Dr. Kevin J Liang is a Research Scientist at Meta Platforms, Inc. , specializing in Deep Learning , Computer Vision , and 3D Reconstruction . He earned his PhD in Electrical & Computer Engineering from Duke University in 2020, with a dissertation on Deep Automatic Threat Recognition for Airport X-Ray Baggage Screening . His research focuses include: 3D Computer Vision (ICON, Fast3R) Few-Shot Learning (Sylph, HyperMix) Federated Learning (WAFFLe) Object Detection (EgoTracks, Self-Supervised Methods) Recent publications demonstrate his leadership in Egocentric Vision (Ego-Exo4D) and Transformer Applications (GliTr). He has received numerous awards including the E Bayard Halsted Fellowship (2017) and Summa cum laude (2015), and serves on program committees for major conferences like NeurIPS and CVPR . As an educator, he developed and taught tutorials for Duke University's Machine Learning School and Coursera courses, covering TensorFlow, PyTorch, and foundational ML concepts for over 600 students.
Matteo Castiglioni is an assistant professor (RTD-A) at the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano. He received his PhD in computer science from the same institution under the supervision of Prof. Nicola Gatti. His academic career spans multiple teaching roles across various programs at Politecnico di Milano. Castiglioni's research focuses on the intersection of artificial intelligence, algorithmic game theory, and multi-agent systems. He specializes in combining machine learning techniques with economic paradigms to build strategic agents capable of operating in complex multi-agent environments. His work addresses fundamental challenges in contract theory, mechanism design, and strategic decision-making under uncertainty. His publication record shows a clear trajectory toward increasingly sophisticated models that integrate learning with strategic behavior. Recent papers demonstrate expertise in constrained optimization, regret minimization, and handling both stochastic and adversarial environments. His work bridges theoretical computer science with practical applications in economics and market design. Castiglioni has taught across multiple academic levels including B.Sc., M.Sc., and Ph.D. programs. He has served as both professor and teaching assistant for courses in Game Theory, Online Learning Applications, and Computer Science and Engineering programs.
Vadym Yermolayev serves as a Professor at the Department of Computer Science and Information Technology within the Faculty of Applied Sciences at Ukrainian Catholic University (UCU). He leads UCU's PhD program in Intelligent Systems and holds an Honorary Professorship at Kherson State University. His academic work focuses on semantic technologies, ontology engineering, and knowledge graph construction, with active participation in international research projects and organizations like ACM and ELLIS. Professor of Semantic Technologies Head of PhD Program in Intelligent Systems Honorary Professor at Kherson State University Member of ACM and ELLIS Research Interests span semantic technologies, ontology engineering, and knowledge representation, with applications in education, industrial analytics, and anti-corruption systems. His work integrates machine learning with formal knowledge modeling and develops frameworks for knowledge ecosystem dynamics. Scientific Contributions include leading research groups at Zaporizhia National University and collaborating on European Commission-funded projects. He has published extensively in ICTERI conference proceedings and developed methodologies for terminology saturation analysis and ontology alignment. Honorary Professor at Kherson State University Member of ACM (Association for Computing Machinery) Member of ELLIS (European Laboratory for Learning and Intelligent Systems) Professional Involvement includes external expert roles for European Commission programs (FP6, FP7, H2020) and industrial consulting with Cadence Design Systems GmbH.
Carlos Gómez Rodríguez is a Full Professor (Catedrático de Universidad) in the Department of Computer Science at the University of A Coruña, Spain, where he leads the FASTPARSE Lab within the LyS Research Group at the CITIC Research Center. His work bridges theoretical and practical aspects of computational linguistics with significant applications in natural language processing. His primary research interests focus on natural language parsing algorithms, with particular emphasis on improving parsing speed for web-scale applications, handling non-projective dependency structures, analyzing morphologically-rich and low-resource languages, and exploring the cognitive aspects of syntax. He maintains a critical perspective on the field's current LLM-driven revolution, advocating for continued research into alternative approaches that prioritize efficiency, explainability, and scientific insight beyond user-facing applications. His recent publications reveal a strong trend toward examining the capabilities and limitations of large language models, particularly in comparison to human language processing, while continuing to advance traditional parsing techniques. His work spans theoretical foundations, practical implementations, and diverse applications including sentiment analysis, opinion mining, and creative writing evaluation. National Young Researcher Price "María Andresa Casamayor" in Mathematics and Information Technologies by Spain's Ministry of Science Honorary Member of the Royal Spanish Mathematical Society CAEPIA 2024 Best Paper Award for research on evaluating creative writing in LLMs Professor Gómez Rodríguez has successfully led significant research projects including the ERC Starting Grant project FASTPARSE, which focused on techniques to improve the speed of parsing algorithms. His work demonstrates consistent funding support for innovative research at the intersection of computational linguistics, cognitive science, and practical NLP applications. He actively collaborates with researchers across Spain and internationally, as evidenced by his extensive publication record with diverse co-authors. He leads the FASTPARSE Lab and is a core member of the LyS Research Group, focusing on advancing both theoretical understanding and practical implementations of language processing technologies. His team explores diverse aspects of natural language processing, from fundamental syntactic analysis to applied sentiment analysis and innovative applications of language technology in domains like healthcare.
