini is a Professor of Systems Engineering at City St George's, University of London , where he has been affiliated since 1995 and served as Director of the Centre for Software Reliability from 2012–2024. His research focuses on dependability assessment , fault tolerance , and defense in depth for safety, security, and reliability in computer-based and socio-technical systems. He has also explored high-speed networking during his earlier career at the Italian National Research Council (IEI-CNR) and as a visiting scientist at UCLA and Bell Communications Research.
Dr. Kla Tantithamthavorn is a Senior Lecturer and Director of Engagement & Impact at Monash University's Faculty of Information Technology. He holds a 2020 ARC DECRA Fellowship and specializes in software engineering, explainable AI, and digital health. His research focuses on defect prediction models and their integration into CI/CD pipelines, with notable contributions like the ScottKnott ESD test R package (14,000+ downloads). He leads projects such as RAISE (Responsible AI Software Engineering) and collaborates with organizations like CSIRO and Atlassian. Education: PhD and M.Eng in Software Engineering from Nara Institute of Science and Technology (Japan). Research areas include empirical software engineering, machine learning for quality assurance, and AI-driven cybersecurity. He serves on editorial boards for IEEE Transactions on Software Engineering (TSE) and Empirical Software Engineering (EMSE). Key Projects: Automated Testing of LLMs (CSIRO), RAISE, LLM4SE (Atlassian) Media Contributions: Featured in articles on emergency care analytics and JITBot defect prediction. His work addresses critical domains like e-Health, with deployed systems reducing patient wait times in Australian hospitals. He actively supervises Honours/Master/PhD students and advocates for 'IT for Social Good' initiatives.
Gemma Catolino is an Assistant Professor at the Department of Computer Science, University of Salerno, and affiliated with the Software Engineering (SeSa) Lab. She has also served as an Assistant Professor at Tilburg University and Eindhoven University of Technology through the Jheronimus Academy of Data Science from September 2022 to December 2023, and previously as a Postdoctoral Researcher at Delft University of Technology and Tilburg/Eindhoven institutions. PhD in Computer Science, University of Salerno (2020), supervised by Prof. Filomena Ferrucci MSc in Management and Information Technology, University of Salerno (2016, magna cum laude) BSc in Computer Science, University of Molise (2014) Her research centers on empirical software engineering, focusing on both technical and social aspects affecting software development. Key areas include code smells, defect prediction, testability, changeability, and the emerging concept of “Community Smells”—social dysfunctions in developer teams. She investigates how human factors, team diversity (especially gender), and developer experience influence software quality and maintenance effort, often using mining software repositories and machine learning techniques. Her recent publications span high-impact journals and conferences such as IEEE TSE, EMSE, JSS, ICSE, and ICSME, with a strong trend toward integrating social and technical metrics for just-in-time defect prediction in mobile applications, analyzing community dynamics, and applying software quality metrics to cybersecurity contexts like dark web analysis. She has also contributed to MLOps and serverless computing. She has received several honors including a DEI research grant (2020), Best Technical Paper at BENEVOL 2019, first and second place in ACM Student Research Competitions (2018, 2017), and the Best Master Thesis award from the Italian Software Metrics Association (2017). Gemma Catolino has been actively engaged in academic service as a referee for top journals like IEEE TSE, EMSE, JSS, and IST, guest editor for special issues, and program/organizing committee member for major conferences including ICSE, MSR, SANER, and MobileSoft, where she served as Program Co-Chair in 2022. She has also contributed as a teaching assistant, lecturer, and course coordinator in machine learning and software engineering courses. She leads and contributes to research projects involving international collaborations, particularly with researchers such as Prof. Filomena Ferrucci, Prof. Andy Zaidman, Prof. Willem-Jam van den Heuvel, and Prof. Alexander Serebrenik. Her work bridges empirical software engineering with practical tool development and socio-technical analysis, positioning her at the forefront of modern software engineering research.
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.
