Roshni Chakraborty is an Assistant Professor at the Institute of Computer Science, University of Tartu, Estonia, where she leads research in the Computational Social Science Group. Her work bridges natural language processing, social network analysis, and machine learning to address challenges in social media analytics, disaster response, and misinformation detection. Research Interests: Her expertise spans: Computational Social Science : Analyzing social dynamics via digital footprints. Disaster Informatics : Developing NLP tools for crisis-related tweet summarization (e.g., ATSumm, IKDSumm). Political & Media Analysis : Detecting biases in community-driven fact-checking and news stance. Network Science : Advancing signed network modeling (SigGAN) and cross-network alignment (HCNA). Publication Trends: Recent articles (2022-2025) focus on generative AI for fact-checking, disaster tweet summarization benchmarks, and political bias analysis. Her work emphasizes practical NLP solutions for social good, leveraging transformer models, adversarial learning, and ontology-driven approaches. Academic Activities: She actively recruits PhD students and research interns for projects in social computing. Her group collaborates on datasets like BD2TSumm and Adsumm, fostering open research in crisis informatics.
Nathaniel Tkacz is a Professor of Digital Media and Culture at Goldsmiths, University of London, serving as Associate Co-Head of the Media, Communications and Cultural Studies Department. He holds a PhD in Culture and Communication from the University of Melbourne (2012). His research critically examines how digital technologies shape culture and society, focusing on apps, data interfaces, and collaborative platforms like Wikipedia. Tkacz’s work blends theoretical inquiry with creative methodologies, exploring topics such as dashboarding practices, financial technologies, and sustainability data innovations. Key research areas include the socio-political dimensions of digital media, open collaborative systems, and the role of data in governance. His methodologies involve ethnographic approaches like 'data diaries' and multi-situated analyses of apps. Tkacz is a co-author of foundational works such as *Wikipedia and the Politics of Openness* (2015) and *Being with Data: The Dashboarding of Everyday Life* (2022). He has led projects on platform migration, pandemic app ecosystems, and equitable knowledge production in Australia. Teaching commitments include leading the MA Digital Media program and supervising students in areas like digital commons, social media alternatives, and digital finance. Tkacz actively engages with interdisciplinary collaborations, notably the Waterproofing Data initiative for climate resilience. His research has been published in journals such as *Global Environmental Change*, *Current Opinion in Environmental Sustainability*, and *Distinktion: Journal of Social Theory*.
Dr. Michael Vierhauser is an Assistant Professor in the Department of Computer Science at the University of Innsbruck. His research focuses on Cyber-Physical Systems (CPS), runtime monitoring, autonomous systems, and software engineering methodologies. He specializes in safety-critical systems, human-machine interaction, and innovative educational technologies for programming and software development. Key research areas include: Runtime monitoring frameworks for CPS and UAVs Development of adaptive systems and self-protective IoT devices Integration of AI into software engineering education Agile methodologies for sustainability assessment His recent publications emphasize field-testing of drone missions, automated simulation testing for aerial systems, and frameworks like FORTE for scalable environmental monitoring. He leads projects like Dronology, an incubator for CPS research, and contributed to tools such as ReMinds and GRuM for runtime monitoring. Dr. Vierhauser collaborates on digital transformation initiatives in underground construction and has designed learning analytics dashboards to improve programming education outcomes. His work bridges theory and practice through real-world applications in emergency response systems and industrial 4.0 contexts.
