Ridha Khedri is a Professor in the Department of Computing and Software at McMaster University . His research spans formal methods in software engineering, cybersecurity, information security ontology, network segmentation, and covert channels analysis. Full Professor since 2000 Contact: khedri@mcmaster.ca Research Interests : Prof. Khedri develops algebraic frameworks for software security, with recent work on network segmentation , ontology engineering , and covert channel detection . His interdisciplinary efforts include hybrid machine learning-ontology models for environmental predictions (e.g., river ice breakup) and digital twin healthcare systems . Article Trends : His 15 most recent works (2016-2025) focus on network security , knowledge representation , and formal verification . Notable trends include automated security testing , ontology modularization , and multi-context reasoning systems . Teaching : He has taught courses like Software Design (CAS 703), Discrete Mathematics (SFWRENG 2DM3), and Algebraic Methods in Software Engineering (CAS 738) since 2017.
Dr. Hongsheng Hu is currently a Lecturer in the School of Information and Physical Sciences at the University of Newcastle, Australia, specializing in the Data Science and Statistics focus area. Prior to this position, he served as a Postdoc Research Fellow at CSIRO's Data61 from October 2022 to August 2024. His academic journey includes a Doctor of Philosophy in Computer Systems Engineering from the University of Auckland in New Zealand, establishing his foundation in advanced computing systems. Dr. Hu's research centers on enhancing the trustworthiness of machine learning systems, with particular emphasis on identifying critical privacy vulnerabilities within machine learning models and developing robust defensive strategies. His work spans several key domains including adversarial machine learning (30% focus), statistical data science (30% focus), and data and information privacy (40% focus). He investigates membership inference attacks, machine unlearning techniques, and privacy-preserving mechanisms in federated learning environments. His research addresses fundamental challenges in AI security, exploring how machine learning models can be compromised through sophisticated privacy attacks and developing methods to mitigate these vulnerabilities while maintaining model utility. Analysis of Dr. Hu's publication record reveals a strong research trajectory focused on machine learning security and privacy. His work consistently addresses vulnerabilities in machine learning systems, particularly examining membership inference attacks, machine unlearning mechanisms, and privacy-preserving techniques in federated learning. The research spans top-tier venues including IEEE Security & Privacy, USENIX Security, NDSS, NeurIPS, IJCAI, AAAI, and WWW, demonstrating both technical depth and recognition by the research community. His publications show an evolving focus from foundational privacy attacks to developing more sophisticated unlearning techniques and robust defense mechanisms, with increasing citation counts indicating growing impact in the field. Active Program Committee member for USENIX Security, NDSS, ICLR, IJCAI, WWW, ICDM, ECML, and PKDD Invited reviewer for IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Pattern Analysis and Machine Intelligence (IPAMI), and ACM Computing Surveys (CSUR) Dr. Hu currently serves as Course Coordinator for STAT6020 and STAT2020 Predictive Analytics at the University of Newcastle. As an academic supervisor, he co-supervises one PhD student working on 'Identifying and Mitigating Vulnerability in Recommender Systems' at Macquarie University. His research collaborations span multiple countries, with significant publication counts in Australia (18), China (12), New Zealand (10), and the United States (7), reflecting an active international research network focused on AI security challenges.
Assoc. Prof. Dr. Yıltan Bitirim is a faculty member at the Computer Engineering Department of Eastern Mediterranean University in North Cyprus. With over two decades of academic experience, he has served in various roles including Vice Chair (2014-2022), Academic Affairs Coordinator (2025-), and committee member for ABET assessment, curriculum development, and faculty recruitment. Current academic rank: Associate Professor Active administrative roles: Senate Member (2023-), Information Technology Commission Member (2023-) Professional memberships: ACM, IEEE Senior Member, Cyprus Turkish Chamber of Computer Engineers Research Interests focus on four primary areas: Information Retrieval Systems – evaluating search engine effectiveness and reverse image search performance Machine Learning – applied to emotion classification, gender recognition, and medical diagnosis Data Mining – used in Turkish word-stemming analysis and user behavior studies Biometrics – specializing in hand/wrist/palm vein recognition systems and voice-based identification Publications demonstrate consistent contributions across disciplines, with recent works (2023-2025) emphasizing: Deep learning applications in biometric authentication Advanced emotion recognition systems Medical AI for diabetes management and retinopathy diagnosis Biometric spoof detection mechanisms Turkish language processing challenges Recommendation system innovations Awards & Recognition : Research Incentive Awards (2020, 2021) Best Paper Award at ICIW 2007 IEEE Senior Member status As an educator, he has supervised numerous thesis committees and taught foundational courses in computer engineering, including CMPE 112 and CMPE 342. His certifications (MCTS, MCITP) reflect technical expertise in Microsoft technologies.
