Professor Paul Brereton is Director of Strategic Alliances at the School of Biological Sciences, Queen's University Belfast, and leads the Institute for Global Food Security. He has coordinated major EU projects including €20M TRACE and €12M FOODINTEGRITY, and currently directs QUB's contributions to the UKRI Sus-Health programme and €11M TITAN Horizon Europe project. Active in food safety, authenticity, and sustainability Co-Director of UKRI Integrating Finance and Biodiversity Programme Chairs European Commission PRIMA Foundation evaluation panel His research spans food chemistry, risk assessment, and policy development, with recent focus on financial instruments for ecological restoration and combined nutritional-environmental metrics. Key projects address antimicrobial resistance, dietary sustainability, and blockchain applications in food traceability. Scientific honors include Fellow of the Royal Society of Chemistry and international awards from AOAC and OIV. His work contributes to UN Sustainable Development Goals 2 (Zero Hunger), 3 (Health), and 12 (Responsible Consumption).
Dr. Liang (Leon) Dong is an Associate Professor in the Department of Electrical and Computer Engineering at Baylor University, where he conducts research and teaches in the areas of signal processing, wireless communications, and artificial intelligence. He leads the Laboratory of Signal Processing, Communications, and Artificial Intelligence, fostering innovation in next-generation communication systems, IoT, and AI-driven applications. PhD, Electrical & Computer Engineering, The University of Texas at Austin (2002) MS, Electrical & Computer Engineering, The University of Texas at Austin (1998) BS, Applied Physics with Minor in Computer Engineering, Shanghai Jiao Tong University (1996) Dr. Dong's research focuses on advancing digital signal processing and wireless communications, with strong emphasis on artificial intelligence applications. His work spans NextG wireless systems , IoT and smart cities , cyber-physical system security , and AI in healthcare and industrial automation . He applies deep learning to domains such as autonomous driving and drug discovery, and investigates energy-efficient, secure, and reliable communication protocols. The recent publications highlight a strong trend toward integrating AI into traditional signal processing and communications. Topics include mRNA vaccine stability prediction , smart city infrastructures , secure cyber-physical systems , and deep learning for biomedical and industrial applications . His work bridges theoretical innovation with real-world impact in defense, transportation, and public health. Dr. Dong has earned recognition as a Senior Member of IEEE and a Member of the American Physical Society. He has also served as Faculty Advisor for Baylor University's InterVarsity chapter. Senior Member, Institute of Electrical and Electronics Engineers (IEEE) Member, American Physical Society (APS) He has successfully advised numerous graduate and undergraduate students, many of whom now hold academic and industry positions at institutions like Stanford, Intel, NASA, L3Harris, and Cummins. His research is generously supported by Baylor's VP for Research, the National Science Foundation, NASA, the Department of Defense (TARDEC), the Michigan Department of Transportation, and industry leaders including Intel, L3Harris, ExxonMobil, and Denso. He actively mentors students through research assistantships and senior design projects. Dr. Dong leads the Laboratory of Signal Processing, Communications, and Artificial Intelligence, which provides a collaborative environment for advancing research in signal processing, communications, and AI. The lab supports graduate and post-doctoral researchers and offers opportunities for undergraduate involvement in AI programming, circuit design, and embedded systems.
Dr. Haibo He is the Robert Haas Endowed Professor in the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island (URI). As an IEEE Fellow and NSF CAREER awardee, his research focuses on computational intelligence, neural networks, and reinforcement learning with applications to smart grids and microgrid systems. Ph.D. in Electrical Engineering, Ohio University, 2006 M.S. in Electrical Engineering, Huazhong University of Science and Technology, 2002 B.S. in Electrical Engineering, Huazhong University of Science and Technology, 1999 His research interests include: Computational Intelligence Adaptive Dynamic Programming Reinforcement Learning Deep Learning for Power Systems Distributed Control in Microgrids Imbalanced Data Learning Recent research trends from publications (2018-2025) show a focus on: Multi-agent reinforcement learning for energy systems Digital twin frameworks for grid security Event-triggered control mechanisms Finite-time convergence algorithms Cyber-attack resilient control systems Evolutionary computation in power networks Awards: IEEE Fellow (2018) NSF CAREER Award (2017) Dr. He leads the Computational Intelligence and Self-Adaptive Systems (CISA) Laboratory at URI, which conducts fundamental research on computational intelligence methods with applications to power systems, data mining, and neural networks.
