Rob Hierons is Professor and Chair in Testing at the University of Sheffield's School of Computer Science. His research develops automated testing techniques for software systems, focusing on model-based testing, distributed systems, and recently autonomous robotics. He aims to enhance software quality through efficient test generation from specifications and code. His work spans theoretical foundations and practical applications, including CSP-based testing models and diversity-based test optimization. Current interests include verification of robotic systems and causal testing frameworks. He has published extensively in software engineering venues and serves in editorial roles for major testing journals.
Sanket Tavarageri is an Assistant Professor in the Computer Engineering Department at San José State University. He holds a Ph.D. in Computer Science and Engineering from The Ohio State University and a B.Tech from National Institute of Technology Karnataka. His research encompasses big data systems, machine learning infrastructure, and high-performance compiler technologies. Research areas include: Polyhedral compilation techniques for deep learning workloads Automatic parallelization and optimization frameworks Hardware-software co-design for computational efficiency Scalable machine learning systems His publication portfolio demonstrates consistent innovation in compiler architecture and parallel systems from 2013-2021. Recent work focuses on AI-enhanced compilation, automatic parallelism for deep learning models, and hardware-aware optimizations. Publications appear in ACM TACO, IEEE Big Data, IPDPS, and PLDI conferences. Tavarageri maintains an active industry connection as a researcher at Google. Tools developed through his BRIGHT laboratory are available on GitHub and Bitbucket. His teaching covers compiler technology, parallel computing, and systems design with emphasis on practical implementation.
Lewis Baumstark is a Professor of Computing in the School of Computing, Analytics, and Modeling at the University of West Georgia. His research spans computer science education, software reverse engineering, and computer architecture, with significant contributions to automated assessment systems and pedagogical analytics. Research domains include: Automated evaluation of student code Version control mining for process analysis Examination format impacts on learning Parallel computing optimizations Publications highlight consistent focus on computer science pedagogy, particularly automated assessment techniques and empirical analysis of student programming behaviors. Recent work emphasizes structural analysis of student-written tests and virtualized grading systems.
Mauricio Barahona is a Professor of Biomathematics and Director of the EPSRC Centre for Mathematics of Precision Healthcare at Imperial College London. He holds appointments in the Department of Mathematics within the Faculty of Natural Sciences. His research integrates dynamical systems, network science, and machine learning to address challenges in healthcare, biological systems, and complex networks. He obtained his PhD in Theoretical Physics from MIT, followed by postdoctoral fellowships at Stanford and Caltech. Key roles include leading the EPSRC Centre since 2016 and contributing to interdisciplinary initiatives such as the Biomathematics Group and the Artificial Intelligence Network. His work spans disease clustering analysis, healthcare system optimization, and applications of AI in precision medicine. He has pioneered methods for analyzing complex networks in biological and clinical contexts, including protein dynamics, antimicrobial resistance spread, and multimorbidity patterns. Research focuses on network-based models for healthcare integration, disease co-occurrence patterns, and machine learning techniques for biomedical data. His lab develops tools like RamanSPy for spectroscopic analysis and HCGA for network phenotyping. Collaborations involve diverse fields from physics to clinical medicine, emphasizing translational applications.
Ziawasch Abedjan is a full professor of computer science and chair of the Data Integration and Data Preparation (D2IP Lab) Group at Technische Universität Berlin, part of the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds a PhD from the Hasso Plattner Institute (2014) and was previously a junior professor at TU Berlin (2016–2020), with postdoctoral work at MIT (2014–2016). His research focuses on scalable methods for processing large heterogeneous datasets, emphasizing automated data preparation, extraction, and cleaning for data science workflows. Education: PhD in Computer Science, Hasso Plattner Institute (2014) MSc in Computer Science, Hasso Plattner Institute (2010) BSc in Computer Science, Hasso Plattner Institute (2008) Research Interests: Dr. Abedjan’s work bridges database systems and machine learning, addressing challenges in data integration, automated data cleaning, and scalable data science tools. His team develops systems like Blend for unified data discovery and MATE for multi-attribute table extraction. Recent projects include exploring the environmental impact of AutoML and advancing catalog enrichment techniques. Awards & Recognition: First Prize, GI Data Science Challenge (BTW 2023) SIGMOD Reproducibility Award (2019) Best Dissertation Award (2013/2014) Teaching & Service: Teaches foundational courses in data structures and databases. Serves on committees for major conferences (SIGMOD, VLDB) and chairs roles such as Reproducibility Chair for BTW (2023/2025). Active in editorial roles for journals including ACM JDIQ and IEEE Data Engineering Bulletin. Labs & Teams: Leads the D2IP Lab, collaborating with universities and industry. Current projects include TDC2 NFDI4DS. Offers thesis topics in data science lifecycle optimization and ML system design.
