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Niloufer SelvaduraiView profile

Professor

• Artificial Intelligence Regulation
• Digital Finance Law
• Intellectual Property Law
• +3 more

Niloufer Selvadurai is Professor and Director of Research & Innovation at Macquarie Law School, where she also co-directs the Data Horizons Research Centre and co-chairs the Australian Law Associate Deans Research Network. A technology law scholar focusing on AI regulation, she examines how laws can calibrate technological innovation benefits and risks. Her interdisciplinary research collaborates with computing, engineering, finance, and health fields. Key leadership roles and recognitions include: Co-Director, Data Horizons Research Centre Program Leader for 'AI and Law' at Centre for Applied AI 2022 Australian Legal Education Award for Excellence in Graduate Research Supervision 2023 Higher Degree Research Supervisor of the Year Her publications extensively cover AI governance, digital finance, copyright, and smart contracts law, with recent work examining AI inventorship and regulatory frameworks for central bank digital currencies.

FENG LingView profile

Adjunct Assistant Professor

• Complex Systems
• Statistical Physics
• Deep Learning
• +7 more

Dr. FENG Ling is an Adjunct Assistant Professor at the National University of Singapore and Manager of the Complex System Group at the Institute of High Performance Computing, A*STAR. His work bridges theoretical and applied research in complex systems, focusing on statistical physics principles underlying deep learning and phase transitions in neural networks, as well as percolation phenomena in inter-dependent networks. Education: PhD in Physics, National University of Singapore (2013) His research spans complexity science, artificial neural networks, and social/economic systems. By analyzing critical states between periodic cycles and chaos, he explores optimal neural network training and explainability. He also develops frameworks for systemic spreading in complex networks, applicable to disease propagation, information diffusion, and blockchain dynamics. The selected publications highlight his contributions to understanding 1/f noise in deep neural networks (2024), reconstructing networked complex systems (2024), and edge-of-chaos training principles (2024). Earlier works investigate generalization in deep learning (2020), viral spreading on social networks (2018), and global spreader identification (2018), reflecting interdisciplinary applications of percolation theory and machine learning. Scientific Awards 9th place in Predicting Generalization in Deep Learning Competition at NeurIPS 2020 Dr. Feng leads the Complex System Group at A*STAR's Institute of High Performance Computing, where his team develops algorithms for maximizing or mitigating systemic spread in social, financial, and blockchain networks. His work integrates nonlinear dynamics, computational modeling, and data science to address challenges in artificial intelligence and networked systems.

Christian WachingerView profile

Professor

• Artificial Intelligence
• Medical Imaging
• Machine Learning
• +3 more

Prof. Christian Wachinger is a Professor of Artificial Intelligence in Radiology at the Technical University of Munich (TUM), part of the TUM School of Medicine and Health. His research focuses on developing AI algorithms for medical imaging, disease prediction, and addressing challenges like AI transparency and data bias. He holds a PhD in Medical Image Processing from TUM and completed postdoctoral training at MIT and Harvard Medical School. Notable awards include the Feodor Lynen Fellowship and Siemens Excellence Award. His lab, Artificial Intelligence in Medical Imaging, pioneers innovations in multimodal data integration and clinical AI applications. Education: PhD in Medical Image Processing, TUM (2011) Diploma in Computer Science, TUM and ENST Paris Honors Degree in Technology Management, CDTM Research Interests: Prof. Wachinger's work bridges AI and medicine, emphasizing neuroimaging, generative models, and clinical translation. Key areas include bias correction in medical datasets, multimodal fusion (MRI/PET), and explainable AI for healthcare. Awards: Feodor Lynen Research Fellowship (2011) German Computer Science Society Award (2011) Siemens Excellence Award (2007) Junior Research Group, Bavaria Center for Digitalization (2017) Grants & Labs: Leads the AI in Medical Imaging lab at TUM, with projects funded by grants focusing on AI-driven diagnostics and healthcare innovation.

