Chris Spencer is a Wellcome Trust Career Development Fellow at the Nuffield Department of Medicine, University of Oxford. His research focuses on statistical genetics, with emphasis on African population genetics, malaria susceptibility (via the MalariaGEN consortium), and stratified medicine applications in hepatitis C (STOP-HCV consortium). He develops methodologies to analyze genetic determinants of host-parasite interactions and their role in disease prevention and treatment. His work explores infectious disease impacts on human immune physiology through natural selection, aiming to translate genetic insights into clinical strategies. Recent studies include structural variation in malaria resistance genes and polymorphisms linked to pneumococcal bacteremia in Kenyan children. His computational tools, such as FINEMAP, advance variable selection in genome-wide association studies. Publications span malaria genetics, viral resistance, and population admixture, reflecting a multidisciplinary approach to genomic medicine. Collaborations with global consortia highlight his commitment to addressing global health challenges through genetics.
Prof. Tim Güneysu is a full Professor and Head of the Security Engineering department at the Faculty of Computer Science, Ruhr-Universität Bochum. He serves as Vice Dean for Strategy and Finances (since 2023) and previously as Speaker of the Horst-Görtz Institute for IT-Security (2020-2023). His academic journey includes roles as Associate Professor at the University of Bremen (2015-2017) and Assistant Professor at Ruhr-Universität Bochum (2011-2015). He also holds positions at the German Research Center for Artificial Intelligence (DFKI) and has conducted postdoctoral research at UMass Amherst. His research focuses on Security-by-Design principles, CAD for Security, and countermeasures against physical attacks. He emphasizes efficient cryptographic implementations and system-level hardware security. Key areas include post-quantum cryptography, side-channel resistant designs, and secure embedded systems. He has contributed over 200 publications in top venues, with recent work on FPGA-based cryptographic accelerators, secure hardware extensions (e.g., KeyVisor), and post-quantum algorithms for IoT. His research themes include agile signature acceleration, fault attack mitigation, and hardware-software co-design for security. Notable projects include CONVOLVE (edge-AI security) and QuantumRISC (quantum-safe systems). His work bridges theoretical cryptography with practical hardware implementations, emphasizing real-world security applications.
Yuebing Zheng is a Professor of Mechanical Engineering & Materials Science and Engineering at the University of Texas at Austin, holding the Cullen Trust for Higher Education Endowed Professorship. He leads a research group innovating optical nanotechnologies for applications in health, energy, and manufacturing. His work focuses on light-matter interactions, optically active materials, and interdisciplinary training. Key roles include Graduate Advisor for the Materials Science Program and past leadership as Associate/Assistant Professor since 2013. Education: PhD in Engineering Science and Mechanics (2010), Penn State University Postdoctoral Researcher (2010-2013), UCLA (Chemistry and Biochemistry) MSc in Physics (2003), National University of Singapore BSc in Physics (2001), Nankai University Research Interests: Optical manipulation technologies (e.g., optothermal tweezers) Nanophotonics and metamaterials Machine learning for materials discovery Biomedical applications (e.g., cell analysis, chiral sensing) Clean energy systems Recent Article Trends: Focus on AI-driven materials design, optothermal microrobotics, and advanced optical systems for energy and biomedical applications. Key innovations include photonic batteries, graphene moiré systems, and steerable active particle swarms. Awards: 2025 SPIE Fellow 2024 Optica Fellow 2017 NIH New Innovator Award 2014 Beckman Young Investigator Multiple best paper awards (2019–2023) Advising & Grants: Supervised over 20 PhD students/postdocs. Active grants from NIH, NSF, ONR, NASA, and industry partnerships. Current lab focuses on optical manipulation, metamaterials, and AI-integrated nanotechnology. Labs/Teams: Director of the Zheng Research Group, affiliated with the Texas Materials Institute. Collaborates on projects merging nanoscience with machine learning and biomedical engineering.
Professor Andrew Martin is a leading academic in Systems Security at the Department of Computer Science, University of Oxford, and a Governing Body Fellow at Kellogg College. His research focuses on trusted computing, cybersecurity, and secure distributed systems, with applications in cloud computing, IoT, and smart grids. He has been instrumental in advancing secure architectures using hardware-based trust mechanisms such as Intel SGX and Trusted Platform Modules. Research Interests: His work spans Systems Security , Trusted Computing , Confidential Computing , Secure Multi-Party Computation , and Privacy in Distributed Systems . He investigates how hardware-enforced security can mitigate risks in large-scale environments, particularly where privacy and trust are paramount. Publication Trends: His recent publications reveal a strong focus on Trusted Execution Environments (TEEs), remote attestation, and secure cloud architectures. He critically examines the feasibility of using hardware enclaves for both defensive and offensive security, emphasizing architectural soundness and real-world applicability. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Professor Martin has supervised numerous PhD and Master’s students in cybersecurity and systems research. His projects include CRANE , Trustworthy Logging , webinos , TCLOUDS , and Secure Networking by Design (SNbD) . He has secured funding for research in trusted computing and secure distributed systems, contributing to both theoretical foundations and practical implementations. Labs and Teams: He leads a research group focused on systems security and trusted computing, collaborating with industry and academic partners on large-scale security challenges. His team works on developing secure middleware, analyzing vulnerabilities in consumer devices, and designing privacy-preserving protocols for smart infrastructure.
