Simon Masnou is a Full Professor at Université Claude Bernard Lyon 1, affiliated with the Institut Camille Jordan (CNRS UMR 5208). He holds leadership roles as Head of the 'Applied Mathematics, Statistics' Master's degree and Head of the 'M2 Maths in Action' program. Previously, he served as Director of the Camille Jordan Institute (2018-2022). His research focuses on applied mathematics, image processing, shape optimization, and geometric measure theory, with contributions to variational models, geometric flows, and applications in computer vision and materials science. Education: PhD in Mathematics (1998, Paris Dauphine) and HDR (2008, Paris 6). Research projects include ANR STOIQUES (2024-2028), PEPR PDE-AI (2023-2028), and collaborations with industry on topics like defect prediction in aluminum production and high-dimensional data analysis. Teaching includes courses on linear algebra, optimization, and machine learning at undergraduate and graduate levels. Key contributions span phase field models, varifold-based surface approximation, and image inpainting. He supervises PhD students in geometric variational problems and computational methods. His work bridges theoretical mathematics with industrial challenges, addressing issues in materials science, medical imaging, and cultural heritage preservation.
Lily Rui Liang is a Professor and Director of the MSCS Program in the Department of Computer Science and Information Technology at the University of the District of Columbia (UDC), School of Engineering and Applied Sciences. She joined UDC in 2004 after completing her Ph.D. at the University of Nevada, Reno. Her educational background includes: Ph.D. in Computer Science and Engineering, University of Nevada, Reno Dr. Liang's research spans cybersecurity , digital image processing , artificial intelligence , and computer science education , with a dedicated focus on broadening participation in computing . Her technical work includes deepfake detection and reinforcement learning for cybersecurity, while her educational innovations develop inclusive curricula for commuter and underrepresented students through Minecraft, robotics, and service learning. Analysis of her recent publications (2024-2018) shows a strategic evolution from core AI/image processing research toward educational interventions, particularly addressing commuter student engagement in urban settings while maintaining technical contributions in multimedia security and deep learning. Her scientific awards and honors include: Fellow of Center for the Advancement of STEM Leadership (CASL) (2019-2020) Fellow of Opportunities for UnderRepresented Scholars (OURS) (2014) Outstanding University Service Award from School of Engineering and Applied Sciences, UDC (2014) Myrtilla Miner Faculty Fellow (2012-2013) Frontiers of Engineering Education (FOEE) Conference participant, NAE (2011) Project Kaleidoscope (PKAL) Summer Leadership Institute participant (2011) Preparing Critical Faculty for the Future (PCFF) program participant (2011) Dr. Liang serves as Co-PI on multiple NSF grants including the UDC-CSEC-ENGAGE Project ($399,924, 2024-2027) for cybersecurity workforce development, CUE-T: HBCU Learning Community ($654,004, 2023-2026), and the AI-CyS Research Partnership ($152,350, 2021-2024) with six HBCUs and national labs. Her mentorship is evidenced through extensive curriculum development projects targeting K-12 and undergraduate students, particularly women and commuters. She leads the MSCS program and coordinates the AI-CyS consortium researching video authentication and autonomous cybersecurity agents, establishing UDC as a hub for HBCU cybersecurity education and AI research.
Raquel Iniesta is a Reader in Statistical Learning for Precision Medicine at King's College London, leading the Fair Modelling and TDA lab within the Department of Biostatistics & Health Informatics. Her expertise spans mathematics, statistics, and machine learning applied to precision medicine, with a focus on ethical AI integration in healthcare. She teaches advanced machine learning and statistical modeling courses and actively engages in scientific communication through workshops and media outreach. Her research emphasizes developing transparent AI models for personalized medicine, particularly in depression, hypertension, and neurodegenerative diseases. Notable contributions include studies on fasciculation analysis in ALS and ethical frameworks for healthcare AI. Publications highlight interdisciplinary approaches, combining machine learning with clinical and genetic data to improve predictive models. Dr. Iniesta leads educational initiatives, including the Machine Learning module and Introduction to Statistics programs, and contributes to public engagement by designing digital content and managing social media for research dissemination.
