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Zhengjie MiaoView profile

Assistant Professor

• Data Management
• Artificial Intelligence
• Machine Learning
• +3 more

Zhengjie Miao is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on data management and artificial intelligence, particularly developing algorithms and tools to enhance data science pipelines. He is part of the SFU Data Science Research Group and previously worked as a Research Scientist at Megagon Labs. Education: PhD in Computer Science from Duke University (advisors: Sudeepa Roy, Jun Yang), M.S. from Columbia University (advisor: Eugene Wu), B.S. from Peking University. Research interests span data management systems, machine learning integration in data science, and human-in-the-loop data preparation. His work emphasizes improving data usability through techniques like data lineage tracking, explainable query systems, and fairness-aware data processing. Recent publications highlight advancements in collaborative annotation with LLMs, automated data standardization, and contrastive learning frameworks for column annotation. Key contributions include the Rotom framework for meta-learned data augmentation, the Watchog system for column annotation, and CAPE for query explanation. His research bridges database theory with practical applications in education and industry.

Noman MohammedView profile

Associate Professor

• Data Security
• Privacy-Preserving Techniques
• Healthcare Data Privacy
• +7 more

Noman Mohammed is an Associate Professor of Computer Science at the University of Manitoba’s Faculty of Science, leading the Data Security & Privacy (DSP) laboratory. He specializes in privacy-preserving techniques for data sharing, addressing challenges in healthcare, genomic, and financial data. In 2020, he received the Terry G. Falconer Memorial Rh Institute Foundation Emerging Researcher Award for his contributions to bridging privacy and data utility gaps. His research focuses on balancing data accessibility and individual privacy through technical solutions like federated learning, differential privacy, and secure genomic data processing. He emphasizes integrating policy guidelines with advanced technologies to mitigate privacy risks from interconnected data sources. Notable achievements include developing toolkits for data anonymization and federated learning frameworks, as well as advancing methods to secure cloud-based data storage and analysis. His work aligns with societal needs for robust privacy mechanisms in an era of expanding personal data collection. Future objectives involve addressing privacy challenges in emerging technologies, such as heterogeneous data integration and scalable systems for personal data management. Despite his research focus, he notably avoids social media platforms.

• Spoken Language Processing
• Natural Language Processing (NLP)
• Paralinguistics
• +6 more

Sarah Ita Levitan is an Assistant Professor in the Department of Computer Science at Hunter College, CUNY, and a member of the doctoral faculty in both Computer Science and Linguistics PhD programs at the CUNY Graduate Center. She previously served as a Postdoctoral Research Scientist at Columbia University, where she completed her PhD in Computer Science in 2019 under Dr. Julia Hirschberg. Research Focus: Spoken Language Processing Natural Language Processing Paralinguistic Analysis Trustworthiness and Deception Detection Acoustic-Procedic and Lexical Feature Extraction Online Radicalization and Misinformation Recent Publications demonstrate expertise in analyzing speech and text for trust cues, deception detection, and mental health prediction. Her awards include grants from NSF, Google, and Columbia University fellowships. She leads the Hunter Speech Lab , mentoring PhD, MS, and undergraduate students in computational linguistics research. Scientific Awards and Grants: NSF EAGER Grant (2023) Google Cyber NYC Grant (2023) NSF AI Institute Grant (2023) Air Force Office of Scientific Research Grant (2020) Brown Institute Seed Grant (2020) Knight News Innovation Fellowship (2018) Teaching: Courses include Natural Language Processing (undergraduate/graduate), Computational Linguistics, Computer Theory, and advanced topics in spoken language processing at both Hunter College and Columbia University.

Joachim RosenthalView profile

Professor

• Coding Theory
• Cryptography
• Algebraic Geometry
• +2 more

Joachim Rosenthal is a Professor of Applied Mathematics at the University of Zurich's Department of Mathematics, leading the Applied Algebra Group. His research integrates coding theory, cryptography, and algebraic geometry with applications in communications and security systems. His primary research explores algebraic coding theory including convolutional codes, subspace codes, and post-quantum cryptography. He develops algorithms for error correction, code optimization, and cryptographic protocol analysis, with emphasis on mathematical structures in finite fields and rings. Professor Rosenthal serves as President of the Swiss Mathematical Society (2024-2025) and sits on IEEE Information Theory Society's Board of Governors. He has organized multiple international conferences on coding theory and cryptography, including the Zurich COST Meeting and Workshop on Convolutional Codes. His editorial contributions include roles at Archiv der Mathematik and SIAM Journal on Applied Algebra and Geometry. Recent publications focus on code construction methods, complexity analysis in group-based cryptography, and algebraic approaches to error-correcting codes.

