Céline Castets-Renard is a Professor at the University of Toulouse Capitole (UT-Capitole), affiliated with the European School of Law and the IRDEIC research institute. She holds a Junior Membership at the University Institute of France (IUF) and serves as Deputy Director of IRDEIC and Director of the Master's program in Law and Computer Science (Digital Lawyers). Her work focuses on digital law, cybersecurity, AI ethics, and EU regulatory frameworks. She has held visiting roles at Yale Law School (ISP Fellow) and Fordham Law School. Research projects include predictive policing risks (funded by the French Ministry of the Interior) and EU data protection norms. She frequently publishes on topics like algorithmic transparency, cross-border data flows, and digital market governance. Education and training details are not explicitly provided in the text. Administrative roles include leading research initiatives and academic programs. Advising students and supervising research projects are part of her responsibilities. Her expertise spans legal-jury roles and organizational leadership in conferences. Blogs and public commentary address emerging issues like AI's impact on professions and digital financial education.
Prof. Katerina Rose is a Professor of Clothing Technology & CAD at Reutlingen University's TEXOVERSUM School of Textiles. She leads the Department of Clothing Technology and CAD, focusing on innovative textile design methodologies. Her expertise includes 3D pattern construction, technical textiles manufacturing, and AI-driven garment design. Her research emphasizes digital avatars for clothing simulation, low-cost 3D scanning technologies, and soft tissue modeling. Notable projects include the InBiO initiative for bio-based automotive interiors and the FLAX365 regional textile chain initiative. She holds a patent (DE102020119338A1) for patternless garment cutting methods. Teaching areas: Clothing Technology, CAD Pattern Construction, Technical Textiles Manufacturing Labs: Sewing & CAD Lab, Material Testing Lab, Electronic Textiles Lab Key collaborations: Hometrica Consulting, SciTePress, Browzwear Recent work explores microwave imaging for body dimension capture and AI-enhanced pattern generation, published in journals like Communications in Development and Assembling of Textile Products. Her contributions bridge textile engineering with digital innovation in fashion and medical applications.
Dr. Sebastiaan Breedveld is an Associate Professor in the Department of Radiation Oncology at Erasmus University Medical Center in Rotterdam, The Netherlands. His research focuses on improving radiation therapy through applied mathematics, multi-criteria optimization, and automated treatment planning. He developed the clinically implemented Erasmus-iCycle algorithm and leads projects like TROTS (Radiotherapy Optimisation Test Set). Education includes a PhD in Medical Physics (cum laude) from Erasmus University Rotterdam and an MSc in Applied Mathematics from Delft University of Technology. Research interests center on developing computational methods for real-time, personalized radiotherapy. Key areas include: Large-scale optimization for treatment planning Deep learning for instantaneous dose prediction Multi-criteria decision analysis for balancing treatment trade-offs Proton therapy and VMAT techniques High-performance computing applications Recent publications demonstrate strong focus on AI-driven automation in brachytherapy, IMPT robustness, and FLASH proton therapy. Work consistently addresses clinical translation of mathematical optimization. Significant scientific awards include: NWO Vidi Grant (2021) for INSTORAD project NWO Veni Grant (2016) MCDM Doctoral Dissertation Award (2015) Erasmus MC Efficiency Grant (2015) PhD with honors (2013) Leads research group supervising 2 post-docs, 11 PhD students, and numerous master/bachelor projects. Major grants support work in proton therapy optimization and automated planning systems. Developed TROTS dataset for benchmarking radiotherapy optimization algorithms and collaborates internationally through projects like BiCycle for automated brachytherapy planning.