Professor Rachel Harrison is a Professor in Computer Science at the School of Engineering, Computing and Mathematics, Oxford Brookes University. Her research focuses on software metrics, machine learning, and requirements engineering with emphasis on empirical and automated software engineering solutions. She has over 160 publications and extensive industry collaborations with organizations like IBM and Philips Research Labs. Her work has been recognized through roles as Editor-in-Chief of the Software Quality Journal and leadership in conferences such as ICSE and ESEM. She leads the Dependable System Engineering Centre (DSERC) and is part of the Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute and the Applied Software Engineering and Data Analytics (ASEDA) Group. Her research projects include AI applications for big data analysis (AIMi), automated review classification (ReClass), and software quality improvement (SEQUIN). Professor Harrison has served on over 50 international program committees and initiated workshops like RAISE and AIRE. Her teaching includes advanced computer science modules and leadership in courses like Essential Maths for University Study and Advanced Software Development . Her work bridges academic research and practical applications, particularly in healthcare technology (e.g., diabetes management systems) and mobile application usability. She advocates for rigorous software quality practices and has contributed to frameworks for requirements validation and risk assessment in software projects.
Dr hab. Krzysztof Węcel serves as Professor and current Head of the Department of Economic Informatics at Poznan University of Economics and Business (UEP), appointed on October 4, 2024. His primary affiliation spans over 25 years with UEP's Department of Economic Informatics, which maintains one of Poland's longest-running academic websites since 1998. He holds dual recognition through habilitation from University of Potsdam (2020) and professorship conferred by UEP (June 24, 2020). His academic milestones: Habilitation degree in Economic Informatics, University of Potsdam (2020) Professor title, Poznan University of Economics and Business (2020) Węcel's research centers on Semantic Technologies and data quality assessment across multilingual Wikipedia, with emphasis on company information verification, citation analysis, and open data applications. His work bridges Big Data analytics with practical business solutions, particularly in maritime logistics where he pioneered evolutionary algorithm-based AIS data processing. Current investigations focus on generative AI's dual role in creating and combating disinformation, including ChatGPT's impact on academic writing and fake news propagation. Recent publications (2022-2025) reveal three dominant trends: First, systematic analysis of Wikipedia's reliability across languages during crises like the pandemic and Ukraine war. Second, development of AI-driven fact-checking frameworks (e.g., OpenFact project's CLEF 2023 victory). Third, exploration of generative AI's societal impact ranging from student creativity to disinformation campaigns. Scientific awards received: Best Paper Award at ICIST 2017 Conference Award for most innovative article at NATCON 2018 conference Microsoft Azure for Research Award (2016) As academic advisor, he leads the 'Semantic Technologies' diploma seminar attracting high-achieving students, with participants winning the 29th UEP Foundation Competition (2025) and Eurostat's Web Intelligence Challenge (2024). His grant portfolio includes the 'Maritime Big Brother' project (2017) for ship voyage prediction using AIS data and Microsoft Azure funding for Wikipedia quality enhancement. Ongoing initiatives include OpenFact (fake news detection) and GOBLIN projects. He actively collaborates with SKN Data Science student circle (evidenced by 2024/2025 inaugural meeting) and international consortia like CLEF and QOD workshops. Departmental leadership involves managing the OpenFact research team that achieved top results in CheckThat! Lab competitions, alongside maritime data analytics groups applying evolutionary algorithms to shipping networks.
Joost Batenburg is a Professor at Leiden Institute of Advanced Computer Science (LIACS) , with a chair in Imaging and Visualization . He is affiliated with the Centrum Wiskunde & Informatica (CWI) and serves as Program Director for the interdisciplinary Society, Artificial Intelligence and Life Sciences (SAILS) initiative. His research focuses on tomographic image processing and reconstruction , where he has published over 80 journal articles and 60 conference papers. Current projects include Universal Three-dimensiOnal Passport for process Individualization in Agriculture (UTOPIA) and Center for Optimal, Real-Time Machine Studies of the Explosive Universe (CORTEX) , both funded by NWO grants. He leads the FleX-Ray Lab , a custom CT system integrated with advanced data processing algorithms. His research spans discrete tomography , real-time imaging pipelines , and AI-enhanced reconstruction methods , with applications in industrial inspection, agricultural analysis, and cultural heritage conservation. Recent articles demonstrate novel approaches to: Single-shot dynamic object tomography using level-set methods and motion modeling X-ray scattering quantification for defect detection in real-time systems Cross-modal image registration between CT scans and physical photographs Auto-differentiation in CT workflows combining classical and machine learning algorithms Scientific Awards: Dutch Award for ICT Research (2018) C.J. Kok Prize (2007) Philips Mathematics Prize (2006) He has supervised numerous PhD candidates including Mary Go, Eani Lachmansingh, and Zhichao Zhong, while maintaining editorial roles at IEEE Transactions on Computational Imaging and Journal of Mathematical Imaging and Vision . His work bridges theoretical mathematics with practical applications in agriculture, industry, and art conservation.