Laks V.S. LakshmananView profile

Professor

• Data Management
• Graph Computing
• Machine Learning
• +5 more

Laks V.S. Lakshmanan is a Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. His research focuses on data management, graph computing, machine learning, and algorithms, with notable contributions to dense subgraph discovery, influence maximization, and healthcare informatics. He teaches advanced courses on databases and data management, including CPSC 404 (Advanced Relational Databases) and CPSC 534L (Topics in Data Management). His awards include the ACM SIGMOD Research Highlight Award, the IEEE Data Science Best Paper Award, and recognition as an ACM Distinguished Scientist (2016). His work bridges theoretical algorithm design with practical applications in social networks, bioinformatics, and healthcare. Key research themes include optimizing graph algorithms for large-scale data, combating misinformation through network analysis, and developing efficient methods for subgraph enumeration and influence propagation. His recent publications explore topics like clinical event prediction (TRACE), cost-effective LLM selection (ThriftLLM), and cross-modal consistency in AI systems. Education: Details not explicitly provided in sources. Grants & Funding: Recipient of NSERC Discovery Accelerator Supplements. Labs/Teams: Engaged in UBC's data management research groups and collaborative initiatives with industry partners.

• AI & Neuroscience
• Biomedical Engineering
• Neurotechnology
• +7 more

Aldo Faisal is a Professor of AI & Neuroscience at Imperial College London's Department of Bioengineering and Faculty of Engineering. He is also the Founding Director of the UKRI Centre for Doctoral Training in AI for Healthcare (£20M), leading the Faisal Lab. His roles include associate investigator at the MRC London Institute of Medical Sciences and affiliation with the Gatsby Computational Neuroscience Unit (UCL). His research bridges AI, neuroscience, and healthcare, focusing on neurotechnology, human behavior analysis, and clinical applications. Education: Studied Computer Science and Physics in Germany, followed by Biology at the University of Cambridge (Emmanuel College). Earned a PhD in Neuroscience under Simon Laughlin, and postdoctoral work with Daniel Wolpert on sensorimotor control. Prior professional experience includes roles at McKinsey & Co. and Credit Suisse. Research Interests: Combines cross-disciplinary approaches to study brain-behavior relationships, developing technologies for neurological disorders and amputees. Key labs include the Brain & Behaviour Lab (neurotechnology) and Behaviour Analytics Lab (behavioral data science). Techniques include machine learning, robotics, and neuroimaging (EEG/fNIRS). Publications: Over 100+ peer-reviewed articles, spanning AI in healthcare, motor learning, and neurotechnology. Recent work emphasizes safety of AI in critical care, wearable biomarkers for neuromuscular diseases, and human-AI collaboration. Awards: UKRI Turing AI Fellowship, Toyota Mobility Prize ($50k), Rosetree Interdisciplinary Award (£300k), and fellowships from German National Merit Foundation and Böhringer-Ingelheim Foundation. Elected to Global Futures Council (WEF, 2016). Grants & Labs: Leads £20M CDT in AI for Healthcare, £2M Turing Fellowship project, and manages labs at Imperial and University of Bayreuth (Germany). Collaborates with institutions like CRUK Convergence Science Centre and Data Science Institute.

Evimaria TerziView profile

Professor

• algorithmic data mining
• network analysis
• recommendation systems
• +3 more

Evimaria Terzi is a Professor and Department Vice Chair at Boston University (BU), affiliated with the Data Management Lab@BU. Her research focuses on algorithmic data mining with applications in network analysis, recommendation systems, ranking, and clustering. She holds a PhD from the University of Helsinki and has held prior roles at IBM Almaden Research Center (2007–2009) and the Helsinki Institute for Information Technology (HIIT) before 2007. Her work spans theoretical and applied domains, including team formation algorithms, fairness in AI, and large language model evaluation. Notable contributions include studies on LSM tree optimization, counterfactual explanations for auditing fairness, and the dynamics of memorization in LLMs. Her recent publications emphasize flexibility in database systems and ethical AI practices. Evimaria’s research has been recognized through her contributions to conferences like WSDM and KDD, where she has served in organizing roles. The themes of her work consistently bridge algorithmic innovation with real-world applications in social networks, healthcare, and collaborative systems.