Stephanie Forrest is a Professor of Computer Science at Arizona State University and serves as Director of the Biodesign Center for Biocomputation, Security and Society . She holds affiliations with the School of Computing and Augmented Intelligence , Global Futures Laboratory , and Santa Fe Institute External Faculty . Education: B.A. from St. John's College M.S. and Ph.D. in Computer Science from the University of Michigan Research Interests: Forrest specializes in the intersection of biology and computation , with key contributions to cybersecurity (anomaly detection, instruction-set randomization), automated software repair (evolutionary methods), and biological modeling (immune systems, SARS-CoV-2 spread). Her work bridges complex adaptive systems , AI/ML , and defense applications . Scientific Contributions: 2020 IEEE S&P Test of Time Award 2019 ICSE Most Influential Paper Award 2011 ACM/AAAI Allen Newell Award NSF Presidential Young Investigator (1991) IEEE Fellow Evolutionary Computation Pioneer award Publications & Grants: Her research appears in top venues (ICSE, IEEE S&P, PNAS) and is funded by the National Science Foundation , DARPA , Air Force Research Lab , and Santa Fe Institute . Key projects include Crispy (CRISPR-inspired DoS defense), GenProg (automated bug correction), and SIMCoV-GPU (agent-based pandemic modeling).
Brent Lagesse is an Associate Professor at the University of Washington - Bothell , affiliated with the Division of Computing & Software Systems under the School of Science, Technology, Engineering & Mathematics . His research focuses on security in emerging environments , particularly secure machine learning and privacy in sensor-rich systems . Ph.D. in Computer Science from the University of Texas at Arlington (2009) Research Interests include: Detecting and locating hidden webcams Scalable AI/ML defense mechanisms Privacy-preserving video sharing AI systems for air quality prediction Automated yeast cell analysis CRISPR/CAS9 guide-donor libraries Article Trends : Recent publications emphasize secure machine learning for smart city applications, privacy-preserving technologies , and resource-constrained security in crowdsensing environments . Collaborative work spans cybersecurity education , environmental monitoring , and context-aware systems . Scientific Awards : Cybersecurity Fulbright Scholar (University of Cambridge, 2018) Johann-von-Spix International Guest Professorship (University of Bamberg, 2019-20) Advising & Grants : Advises current research students Neil Prakasam and Nicholas Handaja NSA grant ($96k) for GenCyber curriculum development (2022) NSF grant ($300k) for AI-enhanced cybersecurity workforce studies (2021) T-Mobile grants for ML security metrics and dataset anonymization (2020-2022) Laboratory : Leads the Security of Emerging Environments (SEE) Lab , developing practical and theoretical frameworks for smart city security and privacy-preserving technologies .