Professor Ebroul Izquierdo holds a position at Queen Mary University of London's School of Electronic Engineering and Computer Science. His academic background includes a BSc, MSc, and PhD. He specializes in Multimedia Signal Processing, Machine Vision, and Mathematical models in image processing. His research focuses on video coding, computer vision, AI applications in agriculture, and security systems. He leads major grants such as the £305k Smart Farm project and the £370k SHIFT initiative. His work includes innovations in camera calibration for sports videos, self-supervised learning models, and federated learning architectures. He is affiliated with the Centre for Multimodal AI and collaborates with organizations like BBC and Innovate UK. His email is ebroul.izquierdo@qmul.ac.uk . Research Grants : Smart Farm and Agri-environmental Big Data Space (£305,116 EPSRC) SHIFT Project (£370,577 EPSRC) KTP: Machine Learning for Sport Broadcast Graphics (£214,061 Innovate UK) Labs & Teams : Centre for Multimodal AI, collaborating with BBC and EU Horizon 2020 initiatives.
Ellen McCreedy is an Associate Professor of Health Services, Policy, and Practice at the Brown University School of Public Health. Her research focuses on pragmatic evaluations of nonpharmaceutical interventions for managing neuropsychiatric symptoms in dementia, particularly in nursing home and assisted living settings. She leads trials testing interventions like personalized music for agitation reduction, enhanced advance care planning, and tunable lighting for sleep improvement. Dr. McCreedy holds a MPH from the University of South Florida, a PhD from the University of Minnesota, and completed a postdoctoral fellowship at Brown University’s Center for Gerontology and Healthcare Research. Education: MPH in Global Health, University of South Florida PhD in Health Services Research, University of Minnesota Postdoctoral Fellowship, Brown University Center for Gerontology and Healthcare Research Research Interests: Dr. McCreedy’s work emphasizes pragmatic clinical trials, focusing on scalable interventions to improve quality of life for older adults with dementia. Key areas include: Nonpharmacological management of agitation and behavioral symptoms Advance care planning and goal-concordant care Implementation science in nursing home environments Health disparities in dementia care Her recent studies explore how electronic health records and wearable devices can better capture behavioral symptoms, and how organizational factors influence intervention fidelity. She serves on the Steering Committee of the NIA IMPACT Collaboratory’s Technical and Data Core, advancing embedded trial methodologies. Labs/Teams: Active collaborator in the Center for Gerontology and Healthcare Research and the IMPACT Collaboratory.
Giorgio Triulzi is an Associate Professor (with tenure) at the School of Management, Universidad de los Andes, Bogotá, Colombia. He serves as the Director of the PhD Program in Management and the Research Master in Management, and has previously led the School's Research Committee. He holds a PhD in Economics and Policy Studies of Technological Change from UNU-MERIT and Maastricht University (Netherlands), with postdoctoral experience at MIT and Singapore University of Technology and Design. His research focuses on the economics and management of innovation, technological change, and capability upgrading, especially in emerging economies. He analyzes patent data, technology performance, and industry output to understand innovation drivers and impacts. His work also explores decision-making methods to reduce uncertainty and promote ambitious innovation investments. Triulzi’s recent publications examine global technological trajectories, knowledge flows through patent citations, technology improvement rate predictions, and innovation in green technologies and coding bootcamps. His methodological contributions include advanced data mining and forecasting techniques using patent data. MISTI grant MIT-Colombia – Universidad de los Andes Seed Fund He advises doctoral students, including Andrés Fernando Durán Cortés, and has secured research funding for collaborative projects. He teaches courses on innovation, entrepreneurship, and strategic management at undergraduate, master’s, and PhD levels. Triulzi is actively engaged in the academic community, presenting at major conferences such as DRUID and the International Joseph Schumpeter Society, and delivering invited talks at institutions like the Inter-American Development Bank and MIT.
