Professor Gregoris Mentzas is a faculty member at the National Technical University of Athens, School of Electrical and Computer Engineering, where he directs the Division of Industrial Electric Devices and Decision Systems. His research focuses on AI-enabled decision systems, knowledge management, and semantic technologies applied to digital enterprises and e-government. With over 350 publications, he ranks among the top 2% most cited scientists globally. Research Interests: Artificial intelligence for decision augmentation, big data analytics in personalized health and smart mobility, semantic web technologies, and industrial internet of things. Current projects investigate trustworthy AI frameworks and hybrid intelligence systems for Industry 5.0. Teaching: Leads courses in Digital Enterprise Management, Strategic Information Systems, and Project Management at undergraduate and postgraduate levels, incorporating industry case studies and experiential learning approaches. Awards & Leadership: Top 2% Highly Cited Scientist (PLOS Biology 2021) 5 Best Paper Awards in international conferences Director of Information Management Unit (1997-present) Board Member of Institute of Communication and Computer Systems (2006-2009) Projects & Funding: Secured over €18 million in research grants through 60+ European projects with industry partners including SAP, IBM, and Siemens. Research outcomes led to three technology spin-offs.
Lesia Mitridati is an Assistant Professor at the Department of Wind and Energy Systems, Technical University of Denmark (DTU). Her research focuses on optimizing energy systems, particularly in renewable energy integration, energy market design, and prosumer behavior modeling. She leads and collaborates on projects involving smart grids, distributed energy resources, and privacy-preserving market mechanisms. Her work contributes to UN Sustainable Development Goals related to affordable and clean energy. Key projects include AI-driven electricity market optimization, hydrogen-wind trading strategies, and risk-aware energy communities. She supervises multiple PhD students in areas like VPP bidding strategies and market-based heat-electricity coordination. Dr. Mitridati has published widely on energy communities, grid services, and reinforcement learning applications. Notable contributions include dynamic pricing frameworks for grid services and privacy-preserving market mechanisms. She co-organizes annual DTU summer schools on future energy systems and AI-driven optimization. Her research integrates machine learning with operational research techniques to address challenges in renewable energy integration, market design, and system resilience. Current initiatives focus on electrolyzer plant bidding strategies and feature-driven trading of renewable resources.
Dr. Rahat Masood is a Lecturer at the School of Computer Science & Engineering (CSE), UNSW Sydney. Her research focuses on cybersecurity, including privacy-preserving technologies, authentication mechanisms, critical infrastructure protection, and network security analysis. She holds a PhD in Information Security and Privacy from UNSW (Data61-CSIRO, Australia), an MS in Computer and Communication Security from NUST, Pakistan, and a B.Sc. in Software Engineering from the University of Engineering & Technology, Pakistan. Her academic contributions span theoretical and applied cybersecurity domains. Dr. Masood’s educational background includes: PhD: Information Security and Privacy (UNSW, Data61-CSIRO, Australia) MS: Computer and Communication Security (NUST, Pakistan) B.Sc.: Software Engineering (University of Engineering & Technology, Pakistan) Her research interests emphasize privacy technologies, authentication systems, and securing distributed energy resources. Recent work includes developing frameworks for quantifying privacy risks and analyzing social media manipulations. She employs data-driven methodologies and machine learning to address challenges such as WiFi device tracking and federated learning security. In her publications, she highlights trends in privacy controls usability, satirical news detection using multilingual models, and threat modeling for critical infrastructure. These studies underscore her commitment to bridging cybersecurity theory with real-world applications. No scientific awards are mentioned in the provided texts. Her teaching and supervision roles at UNSW are active, though specific student advisees or grant details are not listed. She is affiliated with Data61-CSIRO through her PhD and contributes to interdisciplinary cybersecurity efforts within CSE.