Maxime CORDY is a Research Scientist at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) , University of Luxembourg, within the Security, Design and Validation group (SerVal) . He holds a PhD from the University of Namur (Belgium, 2014) and specializes in software engineering, applied artificial intelligence, and cybersecurity. His work focuses on adversarial machine learning, deep learning robustness, and model checking for critical systems. Research interests include adversarial attacks on tabular data , energy system optimization , code understanding models , and software quality assurance . Recent projects address challenges in secure AI deployment, automated test generation, and fault detection in large language models. Publications emphasize empirical studies on adversarial defenses, data augmentation for code models, and energy consumption forecasting. He contributes to tools like Daedalux (variability-aware model checking) and benchmarks like Tabularbench for adversarial robustness evaluation. Current affiliations include leadership within the SerVal group and collaborations on interdisciplinary projects such as MALETSQUE (Machine Learning Techniques for Software Quality Evaluation). His work bridges theoretical computer science with practical applications in energy systems, medical imaging, and space program design.
Professor Carsten Rudolph serves as Deputy Dean at Monash University's Faculty of Information Technology and directs the Oceania Cyber Security Centre (OCSC). He holds a PhD in Information Security from Queensland University of Technology (2002) and a Diplom in Computer Science from Goethe University Frankfurt (1997). His interdisciplinary research focuses on cybersecurity foundations, including cryptographic protocols, AI-driven security, human factors, and national cybersecurity policy. Key areas include securing smart grids, digital health systems, and transnational energy networks. Notable contributions include establishing the OCSC, leading Pacific region cybersecurity maturity reviews with Oxford University, and advancing frameworks for firmware security in virtual power plants. He chairs major projects like RAI4IoE (Responsible AI for Energy) and Post-Quantum Cryptography initiatives. Teaching responsibilities include cybersecurity modules like FIT3173 and FIT3168. Rudolph's research outputs (137+ publications) emphasize phishing detection via AI, blockchain-based energy trading, and resilient smart grid systems. He collaborates internationally on policy development and has advised 12 major research projects funded by agencies like the U.S. Bureau of East Asia and Pacific Affairs.
Robert Falconer serves as Professor of Medicinal Chemistry and Director of the Institute of Cancer Therapeutics (ICT) at the University of Bradford. A registered pharmacist with the General Pharmaceutical Council, he leads a multidisciplinary research team focused on tumor glycocalyx targeting and protease-activated anticancer prodrugs. His academic journey spans from pharmacy training at The School of Pharmacy, University of London to establishing the medicinal chemistry program at ICT in 2005. Pharmacy Degree: The School of Pharmacy, University of London (now UCL School of Pharmacy) PhD in Medicinal Chemistry: London (2000) Professional Registration: General Pharmaceutical Council Professor Falconer's research centers on tumor glycocalyx modulation through polysialyltransferase inhibition and protease-activated drug delivery systems. His work spans osteosarcoma, neuroblastoma, breast, and prostate cancers with emphasis on developing targeted therapies that minimize systemic toxicity. The Falconer group employs computational design, solid-phase synthesis, and advanced biological evaluation techniques to develop novel anticancer agents. His publication portfolio demonstrates consistent focus on tumor-specific activation mechanisms (2023-2025), glycocalyx-targeted metastasis inhibition (2019-2021), and prodrug development (2010-2014). Recent work emphasizes theranostic applications and DNA repair targeting for pediatric cancers. Fellow of the Royal Society of Chemistry Former Honorary Treasurer, RSC Central Yorkshire Local Section Trust (2012-2018) Registered Pharmacist with General Pharmaceutical Council As principal investigator, Falconer has secured substantial funding from Breast Cancer Now, Bone Cancer Research Trust, Worldwide Cancer Research, and Neuroblastoma UK. He leads the £2m ICT Doctoral Training Centre and co-founded spin-out company Incanthera plc, which is advancing the MMP-targeted prodrug ICT2588 to clinical trials. His mentorship includes three PhD students and multiple postdoctoral researchers developing next-generation cancer therapeutics. The Falconer group operates within the ICT's state-of-the-art facilities, collaborating with Stanford University, University of Sheffield, and Ellipses Pharma. Current projects address unmet needs in pediatric oncology while developing platform technologies applicable across multiple cancer types.