Sajad Homayoun is a Tenure Track Assistant Professor at Aalborg University's Department of Electronic Systems within The Technical Faculty of IT and Design. His research focuses on integrating Artificial Intelligence with cybersecurity to develop advanced solutions for threat detection and mitigation. PhD in Computer Science Specialization in Cybersecurity & Machine Learning His technical expertise spans: AI-driven intrusion detection systems Adversarial machine learning (AML) Explainable AI (XAI) for security transparency Time series and graph analysis MLOps implementation with MLflow Python development using pandas, scikit-learn, and TensorFlow Research trends from his publications show strong emphasis on: Combating phishing attacks through ML Applying deep learning to crowdsensing trustworthiness Failure prediction in industrial systems Extracting behavioral patterns from ransomware Blockchain-based security applications He teaches Network Security courses during fall semesters and maintains active collaborations with researchers across Denmark and international institutions.
Ashwathy Ashokan is an Instructor in the Department of Computer Science at the University of Nebraska Omaha's College of Information Science & Technology. With a Master's in Computer Science from UNO and a PhD in Artificial Intelligence and Machine Learning underway, she bridges industry experience (Union Pacific, Microsoft, Wipro) with academia. Her research focuses on algorithmic fairness, recommender systems, and text mining. Research Interests span Text Mining , Information Retrieval , Recommender Systems , Search Engines , Algorithmic Fairness , and Machine Learning . Her recent work in Information Processing & Management (2021) highlights fairness metrics in rating predictions, while earlier publications (2013-2014) explore MapReduce-based topic extraction from timestamped documents. Scientific Awards include the Best Graduate Student for Service Award UNO Advantage Scholarship Special Faculty Development Fellowship Volunteer Activities include involvement with Omaha-based organizations like Girls Who Code, Open Door Mission, and Nebraska AIDS Project. She has served as a Committee Member for UNO's Women in IT Initiative.
Antonella Guzzo is an Assistant Professor (ING-INF/05) at the Faculty of Engineering, University of Calabria, affiliated with the Department of Electronic Informatics and Systems (DEIS). Her research spans Data Mining, Process Mining, Federated Learning, and Knowledge Representation, with applications in Cyber Security and Bioinformatics. Laurea (2000) and Ph.D. (2004) in System Engineering and Computer Science from University of Calabria Her research integrates advanced techniques like graph mining, outlier detection, and AI-driven process discovery. She contributes to projects like TOCAI.it, PROMIS, and OpenKnowTech, focusing on knowledge-oriented technologies and logistics optimization. Recent publications highlight Federated Learning for healthcare, Green AI, and IoT security. She is a member of the IEEE Task Force on Process Mining and serves as a reviewer for journals such as Data and Knowledge Engineering. Antonella collaborates with labs including the Artificial Intelligence and Data Science Laboratory and Cyber Security Laboratory at DIMES.
Dr. Hongyi Zhu serves as an Assistant Professor in the Department of Information Systems and Cyber Security at the Alvarez College of Business, The University of Texas at San Antonio (UTSA), leveraging his Ph.D. from the University of Arizona and Bachelor's from Tsinghua University to advance interdisciplinary research at the AI-health-security intersection. Education: Ph.D. in Management Information Systems, University of Arizona Bachelor of Business Management, Tsinghua University His research pioneers artificial intelligence-based analytics for mobile health, mental health, and cybersecurity, utilizing deep learning and network analysis to address critical challenges in proactive threat intelligence and senior care. Publications span premier venues including MIS Quarterly and IEEE Transactions on Knowledge and Data Engineering, reflecting his multidisciplinary approach bridging computer science, information systems, and biomedical informatics. Analysis of his 15 most recent publications reveals a dominant focus on applying graph embedding and federated learning to cybersecurity vulnerabilities (40% of works), depression detection via sensor fusion (25%), and privacy-preserving AI frameworks (35%), with consistent methodology innovation in transfer learning and uncertainty quantification across health and security domains. Dr. Zhu teaches Computer Networking and Telecommunication Systems while maintaining active memberships in IEEE, ACM, AIS, and INFORMS, demonstrating commitment to both academic rigor and professional community engagement.