Skyler Wang is an Assistant Professor of Sociology at McGill University, specializing in AI, technology, and human-computer interaction. He holds a Ph.D. from UC Berkeley and previously served as a Sociologist at Meta’s FAIR lab. His research critically examines sociotechnical systems' epistemic cultures and social impacts, focusing on AI-driven human-machine interactions in health, relational contexts, and digital platforms. His research interests include AI ethics, platform societies, digital intimacy, and multilingual systems. Notable works include the book project Sharing Bodies in the Sharing Economy , exploring Couchsurfing’s sociosexual dynamics, and applied AI projects like No Language Left Behind (doubling machine translation languages) and SeamlessM4T (awarded TIME’s 2023 Best Inventions). Teaching focuses on Technology & Society, Artificial Intelligence & Society, and Digital Intimacy. Advising roles include Major/Minor and Honours Sociology students. Active in interdisciplinary collaborations through McGill’s Quebec Inter-University Centre for Social Statistics and global AI ethics initiatives. Education: Ph.D. Sociology, UC Berkeley (2023) Key Affiliations: Meta FAIR Lab (prior), McGill Department of Sociology Publications in Nature , CSCW , Big Data & Society , and media features in WIRED, CNN, and NPR
Tianhao Wang is an Assistant Professor in the Department of Computer Science at the University of Virginia School of Engineering and Applied Science. His work focuses on advancing differential privacy and machine learning privacy, with particular expertise in privacy-preserving technologies for data synthesis, adversarial machine learning, and secure AI systems. His research interests span differential privacy mechanisms, secure data sharing, and mitigating privacy risks in modern AI systems. He explores how to protect sensitive information in machine learning models, synthetic data generation, and network analysis while maintaining utility. Recent work highlights include developing benchmarks for private image synthesis (DPImageBench), safeguarding text data from misuse (ExpShield), and analyzing privacy threats in pre-trained language models. His publications reflect a strong emphasis on both theoretical foundations and practical applications of privacy-preserving techniques. Dr. Wang's contributions address cutting-edge challenges in AI ethics, secure machine learning, and privacy engineering, with implications for healthcare, cybersecurity, and data-driven decision-making systems.
Dr. Ziquan Liu is a Lecturer (Teaching & Research) at Queen Mary University of London's School of Electronic Engineering and Computer Science, affiliated with the Centre for Multimodal AI. He holds a PhD from City University of Hong Kong (2023) and dual B.Sc./B.Eng. degrees from Beihang University (2017). His research focuses on trustworthy machine learning, adversarial robustness, and uncertainty quantification in foundation models. He has served as a reviewer for top conferences like NeurIPS, ICLR, and CVPR, earning an Outstanding Reviewer Award in 2021. His teaching includes modules on machine learning for visual data analysis and principles of machine learning. He supervises PhD students in AI safety and reliability, with notable work on conformal prediction, adversarial attacks, and multimodal learning. His research outputs span top venues such as ICML, CVPR, and NeurIPS, addressing challenges in algorithmic fairness, model certification, and cross-modal alignment.
Ryan Henry is an Assistant Professor in the Department of Computer Science at the University of Calgary. His research focuses on applied cryptography, emphasizing the development of secure systems that prioritize user privacy. His work spans designing privacy-enhancing technologies, implementing cryptographic protocols, and analyzing number-theoretic attacks on cryptographic assumptions. He also explores theoretical aspects of cryptographic efficiency and practical deployment challenges. While specific educational background details are not provided in the text, his research contributions highlight expertise in cryptography, secure systems, and privacy-preserving technologies. His work has addressed topics such as Private Information Retrieval (PIR), secure messaging, and blockchain privacy. Key research interests include: Secure Multiparty Computation Privacy-Preserving Data Access Efficient Cryptographic Protocols Zero-Knowledge Proofs IoT Security Cryptocurrency and CBDC Design His recent publications emphasize advancements in distributed systems security, privacy in recommendation systems, and cryptographic efficiency. Notable contributions include the Grotto and Duoram frameworks for secure computation, and proposals for Canadian CBDC frameworks. Despite extensive research output, no scientific awards or grants are explicitly mentioned in the provided text. Collaborations and lab affiliations are not detailed, though his work suggests involvement in interdisciplinary projects on privacy and security technologies.
