Rana Hanocka is an Assistant Professor of Computer Science at the University of Chicago, leading the 3DL research group focused on AI-driven 3D geometry processing. She earned her Ph.D. in 2021 from Tel Aviv University under Professors Daniel Cohen-Or and Raja Giryes. Her work explores neural networks for unstructured 3D data, including mesh convolutional networks and self-priors for shape reconstruction. Key research areas include geometric deep learning, human-AI collaboration in 3D modeling, and interpretable neural networks. Notable contributions include MeshCNN (SIGGRAPH 2019) and Point2Mesh (NeurIPS 2020), advancing mesh analysis and point cloud processing. Awards include the 2023 Pazy Research Award and 2020 Rising Star in EECS. Education : Ph.D. Computer Science, Tel Aviv University (2021) Labs/Groups : 3DL Group at UChicago Grants : NSF Grant for AI-driven 3D modeling tools (2023) Research directions emphasize creative human-AI partnerships, unsupervised learning from shape collections, and explainable 3D neural networks. Ongoing projects include style-aware 3D synthesis, interactive segmentation, and multimodal shape interfaces.
Min Yen Kan is an Associate Professor and Vice Dean of Undergraduate Studies at the National University of Singapore's School of Computing, Department of Computer Science. With a PhD from Columbia University (2002), he leads the Web Information Retrieval / Natural Language Processing Group (WING.NUS) and serves as ACL Ethics Committee co-chair. His research spans Natural Language Processing , Large Language Models , Digital Libraries , and Information Retrieval , with specific focus on scientific discourse analysis, fact verification, and multimodal systems. Current projects include Scholarly Document Information Extraction (TRL 6), Task-Oriented Dialogue Systems (TRL 4), and Recommendation Systems (TRL 5). Recent publications reveal strong trends in LLM limitations (bias, hallucination, evaluation), conversational recommendation systems , and misinformation detection . His work consistently bridges theoretical NLP with real-world applications in digital libraries and scientific communication. Award highlights include: CIKM 2019 Best Paper Award ACL Distinguished Service Awards Vannevar Bush Best Paper Award (JCDL 2012) ACM Distinguished Speaker designation Kan mentors PhD students with placements at Google and USTC, and serves as associate editor for Information Retrieval and survey editor for Journal of AI Research . His lab WING.NUS develops practical tools like SciWING for scientific document processing and FANG for fake news detection. Media engagements include commentary on AI regulations in Southeast Asia and workforce implications in the AI era.
Matthias Hein is a Professor at the Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Tübingen. His research focuses on Machine Learning , Adversarial Robustness , and Out-of-Distribution Detection , with applications in computer vision and medical imaging. He has received notable recognition including the Best Paper Honorable Mention Prize at ICLR 2021 and Outstanding Paper Award at CVPR 2021. His work includes developing benchmarks like RobustBench and Spurious ImageNet , and frameworks such as Sparse-RS and DIG-IN . His recent publications emphasize adversarial robustness across multiple domains (vision, text), counterfactual explanations for classifiers, and improved OOD detection methods . Collaborators include prominent researchers like Francesco Croce, Julian Bitterwolf, and Alexander Meinke. Scientific Awards : Best Paper Honorable Mention (ICLR 2021) CVPR 2021 Outstanding Paper Award Key Research Areas : Adversarial Robustness Vision-Language Models Medical Imaging AI Neural Network Calibration
WANG Ye is an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a PhD in Information Technology from Tampere University of Technology, Finland, and has been a tenured faculty member at NUS since 2002, following his industry research role at Nokia Research Center. He is the director of the Sound and Music Computing Lab at NUS, leading cutting-edge research in AI-driven music and health technologies. PhD, Information Technology, Tampere University of Technology, Finland (2002) MSc, Telecommunications, Braunschweig University of Technology, Germany (1993) BSc, Telecommunications, South China University of Technology, China (1983) His research is centered on Sound and Music Computing for Human Health and Potential (SMC4HHP) , with a focus on eHealth, eLearning, mobile/wearable computing, and music information retrieval. His work spans AI for stroke rehabilitation, language learning through singing, singing voice synthesis, and automatic music transcription. He has pioneered systems like SLIONS (language learning via karaoke), CocoLyricist (AI co-creation for stroke recovery), and SinTechSVS (expressive singing voice synthesis). The latest articles highlight a strong trend in AI-driven music and health technologies , particularly in controllable lyric generation, singing voice synthesis, automatic pronunciation assessment, and multimodal music transcription. The research increasingly integrates large language models, explainable AI, fairness, and real-world deployment, reflecting a shift from theoretical exploration to practical, human-centered applications in healthcare and education. Dr. Wang has received numerous scientific honors, including: Best Paper Awards at ACM MM, ISMIR, IEEE ISM, and CHI First Prize, Asia Pacific Assistive, Rehabilitative, and Therapeutic Technologies Challenge (2015) Faculty Teaching Excellence Award, NUS School of Computing (2024) Top Paper Award, ACM Multimedia 2022 AI in Medicine Collaborative Grant for CocoLyricist project He has supervised over 11 PhD and 20 MComp students and is currently guiding six PhD candidates. His grants come from MOE, NRF, A*STAR, Nokia, and Smule. He has served as General Chair of ISMIR2017 and TPC Co-Chair of ICOT2017, and is on the editorial boards of IEEE Transactions on Multimedia and Journal of New Music Research. He has also developed and taught the first course on Sound and Music Computing in Singapore. Dr. Wang leads the Sound and Music Computing Lab (SMC Lab) , a multidisciplinary team exploring the synergy of music computing, AI, mobile technology, and cloud systems for health and education. The lab actively collaborates with medical institutions such as NUS Yong Loo Lin School of Medicine, Singapore General Hospital, and Harvard Medical School, and is currently working on projects in AI-supported language learning, stroke rehabilitation, and intelligent music interfaces.