Aleksandar BojchevskiView profile

Professor

• Trustworthy Artificial Intelligence
• Graph Neural Networks
• Adversarial Robustness
• +3 more

Aleksandar Bojchevski is a full professor of Computer Science at the University of Cologne, leading the Trustworthy Artificial Intelligence Lab (TAIL). His research focuses on developing robust, interpretable, and privacy-preserving machine learning models, particularly graph neural networks (GNNs). The lab emphasizes trustworthiness in high-stakes applications through methods that handle noisy, adversarial, or anomalous data. Bojchevski holds a PhD and PostDoc from the Technical University of Munich, advised by Stephan Günnemann. Previously, he was faculty at CISPA Helmholtz Center for Information Security. His work bridges theory and practice, addressing adversarial robustness, uncertainty quantification, and scalable GNN techniques. Research interests include: robustness certification for GNNs, conformal prediction, adversarial attack analysis, and privacy-aware machine learning. His lab actively collaborates with RWTH Aachen and organizes events like the Learning on Graphs Meet Up. Recent achievements include a teaching award for the Machine Learning lecture (SS 24) and NeurIPS 2024 acceptance of SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors . Open positions are available in his group focusing on trustworthy ML topics. Key labs/teams: Trustworthy Artificial Intelligence Lab (TAIL), Center for Data and Simulation Science (CDS) as a core scientist.

Wenjing ZhangView profile

Assistant Professor

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +6 more

Dr. Wenjing Zhang is an Assistant Professor in the School of Computer Science at the University of Guelph, Canada. She holds a Ph.D. in Computer Science from the University of Guelph (2024) and was a visiting research scholar at the University of Arizona's Department of Electrical and Computer Engineering (2016–2018). Her research focuses on cybersecurity in AI/ML, including security threats to models, privacy-preserving techniques, and data privacy in generative AI. She leads projects on robust defenses against adversarial attacks, secure federated learning, and privacy-preserving prompt engineering for LLMs. Dr. Zhang’s research areas include: Security in AI/ML (e.g., poisoning, evasion, prompt injection attacks) Model Privacy (protection of internal parameters) Data Privacy (synthetic data generation, privacy-preserving prompt engineering) She has secured a five-year NSERC Discovery Grant (2025–2030) for her research on enhancing security, privacy, and fairness in generative AI. Her work has been published in top-tier venues such as NeurIPS, IEEE Transactions on Information Forensics and Security, and IEEE Transactions on Communications. She is a recipient of the 2022 Westin Scholar Award and serves on technical committees for IEEE conferences including CNS 2025 and ICC 2025. Her team collaborates on interdisciplinary projects involving federated learning, information theory, and reinforcement learning. She actively seeks partnerships in security, privacy, and generative AI applications.

Steven W. PopperView profile

Professor

• Decision Making Under Deep Uncertainty
• Science, Technology, and Innovation Policy
• Economic Development
• +7 more

Steven W. Popper is a Professor of Policy Analysis at the RAND School of Public Policy and an Adjunct Senior Economist at the RAND Corporation. He also serves as Distinguished Professor of Decision Sciences at Tecnológico de Monterrey’s School of Government and Public Transformation, reflecting his international academic engagement. His work bridges economics, science and technology policy, and strategic decision-making under uncertainty. Research Interests: Popper's expertise lies in decision-making under deep uncertainty (DMDU), robust decision making (RDM), assumption-based planning, and science, technology, and innovation policy. He has made seminal contributions to long-term policy analysis, particularly through his co-authorship of Shaping the Next One Hundred Years (2003). His research addresses complex systems in economic development, international relations, and environmental planning. Publication Trends: His recent work applies RDM methodologies to diverse domains such as U.S.-China economic competition, Israeli defense and energy policy, transportation planning, and future technologies. His publications often involve scenario analysis, foresight, and adaptive strategies for policy resilience in uncertain futures. Scientific Awards and Leadership: Founding officer and current Finance Chair, Society for Decision Making under Deep Uncertainty Past Chair, Industrial Science and Technology Section, American Association for the Advancement of Science Consultant to the World Bank, OECD, and multiple national governments Advising and Grants: While specific advisees are not listed, Popper has led numerous high-impact research projects funded by U.S. federal agencies and international bodies. His role as Associate Director of the Science and Technology Policy Institute (1996–2001) involved providing analytic support to the White House Office of Science and Technology Policy, indicating extensive grant-funded research leadership. Labs and Teams: Popper is affiliated with RAND’s research teams focused on policy analysis, innovation, and strategic foresight. He collaborates with interdisciplinary teams applying modeling and simulation tools to public policy challenges, particularly through the Robust Decision Making framework.