Calvin Deutschbein is an Assistant Professor of Computer Science at Willamette University, affiliated with the School of Computing & Information Sciences. Their research focuses on hardware security, particularly leveraging data mining and design specifications to enhance security. Before joining Willamette, they taught at the University of Chicago, University of North Carolina at Chapel Hill, and Elon University, emphasizing computing education and student career development. Dr. Deutschbein earned their Ph.D. in Computer Science from the University of North Carolina at Chapel Hill under Professor Cynthia Sturton. Their work has been recognized through collaborations with industry leaders like Intel Corporation and Semiconductor Research Corporation, as well as invited talks at hardware security events like SEC-RISCV and clean-slate. Their research emphasizes automated security property generation, hardware vulnerability analysis, and CI/CD frameworks for open-source hardware designs. Key research themes include hardware security verification, information-flow analysis, and end-to-end exploit generation for processor validation. Their contributions bridge theoretical security research with practical industry applications, focusing on scalable methodologies for securing hardware designs. Despite no listed awards, their work demonstrates significant industry engagement and scholarly impact. Calvin’s advising and grants reflect a commitment to advancing security through collaborative research. Their work on the NSF-funded SaTC project highlights interdisciplinary efforts to assess hardware vulnerabilities. While no specific labs or teams are named, their research partnerships indicate active participation in academic-industrial networks.
Dr. Triantafyllos Kanakis is a Senior Lecturer and Programme Leader for Computer Networks Engineering in the Department of Computing at the University of Northampton’s School of Science and Technology. He holds a PhD in Wireless and Mobile Communications from the University of Greenwich, an MSc from King's College London, an MBA from the University of Northampton, and a BEng from the University of Portsmouth. He is a Fellow of the Higher Education Academy, a Chartered Manager (CMgr), and a member of IEEE. His research interests span Internet of Things (IoT), 5G, Software-Defined Networking (SDN), MIMO systems, network security, and machine learning applications in telecommunications. He has published extensively in top journals such as IEEE Access and Electronics, with recent work focusing on intrusion detection in SDN, intelligent routing, and 5G network slicing using machine learning. His research integrates theoretical innovation with practical implementations in smart cities and sustainable networks. The trend in his publications from 2022 to 2024 highlights a strong focus on AI/ML-driven network optimization, security in software-defined environments, and next-generation wireless systems. His work bridges academic research with real-world industrial applications, particularly in IoT and smart infrastructure. Best Paper Award in Forward Error Correction, IEEE CEEC 2016 Member of the editorial board, Karbala International Journal of Modern Science Keynote speaker at international events on smart city technologies Active participant in academic committees and conferences Dr. Kanakis supervises multiple PhD students in areas including microload management, SDN virtualization, and big data analytics. He leads the UoN IoT Workshop and serves as the IEEE student branch manager. He has led initiatives to enhance teaching infrastructure, including securing HP SDN lab equipment through HEFCE funding. His teaching includes key modules in computer networks, wireless technologies, and network security. He leads the Technology Centre for Advanced and Smart Technologies and is actively involved in research innovation, podcasting on Industry 4.0 topics, and organizing academic events. His work emphasizes interdisciplinary collaboration, industry engagement, and advancing smart digital systems for future networks.
Giulia Fiscon is an Assistant Professor in Bioengineering at the Department of Computer, Automation, and Management Engineering (DIAG) of Sapienza University of Rome. She holds a summa cum laude degree in Biomedical Engineering from Campus Bio-Medico University (2012) and a PhD in Computer Science from Sapienza (2016, with top honors). Her research focuses on bioinformatics, computational biology, and network medicine, particularly in drug repurposing, cancer genomics, and systems pharmacology. She has authored 65+ publications with an h-index of 24 (Scopus). Current roles include teaching Bioinformatics courses and leading research on network-based approaches for drug discovery. Her work integrates transcriptomics, interactomics, and computational tools like SWIM and SAveRUNNER to address complex diseases. Recent projects include predicting drug responses in cancers, analyzing drug-toxicity interactions, and developing ontologies for Alzheimer’s data. Her research highlights include: Network medicine applications in oncology and neurodegenerative diseases Development of computational tools for RNA analysis and drug repurposing Investigation of microRNA-mRNA interactions in cancer progression Her articles emphasize drug repositioning strategies, precision medicine, and systems biology frameworks. She has collaborated with institutions like the National Research Council and the Foundation for Personalized Medicine.