Song Wang is an Associate Professor in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering. He joined York University as an Assistant Professor in July 2019 and was promoted to Associate Professor in May 2024. He serves as an Associate Editor of ACM Transactions on Software Engineering and Methodology (TOSEM) and has established himself as a prominent researcher at the intersection of Software Engineering and Artificial Intelligence. Dr. Wang earned his Ph.D. in Computer Engineering from the University of Waterloo in December 2018 under Prof. Lin Tan. He received his MS degree from the Chinese Academy of Sciences in June 2014 under the supervision of Prof. Ye Yang, Prof. Wen Zhang, and Prof. Qing Wang. His undergraduate education includes a BE in Software Engineering and a BHRM in Human Resource Management from Sichuan University in June 2011. Prior to academia, he gained industry experience through internships at Microsoft Research, Morgan Stanley Capital International, Yahoo, and Baidu, and co-founded a startup named QualDivine. Dr. Wang's research focuses on two main directions: (1) leveraging AI technologies to address software reliability challenges (AI for SE), and (2) developing software reliability assurance techniques for AI systems (SE for AI). His recent work has particularly focused on how Large Language Models can optimize and reshape software testing practices. His research has practical impact, with tools and techniques that have detected hundreds of true bugs in real-world software systems. His work spans multiple application areas including mobile testing, fuzz testing, and functional testing. His recent publications (2024-2025) demonstrate a strong focus on the intersection of AI and software engineering, with significant contributions in automated vulnerability detection, API recommendation, bias analysis in generated code, and mobile application testing. His research combines empirical studies with innovative technical approaches, often involving benchmarking and systematic literature reviews to establish foundations for future work. He has published over 60 papers in prestigious IEEE/ACM Software Engineering journals and flagship conferences, with over 2,600 citations. Dr. Wang has received four best paper awards: a Distinguished Paper Award at APSEC'23, an ACM Distinguished Paper Award at ICPC'22, an ACM Distinguished Paper Award at ICSE'20, and a Best Paper Award at PROMISE'19. He was recognized as one of the top-10 most impactful early-career researchers in Software Engineering by the Journal of Systems and Software in 2020 and received the TOSEM Distinguished Reviewer Award in 2023. Dr. Wang currently supervises multiple PhD and Master's students including Mohammad Abdollahi, Haoran Xue, Jiho Shin, Nima Shiri Harzevili, and Moshi Wei. He has successfully guided several students to complete their theses, including Reem Al Eithan (Master's thesis defense in April 2025), Moshi Wei (PhD thesis defense in April 2025), and Nima Shiri Harzevili (PhD thesis defense in February 2025). His research group has received funding from various sources to support their work on software engineering and AI. Dr. Wang leads an active research group focused on AI and software engineering at York University. His team includes PhD students, Master's students, and research assistants working on various projects related to software testing, reliability, and AI applications in software engineering. The group has developed tools that have detected hundreds of true bugs in real-world software systems, with some findings documented in Jira issues and GitHub repositories across numerous open-source projects.
Dr. Zhongzhou Chen is an Associate Professor in the Department of Physics at the University of Central Florida (UCF), affiliated with the Physics Education Research (PER) group. He earned his Ph.D. in physics from the University of Illinois Urbana-Champaign (2012), specializing in physics education and multimedia learning. After a postdoctoral role at MIT’s RELATE group (2013–2016), he joined UCF. His research focuses on analyzing student learning data to improve online education effectiveness, designing data-rich online learning environments, and developing assessment methods. Key interests include cognitive mechanisms of learning, scalable instructional design, and curating educational resources. His research integrates online learning platforms like MOOCs with big data analytics to study student behavior and learning outcomes. Notable work includes AB experiments on MOOCs to evaluate instructional methods, drag-and-drop problem formats for deliberate practice, and video production techniques to enhance instructor performance. He leads efforts to modularize online courses for continuous improvement, inspired by grounded cognition theory and adaptive learning principles. Dr. Chen’s contributions span over 30 peer-reviewed publications, including seminal works on MOOC-based research methodologies and AI-driven feedback systems. He collaborates with UCF’s Center for Distributed Learning to develop Obojobo, an open-source platform for modularized instructional design. His work emphasizes practical applications of cognitive science in STEM education, aiming to make learning more equitable and effective through technology.