Alistair Brandon-JonesView profile

Professor

• Operations Management
• Supply Chain Management
• Digital Transformation
• +5 more

Professor Alistair Brandon-Jones (PhD, PGDip, BSc Hons) is a Full Chaired Professor of Operations and Supply Chain Management at the University of Bath's School of Management since 2014. He serves as Head of the Information, Decisions and Operations Division (2020–present) and holds Visiting Professor roles at Hult International Business School and Danish Technical University. His career spans academic leadership positions at Manchester Business School, Bath, and Warwick Business School, where he completed his PhD. Research Interests : Digitalisation of operations and supply chains, professional service operations, healthcare operations, and cybersecurity in AI systems. His work combines empirical methods with cross-sector collaborations, supported by UKRI, ESRC, EPSRC, and the Welsh Assembly Government. Key themes include telehealth integration , responsible AI deployment , and value-based procurement . Teaching & Consulting : He teaches Operations Management, Supply Chain, and Project Management across executive, MBA, and undergraduate levels, with consulting experience for Maersk, NHS, and Qatar Leadership Centre. His textbooks like Operations Management (10th ed., 2022) are globally cited. Scientific Awards : Times Higher Education Teaching Excellence Prize Association of MBAs (AMBA) Award Production Operations Management Society (POMS) Recognition Hult International Business School Teaching Prize Supervision & Projects : Currently supervising doctoral students in digital transformation, he leads two major projects: ESRC Digital Security by Design Social Science Hub (2020–2025) and Welsh Government Healthcare Integration (2009–2009). His research aligns with UN SDG 12 (Sustainable Consumption and Production) through supply chain innovations.

• Edge Computing
• Fog Computing
• Distributed Systems
• +5 more

Umakishore Ramachandran is a Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research spans edge computing, distributed systems, and real-time video analytics, with significant contributions to fog computing infrastructure, mobile systems, and sensor networks. Over a prolific 38-year career, he has authored 142 publications with major contributions in 2022-2025. His research interests focus on bridging the gap between cloud and edge computing, with pioneering work in video analytics systems like EVA and MicroEdge. He investigates resource optimization for latency-sensitive applications, developing novel approaches for load shedding, data management, and container runtime efficiency at the network edge. His work addresses fundamental challenges in distributed camera networks, autonomous vehicle systems, and real-time stream processing. Ramachandran's recent publications reveal a strong emphasis on practical edge computing solutions, with 75% of his 2021-2025 work focusing on video analytics and infrastructure optimization. His research shows increasing collaboration with industry partners while maintaining academic rigor, with publications appearing in top venues like SIGMOD, Middleware, and DEBS. The work consistently addresses real-world constraints of resource-constrained edge environments. Ramachandran has mentored numerous researchers who have become principal investigators on edge computing projects, with notable collaborators including Harshit Gupta, Enrique Saurez, and Zhuangdi Xu appearing as first authors on multiple papers. His work has received significant grant support for projects related to mobile fog computing and distributed video analytics. He leads research in the Edge Computing Laboratory at Georgia Tech, focusing on the development of practical frameworks for real-world deployment of edge infrastructure. Current projects include eCAV for connected autonomous vehicles and MicroEdge for multi-tenant camera processing systems.

• Cybersecurity
• Maritime Cyber Risk Assessment
• Dynamic Positioning Systems
• +12 more

Pelin Bolat is an Associate Professor in the Department of Fundamental Sciences at Istanbul Technical University (ITU), College of Maritime Studies. She is actively engaged in maritime cybersecurity, risk assessment, and maritime safety research. Her work is supported by multiple BAP and EU-funded projects, with ongoing research extending into 2025. Her research interests include cybersecurity in maritime navigation systems, dynamic positioning, port state control, and GHG emissions in the maritime sector. She applies advanced methodologies such as fuzzy FUCOM, CORAS framework, and association rule mining to analyze cyber and operational risks. Her recent publications (2023–2025) reflect a strong trend in maritime cybersecurity, focusing on ECDIS, RADAR, ransomware, and cyber hygiene. These works span high-impact journals in maritime engineering and technology, demonstrating interdisciplinary engagement with computer science, safety engineering, and policy analysis. She serves as a principal investigator on several key projects, including cyber risk assessment of bridge navigation equipment and system dynamic modeling of maritime GHG emission measures. She also mentors 15 theses in progress, indicating her active role in student supervision. Her collaborative network includes researchers like Gökhan Kayişoğlu and international partners. While no formal awards are listed, her leadership in EU and BAP projects underscores her academic prominence.