Stephen Lee is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh, affiliated with Pitt Cyber. His research focuses on distributed systems, cyber-physical systems, and sustainability, emphasizing energy efficiency and cost optimization. Dr. Lee holds a PhD from the University of Massachusetts Amherst, a Master’s from Chennai Mathematical Institute, and a Bachelor’s from St. Stephen’s College, Delhi. He actively seeks students for his research group. Education: PhD, Computer Science, University of Massachusetts Amherst Master’s, Chennai Mathematical Institute Bachelor’s, St. Stephen’s College, Delhi Research Interests: Dr. Lee’s work integrates distributed systems, machine learning, and optimization to enhance sustainability. Key areas include IoT-enabled energy systems, emission-aware computing, and privacy-preserving frameworks. He leads projects like GreenWhisk (serverless emission reduction) and Sat2map (3D building modeling from satellite imagery). Recent Achievements: Best Paper Award in IEEE TPS 2024 DOE-funded Cyber Energy Center (2024) MCSI Seed Grant for Pitt building sustainability (2024) NSF Grant on sustainable distributed infrastructures (2023) Grants & Advising: Secured over $2M in grants, including NSF and DOE funding. Advises on energy-efficient systems and IoT security. Teaches CS 2510 (Operating Systems) and CS 1699 (Systems & Sustainability). Labs & Teams: Directs the Sustainable Systems Research Group, focusing on decarbonizing IT and optimizing renewable energy systems. Collaborates with industry partners on smart grid solutions and edge-cloud systems.
Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.
Sharmistha Guha is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. Her research focuses on Bayesian methods for analyzing complex, high-dimensional data, with applications in neuroscience, network security, and social sciences. She develops techniques for supervised network data analysis, causal inference, and data privacy preservation. Education: Ph.D. in Statistical Science (Duke University, implied by dissertation recognition). Research Interests: Bayesian high-dimensional regression, object-oriented regression, causal inference in randomized trials, probabilistic record linkage, Bayesian nonparametric mixture models, and integration of heterogeneous data types like networks and functional data. Her work addresses challenges in neuroimaging (dMRI/fMRI) and big data analytics. Recent Contributions: Her articles span Bayesian methodologies for network analysis, differential privacy in treatment effect estimation, and applications in environmental health (e.g., PM2.5 exposure studies). Notable trends include leveraging Bayesian hierarchical models for complex data structures and advancing causal inference frameworks. Awards: 2022 Blackwell-Rosenbluth Award (ISBA) 2021 Savage Award Honorable Mention 2024 NSF-DMS Grant 2413721 Grants & Advising: Secured NSF funding for heterogeneous data integration research. Mentored students like Jose Rodriguez-Acosta (NSF GRFP Fellow) and Jacob Pagel (NIH-funded CoSIBS participant). Lab/Team: Leads a research group integrating statistical method development with domain collaborations in neuroscience and network security. Active in organizing workshops and serving on panels to foster academic engagement.