Giovanni Iacca is an Associate Professor at the Department of Information Engineering and Computer Science (DISI) of the University of Trento, Italy, where he leads the Distributed Intelligence and Optimization Lab (DIOL). He serves as Coordinator of the Master's Degree in Computer Science and Deputy Director of the Information Engineering and Computer Science Doctoral School. Dr. Iacca has over 15 years of industrial experience in mechatronics and optimization applied to engineering, logistics, and scheduling. Dr. Iacca received his PhD in 2011 from the University of Jyväskylä, Finland, and his MSc in 2006 from the Technical University of Bari, Italy. His academic career includes: 2021-present: Associate Professor, University of Trento 2018-2021: Tenure-track Assistant Professor, University of Trento 2017-2018: Postdoc, RWTH Aachen University, Germany 2013-2016: Postdoc, EPFL and University of Lausanne, Switzerland 2012-2016: Postdoc, INCAS³, The Netherlands Dr. Iacca's research bridges fundamental and applied aspects of artificial intelligence with particular emphasis on evolutionary computation and explainable AI. His work spans machine learning, optimization techniques, distributed systems, and their practical implementations. Recent research directions include federated learning, interpretable reinforcement learning, neural architecture search, and optimization for resource-constrained environments. He teaches courses on Computer Architectures, Introduction to Machine Learning, Bio-Inspired Artificial Intelligence, Optimization Techniques, and AI in Medicine. His publication record demonstrates a strong trend toward developing transparent and efficient AI systems. Recent papers focus on making complex AI models more interpretable while maintaining performance across diverse domains from healthcare to supply chain management. His work on evolutionary approaches to explainable AI has gained significant recognition in the computational intelligence community. Scientific Awards and Editorial Roles EvoApplications Best Paper Award (2017) UKCI AWARENESS Best Paper Award (2012) IEEE CIS Outstanding Student-Paper Award (2011) IEEE Senior Member (2023) Associate Editor, Evolutionary Intelligence (2024) Editorial Board Member, Memetic Computing (2024) Associate Editor, IEEE Transactions on Evolutionary Computation (2023) Dr. Iacca has successfully supervised multiple PhD students including Andrea Ferigo, Hyunho Mo, and Leonardo Lucio Custode. His research is supported by various grants and collaborations with industry partners like MyAv. He serves as chair for PPSN 2026 and has organized workshops including the Workshop on Awareness and Consciousness in Artificial Intelligence (ACAI). As leader of the Distributed Intelligence and Optimization Lab (DIOL), Dr. Iacca oversees a research team working at the intersection of evolutionary computation, machine learning, and distributed systems. The lab focuses on developing novel algorithms that balance computational efficiency with interpretability, with applications spanning from embedded systems to large-scale distributed computing environments. Current projects include interpretable reinforcement learning, federated neuroevolution, and optimization for edge computing.