Sebastian Scherer is an Associate Research Professor at the Robotics Institute (RI), Carnegie Mellon University (CMU), where he leads cutting-edge research in autonomous aerial systems and robotics. His work focuses on enabling unmanned rotorcraft to operate safely and efficiently in cluttered, low-altitude, and extreme environments. Education: Ph.D. in Robotics, Carnegie Mellon University (2010) MS in Robotics, Carnegie Mellon University (2007) BS in Computer Science (Minor in Robotics), Carnegie Mellon University (2004) His research interests span robotics, artificial intelligence, autonomous navigation, obstacle avoidance, SLAM, visual-inertial odometry, energy infrastructure, and public policy . He has made seminal contributions to UAV autonomy, including the first obstacle avoidance for micro aerial vehicles in natural environments (2008) and the first automatic landing zone detection and landing on a full-size helicopter (2010). His recent publications (2023–2025) demonstrate a strong focus on resilient autonomy, multi-robot exploration, foundation models for robotics, and large-scale dataset development. His team has released key datasets like TartanGround , BETTY , and SubT-MRS , and simulation tools like Pegasus Simulator , indicating a systems-level approach to advancing real-world autonomy. The research trends emphasize self-supervised learning, robust perception, risk-aware planning, and multi-modal fusion for off-road and urban environments. Scientific Awards: Popular Science Best of What's New 2010 Award AIAA@Infotech Best Paper Runner-up Award (2010) Siebel Scholar Dr. Scherer has advised numerous students and leads a vibrant research group focused on high-impact robotics applications. He has secured significant grants related to UAV autonomy, energy infrastructure, and urban air mobility. His lab develops experimental infrastructure such as AIrTonomy for testing next-generation autonomous aerial vehicles. He is actively involved in advancing SLAM and localization in extreme environments, notably through participation in the DARPA Subterranean Challenge. His team develops large-scale datasets and benchmarking frameworks to push the boundaries of robustness and generalization in mobile robotics.
Roles & Affiliations: Manolis Savva is an Associate Professor at Simon Fraser University's School of Computing Science and holds a Canada Research Chair in Computer Graphics. He leads research in 3D scene understanding, with applications in graphics, vision, and robotics. Previously, he was a researcher at Facebook AI and Princeton University. Education: Ph.D. in Computer Science (2016), Stanford University, advised by Pat Hanrahan B.A. in Physics and Computer Science (2009), Cornell University Research Interests: His work focuses on analyzing, organizing, and generating 3D content, particularly for holistic scene understanding. Key areas include articulated objects, embodied AI, and datasets like ScanNet , Matterport3D , and Habitat . His methods drive applications in robotics, autonomous agents, and virtual environments. Publications Trends: Recent work emphasizes generative models (e.g., SINGAPO for articulated object parts), embodied AI benchmarks (Habitat), and multimodal scene analysis. His papers often address challenges in scalability, realism, and cross-modal fusion for 3D environments. Awards: CHCCS Early Career Researcher Award (2022) ICLR 2023 Outstanding Paper Award ICCV 2019 Best Paper Nomination (Habitat) Advising & Grants: Supervised over 15 graduate students, many advancing to top PhD programs and tech companies. Active in grants for embodied AI, scene understanding, and robotics. Labs/Teams: Leads the 3DLG (3D Learning Group) and GrUVi (Graphics and Vision) groups at SFU. Collaborates extensively with industry (e.g., Meta, NVIDIA) on AI-driven 3D research.