Jürgen BernardView profile

Assistant Professor

• Visual Analytics
• Information Visualization
• Interactive Machine Learning
• +6 more

Jürgen Bernard is an Assistant Professor of Computer Science at the University of Zurich (UZH), affiliated with the Digital Society Initiative (DSI). He leads the Interactive Visual Data Analysis (IVDA) Group and holds a joint position in the Department of Informatics within the Faculty of Business, Economics, and Informatics. His research focuses on interactive visual data analysis, explainable machine learning, and human-centered AI interfaces. Bernard completed his PhD at TU Darmstadt in 2015, followed by postdoctoral roles at TU Darmstadt and the University of British Columbia. He has received prestigious awards, including the EuroGraphics Young Researcher Award (2022) and EuroVis Young Researcher Award (2021). His work emphasizes combining human expertise with algorithms in domains like healthcare, climate science, and digital humanities. Key research interests include visual analytics for time-oriented data, interactive machine learning, and applications in healthcare. His projects address challenges such as medical data interpretation, sensor data analysis, and decision-making support systems. Bernard actively contributes to conferences like IEEE VIS and chairs workshops such as VAHC 2023. Education: PhD in Computer Science (2015, TU Darmstadt); Diploma in Computer Science (2009, TU Darmstadt) Grants: SNF Grant on Personalized Visual Analytics (2024), BMW collaboration on manufacturing analytics Labs/Teams: IVDA Group, DSI Health Community

• Distributed Computing
• Distributed Algorithms
• Fault-Tolerance
• +8 more

Marina Papatriantafilou is an Associate Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. Her research focuses on distributed computing, fault-tolerance, parallel algorithms, and concurrency control. She has contributed to methods for fault-tolerant distributed systems, visualization tools for distributed algorithms, and scalable overlay networks. Her academic roles include teaching advanced courses on distributed systems, computer communication, and operating systems. She advises graduate students in areas like distributed algorithms and parallel computing. Key research interests include lock-free synchronization, memory reclamation, and self-stabilizing systems. She has authored over 100 publications in top-tier conferences and journals, with recent work on data streaming frameworks, energy-sharing optimization, and vehicular network processing. Professional involvement includes roles in program committees for conferences like OPODIS, SWAT, and SSS, plus membership in research evaluation boards for Swedish and European funding agencies. She pioneered educational tools like the Lydian environment for distributed algorithm visualization.

Ana PaivaView profile

Professor

• Social Robotics
• Affective Computing
• Virtual Agents
• +5 more

Ana Paiva is a Full Professor in the Department of Computer Science and Engineering at the University of Lisbon's Instituto Superior Técnico (IST), and coordinator of the GAIPS research group at INESC-ID, now focused on AI for People and Society. She holds a Katherine Hampson Bessell Fellowship at Harvard University's Radcliffe Institute for Advanced Study. Her work centers on creating socially intelligent AI and robots through agent-based approaches, emphasizing emotional and cultural competencies. Key research areas include Social Robotics, Affective Computing, and Pro-social Computing. She leads projects like the EU-funded ANIMATAS ITN network and Portugal's AMIGOS/AGENTS initiatives. Notable contributions include the FAtiMA Toolkit for developing emotional AI agents and pioneering work in hybrid human-machine societies. She has received awards such as the EurAI Fellowship (2019) and the Blue Sky Ideas Award at AAAI’18. Her research explores how machines can foster prosocial behaviors like altruism and cooperation, with applications in education, therapy, and ethical AI design. Her advising includes PhD students like Fernando Santos (Victor Lesser Award winner) and Elmira Yadollahi (ACM award recipient). Current projects focus on inclusive robotics for mixed-ability classrooms and ethical frameworks for human-robot collaboration.