Professor Bernhard Kainz is a leading academic in the Department of Computing at Imperial College London and Professor at Friedrich-Alexander-University Erlangen-Nuremberg. He heads the Image Data Exploration and Analysis Lab (IDEA Lab) and co-leads the Biomedical Image Analysis (BioMedIA) group , focusing on human-in-the-loop computing for healthcare applications. His research develops intelligent algorithms for multi-modal healthcare , emphasizing generative and discriminative machine learning methods to enhance diagnostic decision-making and provide real-time guidance during medical procedures. Current projects address democratizing rare healthcare expertise through AI, normative learning for human-like data analysis, and interpretable medical machine learning. Co-founder of Fraiya Ltd. for medical AI solutions Scientific advisor to ThinkSono Ltd. Leader in EPSRC Centre for Doctoral Training in Smart Medical Imaging and UKRI AI for Healthcare CDT He has received numerous accolades including MICCAI best paper awards, IEEE TMI Distinguished Reviewer status, and Imperial President’s Research Team Award. His team's work spans fetal imaging , cardiac ultrasound , digital pathology , and AI explainability in healthcare.
Dr. Roberto Dinapoli is a Principal Researcher and team leader at the Paul Scherrer Institute (PSI), specializing in microelectronics for high-energy physics and photon science. He earned his PhD from Université Montpellier II and holds a degree in Electronic Engineering from Polytechnic University of Bari. Education: PhD in Microelectronics (Université Montpellier II), Electronic Engineering (Politecnico di Bari) His research focuses on radiation-hard hybrid pixel detectors for synchrotrons and free-electron lasers (XFELs), including the development of EIGER, MÖNCH, MYTHEN III, and DOMINO chips. These systems enable high-resolution imaging, charge integration, and single-photon counting under extreme radiation environments. Recent publications highlight advancements in 25 µm pitch detectors, charge transport simulations using deep learning, and quantum efficiency optimization for soft X-rays. His work impacts applications in synchrotron radiation, nanoelectronics metrology, and high-speed imaging. Key Projects: EIGER (24kHz framing), MÖNCH (25µm pixel), MYTHEN III (50µm strips), DOMINO (2GHz memory) Dinapoli actively collaborates on detector patents licensed to DECTRIS and participates in EUROPRACTICE/CADENCE software management. He leads the Chip Design Competence Team (CDCT) at PSI.
Bingcong Li is a postdoctoral researcher at ETH Zurich collaborating with Prof. Niao He and the ODI group. Previously, they completed doctoral studies at the University of Minnesota under Prof. Georgios B. Giannakis, followed by industry experience focused on large language models (LLMs). Education includes a PhD from the University of Minnesota under Prof. Georgios B. Giannakis. Research centers on making computation efficient, accessible, and affordable across heterogeneous resources—from GPU clusters to consumer hardware—through interdisciplinary approaches combining deep learning, optimization, and signal processing. Key research areas address foundational computing architectures, large-scale system sustainability, and personalized AI access. Their work develops theoretically grounded methods for explainable systems, with recent focus on LLM fine-tuning efficiency. Publication trends show consistent contributions to top conferences (NeurIPS, ICML, ICLR) with emphasis on optimization techniques for resource-constrained LLM deployment. Their advising and grant activities aren't explicitly detailed, though they actively participate in academic service through conference talks (EUROPT 2025, ICASSP 2025) and co-organizing events like the Efficient LLMs Fine-tuning Track at AI+X Summit. Lab affiliation centers on ETH Zurich's ODI group under Prof. Niao He, focusing on optimization-driven AI solutions.
Andrew Lawrence Price is a Canadian Postdoctoral Researcher currently working at École Polytechnique Fédérale de Lausanne (EPFL) in both the Computer Vision Laboratory (CVLAB) within the School of Computer and Communication Sciences and the Education Services Centre (ESC). He holds a PhD in Spacecraft Robotics from Tohoku University, Japan (2019-2024), an MASc in Flight Research from National Research Council and Carleton University, Canada (2013-2015), and a B.Eng in Aerospace from Carleton University, Canada (2009-2013). Dr. Price's research focuses on the intersection of computer vision, robotics, and space applications. His work addresses critical challenges in spacecraft pose estimation, particularly for resource-constrained systems where computational capacity is limited. He has developed innovative approaches for network quantization in 6D object pose estimation, enabling high-accuracy performance with significantly reduced computational requirements. His research spans both theoretical development and practical implementation, with applications ranging from small satellite operations to asteroid exploration missions like the Hayabusa2 Minerva-II2 deployment. His publication record demonstrates a clear evolution from earlier work in aerospace acoustics and vibration analysis to his current focus on computer vision for space applications. The most recent publications reveal a strong emphasis on solving practical constraints in space missions, particularly addressing bandwidth limitations in spacecraft communications and computational constraints in onboard processing systems. His work bridges multiple disciplines, connecting aerospace engineering with cutting-edge computer vision techniques. Invited Lecturer, SPACEONOVA 2022 AI For Space Workshop Best Presentation Award, CVPR 2021 GP-Mech Exchange Scholarship, Tohoku University 2020 Recipient of the Japan Monbukagakusho MEXT Scholarship, Japan Government 2019 International Institute of Noise Control Engineering: Young Professional Grant, INTERNOISE 2017 Various Departmental and Dean's List Scholarships, Carleton University 2009-2015 As Academic Referent for the EPFL Spacecraft Team (EST), Dr. Price contributes to student-led space projects, providing guidance on technical aspects of spacecraft development. His GitHub repositories demonstrate active engagement with open-source tools for space applications, including the Orbit and Tumble Integrator project. His work spans both academic research and practical implementation, with code repositories showing his commitment to reproducible research and practical tool development for the space community.
