Daniel Haehn is an Assistant Professor of Computer Science specializing in biomedical imaging and visualization research. His work focuses on developing computational methods to accelerate biological and medical research through web-based tools, machine learning, and interactive visualization systems. Research Interests: Dr. Haehn's research spans biomedical imaging, data visualization, machine learning applications in healthcare, web-based medical tools, human-computer interaction, reinforcement learning, and computer graphics. His work particularly emphasizes creating accessible web-based solutions for medical image processing and scientific visualization. Awards and Recognition: Best Paper Award at IEEE VIS 2021 Best Paper Award at IEEE VIS 2018 Best Paper Award at IUI 2023
Dr. Abdel-Karim Al-Tamimi is a Senior Lecturer in Computer Science and Software Engineering at Sheffield Hallam University (SHU), where he leads the Interactive Data Analytics Group (iDAG) and contributes to the Applied Software Engineering Research Group (ASERG). He previously served as an Associate Professor at Yarmouk University (Jordan), Director of the Entrepreneurship and Innovation Center (EIC), and Department Chair at the Higher Colleges of Technology (UAE). His roles include consultancy in digital transformation via the DIfG program and editorial board memberships for PLOS ONE and Research Reports on Computer Science . Education: MSc and PhD in Computer Engineering, Washington University in St. Louis (2007, 2010) Senior Fellow of the Higher Education Academy (SFHEA) Research Interests: Machine Learning applications in Natural Language Processing (NLP), Multimedia Networks, Computer Security, and IoT. Recent work focuses on conversational agents (e.g., Phyllis chatbot for adolescent health) and cybersecurity education frameworks. Projects include EU-funded initiatives (e.g., H2020, DFG) and interdisciplinary collaborations in AI-driven solutions. Publications: Over 50+ peer-reviewed articles, including recent contributions on optimization algorithms, game-based learning, and threat intelligence models. His work spans journals like Cluster Computing , ACM Transactions on Computing Education , and conferences such as IEEE BigComp. Grants & Awards: Received £10k for the Phyllis chatbot project and £9.8k for Sheffield’s population health initiative. Awards include SFHEA and industry certifications (Huawei HCIA-AI, Cisco CCNA). Advising & Teams: Oversees interdisciplinary projects and labs like the Orange-Yarmouk Innovation Lab. Active in academic entrepreneurship through roles like Co-Founder of Phyllis and mentor at global hackathons.
Univ.-Prof. Dr. Günther Specht is a faculty member in the Department of Computer Science at Universität Innsbruck. His research focuses on machine learning, data mining, and their applications in recommendation systems, music information retrieval, and authorship attribution. He has contributed to projects such as HPT4Rec (a hyperparameter optimization framework for recommenders) and Cloudgene (a cloud computing tool for biomedical pipelines). His work spans database systems, text analysis, and social media analytics. Notable tools include HaploGrep 2 (for mitochondrial DNA analysis) and StyleExplorer (for textual style visualization). Key research areas include: (1) Developing algorithms for music popularity prediction and playlist analysis; (2) Enhancing authorship attribution techniques using grammar profiling and syntax tree analysis; (3) Designing efficient database index structures like Height Optimized Tries; and (4) Investigating ethical aspects of recommendation systems and plagiarism detection. Publications from 2020-2024 highlight advancements in cross-domain text classification, social media behavior analysis, and automated music genre recognition. His work bridges traditional statistical methods with modern machine learning, emphasizing practical applications in both academic and industry contexts.
Gias Uddin is an Associate Professor in the Electrical Engineering and Computer Science department at York University's Lassonde School of Engineering. He also holds an Adjunct Professor position at the University of Calgary. Previously, he served as an Assistant Professor at the University of Calgary from 2020 to 2023. Dr. Uddin's research lies at the intersection of software engineering (SE) and artificial intelligence (AI), with specific focus areas including the assessment of AI trustworthiness using SE (SE4AI) and improving the productivity of software and knowledge professionals using AI-enabled software assistants. His work spans three key domains: Democratized Data Science (HCI → AI4DS, SE4DS), Modernized Software Issue Management (HCI → AI4SE), and Usable Cybersecurity Engineering (HCI → AI4CE). He is particularly interested in how AI can democratize the adoption of machine learning techniques across stakeholders during ML systems development. His recent publications demonstrate a strong focus on practical applications of AI in software engineering, with particular emphasis on detecting hallucinations in LLMs, improving API documentation, and developing AI-assisted tools for software issue management. His research has resulted in numerous publications at top-tier software engineering conferences including ICSE, ASE, and FSE. Distinguished paper award at FSE 2025 for work on hallucination detection in LLMs York University Research Award (2025) IBM Champion recognition for 2024 and 2025 CAS Project of the Year Award at IBM TechXChange 2024 Multiple NSERC-funded research grants Dr. Uddin has successfully secured multiple research grants including an NSERC Discovery Grant, NSERC Alliance International Catalyst Grant on 'Hallucination Detection in LLMs using Metamorphic Testing,' and several industry-funded projects with IBM. He has supervised numerous graduate students who have gone on to successful careers in both academia and industry, with many receiving prestigious scholarships and awards. As the founding director of the Data Intensive Software Analytics (DISA) Lab, he leads a team focused on developing innovative AI-assisted tools for software developers and data scientists.