Tom Holberton is an Associate Professor (Teaching) at The Bartlett School of Architecture, University College London, where he also completed his BSc, DipArch, and MArch degrees. A chartered architect (RIBA ARB) with over ten years of research and teaching experience at UCL's Unit 21, he leads innovative design pedagogy integrating artificial intelligence and computational techniques. His educational background includes: BSc in Architecture from the Bartlett School of Architecture DipArch from the Bartlett School of Architecture MArch from the Bartlett School of Architecture His research pioneers the application of artificial intelligence within architectural design, specifically investigating the latent space of deep-learning models as a novel design medium. This work examines historical computational design paradigms alongside emerging technologies including Generative Adversarial Networks (GAN), Large Language Models (LLM), and crossmodal systems like DALL-E and Midjourney, critically assessing their impact on design agency and creative processes. Analysis of his 94+ publications reveals consistent thematic threads: integration of Japanese cultural elements with Western design practices, sustainable urban interventions in London, and experimental applications of AI in architectural representation. His projects frequently bridge heritage preservation with technological innovation, particularly in exhibitions and public space transformations. His distinguished awards include: RIBA Bronze Medal RIBA Sergeant Award for Excellence in Drawing RIBA Skidmore Owings Merrill Foundation Scholarship Victor Chu Prize Hamilton Prize Faculty Medal As an educator, Holberton serves as Design Unit Tutor for both BSc (UG21) and MArch (PG21) Architecture programmes, Practice Tutor for Design Realisation, and Technical Thesis Tutor. Since 2020, he co-pioneered a vertical teaching model integrating four academic years. His research attracts Arts Council funding for Japanese craft-AI collaborations and additive manufacturing partnerships, with projects featured in Monocle, Wallpaper, and Japanese national media. He founded the research studio SoHoKo (2013) specializing in AI-driven installations and exhibitions for institutions including the Victoria and Albert Museum, British Museum, and Japan House London. As a member of UCL's AI in Education working group, he investigates AI's pedagogical implications while serving as D&AD Awards advisor and judge.
Dr. Umran Ali is a Senior Lecturer at the University of Salford's School of Arts, Media and Creative Technologies. He leads video game design programs and serves as a consultant for higher education institutions globally. His research focuses on virtual natural environment design, blending game design with geography, geology, and landscape architecture. Notable work includes the Virtual Amazon rainforest project (2019), an immersive educational tool for environmental engagement. He holds a PhD in Higher Education Research and Practice (2016) alongside qualifications in Creative Technology and Computer Games. Teaching expertise spans Game Design, Narrative Storytelling, Virtual Reality, and AI. He actively collaborates with organizations like the Landscape Institute and CIEEM, contributing to interdisciplinary projects at the intersection of gaming and environmental science. His research explores how virtual landscapes can reflect real-world ecological systems, with publications examining game ecosystems, architectural level design principles, and the cultural significance of digital landscapes across eras (1990s to modern). Dr. Ali’s work bridges academia and practice through exhibitions like the Virtual Landscapes Exhibition (2017) and conferences presentations on topics like 'Virtual Ecosystems & Video Games' (2018). He is affiliated with the Arts, Media and Communications Research Centre, advancing research into interactive technologies and their societal impacts.
Ahmed Farooq is a University Lecturer and Senior Researcher at the Tampere Unit for Computer-Human Interaction (TAUCHI), Tampere University. He holds a PhD in Computer Science and has over 20 years of experience in haptic and multimodal interaction systems. His current role combines academic teaching (e.g., HI 520 Haptic Interaction course) with advanced research in haptic mediation, wearable technologies, and AI-driven interaction systems. Affiliations: TAUCHI Research Group, Tampere Institute of Advanced Studies (TIAS) Positions: Permanent Lecturer (2024–present), TIAS Postdoctoral Fellow (2022–2024), Visiting Researcher at McGill University (2020) and Purdue University (2019–2020) Research Interests: Haptic signal mediation, 3D-printed haptic waveguides, magnetorheological fluid actuators, AI integration in multimodal systems (e.g., TAUCHI-GPT), and applications in automotive UI, VR/XR, and food interaction systems. His work focuses on reducing driver distraction, enhancing tactile feedback in virtual environments, and developing ethical AI tools. Key Projects: Origo Steering Wheel (German Design Award, 2021), Augmented Eating Experiences (AEE), Huawei Haptic Mediation, and TAUCHI-GPT open-source AI framework. These projects involve collaboration with industry partners like Huawei and Bentley University. Publications: Over 60 peer-reviewed articles in top venues (ACM, IEEE, EuroHaptics) focusing on haptic actuation, multimodal interaction, and AI ethics. Recent work explores prompt injection attacks in LLMs and implantable haptic actuators. Awards: TIAS Postdoctoral Fellowship (2022–2024), Finnish Cultural Foundation Mobility Grant (2018). His research has led to 9 patents (e.g., multifunctional haptic actuator, 2024) and 6 granted US/Japan patents.