Daniele Apiletti is an Associate Professor at the Polytechnic University of Turin , affiliated with the Department of Control and Computer Engineering (DAUIN). He serves as a member of the Interdepartmental Center SmartData@PoliTO and acts as Academic Advisor for the Master's degree program in Data Science and Engineering. Research Groups: DBDM - Database and Data Mining Group (DAUIN) ERC Sectors: Algorithms, Artificial Intelligence, Machine Learning, Web and Information Systems Research Interests span Big Data Analytics, Data Science, Machine Learning, Computer Vision, and Quantum Computing. His work focuses on integrating data-driven and theory-guided approaches for heterogeneous data querying, cloud continuum machine learning, and spatio-temporal models for crisis management. Recent Publications highlight trends in medical image segmentation, predictive industrial modeling, and fault-tolerant data systems. Key subfields include AI in healthcare, scalable manufacturing analytics, and vision-language models for game tutorials. Teaching roles include course ownership of Big Data: Architectures and Data Analytics and Internships across multiple academic years. He has collaborated on courses in Data Science, Database Technologies, and Data Management. PhD Students Supervised: Etibar Vazirov (Cloud Continuum Machine Learning) Gabriele Scaffidi Militone (Cloud Storage Microservices) Daniele Rege Cambrin (Spatio-Temporal Ecology Models) Simone Monaco (Theory-Guided Data Science) Research Projects include commercial contracts on: - Natural language querying of corporate research archives - National tourism ecosystem platforms - AI for thermotechnical system design - Machine Learning in clinical trials and supply chains
Jingrui He is a Professor and MSIM Program Director at the School of Information Sciences, University of Illinois Urbana-Champaign. She holds multiple faculty affiliate positions including with the Department of Computer Science, National Center for Supercomputing Applications (NCSA), Illinois Informatics, Center for Digital Agriculture (CDA), and Mayo Clinic Arizona. Her research spans machine learning with applications in diverse domains including healthcare, agriculture, security, and finance. Dr. He received her PhD in Machine Learning from Carnegie Mellon University in 2010. Her research focuses on heterogeneous machine learning, active learning, neural bandits, and self-supervised learning. She addresses complex data challenges where multiple types of heterogeneity coexist, developing methods for exploring, understanding, characterizing, and predicting real-world data through statistical machine learning techniques. Her recent publications demonstrate a strong focus on graph learning, federated learning, fairness in AI, and neural bandit algorithms. She has developed innovative approaches for class-imbalanced graph learning, Byzantine-robust federated learning, and privacy-preserving graph machine learning. Her work bridges theoretical foundations with practical applications across multiple domains. Her scientific awards include the Amazon Research Award (2025), ACM Distinguished Member (2023), AAAI Senior Member (2023), FAccT Distinguished Paper Award (2022), NSF CAREER award (2016), and multiple IBM Faculty Awards. She has been recognized as an excellent teacher and received Best Paper awards at major conferences including ICDM and SDM. Dr. He directs the iSAIL Lab and leads several major research projects including the AI Institute for Future Agricultural Resilience Management and Sustainability (AIFARMS). She has successfully mentored numerous doctoral students who have become co-authors on her publications. Her research has been funded through prestigious grants including the NSF CAREER award and IBM Faculty Awards.