Ronghua Xu is an Assistant Professor at the Department of Applied Computing, Michigan Technological University, specializing in blockchain, IoT, and edge computing. He is a member of the ICC Center for Cybersecurity. Ph.D. (2023), M.S. (2018) in Electrical and Computer Engineering, Binghamton University M.S. (2010) in Mechanical and Electrical Engineering, Nanjing University of Aeronautics & Astronautics B.S. (2007) in Mechanical Engineering, Nanjing University of Science & Technology His research focuses on decentralized security networks, NextG network intelligence, and blockchain applications in IoT systems. Key themes include scalability, interoperability, and resilience in smart vehicular and urban air mobility networks. Recent publications highlight blockchain-enabled architectures for secure data access, federated learning, and edge resource management. Awards include the Graduate Student Excellence Award (2023) and ICC Rapid Seeding Awards (2024). Ronghua Xu actively seeks self-motivated Ph.D. students for his research group. He previously worked at Siemens (2010–2016) on software development and system integration.
Sofie Haesaert is an Assistant Professor in the Control Systems group at the Department of Electrical Engineering, Eindhoven University of Technology. Her work focuses on formal verification and control synthesis methods for cyber-physical systems, particularly through stochastic simulation relations and temporal logic specifications. Education: BSc (cum laude) and MSc (cum laude) in Mechanical Engineering and Systems & Control from Delft University of Technology; PhD from Eindhoven University of Technology (2017) Experience: Postdoctoral researcher at Caltech (2017-2018), then returned to TU/e as Assistant Professor Her research interests include: Cyber-physical systems verification Stochastic control methods Temporal logic specification Markov decision processes Formal methods in control engineering Model abstractions and simulation relations Recent publications show strong focus on: Stochastic temporal logic control Robust and risk-aware control Multi-agent system verification Formal synthesis via simulation relations AI integration in control systems Software tools for formal control Scientific achievements: Veni Grant recipient (2020) Co-developer of the SySCoRe toolset for stochastic control synthesis Contributor to formal verification benchmarks through ARCH-COMP reports She contributes to education through courses on: Control principles for engineered systems Control challenges in autonomous racing Supervisory control of cyber-physical systems Haesaert collaborates across disciplines including computer science, applied mathematics, and robotics, with over 750 citations and significant contributions to formal control theory for stochastic systems. Her work bridges theoretical developments with practical applications in autonomous systems and complex control architectures.
Christian Wolff is a University Professor and Chair of Media Informatics at the Institute for Information and Media, Language and Culture at the University of Regensburg. Since April 2022, he has served as the founding Dean of the Faculty of Computer Science and Data Science, while maintaining secondary membership in the Faculty of Languages, Literature and Cultural Studies (SLK). His academic career spans over three decades with significant contributions to multiple disciplines at the intersection of computer science and humanities. Wolff's research interests center around multimedia and multimodal information systems, electronic publishing, and text technology, particularly text mining. His work bridges computer science with digital humanities, legal informatics, and social media analysis. Recent publications demonstrate a strong focus on large language models, sentiment analysis applications across various domains, legal technology innovations, and virtual reality research for cognitive studies. His interdisciplinary approach has produced significant contributions in both technical and humanities domains. His recent publication trends reveal a strategic shift toward applied AI research, particularly in legal technology (LegalTech), social media analysis, and sentiment analysis using large language models. The publications show increasing collaboration across disciplines, connecting computer science with law, political science, literature, and psychology. His work on the digital basis document for legal proceedings represents a major practical application of his research in the German justice system. East Bavarian Cultural Prize Doctoral Award of the University of Regensburg Wolff has led numerous interdisciplinary research projects connecting computer science with humanities and legal studies. His leadership extends to institutional roles including Dean of Research, Vice Dean, and Dean of Faculty positions. He has been instrumental in establishing the new Faculty of Computer Science and Data Science at the University of Regensburg, demonstrating significant impact on institutional development and research infrastructure. Wolff directs research initiatives focused on text technology, digital humanities, and legal informatics. His work with the INDIGO - Internet and Digitization Eastern Bavaria initiative and the TRIO project demonstrates commitment to regional technology transfer and innovation. The interdisciplinary nature of his research groups connects computer scientists with legal scholars, linguists, and social scientists to address complex digital transformation challenges.