Bei Yan is an Assistant Professor at the School of Business, Stevens Institute of Technology. His research focuses on technology-supported collaboration and influence processes in groups, including human-machine teaming with intelligent personal assistants and large-scale crowdsourcing communities. He employs experimental methods and computational analysis of big data to study collective intelligence and network structures. Previously, he served as a Project Scientist at the University of California, Santa Barbara. Education: PhD in Communication (2018), University of Southern California MA in Global Communication (2012), University of Southern California MS in Global Media and Communications (2011), London School of Economics BS in Marketing Management (2010), Renmin University of China Research Interests: Key areas include: Human-AI collaboration dynamics Crowdsourcing and collective intelligence Network analysis of social influence and polarization Big data/text mining applications Recent Work Trends: Recent studies emphasize the role of AI in team decision-making, crisis communication networks, and semantic analysis of political polarization. His work bridges communication studies, social psychology, and computational methods. Awards: Best Paper Award (AOM 2023) Best Paper Award (AOM 2020) Dennis Gouran Research Award (2020) Top Paper Award (Global Cultural Industries 2019) Grants & Funding: NSF CRII Early Career Award ($221,000, 2021-2024) USC Research Enhancement Fellowship (2017-2018) Annenberg Graduate Fellowship (2012-2013) Labs & Teams: Active in interdisciplinary research groups focusing on computational social science and human-AI interaction. Collaborates with institutions like the University of California and the London School of Economics.
Dr. Michael Scott Brown is a Professor in the Department of Information Systems at the University of Maryland Baltimore County (UMBC). Previously, he served as the Graduate Program Director for Online Masters in Information Systems and related certificates at UMBC. Before that, he was Program Director of Computer Science at the University of Maryland Global Campus (UMGC), overseeing software engineering, database systems, and computer science programs. He has extensive industry experience, including roles at Sun Microsystems, Valhalla Data Systems, and CACI, where he contributed to software engineering and project management. Education: Dr. Brown holds a Doctor of Philosophy in Computer Science (Nova Southeastern University, 2010), a Master of Science in Computer (Shippensburg University, 1997), and a Bachelor of Arts in Mathematics/Computer Science (Shippensburg University, 1992). His thesis focused on a species-conserving genetic algorithm for multimodal optimization. Research Interests: His work spans genetic algorithms, software engineering, educational technology, and data analysis. Notable projects include collaborations with NASA on flight system testing, development of ranking systems for high school sports, and contributions to Cytoscape modeling tools. He has been awarded a Fulbright Grant (2019) and two Effective Practice Awards from the Online Learning Consortium (2016, 2018). Recent Articles Trends: His publications emphasize predictive modeling in education (student retention, test scores), machine learning applications, and optimization techniques. He frequently explores intersections between algorithms (genetic, SVM) and real-world challenges like cryptocurrency forecasting and UAV path planning. Awards: Fulbright Grant (2019), OLC Effective Practice Awards (2016 & 2018), NSF grants, and others. Advising & Grants: Supervised projects at NASA and Cytoscape, managed large educational programs, and secured grants like NSF AIM-PQC. His teaching spans 12 institutions, emphasizing adult and online education. Labs/Teams: Collaborated on NASA Core Flight System testing, Washington Post sports rankings, and the EVA Training System for astronauts. Active in interdisciplinary research through projects like the Open Research Laboratory.
Effat Farhana is an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. She holds a Ph.D. in Computer Science from North Carolina State University and a B.S. in Computer Science and Engineering from Bangladesh University of Engineering and Technology. Her research focuses on Machine Learning, Data-Centric AI, and their applications in education and healthcare, alongside empirical software engineering and natural language processing. She explores topics like student learning analytics, AI-driven educational tools, and defect analysis in software projects. Her work bridges cognitive science and AI through projects like theory of mind modeling for humans and AI systems, and developing frameworks for K-12 physics instruction. She also investigates software engineering challenges in infrastructure as code and pandemic-related software systems. Her contributions span both foundational AI research and applied educational technology solutions. Farhana's publications emphasize empirical studies of software defects, cognitive aspects of learning, and AI's role in improving education and healthcare systems. She collaborates on projects involving online learning platforms, science literacy tools, and cognitive-inspired neural architectures.