Tomislav Kovac is a Professor of Architecture at RMIT University's School of AUD, directing the Advanced Architecture stream. His work spans experimental design, digital environments, and urban planning, with notable projects like the proposal for the New World Trade Centre in New York. He has held roles including Studio Director at die Angewandte (Vienna), Visiting Critic at Columbia University, and Adjunct Professor at RMIT. His firm, Tom Kovac Architecture, collaborates globally on projects such as the Ikon Tower (San Francisco) and Hyper Centre (Toulouse). Awards include the '40 Under 40' by RIBA (1996), multiple RAIA recognitions, and the Louis Poulsen Lighting Design Award (1991). Exhibitions include the Venice Biennale, Centre Pompidou, and FRAC Centre. His research emphasizes parametric design, generative systems, and sustainable urban development. Education: Not explicitly stated in the text. Exhibitions: Over 30 international exhibitions from 2003–2018, including the Venice Biennale, Centre Pompidou, and FRAC Centre. Key Projects: World Trade Center proposal, Alessi Mutants series, and urban planning initiatives. His work bridges architecture, design, and technology, with a focus on innovation and cultural expression. Collaborations span academic institutions and global firms, reflecting his interdisciplinary approach.
Pier Paolo Peruccio is a Full Professor of Design at the Polytechnic University of Turin, affiliated with the Department of Architecture and Design (DAD) and the Future Urban Legacy Lab. He is the Director of the Sydere Center (Systemic Design Research and Education) at the University of Turin and holds leadership roles in international design organizations including the World Design Organization (WDO/ICSID), CUMULUS, PLART Foundation, and the Sustainability and Circular Economy Laboratory at the University of Gastronomic Sciences in Pollenzo. His research interests include the history of design, systemic design, design for sustainability, environmental sustainability, corporate culture, and historical research methodology. He teaches courses such as History of Systems Thinking, History of Design, and Systemic Design Theory and History across various programs including Design and Technology, Management, Production and Design, and Sustainable Design for the Food System. The recent publications of Pier Paolo Peruccio reflect a strong interdisciplinary trend, combining design history with contemporary challenges such as blockchain, AI, circular economy, and sustainable living. His work bridges historical analysis with systemic innovation, focusing on corporate archives, heritage, and behavioral design. He frequently explores themes of sustainability, digital transformation, and the socio-cultural role of design. Member, Board of Directors – World Design Organization (2019–2023) Member, Scientific Committee – PLART Foundation (2020–2028) Member, Inspiration Board – Sustainability and Circular Economy Laboratory, University of Gastronomic Sciences (2021–2027) Full Member – CUMULUS Association (2014–2025) As a supervisor and academic leader, Peruccio has guided numerous PhD candidates and led a wide array of research projects funded by public and private institutions. These include initiatives on the heritage of RIVA Shipyards, Olivetti’s corporate identity, smart districts, and sustainable urban development. He has also contributed to major editorial projects, co-directing book series and publishing authoritative volumes on Carlo Mollino and Italian design history. His work emphasizes the integration of historical insight with forward-looking, systemic design solutions. He is actively involved in labs and research centers such as the Sydere Center and the Future Urban Legacy Lab, which foster interdisciplinary collaboration on sustainable and systemic design. These platforms support practice-based research, innovation in design education, and international partnerships aimed at addressing complex socio-ecological challenges through design.
Andrea Tonoli is a Full Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at Polytechnic University of Turin. He serves as Scientific Advisor for partnerships with DAYCO, ITALDESIGN GIUGIARO, and STELLANTIS, and leads Spoke 2 'Sustainable Road Vehicle' at the National Center for Sustainable Mobility (MOST). He coordinates the DIMEAS-LIM research group and participates in CARS and PEIC interdepartmental centers. His teaching includes courses on Car Body Design, Motor Vehicle Design, and Mechatronic Systems Simulation. His research focuses on electric/hybrid powertrain optimization, mechanical design of electric traction machines, energy management in autonomous vehicles, longitudinal/lateral dynamics, virtual sensors for vehicle state estimation, electro-hydraulic/electromechanical shock absorbers, and magnetic bearings. Key subfields include rotordynamic analysis, harmonic finite element modeling for turbines, maglev stability, mechatronic systems for autonomous vehicles, and sustainable transportation. Recent publications emphasize electrodynamic maglev damping, compact assistive knee prostheses, electromagnetic shock absorber testing, and multi-objective optimization of regenerative suspensions. His work spans automotive, aerospace, and biomedical domains with applications to energy recovery, vibration control, and sustainable mobility. He supervises PhD students in mechatronics and automotive engineering, and leads numerous funded research projects including SPHERE (PNRR), MINERVA (PNRR), SmartCorners (EU), and industry collaborations with Hyperloop Transportation Technologies, Dayco Europe, and Racing Force Spa.