Dr. M M Manjurul Islam is a Research Associate in Artificial Intelligence for Smart Manufacturing at Ulster University's School of Computing, Engineering and Intelligent Systems. His research focuses on applying advanced AI techniques to solve critical challenges in manufacturing systems, with particular expertise in fault diagnosis, predictive maintenance, and semiconductor production optimization. His research interests span Artificial Intelligence , Smart Manufacturing , Fault Diagnosis , Machine Learning , Deep Learning , Predictive Maintenance , and Semiconductor Manufacturing . He has made significant contributions to the application of convolutional neural networks, support vector machines, and generative adversarial networks in industrial settings, particularly for bearing fault diagnosis and wafer defect classification. Dr. Islam's recent publications (2023-2025) demonstrate a strong focus on practical AI applications in manufacturing, with multiple chapters in the Springer Series in Advanced Manufacturing. His work shows an evolving trajectory from traditional machine learning approaches to more sophisticated deep learning and explainable AI techniques, with increasing emphasis on semiconductor manufacturing challenges and trustworthy AI systems. His research contributes to UN Sustainable Development Goals, particularly in industrial innovation and infrastructure. He is an active member of professional organizations including IEEE and Advance HE, serving as Chair for both networks. According to Scopus data, Dr. Islam has accumulated 1,706 citations with an h-index of 16, reflecting the impact of his research in the field. His publication record shows consistent productivity, with research outputs spanning from 2015 to anticipated publications in 2025.
Daye Nam is an Assistant Professor in the Department of Informatics at the University of California, Irvine, where they design, build, and evaluate AI tools for developers using natural language processing techniques. Their work sits at the intersection of software engineering, artificial intelligence, and human-computer interaction, with a strong focus on creating useful and usable tools that make software development more accessible, efficient, and enjoyable. Education PhD in Software Engineering, Carnegie Mellon University (2018-2024) MS in Computer Science, University of Southern California (2016-2018) BS in Computer Science, Yonsei University (2012-2016) Research Interests Dr. Nam's research focuses on designing, building, and evaluating AI tools for programmers at all levels, with an emphasis on making these tools both useful and usable. Their work spans several key areas including machine learning for software engineering (ML4SE), developer experience, and human-AI interaction. They employ a user-centered approach that involves conducting empirical studies to understand programmers' needs, building and training machine learning models based on those insights, creating tools for programmers, and evaluating them using human-computer interaction methods. Their research has particular relevance to AI-powered developer tools, API documentation and discovery, and educational applications of AI for programming students. Publications and Research Trends Dr. Nam's recent publications demonstrate a clear trajectory toward understanding and improving how developers interact with AI systems. Their work increasingly focuses on empirical studies of developer-AI interaction, particularly with large language models for code generation and understanding. There's a strong emphasis on understanding trust in AI systems among developers, measuring the actual impact of AI on development speed, and designing tools that balance automation with user control. Their research methodology often combines log analysis, user studies, and the development of novel AI-powered tools that address specific developer pain points. Scientific Awards and Honors Best Tool Paper Award at ASE ACM Student Research Competition 2nd Place SIGSOFT CAPS Student Travel Award for FSE ACM SIGSOFT NSF Travel Award NSF Travel Award for ICSE SIGSOFT Best Research Award from University of Southern California Teaching and Service Dr. Nam teaches SWE 233: Intelligent User Interfaces at UC Irvine, guiding students through the design and evaluation of AI-powered interfaces for software development. They have previously served as a Teaching Assistant and Co-Instructor for Foundations of Software Engineering at Carnegie Mellon University. In terms of service, they've been on program committees for major software engineering conferences including ICSE, ASE, and FSE, and have reviewed papers for journals like TOSEM and Empirical Software Engineering. They've also been active in student support programs, organizing and mentoring for graduate applicant support initiatives.
Leonardo Orazi is a Full Professor at the University of Modena and Reggio Emilia's Department of Engineering Sciences and Methods. He specializes in advanced manufacturing technologies, particularly laser processing, polymer engineering, and biomedical surface functionalization. His teaching roles include courses on Smart Manufacturing, Injection Molding, and Additive Manufacturing in Digital Automation and Mechatronic Engineering programs. Research focuses on laser-induced periodic surface structures (LIPSS), material characterization, and micro/nanostructuring for biomedical and industrial applications. Develops innovative manufacturing processes for antibacterial surfaces, microfluidic devices, and enhanced material properties. Research Interests: Laser texturing, polymer processing, surface engineering, additive manufacturing, and simulation-driven design. Labs/Teams: Active in laser-matter interaction research and collaborative projects on biomaterial functionalization. His work bridges computational modeling (e.g., Moldflow simulations) with experimental validation. Publications: Over 40 peer-reviewed articles since 2010, emphasizing laser-based manufacturing advancements, polymer molding optimization, and biomedical material surface treatments. Recent work includes antibiofouling polymer functionalization via ultrafast lasers and fiber orientation modeling in composites.