Neil Lawrence is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge's Department of Computer Science and Technology. He also holds positions as a Senior AI Fellow at The Alan Turing Institute and a Visiting Professor of Machine Learning at the University of Sheffield. After three years as Director of Machine Learning at Amazon, Lawrence recently returned to academia, bringing extensive industry experience to his academic work. Lawrence's research focuses on the intersection of machine learning with the physical world, particularly in uncertainty quantification and end-to-end solutions for real-world applications. His work was initially inspired by deploying machine learning systems in African contexts, where comprehensive solutions are often required. His technical expertise spans over two decades in machine learning methods, with a growing interest in public understanding of machine learning, policy decisions, and data governance implications. His recent publications reveal a diverse research portfolio spanning climate science, healthcare applications, systems engineering for AI deployment, data governance, and theoretical machine learning. Lawrence's work demonstrates a consistent theme of bridging theoretical machine learning with practical applications across multiple domains, with particular attention to uncertainty quantification and the societal implications of AI systems. Lawrence serves on the board of the AISTATS conference and the ELLIS Foundation, and acts as the founding and series editor for the Proceedings of Machine Learning Research. He is also the co-host of the Talking Machines podcast, demonstrating his commitment to public engagement with machine learning concepts. At Cambridge, Lawrence teaches Advanced Data Science (Part II) and Machine Learning and the Physical World (MPhil ACS, Part III), contributing to both undergraduate and postgraduate education in computer science. His work with the Accelerate Programme for Scientific Discovery and the Data Trusts Initiative positions him at the forefront of developing frameworks for responsible and effective AI deployment in scientific and societal contexts.
Rob Gleasure is a Professor in the Department of Digitalization at Copenhagen Business School (CBS), Denmark. His research focuses on the intersection of information systems, digital technologies, and human behavior, with particular expertise in blockchain technology, crowdfunding, AI applications, and technology adaptation. Based at Solbjerg Square 3 in Frederiksberg, he contributes to CBS's mission of advancing knowledge in business and society through digital transformation. Professor Gleasure's research spans several key areas within information systems and digital innovation: Digital finance and blockchain technologies, including cryptocurrency and financial applications Crowdfunding platforms and digital fundraising mechanisms Artificial intelligence applications in various domains including healthcare and banking Human-computer interaction and the psychological aspects of technology adoption Digital collaboration and the affective dimensions of online work Quantum computing infrastructure and emerging technologies His recent publications reveal an evolving research trajectory that increasingly addresses the societal implications of digital technologies. Gleasure has moved from foundational work on crowdfunding and blockchain to more complex examinations of AI ethics, gender bias in technological systems, and the psychological impacts of digital media. His research demonstrates growing attention to sustainable development goals, particularly those related to responsible consumption and production, reduced inequalities, and climate action. The interdisciplinary nature of his work bridges business, technology, and social sciences, often employing both qualitative and experimental methodologies. Professor Gleasure has served as a supervisor for numerous students (21 supervisor tasks mentioned) and has been active in academic service including co-chairing the ACM Collective Intelligence Conference in 2021. His research has attracted media attention, with contributions to discussions on cryptocurrency, carbon offsetting in aviation, and AI applications.
Edith C. H. Ngai is an Associate Professor in the Department of Information Technology at Uppsala University, Sweden. She leads the Smart City Arena initiative and serves as project leader for the national GreenIoT project on energy-efficient IoT for sustainable city development funded by Vinnova. Her academic career spans multiple prestigious institutions including Chinese University of Hong Kong, Imperial College London, Simon Fraser University, UCLA, and Tsinghua University. Dr. Ngai's research focuses on Internet-of-Things, mobile crowdsensing, network security and privacy, cloud computing, and data analytics, with particular applications in smart cities and healthcare. Her work bridges theoretical foundations with practical implementations for sustainable development. She has pioneered research in energy-efficient IoT systems, data privacy in participatory sensing, and mobile health monitoring applications. Her recent publications demonstrate strong trends in IoT for smart cities, privacy-preserving techniques in social sensing, and energy-efficient data collection systems. The research spans both theoretical contributions and practical implementations, with applications ranging from urban environmental monitoring to healthcare solutions. Her work consistently addresses the tension between functionality and privacy in connected systems. Professional recognition includes: ACM Senior Member (2016) IEEE Senior Member (2015) ACM/IEEE IPSN Best Paper Runner-Up (2013) IEEE IWQoS Best Paper Runner-Up (2010) VINNMER Fellow from Swedish government agency (2009) Dr. Ngai actively mentors PhD and Master's students, with numerous graduates working at leading technology companies including Google. She serves as Associate Editor for IEEE Access, IEEE Transactions on Industrial Informatics, and IEEE Internet-of-Things Journal. Her current research projects include EU SimpliCITY, EU CRUNCH, and the GreenIoT platform for sustainable development, with funding from European Commission, Swedish Research Council, and Vinnova. She leads the Uppsala Urban Computing Lab, which focuses on IoT and mobile crowdsensing for smart cities, network security and data privacy, and smart sensing for healthcare applications. The lab develops integrated decision support tools for smart cities and citizen engagement platforms.