Professor Peter Chin is a Professor of Engineering at Dartmouth College and Director of the Learning, Intelligence + Signal Processing (LISP) Lab. He holds affiliations with the Thayer School of Engineering and serves as Associate Editor of IEEE Transactions on Computational Social Systems. His research bridges signal processing, machine learning, game theory, and differential geometry, with applications in cybersecurity, healthcare, and network analysis. Education: Bachelor of Science in Electrical Engineering, Computer Science, and Mathematics from Duke University (1993) Doctor of Philosophy in Mathematics from MIT (1998) Research Interests: Chin’s work focuses on fundamental questions at the intersection of machine learning, game theory, and signal processing. His lab explores topics like adversarial defense mechanisms, topological machine learning, and computational neuroscience. Recent projects include cybersecurity resilience modeling, medical imaging enhancements via GANs, and multi-agent reinforcement learning frameworks. Publications Trends: His most recent articles address cutting-edge challenges in cybersecurity (e.g., autonomous defense systems), medical AI (e.g., Alzheimer’s classification), and adversarial robustness. A notable 2025 focus is on quantitative resilience modeling for cyber defense, reflecting growing demand for AI-driven security solutions. Awards: Faculty Scholar Award, Duke University George Sherred III Award, Duke University Julia Dale Memorial Award, Duke University Grants & Leadership: Recipient of DARPA cybersecurity research grants Co-chair for SPIE/DSS Cyber Sensing Conference (2013–2020) Developed novel compressive sensing microscope for biological imaging LISP Lab: This interdisciplinary lab pioneers projects like nFlip (multiplayer security game models) and topological machine learning frameworks, emphasizing practical applications of theoretical advancements.
Dr. Kanika Goel is a Lecturer in the School of Information Systems at Queensland University of Technology (QUT), specializing in Business Process Management (BPM), Data Governance, and Process Analytics. She holds a PhD from QUT and has over 9 years of teaching experience, coordinating programs such as BIT Honours (IN10) and Masters of Philosophy (IN80). Her research focuses on process-oriented data analytics, data quality, and process mining, with industry collaborations spanning health, retail, and asset management sectors. She is a Lean Six Sigma Green Belt certified trainer and a Fellow of the Higher Education Academy (FHEA). Dr. Goel has led several industry-funded projects, emphasizing applied research in data governance, process mining, and process improvement. Notably, she received the Vice-Chancellor's Award for Excellence (2019) for innovative BPM integration in research management systems. Her work bridges academic research and real-world applications, contributing to journals like Business and Information Systems Engineering and IEEE Access . She teaches courses on Business Process Technologies, Modern Data Management, and BPM units in QUT's continuing professional education programs. Her articles explore topics like data imperfections in healthcare systems, process standardization strategies, and privacy risks in NoSQL databases. She advocates for digital literacy and has published on initiatives to build tech-savvy communities. Dr. Goel is also involved in supervising research topics such as prescriptive process analytics and process-data governance patterns.
Assoc Prof Frederique Elise Oggier is an Associate Professor in the School of Physical and Mathematical Sciences, Division of Mathematical Sciences at Nanyang Technological University (NTU). She holds a PhD from the Swiss Federal Institute of Technology (EPFL) and has held visiting positions at Caltech and the Research Center for Information Security (Tokyo). Her research focuses on algebraic coding theory, lattice-based cryptography, and applications of number theory to secure and reliable communication systems. Education: Bachelor’s and Master’s in Mathematics from the University of Geneva PhD in Mathematics from EPFL Research Interests: Her work bridges abstract algebra with practical coding challenges, emphasizing lattice codes for wiretap channels, distributed storage systems, and security protocols. Specialized in algebraic structures like cyclic division algebras and modular lattices, her contributions advance both theoretical foundations and real-world implementations of secure communication systems. Publications: Her recent work addresses cutting-edge topics such as MDS codes, non-GRS code constructions, and lattice-based security in noisy channels. These contributions highlight her expertise in coding theory and its interdisciplinary applications. Grants/Advising: While specific grants are not detailed, her prolific publication record reflects sustained research activity. She advises on projects related to distributed storage and secure coding, though student names are not listed in the provided texts. Labs/Teams: Affiliated with NTU’s mathematical sciences division, she collaborates with global researchers on projects such as lattice coding for 5G/6G systems and cryptographic protocols leveraging algebraic number theory.