Luke Rendell is a Professor in Biology at the University of St Andrews, where he holds multiple affiliations including the Scottish Ocean Institute, Sea Mammal Research Unit, Centre for Biological Diversity, Centre for Social Learning and Cognitive Evolution, and the Institute of Behavioural and Neural Sciences. His academic title of Reader (equivalent to Professor in the US system) reflects his senior status within the university. Dr. Rendell's research program centers on the evolution of learning, behavior, and communication in marine mammals. He has conducted extensive work on sperm whale society and ecology, demonstrating how long-lasting social groups use distinctive vocal dialects that appear culturally transmitted. His research spans culture in whales and dolphins, learning in archerfish, human social learning, and evolutionary modeling. Through the East Coast Marine Mammal Acoustic Study (ECOMMAS), he deploys passive listening buoys along the Scottish coastline to monitor windfarm impacts on marine mammals. His recent publications reveal a strong focus on cetacean culture, vocal communication, and conservation. His work increasingly integrates machine learning with traditional bioacoustics to analyze whale communication patterns. There's a clear trend toward examining the conservation implications of cultural transmission in marine mammals, particularly how cultural knowledge affects population resilience to environmental changes. Supervised 9 PhD students to completion since 2009 Principal Investigator on multiple research grants from Scottish Funding Council, Leverhulme Trust, and NERC Co-author of the influential book "The Cultural Lives of Whales and Dolphins" Dr. Rendell maintains active science communication efforts through projects like "Sea Symphonies" that blend marine science with artistic expression. His work contributes to UN Sustainable Development Goals related to life below water and biodiversity conservation.
Dr. Stavros Shiaeles is an Associate Professor in Cybersecurity at the Faculty of Technology , University of Portsmouth, and Co-Director of the Portsmouth AI and Data Science Centre (PAIDS) . With over 130 publications and 3000+ citations, he specializes in cybersecurity, applied AI, and threat mitigation frameworks. Academic Qualifications : PhD in Electrical and Computer Engineering (Democritus University of Thrace, 2013), MEng in Electrical and Computer Engineering (Democritus University of Thrace, 2007), MBA in Human Resource Management (University of Plymouth, 2016), and PG Cert in Academic Practice (University of Plymouth, 2017). Research Interests span cybersecurity, malware detection, blockchain, 6G networks, AI/ML applications, digital forensics, and post-quantum cryptography. His work addresses threats in IoT, financial systems, and critical infrastructure while exploring SDG4 (Quality Education) through cybersecurity training. Recent publications emphasize AI-driven anomaly detection (e.g., ransomware behavior analysis, 6G traffic monitoring), deepfake forensics, synthetic image attribution, and hybrid blockchain/AI security architectures. He also curates datasets for malware analysis and synthetic media classification. Scientific Awards : IEEE SMC TCHS Outstanding Service Award (2021). Grant Funding : Over €18M secured in EU Horizon 2020 grants, including €8M as Principal Investigator for the ongoing XTRUST-6G project. Active in KTPs, consulting, and research commercialization opportunities.
Kotaro Hara is a Full-time Faculty Assistant Professor of Computer Science at Singapore Management University's School of Computing and Information Systems. His research focuses on Human-Computer Interaction, Human-Machine Collaborative Systems, and Accessibility Design, with applications in urban mobility, digital transformation, and aging population support. He holds a PhD from the University of Maryland (2016). Research Areas: Accessibility, Crowdsourcing, Future of Work, Urban Sustainability Recent publications explore Augmented Reality for low-vision runners, conversational agents for dementia care, and novel tactile data visualization methods. His work integrates natural language processing, multimodal interfaces, and AI ethics frameworks across diverse domains. Academic collaborations include advising PhD candidate Sheshadri Smitha. Contact: kotarohara@smu.edu.sg
Kathleen DeRose is a Clinical Associate Professor of Finance at the Leonard N. Stern School of Business, New York University. She serves as Director of the Fubon Center for Technology, Business, and Innovation and as Academic Director of the MS in FinTech program. She joined Stern in 2016 as an Executive-in-Residence and became a full-time faculty member in 2017. Education: M.Sc. with distinction in Contemporary Chinese Studies, University of Oxford M.B.A., TRIUM Global Executive MBA (NYU Stern, HEC Paris, London School of Economics) B.A. in American History, Princeton University Her research and teaching focus on the intersection of finance and technology, particularly in FinTech, entrepreneurial finance, asset and wealth management, and China's political economy. She explores innovative applications of machine learning in investment management and investigates behavioral phenomena such as lotteries in financial decision-making and their use in public funding mechanisms. The available publications reflect a strong orientation toward practical and evolving challenges in investment management, including machine learning applications and quantitative strategies. These works demonstrate a blend of technical rigor and real-world relevance in modern finance. Scientific Awards: Humphrey Ko prize for best master’s thesis, University of Oxford Kathleen DeRose has extensive industry engagement through her board memberships at London Stock Exchange Group plc, Enfusion, Experian, and Voya Financial, and previously chaired Evolute AG. She brings significant professional experience from leadership roles at Credit Suisse, Deutsche Bank, and Bessemer Trust into her academic role. She has led curriculum development, joint research projects with industry, and major FinTech events at Stern. She also teaches at NYU Shanghai, contributing to global FinTech education. She leads the Fubon Center's FinTech Initiative and the MS in FinTech program, fostering collaboration between academia and industry, supporting innovation in financial technology, and training the next generation of FinTech leaders.