Timothy Menzies is a full Professor in the Department of Computer Science at North Carolina State University's College of Engineering. He serves as the director of the Irrational Research lab (mad scientists r'us) and holds editorial positions as editor-in-chief of the Automated Software Engineering journal and associate editor for IEEE Transactions on Software Engineering. With over 300 publications and more than 24,000 citations, Menzies is a globally recognized leader in software engineering research. Menzies' research focuses on developing computer systems that make optimal decisions with minimal data, specializing in artificial intelligence, intelligent agents, data sciences, analytics, and software engineering. His pioneering work in data-driven, explainable, and minimal AI for software systems has redefined defect prediction, effort estimation, and multi-objective optimization. He is particularly known for his contributions to empirical software engineering, emphasizing transparency and reproducibility. As the co-creator of the PROMISE repository, he helped establish modern empirical software engineering by demonstrating that small, interpretable AI models can outperform larger, more complex ones. Menzies' recent publications reveal several key trends in his research: a growing emphasis on ethical considerations in AI deployment, particularly in sensitive domains like legal systems; continued innovation in software analytics with a focus on hyperparameter optimization tailored specifically for software engineering tasks; exploration of causal relationships in software analytics; and development of techniques that work effectively with limited data, including landscape analysis, surrogate learning, and active learning approaches. Mining Software Repositories Foundational Contribution Award (2017) Carol Miller Graduate Lecturer Award (2016) IBM Faculty Award (2016, 2017) ACM Fellow (2025) ASE Fellow (2024) IEEE Fellow Professor Menzies has advised 24 Ph.D. students throughout his career, with recent completions including Andre Motta (April 2025) and Xueqi Yang (October 2024). His research has secured over $19 million in funding from prestigious agencies including NSF, DARPA, and NASA, as well as industry partners like Meta, Microsoft, and IBM. Current grants focus on improving machine learning model efficiency, adapting empirical software engineering methods to computational science, vulnerability detection, and software analytics at scale using transfer learning across 10,000+ GitHub projects. Menzies has developed innovative approaches to help developers navigate the challenges of AI implementation while maintaining ethical standards and practical effectiveness. As director of the Irrational Research lab, Menzies leads a team focused on creating AI tools that are not only intelligent but also fair, transparent, and trustworthy. The lab's work emphasizes practical applications of AI in software engineering while addressing the human factors involved in developer-AI collaboration. Current projects include developing methods for better fuzzing with L3harris, improving vulnerability detection through smart pruning techniques, and creating AI platforms for workforce empowerment through credential gap diagnostics.
Hakan Basarir is a Professor in the Department of Mining Engineering at the Norwegian University of Science and Technology (NTNU), Trondheim, Norway. His research and teaching focus on mining rock mechanics, rock mass characterization, underground support systems, and the application of soft computing methods in mining engineering. PhD in Mining Engineering (2002) 20+ years of research and teaching experience 60+ publications in journals and conferences Research Interests include rock mass property prediction using measurement while drilling (MWD) techniques, numerical modeling of mining structures, optimization of mine support systems, and sustainable material development. His work integrates machine learning and computational methods to address challenges in mining geomechanics and backfill design. Recent Publications highlight advancements in AI-driven lithology prediction, eco-concrete formulation, and backfill mixture optimization. He has also contributed to tunnel stability analysis and seismic rock slope modeling. Teaching includes advanced courses in mining engineering, mineral production modeling, and specialization projects in geotechnology.