Mário Marques Freire is a Full Professor at the University of Beira Interior, where he serves as President of the Faculty of Engineering and Professor in the Department of Computer Science. He is also a Senior Researcher at Instituto de Telecomunicações, leading the Secure and Intelligent Networked Software Systems Lab. His academic qualifications include a Dr. habil. in Computer Science (2007) and Ph.D. in Electrical Engineering (2000) from the University of Beira Interior, complemented by an M.Sc. in Systems and Automation and B.Sc. in Electrical Engineering from the University of Coimbra. Freire's research spans Computer Systems and Networks, with specific expertise in Cloud Systems, Computer Networking, Security & Privacy, and AI applications. His work focuses on infrastructure virtualization, encrypted traffic classification, DDoS attack detection, and malware analysis. Recent publications demonstrate strong emphasis on cloud security, AI-enhanced defect prediction, and IoT security frameworks. He has received several scientific awards including multiple Conference Best Paper Awards and an ACM Certificate of Appreciation for his long-term contributions. Freire has supervised over 35 graduate students (10 PhD graduates) and led numerous research projects such as 'Towards the assurance of SECURity by dESIGN of the Internet of Things' (FCT/COMPETE/FEDER) and 'Cloud Computing Competence Center'. At the University of Beira Interior, Freire has held significant administrative roles including Vice-Rector (2020-2021), Director of Computer Science Services (2009-2013), and multiple terms as President of the Faculty of Engineering. He leads the Network Applications and Services research group at Instituto de Telecomunicações, focusing on secure networked systems.
Mingda Xu is a Research Fellow at the Australian National University (ANU), working in Prof. Stephen Gould's group in collaboration with Seeing Machines Ltd. His research focuses on autonomous systems, combining machine learning, computer vision, and robotics. He holds a PhD in Robotics from Queensland University of Technology (2019–2022) and a Master's in Mathematics from the University of New South Wales (2016–2018). His research interests include unsupervised learning, optimal control, differentiable optimization, and visual localization. Notable projects involve feature-shaping methods for anomaly detection, diffusion models for pose estimation, and model-based control techniques like differentiable dynamic programming. He has also explored applications in visual place recognition and SLAM (Simultaneous Localization and Mapping). Key projects: Unsupervised learning for long-form videos, differentiable DTW for visual place recognition, and discrete-time optimal control. His work has been recognized with a CVPR 2024 Best Paper Award nomination. He collaborates actively with industry and academia, contributing to the development of scalable and robust autonomous systems. He is registered to supervise research students and has contributed to multiple grants focused on advancing robotics and AI technologies. Mingda's lab work integrates robotics, computer vision, and machine learning, emphasizing end-to-end solutions for real-world applications in autonomous systems.
Chuan He is an Assistant Professor in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA) and the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research bridges continuous optimization and machine learning, focusing on algorithmic efficiency and theoretical foundations. Education: Ph.D. in Industrial and Systems Engineering, University of Minnesota, USA (2019–2023) B.S. in School of Mathematical Sciences, Xiamen University, China (2015–2019) His research interests include deep learning, decentralized and large-scale optimization, high-order methods, and applications in healthcare, scientific computing, and engineering. He develops algorithms with strong theoretical guarantees, particularly in nonconvex optimization settings. His work emphasizes improving the speed, reliability, and scalability of machine learning training processes. His recent publications focus on Newton-CG based methods, augmented Lagrangian techniques, and federated learning under constraints. These contributions span top journals in operations research, optimization, and machine learning, reflecting a strong trend toward integrating second-order optimization with practical machine learning challenges. Scientific Awards: No awards explicitly mentioned in the text. Chuan He advises and collaborates within the WASP Mathematics research environment, particularly in the 'Optimisation for machine learning' group. He previously held a postdoctoral position at the University of Minnesota under Professor Ju Sun. His research is supported through institutional affiliations with WASP and Linköping University. He actively contributes to the academic community through conference presentations at INFORMS, SIAM, and NeurIPS workshops. He is involved in the 'Optimisation for machine learning' research group at MAI, which aims to develop more efficient and theoretically sound algorithms for machine learning. The group focuses on replacing heuristic methods with principled approaches, reducing computational costs in training models.
Nicolo Colombo is a Senior Lecturer in Computer Science at the Department of Computer Science, Royal Holloway, University of London. He is affiliated with the Centre for Intelligent Systems and Centre for Reliable Machine Learning. His research focuses on machine learning, statistics, physics, and their applications in areas like gravitational wave detection, uncertainty quantification, and conformal prediction. Colombo collaborates internationally on projects involving astrophysical signal analysis and algorithm development. He leads or co-leads two research projects: 'Revealing the secrets of neutron-star interiors with AI and the SKA' (Medical Research Council MRC-funded, 2024-2028) and the Turing Network Development Award (Alan Turing Institute, 2022). His work contributes to UN Sustainable Development Goals through technological advancements in machine learning and data science. Education: Ph.D. in relevant field (details not specified in text) Colombo's research interests span neural networks, probabilistic modeling, and their applications in astrophysics and energy systems. His recent work emphasizes conformal prediction for improving algorithm reliability in gravitational wave detection and route planning. His articles explore cutting-edge techniques like normalizing flows, graph autoencoders, and uncertainty calibration. Notable collaborations include work with institutions like the Alan Turing Institute and international researchers in physics and computer science. He has secured grants from major funding bodies and actively participates in academic networks. His labs focus on advancing AI methodologies for real-world challenges in science and engineering.
Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he has been faculty since 2017 (initially as Assistant Professor until 2022). He also holds a position as Senior Principal Researcher at Microsoft AI since 2024, having previously served as Principal Researcher (2022-2024) and Visiting Researcher (2018-2022) at Microsoft Research. His academic career spans prestigious institutions including MIT, where he completed his PhD in Mathematics. 2024-Now: Member of Technical staff / Senior Principal Researcher in Microsoft AI 2022-2024: Principal Researcher in Microsoft Research 2022-Now: Associate Professor in University of Washington 2017-2022: Assistant Professor in University of Washington 2018-2022: Visiting Researcher in Microsoft Research 2016-2017: Postdoc in Microsoft Research Dr. Lee received his PhD in Mathematics from MIT (2012-2016) and his undergraduate degree in Mathematics from the Chinese University of Hong Kong (2008-2012). His exceptional academic journey was recognized with the MIT Presidential Fellowship and the Charles W. and Jennifer C. Johnson Prize. Lee's research fundamentally advances algorithms across multiple domains, particularly in convex optimization, convex geometry, spectral graph theory, and online algorithms. His work bridges continuous and discrete mathematics to develop state-of-the-art algorithms for fundamental problems in computer science and optimization. Notably, he has developed breakthrough approaches for linear programming, maximum flow problems, and optimization in high-dimensional spaces. His research has evolved from foundational theoretical work to more applied areas including differential privacy and connections to machine learning. Analysis of his recent publications reveals a strong trajectory toward practical applications of theoretical optimization, with significant contributions to differentially private machine learning, efficient sampling methods, and connections between optimization theory and deep learning. His work consistently demonstrates how deep theoretical insights can yield practical algorithmic improvements across computer science. Lee's exceptional contributions have been recognized with numerous prestigious awards including the Packard Fellowship, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, A.W. Tucker Prize, and multiple Best Paper Awards at top theoretical computer science conferences (FOCS, SODA, NeurIPS). He has also received the NSF CAREER Award and MIT's Sprowls Award for his doctoral thesis. Packard Fellowship (2020) Sloan Research Fellowship (2020) Microsoft Research Faculty Fellowship (2019) Best Paper Awards at FOCS, SODA, and NeurIPS A.W. Tucker Prize NSF CAREER Award As an advisor, Lee has mentored PhD students including Haotian Jiang, whose work earned a Best Student Paper award at SODA. His research has been supported by significant grants from NSF and Microsoft Research. Lee actively contributes to the academic community through service on program committees for FOCS, SODA, and other major conferences, as well as organizing workshops on continuous approaches to discrete optimization. He has also taught graduate courses including Theory of Optimization and Continuous Algorithms and undergraduate courses on algorithms. Lee's work bridges theoretical computer science and practical applications, with his recent research expanding into differential privacy for machine learning and connections between optimization theory and deep learning. His collaborative work spans institutions including MIT, Microsoft Research, and the University of Washington, reflecting his position at the intersection of theoretical and applied computer science.
Clemens Cavallin is a Professor at the NLA University College , specifically within the Kalfaret campus in Bergen. His academic work spans Theology, Religious Studies, Philosophy , and the intersection of Artificial Intelligence and Creativity . He holds a Ph.D. in History of Religions from the University of Gothenburg, Sweden, and his research focuses on ethical, philosophical, and pedagogical dimensions of religion and technology. Research Interests : Christian humanism and AI ethics Religious education in early childhood Phenomenology and spirituality Literary analysis of religious themes (e.g., Michael D. O’Brien’s works) Recent Publications (2022–2024) explore topics such as: The ethical implications of AI in education Spirituality in artistic practice Thomistic models of perfection in Christian life Comparative studies of preschool religious curricula in Nordic countries He leads the MishMash - Centre for AI & Creativity (2026), focusing on creative AI applications in education, and has contributed extensively to debates on globalization, religious studies, and Hinduism’s academic portrayal.