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).
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
Dr. Joonsang Baek is an Associate Professor at the School of Computing and Information Technology, University of Wollongong (since 2021). His research focuses on Cybersecurity , Cryptography , and Network Security , with notable contributions to digital signature revocation, attribute-based encryption, and privacy-preserving protocols. Member, Institute of Cybersecurity and Cryptology (since 2017) Supervision interests: Cybersecurity, Applied Cryptography, Network Security Research Highlights: Innovations in withdrawable signatures, secure cloud data sharing, and 5G authentication protocols. His work combines theoretical cryptography with practical applications in edge computing and AI-driven security systems. Grants: Led projects on ransomware datasets, dynamic access control, and TLS optimization. Collaborated with Data61, Discovery Projects, and industry partners on cybersecurity resilience initiatives. Collaborations: Frequent co-author with researchers like Willy Susilo, Cao Cao, and Xinyu Liu. Active in ACM Asia CCS, IEEE Transactions, and LNCS publications.
Simo Hosio is an Academy Research Fellow (2022-2027) and Professor of Computer Science and Engineering at University of Oulu's Center for Ubiquitous Computing, where he leads the Crowd Computing Research Group. He also maintains a visiting position at University of Tokyo, Japan. Having graduated as the first Finnish scholar under Microsoft Research Cambridge's Ph.D. scholarship program, he has published over 150 peer-reviewed scientific articles spanning two decades of research. Hosio's research spans three primary domains: crowdsourcing methodologies, human-computer interaction, and digital health applications. His work pioneers novel approaches to online labor markets, investigates the suitability of crowdsourcing for diverse applications, and explores HCI aspects of digital health solutions for chronic conditions. His research group, founded in 2020, has secured nearly two million USD in funding, demonstrating significant research impact and recognition. Analysis of Hosio's recent publications reveals a strong trend toward interdisciplinary research at the intersection of crowdsourcing, healthcare technology, and emerging AI systems. His work increasingly focuses on practical applications of crowd computing in health contexts, with growing attention to mental health, women's health, and workplace well-being solutions. The integration of AI and machine learning techniques with traditional HCI approaches represents another significant trajectory in his recent scholarship. Distinguished Paper Award (2024) Best Paper Honourable Mention Award (2022) PMCJ Best Research Paper (awarded in 2024) Best Paper Award (2022) Best Full Paper Award (2015) Honorable Mention Award (2014) Best Paper Presentation award (2010) As an educator, Hosio has taught Human-Computer Interaction (2019-2025) to over 260 students in 2024, Social Computing (2018-2021) to approximately 60 students annually, and Applied Computing (2015-2018) to around 50 students each year. His research group's nearly two million USD in secured funding demonstrates significant grant acquisition success, supporting innovative work at the intersection of crowd computing, health technology, and human-centered AI systems. The Crowd Computing Research Group, founded by Hosio in 2020, represents a significant research infrastructure focused on advancing methodologies for crowd-powered systems. The group's work spans from fundamental research on crowd labor markets to applied projects in healthcare, workplace well-being, and social computing, demonstrating a strong commitment to both theoretical advancement and practical impact.
Maria Luce Lupetti is a Fixed-term Assistant Professor at the Department of Architecture and Design (DAD) at Politecnico di Torino . She actively contributes to the College of Architecture and Design and is a member of the College of Mechanical, Aerospace and Automotive Engineering . Her academic role involves teaching Cognitive Ergonomics and HMI in the Automotive Engineering master's program and collaborating on Design and Communication courses. Editorial Roles: Associate Editor for the journal INTERACTIONS since 2024 Conference Leadership: Participating in organizing committee for CHI 2026 Research Projects: Scientific Director of PARJAI (2025-2028) on Participatory Design Justice for Ethical AI Transitions Research Focus : Maria's work bridges Design with Human-Robot Interaction , focusing on Ethical AI , Speculative Design , and Urban Robotics . Her recent publications explore: Speculative design for sustainable digital futures Contextual adaptability challenges in urban robotics Design taxonomies for demystifying AI Trustworthy embodied agents in healthcare Creative applications of AI in HRI Ethical frameworks for energy futures Design Philosophy : She emphasizes participatory approaches, critical design thinking, and transdisciplinary collaboration to address societal challenges at the intersection of technology, ethics, and human factors. Her work appears in leading venues including CHI , HRI , and Frontiers in Neurorobotics , while also contributing to edited volumes and journal special issues.