Payam Barnaghi is a Professor and Chair in Machine Intelligence Applied to Medicine at Imperial College London's Department of Brain Sciences, part of the Faculty of Medicine. He holds multiple leadership roles, including Co-Director of the School of Convergence Science in Human and Artificial Intelligence and Deputy Head of Neurology. His research focuses on AI-driven healthcare solutions, particularly in neurosciences and dementia care. He leads the Translational Machine Intelligence group at the UK Dementia Research Institute (UK DRI) and is a Visiting Professor at University College London's Institute of Child Health. His affiliations include the NVIDIA Deep Learning Institute, the British Heart Foundation Centre for Research Excellence, and the UK DRI Care and Research Technology Centre. He has received awards such as the Wellcome Trust Mental Health Ideathon Award (2023) and the IEEE Outstanding Leadership Award (2017). His work emphasizes remote patient monitoring, digital biomarkers, and explainable AI for early health event detection. Key projects include the TIHM (Technology Integrated Health Management) initiative for dementia care, leveraging wearable sensors and machine learning. He contributes to interdisciplinary efforts in smart care ethics and has published extensively on topics like neural network applications, healthcare data analysis, and clinical decision support systems.
Robert M. Westervelt is the Mallinckrodt Professor of Applied Physics and Physics at Harvard University, holding dual appointments in the Department of Applied Physics and the Department of Physics. He directs the NSF Science and Technology Center for Integrated Quantum Materials and the Center for Nanoscale Systems. His research focuses on quantum materials, nanoelectronics, and biomedical microfluidics. Education: PhD in Physics from University of California, Berkeley (1977), followed by a postdoctoral appointment at Berkeley before joining Harvard. Research Interests: His work includes scanning probe microscopy of nanostructures, programmable microfluidic chips for cell manipulation, and quantum engineering using atomic-layer materials and topological insulators. Current projects aim to develop atomic-scale electronics and photonic devices using quantum materials like diamond nitrogen vacancy centers. Grants/Recognition: NSF grants support his quantum engineering and AI initiatives. The Center for Nanoscale Systems provides advanced nanofabrication facilities under his leadership. Labs/Teams: Directs the Westervelt Research Group and oversees shared facilities at the Center for Nanoscale Systems, fostering interdisciplinary collaboration in nanotechnology and quantum science.
Stephanie Käs is a Researcher at RWTH Aachen University specializing in Human Pose Estimation (HPE) and gesture recognition using CNN-based methods and Video Language Models applied to fisheye imagery. Her interdisciplinary background spans particle physics and railway engineering data science projects, with strong emphasis on science communication and agile project management. Her research focuses on overcoming challenges in 3D human pose estimation from distorted fisheye images, temporal consistency in motion recognition, and gesture-based human-robot interaction. She actively develops novel approaches for monocular 3D pose estimation and foundation model applications in robotics, with contributions to datasets like FISHnCHIPS for fisheye image analysis. Stephanie supervises multiple ongoing theses including motion recognition, visual anonymization, and anatomical realism evaluation in AI-generated imagery. She leads the Stratospheric Balloon Research Project (StratoGI) at JLU Gießen and has extensive teaching experience in machine learning, computer vision, and statistics at RWTH Aachen and JLU Gießen.