Joshua Weber is a Research Associate at the University Center, School of Social Work FHNW , where he focuses on digitalization in social work and specialized software development. He is concurrently a doctoral student at the University of Cologne, Faculty of Human Sciences since 2018. Education: Master's (2012–2014) and Bachelor's (2008–2012) in Social Work from Evangelical University of Freiburg Professional Experience: Research roles at University Center (2017–present), Evangelical University of Freiburg (2014–2016), and Mannheim University of Applied Sciences (2014–2015) His research explores digitalization in social work , including specialist software, curricular integration, and materiality-sensitive approaches. He has published extensively on AI empowerment, ecosystemic competency models, and sociotechnical systems in peer-reviewed journals and conferences. Recent work includes collaborations on Actor-Network Theory and digital artifacts in social contexts. Joshua teaches courses like BA 101: Social Work as a Science and Profession and BA 115: Support for BA theses Digitalization , emphasizing digital transformation in social work. He is active in the German Society for Social Work (Specialist Group: Digitalization) and Swiss Society for Social Work (Expert Commission: Digitalization) . Key contributions include framing digital participation in software development, AI-driven educational tools, and ethical considerations in digital social work. His work bridges academic research with practical implementation through projects like Fachsoftware selection frameworks and digital competency models for universities.
Dr. Anastasios Tsiamis is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich, working in the Automatic Control Laboratory (Professur Control and Computation). His research focuses on the intersection of control theory and machine learning, specifically investigating how system theoretic properties affect the statistical difficulty of learning in system identification, online estimation, and control. Dr. Tsiamis received his Diploma (MEng, five-year degree) in Electrical and Computer Engineering from the National Technical University of Athens (NTUA). He completed his Ph.D. in Electrical and Systems Engineering at the University of Pennsylvania under Professor George Pappas, following graduate research with Professor Petros Maragos and undergraduate work with Professor Kostas J. Kyriakopoulos at NTUA. His primary research areas include Statistical Learning and Control, Data-Driven Control, Online Learning, Risk-Aware Control, and Security and Privacy in Networked Control Systems. Dr. Tsiamis has made significant contributions to understanding the fundamental statistical limits of learning in control systems, particularly focusing on sample complexity. His work on risk-aware optimization develops algorithms that safeguard against catastrophic events while maintaining good average performance, and his security research addresses eavesdropping attacks in remote estimation and motion planning. Analysis of Dr. Tsiamis's recent publications reveals a strong focus on data-driven approaches to control theory with emphasis on distributionally robust methods, risk-aware optimization, and finite sample guarantees. His work bridges theoretical foundations with practical applications across system identification, online learning, and adaptive control, providing rigorous non-asymptotic guarantees for learning-based control algorithms. Dr. Tsiamis has received several notable research recognitions: Best student paper award at IEEE 61th Conference on Decision and Control (2022) Spotlight Presentation at 41st International Conference on Machine Learning (2024) Finalist for best student paper award at American Control Conference (2019) Finalist for young author prize at IFAC World Congress (2017) Oral presentation at 2nd L4DC Conference (2020) Dr. Tsiamis teaches Linear System Theory (227-0225-00L) at ETH Zürich and collaborates extensively with Professor John Lygeros, Professor Manfred Morari, and researchers from the University of Pennsylvania. His publication record demonstrates strong collaborative research across multiple institutions while advancing theoretical foundations of learning-based control. As an active member of the Automatic Control Laboratory at ETH Zürich, Dr. Tsiamis contributes to advancing control systems science through rigorous mathematical analysis and innovative algorithmic development, with applications spanning robotics, energy systems, and networked control.