Dr. Volkan Acun is a Research Fellow at the School of Science, Engineering & Environment at the University of Salford, UK. His work focuses on soundscapes, environmental acoustics, and the application of machine learning to auditory perception analysis. He is affiliated with the Acoustics Innovation Institute , contributing to research on noise barriers for sustainable technologies like air source heat pumps. Research Fellow, University of Salford Affiliated with Acoustics Innovation Institute Focus on environmental acoustics and soundscape modeling Dr. Acun’s research explores how humans perceive sound in diverse environments, including historical spaces , open-plan offices , and public study areas . He employs methodologies like grounded theory , structural equation modeling , and machine learning to decode auditory experiences and emotional responses. Recent work highlights include high-fidelity analysis of air source heat pump noise , which intersects with the UK’s net-zero carbon goals, and studies on predictive soundscape modeling using AI. His publications span journals like Applied Acoustics and Building Acoustics .
Dr. Guy C. Hembroff is an Associate Professor in the College of Computing at Michigan Technological University (MTU), serving as Graduate Program Director for the MS in Health Informatics and PhD in Computational Science and Engineering programs. He holds a PhD in Computational Science and Engineering from MTU, alongside an MPA in Public Administration (Northern Michigan University) and a BS in Finance and Economics (MTU). His research focuses on human health applications of machine learning, computer vision, cybersecurity, and medical device innovation, particularly in areas like medical image analysis and mHealth solutions. He leads the Biomedical Data Science (BDS) Lab, a multidisciplinary team addressing pressing healthcare challenges through AI-driven solutions. Education: PhD, Computational Science and Engineering, Michigan Tech MPA, Public Administration, Northern Michigan University BS, Finance and Economics, Michigan Tech Research Interests: Dr. Hembroff’s work spans healthcare AI (deep learning, medical image segmentation, generative AI), cybersecurity for healthcare systems, and biometric development. His lab collaborates with medical institutions to advance disease surveillance, healthcare interoperability, and secure patient data systems. Recent projects include automated quality control of radiographs and blockchain-based patient data auditing. Advising & Grants: Advises four PhD students on topics like medical image segmentation, mental health intervention modeling, and privacy-preserving LLMs. His work integrates clinical, engineering, and cybersecurity expertise to drive practical healthcare solutions. While specific grant details are not listed, his research emphasizes translational applications in global health equity and rural healthcare access. Labs/Teams: The BDS Lab combines clinical informatics, AI, and cybersecurity to innovate in areas such as medical device development and health data security. Current projects include blockchain for secure patient data and AI-driven mHealth tools for underserved regions.
Diomidis Spinellis is a Professor at the Department of Management Science and Technology, Athens University of Economics and Business. He is a leading researcher in software engineering, IT security, and cloud systems engineering, with over 300 publications and 10,000 citations. He has authored award-winning books including Code Reading , Code Quality: The Open Source Perspective , and Effective Debugging: 66 Specific Ways to Debug Software and Systems (2016). As a Senior Member of ACM and IEEE, he served as Editor-in-Chief of IEEE Software (2015–2018) and contributed to open-source tools like CScout, UMLGraph, and dgsh. Award-winning author in software engineering Developer of critical open-source tools Editorial leadership in IEEE Software Contributor to macOS and BSD Unix His research spans code quality, software evolution, security, and developer productivity. Articles highlight Unix modernization, AI-assisted coding, dependency analysis, and incident management. He has served on the IEEE Computer Society Board of Governors and holds degrees from Imperial College London (MEng, PhD). Scientific contributions include open-source datasets (e.g., VulinOSS, Alexandria3k) and innovative tools for software analysis. His work bridges academic research and industrial practice, with case studies on Eclipse, Android APIs, and ING’s incident management.