Dr. Yanjie Fu is an Associate Professor in the School of Computing and AI at Arizona State University, part of the Ira A. Fulton Schools of Engineering. He maintains his office in BYENG 506 at the Tempe campus and can be reached at yanjie.fu@asu.edu. Dr. Fu received his Ph.D. from Rutgers University in 2016, the B.E. degree from the University of Science and Technology of China, and the M.E. degree from the Chinese Academy of Sciences. His industry research experience includes positions at Microsoft Research Asia and IBM Thomas J. Watson Research Center. His research focuses on developing disruption-robust machine intelligence that can handle imperfect and complex data. Dr. Fu's work spans two major efforts: Data for AI (D4AI), exploring how structure knowledge of data can guide AI, and AI for Data (AI4D), investigating how AI can augment, reprogram, and knowledgeize data. His current research interests include space-time intelligence, data-centric AI, sim2decision, multimodal reasoning, and LLM with agentic AI. His lab has contributed projects including D4AI-spatial, D4AI-timeseries, D4AI-causal outliers, AI4D-RL, AI4D-Gen, and AI4D-LLM. Dr. Fu's recent publications reveal a strong trend toward integrating causal reasoning with deep learning for robust anomaly detection, advancing time series forecasting with novel normalization techniques, and applying generative AI to urban planning. His work increasingly bridges traditional machine learning with large language models, particularly focusing on data-centric approaches for tabular data transformation and feature engineering. US NAE FOE early career engineer (2023) US NSF CAREER (2021) NSF CRII (2018) ACM KDD18 Best Student Paper Finalist IEEE ICDM Best Paper Finalist (2014, 2021, 2022) ACM SIGSpatial Best Paper Runner-up (2020) 2022 Baidu Scholar global top Chinese young scholars in AI 2021 Aminer.org AI 2000 Most Influential Scholar Award Honorable Mention Dr. Fu has successfully mentored multiple Ph.D. students who have secured tenure-track faculty positions at prestigious institutions including University of Kansas, Chinese Academy of Sciences, Great Bay University, Portland State University, and University of Macau. His research has been supported by significant grants including the NSF CAREER award, and he currently serves as Associate Editor of ACM Transactions on Knowledge Discovery from Data. He is also a senior member of both ACM and IEEE. Dr. Fu leads a research group focused on developing trusted and safe machine intelligence. The lab connects computing issues across representation learning, self-supervised learning, interactive learning, adaptive learning, and stream learning to build disruption-robust frameworks. The group executes two key steps: data representation construct (integrating structure knowledge, self-optimization, explainability) and learning strategy construct (integrating robust representations with adaptive and interactive learning).
George Runger is a Professor at the School of Computing and Augmented Intelligence, Arizona State University. His work focuses on analytical methods for knowledge generation and data-driven organizational improvements, particularly in machine learning for large-scale data, real-time analysis, and applications to surveillance, decision support, and population health. Previously, he was a senior engineer and technical leader at IBM. Education: Ph.D. in Statistics, University of Minnesota (1982) Runger's research bridges machine learning, data mining, and statistical process control (SPC) to address challenges in manufacturing, healthcare, and semiconductor systems. His work includes developing artificial contrasts for signal detection, ensemble feature selection, and self-learning decision rules for adaptive SPC. His funded projects span NSF, DOD-NAVY-ONR, and Semiconductor Research Corporation grants, emphasizing supply chain analysis, dimensional metrology, and energy efficiency diagnostics. He has co-authored foundational texts like Applied Statistics and Probability for Engineers and Engineering Statistics . Scientific Awards: Inaugural Department Editor for Healthcare Informatics, INFORMS Transactions on Healthcare Systems Engineering Runger actively contributes to academia as a reviewer for journals like Management Science and IEEE Transactions on Knowledge and Data Engineering , and as a panel member for NSF and INFORMS workshops. He co-directs ASU's Quality and Reliability Engineering Laboratory and the Modeling and Analysis of Semiconductor Manufacturing team.
Oktay Türetken is a Full Professor at Eindhoven University of Technology (TU/e) in the Industrial Engineering and Innovation Sciences school, specializing in Information Systems . He chairs the Industrial Engineering Bachelor Program and contributes to the European Research Center for Information Systems (ERCIS) in Münster, Germany. PhD in Information Systems Research Fellow at European Research Institute in Service Science (2008-2012) Adjunct Lecturer at METU’s Software Management program (pre-2008) His research focuses on digital innovation and collaborative business models , particularly in urban mobility. He develops methods for operational design , business process management , and data analytics maturity models . Key projects include MobilitEU and EU-funded initiatives in smart mobility. Recent publications address KPI frameworks for business models, platform design for shared mobility, and maturity models for organizational capabilities. He leads multi-disciplinary teams and has co-authored over 129 research outputs, including 2025 articles in Production Planning & Control and Business & Information Systems Engineering . 2021 : 1st Prize at DESRIST for paper prototype 2022 : 1st Prize for MaaS Platform Features research He supervises PhD candidates Ginger Korsten , Yasmin Rettab , and Stella Lo Giudice , and teaches courses like Digitalization and Enterprise Systems and Green and Digital Transformation . His work bridges academic research with industry consultancy via Cogniton Business Engineering Consultancy.