Wenbin Zhang is an Assistant Professor in the Knight Foundation School of Computing & Information Sciences at Florida International University and an Associate Member at the Te Ipu o te Mahara Artificial Intelligence Institute. His research focuses on the theoretical foundations of machine learning with societal impact, including fairness, generative AI, health informatics, and interdisciplinary applications in healthcare, digital forensics, and energy. He has received awards such as the NSF CRII Award and recognition in the AAAI’24 New Faculty Highlights. Zhang serves in organizing committees for major conferences like AAAI, WSDM, and AIES, contributing to academic leadership. Ph.D., University of Maryland, Baltimore County (2020) Research interests span societal aspects of AI, generative models, and fairness-aware machine learning. His work bridges theory and practice, addressing ethical challenges in AI deployment across domains like healthcare and cybersecurity. Key contributions include frameworks for fair graph learning, bias mitigation in LLMs, and adaptive pruning techniques for large models. Publications emphasize fairness in ML systems, digital forensics, and interdisciplinary applications. Over 50 papers span venues like FAccT, ICDM, and AAAI, with multiple best-paper recognitions. His NSF-funded research highlights innovation in fair AI and robust model adaptation. Scientific Awards: NSF CRII Award, FAccT’23 Best Paper Candidate, ICDM’23 Best Paper Academic service includes roles as Travel Award Chair (AAAI’24), Volunteer Chair (WSDM’24), and Student Program Chair (AIES’23). Teaching and mentorship are integral to his mission, fostering next-generation AI researchers through rigorous training in ethics-driven innovation. Labs/Teams: Active collaborations with the Te Ipu o te Mahara Institute, focusing on AI ethics and societal impact.
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.
Jeong-Hyon Hwang is an Associate Professor at the University at Albany, State University of New York , affiliated with the College of Nanotechnology, Science, and Engineering and the Department of Computer Science . As Director of the Data Management Systems (DMS) Lab, he focuses on scalable graph databases, trajectory data compression, and real-time stream processing. His work on the G* graph database system, funded by the NSF CAREER award IIS-1149372 , enables efficient storage and analysis of distributed dynamic graphs. PhD in Computer Science, Brown University (2008) MS in Computer Science, Brown University (2003) MS in Computer Science and Engineering, Korea University (2000) BS in Computer Science and Engineering and Mathematics Education, Korea University (1998, 1994) Dr. Hwang’s research spans Databases and Distributed Systems , with specific emphasis on graph database systems , trajectory data management , and fault-tolerant stream processing . His publications highlight advancements in Internet-scale data management , real-time analytics , and load balancing for dynamic environments. The 15 most recent publications reflect trends in graph algorithms , stream processing reliability , and trajectory compression . Key subfields include distributed graph storage , centrality estimation , non-relational stream models , and high-availability solutions for wide-area networks. Scientific Awards : NSF CAREER award (2012) Best Poster Award, IEEE ICDE (2014) Best Poster Runners-Up, ACM SIGSPATIAL GIS (2010) IBM Open Collaborative Faculty Award (2010) National Scholarship, South Korea (2001-2005) New Software Award, South Korea (2001) Dr. Hwang leads the DMS Lab , developing open-source systems like G* for graph storage and querying. He has authored patents, co-authored Korean translations of technical books, and contributed to foundational research in high-availability algorithms and stream processing engines .
Dr. Michael Bewong is a Senior Lecturer in Computing at Charles Sturt University, specializing in Data Science, AI, and Cyber Security. He holds a PhD and BSc (Hons) from the University of South Australia. His roles include teaching database-related subjects and conducting research in data analytics and cyber security. Previously, he worked as a Research Fellow at UniSA, collaborating with Data to Decisions CRC on projects like 'Beat the News' (predictive data mining) and 'Predicting Cyber Security Exploits' (machine learning for vulnerability analysis). Education: PhD in Computing, University of South Australia BSc (Hons) in Computing, University of South Australia Research Interests: Focuses on applying data analytics and machine learning to solve real-world problems in cyber security, agriculture, and health. His work integrates AI-driven solutions for cyber threat detection, privacy-preserving data publication, and smart farming systems. Current projects include analyzing UAV threats to agriculture and developing frameworks for false information detection on social media. Articles Trends: Recent publications emphasize cyber security (e.g., threat hunting, exploit prediction), AI ethics (fairness algorithms), and precision agriculture (UAV threats, data-driven farming). Collaborations span interdisciplinary teams addressing challenges in graph data imputation, federated learning, and privacy techniques like LDP. Awards: Holds 12 prizes (specific names not listed in provided texts). Advising & Grants: No explicit student advisees listed. Collaborations include grants from Data to Decisions CRC and Food Agility Cooperative Centre. Labs/Teams: Affiliated with Data Science and Engineering Research Unit, Cyber Security Research Group (CSRG), and Advanced Network Research Group (ANRG) at Charles Sturt University.