V. K. Cody Bumgardner serves as Associate Professor of Pathology and Laboratory Medicine , Assistant Dean for Artificial Intelligence and Data Science , and Division Chief of Pathology Informatics at the University of Kentucky College of Medicine . He also directs the IBI Center for Applied AI , focusing on AI integration in healthcare. Academic Rank: Associate Professor Leadership Roles: Assistant Dean, Division Chief, Director Research Focus: AI/ML in pathology, medical imaging, and clinical data Education: PhD in Computer Science (University of Kentucky, 2017). Research Themes combine artificial intelligence with clinical applications, including: Digital pathology automation Edge computing for healthcare Large language models in structured medical tasks Opioid crisis forecasting systems Medical imaging enhancement techniques Recent Publications demonstrate cross-disciplinary innovation in AI-driven clinical diagnostics , secure healthcare infrastructure , medical imaging enhancement , and addiction epidemiology .
Saheed Popoola, PhD, is an Assistant Professor at the University of Cincinnati's CECH - School of Information Technology. His research spans software engineering education, computational technology in K-12 classrooms, and model-driven engineering. He actively explores strategies to enhance collaborative learning environments through student-driven software projects and investigates challenges in open-source development and cybersecurity incident analysis. His work bridges empirical software engineering practices with educational technology, focusing on topics such as static analysis tool efficacy, user sentiment analysis in software communities, and ensemble learning techniques for network security. He has contributed to tools like Loupe for model analysis and has examined LabVIEW/Simulink model evolution patterns. Recent studies include the impact of computational thinking in K-12 education, early heart disease detection via machine learning, and strategies for managing diverse classroom environments. His research emphasizes practical applications in both academic and industrial software development contexts. Notably absent from the provided data are specific details about grants, awards, or direct student advising. However, his involvement in initiatives like the student software solutions center highlights a focus on experiential learning and community-driven development practices.
Prof. Vassil Vassilev is a Professor of AI and Intelligent Systems and Head of the Cyber Security Research Centre at London Metropolitan University. His work spans artificial intelligence, cyber security, and data integration with a focus on applications in urban systems, malware analysis, and cyber-physical systems. He leads initiatives in secure transaction risk assessment (CyDRA) and smart city technologies. Research interests include reinforcement learning for cyber incident response, AI policy validation frameworks, and adaptive security systems. He explores hybrid AI models, IoT-based environmental monitoring, and semantic frameworks for vulnerability detection. His work bridges theoretical AI advancements with practical implementations in cyber defense, urban data spaces, and financial security. Grants and funding support his projects on cyber security analytics, threat intelligence, and low-cost data platforms. He collaborates on initiatives like the first urban data space in Bulgaria and innovative authentication techniques using audio steganography. His leadership at the Cyber Security Research Centre drives applied research in both academic and enterprise contexts.
Mostafa Rahimi Azghadi is a Professor and Head of the Electrical and Electronic Engineering Discipline at James Cook University (JCU). He specializes in neural-inspired computing, machine learning, and AI applications in agriculture, aquaculture, medicine, and biosecurity. He has secured over $20M in research funding and holds leadership roles in several ARC research hubs and centers, including Deputy Director of the ARC Training Centre in Plant Biosecurity and AI Leader of the ARC Industrial Transformation Research Hub for Aquaculture. Education: PhD in Electrical and Electronic Engineering (University of Adelaide, 2014), with prior degrees from Iranian National University. Affiliations: Acting Director of JCU's Agriculture Technology and Adoption Centre, IEEE Northern Australia Section Chair, and Associate Editor of Frontiers in Neuroscience and IEEE Access. Research Interests: Focus on AI, deep learning, neuromorphic engineering, and hardware acceleration. His work spans applications in sustainable agriculture, biosecurity, and healthcare, including projects on fish phenotyping, stress monitoring via wearables, and memristive neuromorphic systems. Publications & Impact: Over 100 journal articles and conference papers. Recent work includes advances in memristive neural networks, underwater fish segmentation, and stress prediction models. His research bridges theory and practice, emphasizing real-world problem-solving. Awards: Top 2% global citation rank in EEE (2020), QLD Young Tall Poppy Science Award (2017), and multiple best paper awards. Grants & Funding: Secured major grants for aquaculture genomics, sugarcane health monitoring, and robotic weed control. Teaching: Coordinates and lectures on embedded systems and sensors. Emphasizes hands-on learning and industry relevance, with a focus on connecting theory to practical engineering challenges.
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