Muchao YeView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• AI Safety
• +7 more

Muchao Ye is an Assistant Professor in the Department of Computer Science at the University of Iowa. He earned his Ph.D. from Pennsylvania State University's College of Information Sciences and Technology in 2024 and a Bachelor of Engineering in Information Engineering from South China University of Technology. Ph.D., Information Sciences and Technology, Pennsylvania State University (2024) B.Eng., Information Engineering, South China University of Technology His research focuses on the intersection of Artificial Intelligence, Machine Learning, and AI Safety, particularly adversarial robustness in language models and vision-language models. He designs methods to enhance the security and reliability of deep learning systems for safety-critical applications like video surveillance and healthcare. Recent publications highlight adversarial robustness frameworks (e.g., UniT , PAT ), vision-language models for explainable video anomaly detection ( VERA ), and healthcare risk prediction techniques ( MedPath , MedRetriever ). His work appears in top venues such as NeurIPS, KDD, AAAI, ACL, and CVPR. Professional experience includes Applied Scientist internships at Amazon (2022–2023) and teaching roles at the University of Iowa and Pennsylvania State University. He serves as a reviewer for conferences like NeurIPS, ICML, and journals including IEEE TPAMI.

Jia ZhangView profile

Professor

• Machine Learning
• Natural Language Processing
• Information Retrieval
• +10 more

Dr. Jia Zhang is the Inaugural Robert H. Dedman Jr. Endowed Department Chair and Professor of Computer Science at Southern Methodist University (SMU Lyle School of Engineering). She holds the Cruse C. and Marjorie F. Calahan Centennial Chair in Engineering and has a courtesy appointment in the Department of Operations Research and Engineering Management. Her research focuses on applying machine learning, natural language processing, and information retrieval to data science infrastructure, particularly scientific workflows, provenance mining, software discovery, knowledge graphs, cloud computing, immune AI, and applications in earth science and healthcare. Education: Ph.D. in Computer Science, University of Illinois at Chicago M.S. in Computer Science, Nanjing University B.S. in Computer Science, Nanjing University Dr. Zhang's work emphasizes data science infrastructure and machine learning for scientific workflows and knowledge graphs. Her recent publications highlight deep learning , graph neural networks , and optimization algorithms in cloud computing, cybersecurity, and environmental applications. Key trends include spatiotemporal modeling , hybrid neural architectures , and AI-driven service ecosystems . Scientific Awards: Best Paper Awards IEEE SCC (2011, 2017) Best Student Paper Awards IEEE ICWS (2014, 2018), IEEE ICCC (2018) Distinguished Paper Award ICSOC (2023) First Outstanding Service Award IEEE Technical Committee on Services Computing (2016) She has secured over $5 million in federal grants (as PI) and $11 million as PI/Co-PI from NSF, NASA, NIH, UTSW, Ericsson, SAP, and Google. Her lab (Caruth Hall 308) actively recruits research assistants. She previously served as a faculty member at Carnegie Mellon University, Northern Illinois University, and Nanjing University, and worked in industry as a software architect.

• Reliability Engineering
• Hardware Security
• Low-Power Design
• +4 more

Dr. Saqib Khursheed is an Assistant Professor in the Department of Electrical Engineering and Electronics at the University of Liverpool, UK. He holds a PhD in Electronics and Electrical Engineering from the University of Southampton (2010), where he later worked as a Senior Research Fellow on EPSRC-funded projects. His research focuses on reliability, testability, and hardware security of low-power/high-performance systems and 3D integrated circuits. He has served in leadership roles at major conferences such as IEEE DFT (General Co-Chair 2018) and ETS (Program Co-Chair 2017). Dr. Khursheed is a Senior Member of IEEE and Fellow of the Higher Education Academy. His professional activities include roles as Guest Editor for IEEE Design & Test (2016) and IET Computers & Digital Techniques (2018). He currently chairs the Examinations Officer committee in his department and serves on multiple university-level committees (Senate Progress, Quality Assurance). He has organized workshops like the Friday Workshop on 3D Integration (2012-2015) and reviews for top-tier journals/conferences in his field. Dr. Khursheed’s funded projects include the eFutures Sandpit Award (2018) for secure microelectronics design. His teaching responsibilities include coordinating modules like Advanced Low Power Computer Architecture (ELEC470) and Digital Electronics & Microprocessor Systems (ELEC211). His research has led to innovations in hardware security (e.g., PCB Trojan detection via machine learning), age estimation of ICs, and fault tolerance in 3D ICs.