John Dempsey is a Professor in the Coulter School of Engineering & Applied Sciences at Clarkson University, affiliated with the Department of Civil & Environmental Engineering. His research focuses on fracture mechanics, ice mechanics, and materials science, with applications to polar engineering and structural analysis. His publications demonstrate a consistent focus on ice fracture behavior under varying thermal and mechanical conditions. Recent work explores size/rate effects in freshwater ice fracture, T-stress extraction methods, and in-situ testing protocols. The research employs laboratory experiments, field measurements, and computational modeling to address fundamental questions in material failure. No awards, students, or lab affiliations were mentioned in the provided text.
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.
Dinesh Verma is a Professor and Executive Director of the Systems Engineering Research Center (SERC) at Stevens Institute of Technology. He previously served as Founding Dean of the School of Systems and Enterprises (2007–2017). His roles include adjunct positions at Georgetown University (Visiting Professor, Department of Biochemistry), and advisory roles at institutions like the Embedded Systems Institute (Eindhoven, Netherlands) and the Defense Science Board (U.S.). Education and Professional Background: Verma’s career spans academia and industry, including roles at Lockheed Martin, Virginia Tech (Systems Engineering Design Laboratory), and as an Invited Lecturer at the University of Exeter (1995–2000). Research Interests: Focus on systems engineering fundamentals, including conceptual design evaluation, system architecture, life cycle costing, and supportability engineering. Recent work emphasizes digital engineering integration, ontology-based model interoperability, and systems security for modular open systems. Grants & Patents: Directed over $175M in academic/research programs. Holds patents in life-cycle costing, fuzzy logic for design evaluation, and collaborative engineering tools. Awards: Honorary Doctorate (Linnaeus University, 2007) and Honorary Master’s (Stevens, 2008). INCOSE Fellow (International Council on Systems Engineering). Key Contributions: Authored 100+ publications, including textbooks on Maintainability, Economic Decision Analysis, and Space Systems Engineering. Co-founded the Defense Science Board’s Digital Engineering initiative and chairs DoD/MITRE mission engineering collaborations.
Dr. Guiling Wang is a Distinguished Professor of Computer Science and Associate Dean of Research and External Relations at New Jersey Institute of Technology (NJIT). She holds a Ph.D. in Computer Science and Engineering from The Pennsylvania State University (2006) and a B.S. in Software Engineering from Nankai University, China (2002). Her research focuses on machine learning, blockchain technology, deep learning, and intelligent transportation systems. Dr. Wang’s work spans interdisciplinary areas such as: Machine Learning and Reinforcement Learning Blockchain-based systems and decentralized applications Intelligent traffic signal control and autonomous systems Data privacy and cybersecurity Generative adversarial networks (GANs) and vision-language models Her recent research emphasizes practical solutions for: Optimizing traffic management through multi-agent reinforcement learning Developing robust watermarking techniques for digital security Applying LLMs for financial decision-making and portfolio management Designing blockchain frameworks for vehicular networks and edge computing Notable contributions include: Proposing novel architectures like MCFN for high-performance watermarking Advancing RAGIC for risk-aware stock interval construction Developing PairUpLight for multi-intersection traffic coordination Pioneering blockchain-based systems iBCTrans and VDKMS for vehicular networks Dr. Wang’s research is supported by grants from federal agencies and industry collaborators, with applications in smart cities, financial technology, and healthcare systems.
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.
Pourang Irani is an Adjunct Professor in the Department of Computer Science at the University of Manitoba's Faculty of Science. His research focuses on designing software tools for ubiquitous interaction and analytic tasks, emphasizing accessibility across devices and environments. He holds an email address associated with the University of British Columbia (UBC), suggesting potential cross-institutional collaborations. Research Interests: Mobile and ubiquitous user interfaces Visual and ubiquitous analytics Information visualization Navigation and distributed user interfaces Gaming and input device innovations While no specific grants, awards, or students are listed, his work bridges human-computer interaction and pervasive computing environments. No lab affiliations or future projects are detailed in the provided text.
Jeffrey Wall is an Associate Professor of Management Information Systems & Analytics at the College of Business, Michigan Technological University. He holds a PhD in Information Systems from the University of North Carolina at Greensboro, along with an MS in Public Administration (minor in IS) from Brigham Young University and a BA in Organizational Communication from the University of Utah. His research focuses on AI/ML applications in finance/accounting, simplification of AI workflows, information security behavior, and power dynamics. He has developed proprietary e-commerce systems and an ERP system, and currently oversees two open-source software projects with student collaboration. Teaching interests include business process automation, AI for business, systems analysis, and programming for analytics. Dr. Wall’s work emphasizes entrepreneurship and the practical application of technology in small businesses. His research spans behavioral aspects of information security, organizational policy compliance, and cross-cultural privacy behaviors. He has authored over 15 peer-reviewed articles on topics ranging from fear appeals in security messaging to collaborative theorizing in academic research. His projects often bridge theory and practice, particularly through student-led open-source initiatives.