Marta Arce Urriza is a Post University Professor at the Public University of Navarra, specializing in Business Management within the Department of Business Management at the Institute for Advanced Research in Business and Economics (INARBE). Her research focuses on consumer behavior in online and offline channels, multichannel retail strategies, and the impact of emerging technologies like AI on marketing and consumer adoption. She has contributed to doctoral programs in Economics, Business, and Law at her university and collaborates with institutions like the Autonomous University of Barcelona. Education: Earned a Doctorate in Business Administration and Management from the Public University of Navarra in 2009. Participated in research stays at the University of Chicago and University of Groningen. Research Interests: Consumer adoption of new technologies (e.g., AI chatbots, 3D printing) Online review dynamics and their influence on consumer decisions Privacy concerns in virtual assistant interactions Strategic comparisons between private labels and national brands in multichannel environments Recent Article Trends: Focus on generative AI’s role in retail, privacy implications of voice assistants, and leveraging text mining for brand analysis. Articles span journals like Journal of Retailing and Consumer Services and Technology in Society . Grants & Collaborations: Lead or co-PI in projects such as 'Generative AI Solutions for Intelligent Storytelling' (2025–2027) and 'Interaction with Virtual Assistants' (2022–2025), funded by Navarra Government and the Spanish Ministry of Science. Active in editorial roles for journals like Journal of Interactive Marketing . Labs/Teams: Involved in interdisciplinary research teams focusing on AI ethics, consumer behavior analytics, and multichannel strategy. Supervised doctoral student Miriam Alzate Barricarte’s thesis on electronic word-of-mouth implications.
Dr. Mary Hall is a Professor in the School of Computing at the University of Utah, specializing in compiler optimization, parallel computing, and high-performance computing (HPC). Her work focuses on autotuning techniques, compiler-driven performance optimization, and minimizing data movement in computations to enhance efficiency. She has contributed significantly to frameworks like Bricks and Peak , advancing code generation for GPUs and block-structured grids. Her research also addresses educational initiatives, such as improving student retention in introductory computing courses and fostering diversity in the computing workforce through NSF-funded programs. Her research interests span compiler technology, stencil computations, and energy-efficient HPC applications. Key projects include optimizing geometric multigrid methods, developing communication-avoiding algorithms, and integrating machine learning into autotuning. She has led efforts to streamline performance portability across heterogeneous architectures and has published extensively on scheduling languages and compiler-driven optimizations. Mary Hall’s contributions include advancing data layout strategies for sparse tensors and DNNs, as well as fostering reproducibility in computational research through collaborative NSF REU programs. Her work emphasizes practical tools like ytopt and Rigel , which automate performance tuning for scientific applications. She remains active in both academic and industrial HPC communities, addressing challenges in extreme heterogeneity and scalable computing.