Aybars TuncdoganView profile

Associate Professor

• Digital Innovation
• Information Security
• Marketing Strategy
• +3 more

Aybars Tuncdogan is a Reader in Digital Innovation and Information Security at King’s Business School, King’s College London. He holds affiliations with the King’s AI Institute, King’s Cybersecurity Centre (Informatics), and King’s Cybersecurity Group (War Studies). A Fellow of the Higher Education Academy, he also serves on the editorial review board of Industrial Marketing Management . Education : PhD in Management, Rotterdam School of Management, Erasmus University MPhil in Business Research (Distinction), Erasmus University Bachelor’s in Business Management & Computer Science (Honors), Earlham College Research Interests : His work focuses on three pillars of digital innovation: generation (crowdsourcing, AI), marketing (digital brand personality, sales ambidexterity), and protection (information security, corporate espionage). He integrates psychology (individual differences, social identity) and computer science (machine learning/AI) frameworks. Publications Trends : Recent work addresses cybersecurity challenges in retail, AI ethics in healthcare, and interdisciplinary innovation. He frequently publishes in top-tier journals like Journal of Management and Scientific American , blending academic rigor with practitioner impact. Awards & Contributions : 2015 Best Paper Award (shared) Edited books including Oxford Handbook of Individual Differences and Strategic Renewal Teaching & Pedagogy : Develops innovative teaching methods like inquiry-based learning to foster student creativity. Taught modules on digital marketing, consumer behavior, and research methods. Labs/Teams : Contributes to cybersecurity initiatives at King’s, focusing on AI-driven defense mechanisms and organizational cyber resilience strategies.

Jim SmithView profile

Professor

• Interactive Artificial Intelligence
• Statistical Disclosure Control
• Privacy Leakage from AI Models
• +7 more

Jim Smith is a Professor in Interactive Artificial Intelligence at the University of the West of England (UWE), Bristol, affiliated with the School of Computing and Creative Technologies and the Department of Computer Science and Creative Technologies. He serves as Director of the Computer Science Research Centre and leads the AI@UWE theme. His research is supported by UKRI, Innovate UK, and partnerships with organizations including Health Data Research UK, Office for National Statistics, NHS Scotland, and DSTL. University: University of the West of England School: School of Computing and Creative Technologies Department: Department of Computer Science and Creative Technologies Role: Professor in Interactive Artificial Intelligence Leadership: Director, Computer Science Research Centre Research Interests : Jim Smith's work focuses on Interactive Artificial Intelligence, particularly at the intersection of AI and privacy preservation when using sensitive data for public good. His research includes statistical disclosure control, privacy leakage from AI models, evolutionary computation, machine learning, and systems that learn through human interaction or self-adaptation. He explores how AI can automate privacy checks in research outputs and assess vulnerabilities in trained models. Recent Publications : His recent work spans AI privacy in trusted research environments (e.g., SACRO, SDC-Reboot), dialogue act classification, human-robot interaction, and visualization of deep learning models. Themes include privacy-preserving AI, automated disclosure control, interactive machine learning, and neuromorphic computing. Machine Learning & Privacy Evolutionary Computation Interactive AI Systems Human-Computer Interaction Statistical Disclosure Control Federated Learning Security Scientific Awards : No specific awards are mentioned in the provided texts. Advising and Grants : He currently supervises PhD students on topics including spatio-temporal air quality modeling, federated learning privacy, and threat detection in mobile networks. He leads Innovate UK and UKRI-funded projects such as SACRO and SDC-Reboot, focusing on AI-driven solutions for data confidentiality in public sector research. Interactive Machine Learning for Claim Settlement (Innovate UK) SDC-Reboot (DARE UK/Health Data Research UK) Threat Identification in Mobile Networks (Ribbon Communications) Labs and Teams : He leads the AI@UWE initiative and the Computer Science Research Centre at UWE. His work involves collaboration through DARE UK and open-source development via the AI-SDC GitHub organization, which hosts tools from SACRO and GRAIMATTER projects.