Peter JansenView profile

Associate Professor

• Natural Language Processing
• Artificial Intelligence
• Cognitive Science
• +5 more

Peter Jansen is an Associate Professor at the University of Arizona's College of Information Science with a joint appointment at the Allen Institute for Artificial Intelligence (Ai2). He specializes in natural language processing (NLP), cognitive artificial intelligence, and automated scientific discovery. His research focuses on virtual environments for scientific reasoning, such as ScienceWorld and DiscoveryWorld, and methods for explainable AI through projects like the Explanation Bank. Education: PhD in Psychology and Neuroscience from McMaster University, and a Bachelor of Independent Studies from the University of Waterloo. His interdisciplinary background combines NLP, computer science, physics, and electrical engineering. Key projects include open-source hardware initiatives like the science tricorder (featured in over 50 media outlets and exhibited at the German Museum of Technology in Berlin) and TextWorldExpress, a text-game simulator. His work emphasizes grounding science education through sensing and developing AI systems capable of systematic reasoning and explanation generation. Current roles include advising students in automated scientific discovery and contributing to cross-listed courses in Computer Science and Linguistics. He maintains an active presence in academic outreach through teaching, conferences, and publications.

• Legal Informatics
• Artificial Intelligence & Law
• Computable Legal Reasoning
• +6 more

Giuseppe Contissa is an Associate Professor at the Department of Legal Sciences, University of Bologna. His work focuses on Legal Informatics, AI & Law, Legal Theory, and Law & Technology, particularly in robotics and automated systems. He holds a PhD in Legal Informatics from the University of Bologna and has extensive experience in research projects like ALIAS (air traffic management liability) and CLAUDETTE (AI-driven contract analysis). He is affiliated with ALMA Ai (Human-Centered AI Institute) and ARCES (Electronic Systems Research). Education: PhD in Legal Informatics (2006, University of Bologna), Master's in Legal Informatics (2002), Laurea in Jurisprudence (2001). Research Interests: Computable legal reasoning, AI ethics in healthcare, liability frameworks for autonomous systems, socio-technical systems governance, and automated vehicle regulation. He leads projects on AI certification in aviation and consumer protection through AI systems. Key Projects: CrossJustice (digital criminal procedure rights), SAFELAND (air traffic safety), and COMPULAW (computable law models). He advises the Council of Europe on AI ethics in justice systems. Labs/Teams: CIRSFID (Computer Law Research), ALMA Ai, and collaborations with CodeX (Stanford).

Martin ServinView profile

Associate Professor

• Digital Physics
• Computational Modelling
• Robotics
• +3 more

Martin Servin is an Associate Professor at the Department of Physics, Umeå University, and leads the Digital Physics research group within the UMIT Research Lab. His work focuses on computational modeling and simulation of granular materials, robots, and vehicles, with applications in AI-based control and perception. He holds a doctoral degree from Umeå University (2003) and has pioneered research in real-time physics simulation, particularly in the context of autonomous machinery and off-road robotics. Research Interests : Digital physics, granular materials simulation, autonomous systems, reinforcement learning, and simulation-to-reality transfer. His group develops advanced simulation tools for industries like forestry, mining, and construction. Key Projects : Mistra Digital Forest (2019–2026) AILUR (Digital Twin for AI-controlled Lunar Robotics) XSCAVE (Explainable, Safe Control for Heavy Machinery) Publications emphasize simulation methodologies, AI integration, and real-world validation across robotics, vehicle dynamics, and granular mechanics. Notable contributions include work on wheel loader dynamics, deep reinforcement learning for control systems, and terrain modeling. Awards include the Spin-off award for industry-grade physics in Unreal Engine (2018) , recognizing his role in Algoryx Simulations, a spin-off company commercializing his research. Labs/Teams : UMIT Research Lab, Digital Physics Group, and collaborations with Algoryx Simulations.