Weiqun Geng is a Senior Scientist at the Institute of Thermal and Fluid Engineering within the University of Applied Sciences and Arts Northwestern Switzerland (FHNW), School of Engineering and Environment . Education BSc in Energy Engineering, Zhejiang University (1982) MSc in Energy Engineering, Zhejiang University (1988) PhD in Aerospace Engineering, University of Stuttgart (1993) Research Interests focus on combustion engineering , heat transfer , and CFD modeling applied to gas turbines and environmental systems . His work includes: Optimization of fuel mixing and burner design for emission reduction Investigation of thermoacoustic instabilities and combustion stability CFD-driven combustor performance analysis Development of simplified reaction mechanisms for combustion modeling Publication Trends reveal expertise in combustion diagnostics , emission control , and CFD validation for industrial applications, with a focus on gas turbines and alternative fuels .
Dr. Vasileios Nittas is a postdoctoral researcher at the University of Zurich, affiliated with the Epidemiology, Biostatistics and Prevention Institute (EBPI) . His academic rank is Researcher , and he specializes in leveraging digital health technologies for disease prevention and equitable health promotion . His work integrates qualitative and quantitative methods to address systemic challenges in digital health ecosystems. Institution: University of Zurich Department: EBPI Academic Rank: Researcher Nittas's research spans machine learning in precision oncology , cultural adaptations of digital health interventions , and equity implications of technologies like mobile apps and wearables. His recent work explores mHealth for HIV prevention , virtual reality therapy , and long COVID epidemiology , reflecting a focus on both technical innovation and social determinants of health. His publications highlight trends in digital biomarker development , remote study methodologies , and policy frameworks for equitable health technology implementation. Themes include mobile health (mHealth) , AI in healthcare , and pandemic response strategies .
Federico Germani, a postdoctoral researcher at the Institute of Biomedical Ethics and History of Medicine (IBME) within the Faculty of Medicine at the University of Zurich, holds a PhD in Molecular Life Sciences and additional qualifications in International Relations. He founded Culturico, a non-profit cultural platform combating misinformation, and serves as a rapporteur for the World Health Organization (WHO) on infodemic ethics. His work bridges biomedical research, digital society, and public health ethics. Bachelor's in Biology, University of Milan (2013) Master's in Cellular and Molecular Biology, University of Milan (2015) PhD in Molecular Life Sciences, University of Zurich (2019) Graduate Diploma in International Relations, University of London (2018) Federico's research focuses on digital misinformation, pandemic preparedness, and ethical frameworks for AI in health communication. His expertise spans infodemic management, information literacy, and the intersection of technology and ethics. His recent work explores AI-driven disinformation mechanisms, systematic evaluation biases in language models, and critical thinking interventions. His 15 most recent publications (2025-2024) address AI ethics, pandemic communication, and misinformation analysis. Articles include topics such as algorithmic accountability, emotional bias in AI, and preference epidemiology for patient-centered healthcare.
Dr. Lea Stahel is a Senior Research and Teaching Associate at the Department of Sociology, Faculty of Arts and Social Sciences, University of Zurich. Her research focuses on digital sociology, with specific interests in online aggression, algorithmic influences, social norms, and digital inequality. Education: Dr. phil. in Sociology (University of Zurich, 2018), MSc in Political Psychology (Queen's University Belfast, 2011–2012), BA in Psychology (University of Basel, 2006–2009) Research Interests: Lea investigates sociological multilevel perspectives on digital hate speech, legitimacy construction in networked publics, and the intersection of technical recommendations with social influences on decision-making. Her work spans health, work, and political domains in digital contexts. Project Leadership: She leads initiatives like Hostility towards Members of Parliament in Switzerland (FDJP-funded) and Cyber Safari – Zurich Learning Path to Digital Resilience (DIZH/DSI-funded). Collaborative projects include Algorithm vs. Friends (DSI) and External Pressure on Journalists (FAN). Scientific Communication: Lea contributes to policy reports for Swiss federal agencies and academic discourse through book chapters and conference presentations, including at the International Conference on Social Media & Society and the Third Workshop on Abusive Language Online.