Dr. Marc Schmitt serves as a Research Associate in the Department of Computer Science at the University of Oxford while concurrently leading as Managing Director of the DEIM Research Institute in Germany. His interdisciplinary work bridges academic research and industry applications across artificial intelligence, cybersecurity, and financial systems. Academic Background: PhD in Computer and Information Sciences (AI in Finance), University of Strathclyde MSc in Quantitative Finance, University of Strathclyde MSc in Software Engineering, University of Oxford BA in Business Administration, Technische Hochschule Nürnberg Georg Simon Ohm Dr. Schmitt's research focuses on AI-driven decision-making at the intersection of finance, business analytics, and cybersecurity. His work examines how intelligent systems integrate into organizational structures while addressing systemic risks in digital ecosystems. Recent investigations include generative AI threats in social engineering, no-code AutoML applications, and policy frameworks for AI-enhanced security systems. His publications demonstrate consistent methodological innovation across theoretical and applied domains. Analysis of his publication trajectory reveals growing emphasis on generative AI security implications (2024-2025), with foundational work in business analytics applications (2023). The research shows strong interdisciplinary connections between computer science, financial economics, and human-centered design principles, reflecting his unique background spanning technical and business domains. Prior to academia, Dr. Schmitt held strategic positions including Senior IT Partner for Equity Finance at Siemens Financial Services and management consulting roles at d-fine and Deloitte, where he advised Fortune 500 companies on digital transformation and risk management. His industry experience directly informs his research approach, emphasizing practical implementation challenges alongside theoretical innovation.
David O'Brien is an Assistant Professor at Tulane University's Murphy Institute and Department of Philosophy , with an additional appointment as Assistant Professor in the Department of Educational Policy Studies at the University of Wisconsin-Madison. He holds a PhD in Philosophy from the University of Wisconsin-Madison (2019) and served as a Faculty Fellow-in-Residence at Harvard University's Edmond & Lily Safra Center for Ethics (2022-2023). His research focuses on political philosophy , normative ethics , applied ethics , and the philosophy of education , with notable work on: Egalitarian principles in higher education Feminist critiques of abortion ethics Algorithmic fairness and machine learning Parental partiality and distributive justice Conservatism in axiological theory Recent publications explore egalitarian machine learning (2023), fairness in abortion ethics (2023), and levelling down objections in nonconsequentialist frameworks (2018). He has received recognition through peer-reviewed citations and Harvard's fellowship (2022-2023). Email: dobrien10@tulane.edu
Fernando Manuel Marques Batista is an Associate Professor at ISCTE – University Institute of Lisbon, Department of Information Science and Technology, and an integrated researcher at INESC-ID Lisbon. He serves as the Executive Coordinator of the Human Language Technologies (HLT) Scientific Area at INESC-ID and is a member of its Scientific Council. He previously held leadership roles including President of the Pedagogical Council of ISCTE-IUL (2017–2019) and member of its Standing Committee (2015–2017). Research Interests: Natural Language Processing Machine Learning Text and Speech Processing Sentiment and Emotion Analysis Hate Speech Detection Social Media Analytics Automatic Speech Recognition and Transcription His recent publications reflect a strong focus on applying NLP and machine learning to social media, with particular emphasis on hate speech detection, sentiment analysis, and user behavior modeling. He has also contributed significantly to speech processing, including punctuation restoration and prosody modeling, and to digital humanities through medieval text analysis. His work spans both technical innovation and real-world applications in tourism, finance, and public discourse. Scientific Recognition: Senior Member of IEEE (since 2016) Member of ISCA (International Speech Communication Association) Fernando Batista actively advises numerous PhD and Master’s students, supervising research in areas such as generative AI, hate speech detection, sentiment analysis, and economic forecasting. He has coordinated research projects like SPEDIAL and AppRecommender and is involved in organizing major conferences including PROPOR, EAMT, IPMU, and the Lisbon Machine Learning Summer School (LxMLS), where he has served in editorial and technical roles. Research Labs and Teams: He is a key member of the HLT@INESC-ID research group, contributing to its leadership and scientific direction. This group focuses on human language technologies, including speech processing, natural language understanding, and multilingual systems.