Emek Demir serves as an Associate Professor in the Department of Molecular and Medical Genetics at Oregon Health & Science University's School of Medicine, where he directs the Computational Biology program at the Brenden-Colson Center for Pancreatic Care. His academic journey includes a Ph.D. in Computer Engineering from Bilkent University (2005) under Ugur Dogrusoz and postdoctoral training with Chris Sander at Memorial Sloan Kettering Cancer Center's Computational Biology Center. Dr. Demir's research centers on Pathway Informatics, integrating detailed biological pathway information with omic data to solve cancer biology problems. His work spans pathway curation, visualization, NLP, data standardization, machine learning, and mechanistic simulation. He pioneered the BioPAX pathway data standard and developed Pathway Commons—the largest process-level pathway database with over 2 million interactions and 400,000 detailed human reactions. His publication record demonstrates consistent innovation in computational oncology, with recent work focusing on transcription factor activity prediction, spatial tumor mapping, and causal network analysis. Key contributions include algorithms for detecting altered cancer sub-networks, identifying transcription factor modulators, and inferring active networks from proteomic data. His research bridges computational methods with clinical applications in leukemia, prostate cancer, and glioblastoma. Recipient of leadership roles in major NIH-funded initiatives Principal developer of Pathway Commons and BioPAX standards Extensive collaborations with Memorial Sloan Kettering and OHSU clinical departments Dr. Demir directs a computational biology program focused on translating pathway knowledge into clinical insights for pancreatic cancer, with ongoing projects in spatial omics, multi-dimensional tumor atlases, and antiviral nanomaterial applications.
Yazhou Tu is an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University. He specializes in Cyber-Physical System Security, Side Channel Analysis, and Privacy in embedded systems. His work focuses on addressing vulnerabilities in sensors, actuators, and IoT devices to enhance security and privacy in critical systems. Education: Ph.D. Computer Science, University of Louisiana at Lafayette M.S. Software Engineering, Tsinghua University B.S. Software Engineering, Wuhan University Research Interests: Dr. Tu’s research explores cutting-edge topics such as adversarial control of embedded systems, acoustic and magnetic side-channel attacks, and privacy risks in robotic and biomedical systems. He develops novel defense mechanisms like ADC-Bank and Transduction Shield to counteract physical signal injection attacks. Key Contributions: His work spans sensor security, IoT exploitation, and healthcare technology, with notable studies on 3D printer IP theft, vehicle control via smart glasses, and keystroke tracking via audio. He also investigates physics-informed ML for porous media and glucose monitoring systems. Awards & Grants: No specific awards or grants listed in the provided data, but his prolific publication record highlights sustained research excellence. Labs & Collaborations: Engaged in Auburn’s Center for Artificial Intelligence and Cybersecurity Engineering and other interdisciplinary initiatives focused on secure embedded systems and cyber-physical infrastructure.
Audris Mockus is the Ericsson-Harlan D. Mills Chair Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. He holds a PhD in Statistics from Carnegie Mellon University and has extensive experience in software engineering research, particularly in Digital Archaeology and Open Source Software analysis. His work focuses on understanding developer behavior, software evolution, and global software development challenges. Education: PhD in Statistics, Carnegie Mellon University, 1994 MS in Applied Mathematics, Moscow Institute of Physics and Technology, 1991 BS in Applied Mathematics, Moscow Institute of Physics and Technology, 1988 Research Interests: Mockus specializes in recovering and analyzing digital traces of software development to study developer behavior, software quality, and project dynamics. His research includes quantifying open-source supply chains, measuring developer expertise, and improving software defect prediction. He has pioneered methods for analyzing version control systems and global software teams. Awards and Grants: Recipient of multiple best paper awards at ICSE, FSE, and other conferences. Lead PI on NSF grants totaling over $2M for projects like the World of Code (WoC) infrastructure and forensic image curation. Patents in data visualization and software defect prediction. Advising and Labs: Mockus has advised PhD students on topics like npm ecosystem analysis and forensic image clustering. His research group collaborates with institutions like the National Institute of Justice on projects like Image Cloud Platform for forensic data curation.