Natalia Andrienko is a Professor of Computer Science at City University London and Lead Scientist in the Knowledge Discovery department at Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme. Her work bridges visual analytics with mobility data science and machine learning, focusing on human-in-the-loop systems for pattern discovery and spatiotemporal data exploration. Professor, Computer Science, City University London (2013-present) Lead Scientist, Knowledge Discovery, Fraunhofer Institute (1997-present) Research interests center on visual analytics methodology for spatiotemporal data, human-centered machine learning, and mobility pattern analysis. She develops frameworks for interactive dashboards, trust visualization in ML, and semantic exploration of location-based data, with a focus on scalable and privacy-respecting techniques. Her recent publications investigate hybrid human-machine discovery of movement patterns, contextual visual analytics for multivariate events, and the integration of temporal periodization with spatial analysis. Articles emphasize applications in sports analytics, transportation systems, and collaborative visual analysis workflows. Key collaborations include work with Gennady Andrienko and Salvatore Rinzivillo. She has contributed to journals like Visual Informatics , IEEE Transactions on Visualization and Computer Graphics , and International Journal of Cartography , maintaining active research output across visual analytics, mobility science, and geospatial data modeling.
Jilles Vreeken is a Professor of Computer Science at Saarland University and tenured faculty at the CISPA Helmholtz Center for Information Security, where he leads the Exploratory Data Analysis research group. He is also an ELLIS Fellow and Faculty of the Saarbrücken Unit on AI and ML. His work bridges theoretical foundations with practical applications in causal inference, unsupervised learning, and exploratory data analysis. Dr. Vreeken's research focuses on developing theory and algorithms for answering fundamentally exploratory questions about data: "what is going on in my data?", "what causes what and how?", and "what can we learn from this model?" without making unnecessary or unjustified assumptions. He takes a principled approach based on information theory to identify what is worth knowing, then develops efficient algorithms for extracting useful interpretable results. His work spans causal inference under realistic conditions (including hidden confounding, selection bias, and non-i.i.d. data), summarizing complex data and models in understandable terms, and combining these threads to create more robust and useful models across diverse data types. His recent publications demonstrate a strong trend toward causal discovery in increasingly realistic settings, including non-stationary time series, event sequences, and scenarios with hidden confounders. He has made significant contributions to federated learning, interpretable machine learning, and pattern mining. His work consistently applies information-theoretic principles to develop methods that are both theoretically sound and practically useful for extracting insights from complex data. Dr. Vreeken has received numerous prestigious awards including: IEEE ICDM'18 Tao Li Award for Excellence in Research IEEE ICDM'18 Best Paper Award UdS-CS'15 Busy Beaver Teaching Award ACM SIGKDD'11 Best Student Paper Award ACM SIGKDD'10 Doctoral Dissertation Runner-Up Award ECML PKDD'09 Best Student Paper Award As an advisor, Dr. Vreeken has mentored numerous doctoral researchers to completion, including Dr. Osman Ali Mian, Dr. David Kaltenpoth, Dr. Boris Wiegand, Dr. Sebastian Dalleiger, Dr. Janis Kalofolias, Dr. Jonas Fischer, Dr. Alexander Marx, Dr. Panagiotis Mandros, Dr. Kailash Budhathoki, Dr. Roel Bertens, Dr. Koen Smets, and Dr. Michael Mampaey. He has secured significant research funding as PI for multiple projects including "AI for Prediction and Therapy Guidance in Acute Stroke" (HAICU, 2025-2028), "Neuro-Explicit Models of Language, Vision and Action" (RTG, DFG, 2023-2028), and "Crushing Antimicrobial Resistance using Explainable AI" (HAICU, 2021-2024). Dr. Vreeken leads the Exploratory Data Analysis (EDA) research group at CISPA, which focuses on developing theory and algorithms for discovering novel insights from data, learning inherently interpretable models, and drawing reliable causal conclusions. The group has produced numerous influential algorithms and frameworks in causal inference, pattern mining, and exploratory data analysis, with applications spanning healthcare, materials science, and cybersecurity.