Rianne Conijn is an assistant professor in the Human-Technology Interaction group at Eindhoven University of Technology (TU/e), Netherlands. Her research bridges data-driven methodologies (machine learning, statistical modeling) with human-centered design to enhance learning analytics, explainable AI, and writing process analysis. She holds a joint PhD (cum laude) from Antwerp University and Tilburg University, and an MSc (cum laude) in Human-Technology Interaction from TU/e. Academic Background: MSc (2015, TU/e, cum laude), PhD (2020, Antwerp University & Tilburg University, cum laude). Research Focus: Learning analytics, keystroke logging, explainable AI for education, data dashboards, and self-regulated learning dynamics. Teaching: Courses in Advanced Research Methods, Human-AI Interaction, Behavioral Research Methods, and AI ethics in education. Her recent publications explore parallel language planning in writing, longitudinal self-regulated learning strategies, and generalizability of academic performance prediction models. She leads an NWO Veni project on Human-Centered AI in education, emphasizing tailored explanations for student-AI collaboration. Scientific awards include cum laude distinctions for her MSc and PhD, and the NWO Veni grant. Collaborative work spans institutions in the Netherlands, Norway, and the U.S., with applications in intelligent tutoring systems and ethical AI deployment in exams. Key trends across her work: integration of machine learning with educational theory, leveraging keystroke data for cognitive process insights, and prioritizing actionable, explainable AI systems for student support. Publications span journals like the Journal of Experimental Psychology: General , Computers and Education , and IEEE Transactions on Learning Technologies . Scientific Awards: NWO Veni grant for Human-Centered AI in education Cum laude for MSc and PhD Grants & Collaborations: National Science Foundation grants (2016868, 2302644) for biometric feedback in writing UK Research and Innovation grant (ES/W011832/1) for real-time AI scaffolding TU/e Boost! Program grant for self-regulated learning analysis Labs & Teams: EAISI Foundational (Eindhoven AI Systems Institute) Human Technology Interaction group at TU/e Collaboration with Norwegian Reading National Center (University of Stavanger) Project teams for Waterproof ITS and ProWrite grants
Miloš Racković serves as a full Professor in the Department of Mathematics and Informatics at the University of Novi Sad, Serbia. He maintains active academic engagement through the Laboratory for the development of information systems, with his office located in the Information technologies and systems office (DMI&DF) on the second floor, room 49. Contact is available via telephone (485)-2868 or email rackovic@dmi.uns.ac.rs, and his personal website (http://www.is.pmf.uns.ac.rs/rackovicm/) provides additional resources. His research spans foundational and applied computer science, with seminal contributions in fuzzy database systems including PFSQL query language development and prioritized fuzzy logic for relational databases and XML. He has pioneered deep learning methodologies through innovative classification techniques using negative and missing features in convolutional neural networks. Additional expertise includes high-performance computing implementations of Lattice Boltzmann methods using OpenCL, robotics (symbolic modeling and trajectory planning), and blockchain applications for Industry 4.0 production processes. His sports analytics work applies neural networks to basketball player and referee movement analysis. Analysis of his 2012-2025 publications reveals a strategic evolution toward interdisciplinary applications, particularly in industrial transformation (blockchain-enabled traceability) and sports analytics. His work consistently bridges theoretical computer science with practical implementations, demonstrating increasing focus on real-world problem solving while maintaining strong foundations in database theory and computational methods. Professor Racković leads the Laboratory for the development of information systems, which focuses on advancing information system methodologies through formal modeling extensions (including Petri net innovations) and practical implementations for uncertainty management. The laboratory's work spans from foundational research in fuzzy logic systems to applied projects in high-performance computing and blockchain integration, fostering innovation in information technology development.