Francesca Dalia FaraciView profile

Senior Lecturer

• Biomedical Signal Processing
• Medical Data Analysis
• Wearable Technology
• +5 more

Francesca Dalia Faraci is a Senior Lecturer and Researcher at the Department of Innovative Technologies (DTI) of SUPSI. She holds a PhD in Electronics from the University of York (UK) and a Laurea in Physics from the University of Genoa (Italy). Her research focuses on biomedical signal processing, statistical data analysis, and wearable technology applications in healthcare. She leads projects such as Cardio MIPA (arrhythmia monitoring) and AutoPlay_DD (child development analysis via smart toys). Key contributions include developing algorithms for sleep staging, arrhythmia detection, and synthetic ECG generation. Awards include the GOSPEL and DoE School Certificates. She coordinates research projects and teaches courses in Physics and Applied Statistics. Education: PhD in Electronics (University of York, 2006), Laurea in Physics (University of Genoa, 2002) Roles: Senior Lecturer, Researcher, Project Coordinator Key Projects: Tackling teleWorking Impacts, my Doctor's Lifestyle (lifestyle medicine), Telemonitoring Breast Cancer Her research bridges AI and clinical practice, emphasizing ethical frameworks and complementarity between clinicians and AI systems. Recent work includes causal inference for sleep dynamics and bias quantification in healthcare algorithms.

Simone AonzoView profile

Lecturer

• Cybersecurity
• Malware Analysis
• Android Security
• +5 more

Simone Aonzo is a Class 2 Lecturer in the Digital Security department at EURECOM, a leading research and education institution in France focused on digital sciences. He is actively engaged in cybersecurity research with a strong emphasis on malware analysis, Android security, and machine learning applications in threat detection. His research interests span several critical areas in modern cybersecurity, including: Android and Windows malware analysis Evasive techniques used by malware Machine learning vulnerabilities in security systems Binary analysis and cryptographic function detection Phishing and social engineering attacks Security hardening using AI The trends in his recent publications (2024–2025) reflect a deep focus on adversarial machine learning, evasive malware behaviors, and the application of AI in both offensive and defensive security contexts. His work frequently appears in top security venues such as RAID, NDSS workshops, and IEEE-affiliated conferences, demonstrating his active contribution to the research community. He has no listed scientific awards in the provided text. Simone Aonzo is involved in mentoring and training through academic collaborations and educational events such as the PEPR Cybersecurity Winter School. While specific advisees are not listed, his collaborative publications suggest an active role in guiding junior researchers. There is no mention of grants, but his publication output implies ongoing research funding. He also contributes to community knowledge through talks and workshops. He is associated with the Digital Security research group at EURECOM, which focuses on cutting-edge cybersecurity challenges, including malware analysis, privacy, and system security. The team employs advanced techniques in machine learning, reverse engineering, and automated analysis to study modern threats.

Zheng ZhangView profile

Researcher

• Human-AI Interaction
• Human-Computer Interaction
• Machine Learning
• +3 more

Zheng Zhang is a Ph.D. candidate in the Department of Computer Science and Engineering at the University of Notre Dame, where he focuses on human-AI interaction. He also works as an Applied Scientist in Adobe's GenAI team, developing interactive AI systems for user experience enhancement. He has held internships at Apple, AWS AI, and Meta Reality Labs. Education: Ph.D. (in progress) at University of Notre Dame M.S. in Computer Science from University of Rochester and University of Minnesota B.Eng. in Software Engineering from Shaanxi Normal University His research explores the intersection of human-computer interaction (HCI) and machine learning (ML), emphasizing systems that provide adaptive, context-sensitive support for complex cognitive tasks. Key areas include collaborative tools for team ideation, incremental learning from user demonstrations, and multimodal context-aware interfaces. Recent publications demonstrate a focus on AI-augmented collaboration through large shared displays (LADICA), audio-visual annotation (PEANUT), and interactive qualitative coding (PaTAT). His work increasingly integrates generative AI with real-time human input, particularly for co-located teams and educational applications. He serves as a Program Committee member and reviewer for top HCI/ML conferences including CHI, UIST, IUI, and ACL. Teaching experience spans courses in human-AI systems, algorithms, and data structures.