Prof. Gabriela Alves Werb, Ph.D. is a faculty member affiliated with the Bundesbank Research Data and Service Centre (RDSC) and holds her academic background from Johann Wolfgang Goethe University Frankfurt am Main. Her research spans interdisciplinary areas at the intersection of finance, machine learning, and digital economy, with a focus on non-financial risks and search engine analytics. Education: Doctoral Dissertation (2020), Goethe-Universität Frankfurt Research Interests include machine learning applications in financial risk assessment, user-generated content analysis, and digital marketplace dynamics. Her work bridges computational methods with economic policy challenges. Recent Publications highlight trends in climate-related financial data, predictive modeling in marketing, and the economic implications of search engine visibility. Key subfields span ESG investing, SEO vulnerability, and digital risk assessment.
Thomas Hellstrom is a Professor at the Department of Computer Science , Umeå University, Sweden. He leads the Intelligent Robotics group and is affiliated with the Center for Transdisciplinary AI . His research spans human-robot interaction (HRI) , deep learning applications , robot ethics , and field robotics for agricultural and forestry automation. Coordinated EU projects: INTRO (FP7/ITN), SOCRATES (H2020), CROPS, SWEEPER Developed intelligent walker for stroke patients with CMTS/MT-FoU/Umeå Stroke Center Key contributions in robot learning , causal reasoning , and natural language understanding Research Focus : His work emphasizes understandability in robot behavior, including causal modeling , multi-modal communication , and ethical frameworks for autonomous systems. Current project ROCC (Swedish Research Council) explores robot causality, while SOCRATES addressed social robotics in eldercare. Scientific Awards : • Erdös-Bacon-Sabbath number ≤ 13 Grants & Funding : • ROCC (2023, 3.7M SEK, Principal Investigator) • SCAI (2022, 3.7M SEK, Co-Applicant) • VINNOVA (2019, 3.47M SEK, Co-Applicant)
Magdalini Eirinaki is a Professor and Academic Program Coordinator for the MS in Artificial Intelligence at San José State University's Charles W. Davidson College of Engineering. With a career spanning two decades, her work bridges recommender systems , machine learning , and smart city applications . PhD in Computer Science (2006), Athens University of Economics and Business MSc in Advanced Computing (2000), Imperial College London BSc in Computer Science (1998), University of Piraeus Her research focuses on machine learning and recommender systems with extensions to generative AI , privacy-sensitive algorithms , and social network analysis . Recent publications explore federated learning , multi-resolution diffusion models , and autonomous network defense using reinforcement learning. Current projects include NSF-funded CollaborAIte (2024) EU Horizon/Marie Sklodowska-Curie's MUSIT (2024) IBM SkillsBuild Cloud Credits for Sustainability (2024) She has received multiple teaching and mentorship awards including: Newnan Brothers Award (2019) Applied Materials Award (2017) 5-time SJSU Distinguished Faculty Mentor Award Dr. Eirinaki advises students in AI , ML , and smart city projects, with recent graduates presenting at IEEE CAI (2025) and CSU Conference (2025).
Jana Gonnermann-Müller is a research assistant at the Chair of Business Information Systems, Processes and Systems at the University of Potsdam . Holding an M.Sc. in Psychology from Humboldt University of Berlin, her work focuses on human-machine interaction, particularly examining learning, decision-making, and trust in technological collaborations. Degree: Master of Science (M.Sc.) in Psychology Alumni: Humboldt University of Berlin Current Role: Scientific Associate at University of Potsdam Research explores cognitive factors in augmented reality and AI assistance systems , employing experimental designs and eye tracking methodologies. Key projects include investigating visual guidance in AR, comparing AR display types, and developing frameworks for generative AI in education. Publications (2023-2025) demonstrate expertise in digital education , cognitive load optimization , and technology-enhanced learning . Collaborations span institutions like Hong Kong Polytechnic University , University of California, Davis , and Weizenbaum Institute , with corporate consulting on learning technology implementation. Technical focus areas include: Human-System Design Optimization Eye Tracking & Usability Studies AI Recommender Systems Industry 4.0 Implementation Research applications extend to: Secondary Education Technology Assembly Line AR Integration Change Management Systems Maintenance AR Visualization