Alberto Gambino serves as Full Professor of Private Law (IUS/01) and Deputy Vice-Rector at the European University of Rome. He holds multiple prestigious positions including Commissioner of the European Commission against Racism and Intolerance (ECRI) of the Council of Europe in Strasbourg, Member of the National Bioethics Committee, and Judge of the Patent and Trademark Appeals Commission at the Ministry of Enterprise and Made in Italy (MIMIT). Additionally, he is President of the Science & Life Study Center, CEI, and the Italian Academy of the Internet Code (IAIC). As a civil cassation lawyer, he owns the Gambino law firm and participates in managing research organizations and implementing national and international research projects. Professor Gambino's research spans numerous legal domains with particular emphasis on Civil Law, Corporate Law, Mergers and Acquisitions, Corporate Governance, and Intellectual Property. His work bridges traditional legal frameworks with emerging digital and technological challenges, particularly in the areas of bioethics, data protection, and internet governance. He has made significant contributions to understanding the intersection of law with information technology, media, telecommunications, and consumer protection in both national and European contexts. His scholarly output reveals a consistent focus on evolving legal challenges in the digital age, with recent publications examining antitrust issues in digital markets, sports governance, intellectual property in AI systems, and data protection frameworks. His work demonstrates a sophisticated understanding of how traditional legal principles must adapt to contemporary technological and social realities while maintaining core legal values. Commissioner of the European Commission against Racism and Intolerance (ECRI) of the Council of Europe Member of the National Bioethics Committee Judge of the Patent and Trademark Appeals Commission at MIMIT President of the Science & Life Study Center President of CEI President of the Italian Academy of the Internet Code (IAIC) Professor Gambino actively contributes to legal practice through his law firm while maintaining a robust academic profile. His editorial work with prestigious scientific journals and involvement in national and international research projects demonstrate his commitment to advancing legal scholarship. His expertise in both theoretical and applied law positions him as a key figure in shaping contemporary legal discourse, particularly in the intersection of traditional legal frameworks with emerging digital challenges.
Tony Cookson is a Professor of Finance and the Michael A. Klump Endowed Professor at the Leeds School of Business , University of Colorado Boulder, where he has served since 2013. His research spans empirical finance , household and corporate financial decision-making , and the impact of social media and legal institutions on financial behavior. He has published in top journals like the Journal of Finance , Journal of Financial Economics , and Management Science , focusing on topics from investor disagreement to fracking-induced debt repayment . Education : Ph.D. in Economics (University of Chicago), M.S. in Statistics and Applied Economics (Montana State University), B.S. in Economics (Montana State University) His research interests include: How social media shapes investor sentiment and trading patterns The economic consequences of fracking and casino policy Legal institutions and their role in credit market development Behavioral finance through LLM-driven investor personas His scientific awards include the Best Paper in Investments and Asset Pricing (MFA 2023) , NASDAQ Best Paper in Asset Pricing (WFA 2021) , and Finalist for TIAA Paul A. Samuelson Award (2021) . He serves as Editor at the Review of Corporate Finance Studies and Associate Editor at multiple top journals.
Ben Collier is an Assistant Teaching Professor of Business Analytics at the Tepper School of Business , Carnegie Mellon University. He holds a PhD in Information Systems and Organizational Behavior from Carnegie Mellon and has extensive experience in data science leadership roles in industry. PhD, Information Systems and Organizational Behavior (2012), Carnegie Mellon University MS, Information Systems and Organizational Behavior (2009), Carnegie Mellon University MBA, Information Systems (2007), University of Wisconsin-Madison BBA, Management Computer Systems and Mathematics (2004), University of Wisconsin-Whitewater His research focuses on data mining for business , data visualization , and large-scale experimental design , with applications in healthcare analytics, online community dynamics, and gender equity in technology. His recent work includes monetization data science for Duolingo's $6.5 billion IPO and developing UPMC's CognitiveRx analytics engine. Ben's publications span topics including gender gaps in Wikipedia , leadership in open collaboration communities , and conflict resolution in crowdsourced platforms . He has served on CMU committees for curriculum review and summer summit planning, and actively advises MSBA capstone projects.