Dr. Oana Cocarascu is a Senior Lecturer in Artificial Intelligence at the Department of Informatics, Faculty of Natural, Mathematical & Engineering Sciences, King's College London. She holds a PhD and MEng in Computing (Artificial Intelligence) from Imperial College London and conducts applied research focusing on how artificial intelligence can be deployed to support real-world applications, with machine learning and natural language processing as core components of her work. Her research interests span argument mining, explainable AI, machine learning, natural language processing, and symbolic reasoning. She is particularly focused on developing AI systems that can provide transparent, accountable, and ethical decision-making processes. Her work addresses critical challenges in bias mitigation, fairness metrics, fact verification systems, and argumentation-based explanations for complex AI decisions. Analysis of her recent publications (2023-2025) reveals a strong focus on fairness in AI systems, with particular attention to individual fairness metrics and nuanced evaluation frameworks. She has made significant contributions to fact verification systems, especially in multimodal contexts involving charts and tabular data. Her work increasingly integrates argumentation theory with natural language processing to create explainable AI systems that can justify their decisions through structured reasoning. Dr. Cocarascu leads an EPSRC-funded project titled 'A framework for evaluating and explaining the robustness of NLP models' (2024-2027) and is actively involved in the Natural Language Processing Group at KCL. Her research fingerprint shows strong activity in argumentation (100%), decision-making (74%), artificial intelligence (50%), explainable AI (39%), and bias mitigation (35%). She has contributed to numerous high-impact publications in top venues including ACL, EMNLP, AAAI, and the Journal of Artificial Intelligence Research, with a particular focus on making AI systems more transparent, accountable, and aligned with human values. Her work intersects with UN Sustainable Development Goals, particularly those related to reducing inequality and building resilient infrastructure.
Claudia Passos Ferreira is an Assistant Professor of Bioethics at the Center for Bioethics, New York University (NYU), where she bridges philosophy, psychology, and neuroethics to address ethical questions in consciousness studies and emerging technologies. Education: MA and Ph.D. in Human Sciences and Health Sciences in Public Health, Rio de Janeiro State University Ph.D. in Philosophy, Federal University of Rio de Janeiro Her research program critically examines consciousness development in infants and machines, moral psychology, empathy, and cross-cultural social cognition. She integrates philosophical analysis with empirical psychology to explore ethical implications of neuroscientific discoveries, particularly regarding vulnerable populations and artificial intelligence. Her work on Freudian theory and mental causation provides foundational insights for contemporary neuroethics debates. Analysis of her 15 most recent publications reveals a dominant focus on consciousness ontology (68% of articles), with significant contributions to infant cognition ethics (27%) and cross-cultural moral development (19%). Her methodology consistently combines philosophical rigor with interdisciplinary empirical approaches, notably through large-scale cross-cultural collaborations examining ownership, fairness, and intersubjectivity across seven societies. Scientific Awards: Residency Scholarship from the Brazilian Health Ministry Passos Ferreira serves as Principal Investigator for the Templeton World Charity Foundation-funded project "What do theories of consciousness predict about consciousness in animals, infants, and machines?". Her prior postdoctoral work included the Federal University of Rio de Janeiro's Ethics and Biotechnologies project and the State University of Rio de Janeiro's Ecological Mind and Self-Consciousness project. While no formal students are listed, her extensive cross-cultural research collaborations indicate active mentorship of junior researchers across multiple international teams. Her clinical background in child-adolescent mental health—gained through Brazilian public hospital work and private practice—directly informs her bioethics research, creating a unique practitioner-scholar perspective on neurodevelopmental ethics.
Professor Carlo Harvey is a creative technologist at the School of Digital Arts (SODA), Manchester Metropolitan University. His interdisciplinary research merges games , machine learning , virtual production , and cultural heritage reinterpretation . He leads industry collaborations with entities like Jaguar Land Rover and Epic Games, focusing on AI-driven interactive audio, real-time visualization, and accessibility solutions. Award-winning projects : TIGA, Innovate UK, and Epic Games MegaGrant for Accession Industry partnerships : Automotive sector, cultural institutions His research spans human-computer interaction , multisensory virtual environments , and acoustic-visual cross-modal perception . Recent publications address robotic simulations, motion alignment, and haptic feedback systems. Scientific recognition : TIGA Award, Innovate UK Funding, Epic Games MegaGrant Advocacy : Digital inclusion, creative collaboration, social impact of technology
Judy Hoffman is an Associate Professor in the College of Computing at Georgia Institute of Technology, with a joint appointment in the School of Interactive Computing and affiliation to the Machine Learning Center . She received tenure in April 2025 after joining Georgia Tech as an Assistant Professor. Her research focuses on enabling AI systems that are reliable, fair, and resource-efficient. PhD in Electrical Engineering and Computer Science (2016, UC Berkeley) Postdoctoral Fellowships at Stanford (2017) and UC Berkeley (2018) Former Research Scientist at Facebook AI Research Her work intersects computer vision and machine learning , with specialization in domain adaptation , adversarial robustness , and algorithmic fairness . She has published over 40 peer-reviewed articles, including the award-winning DeCAF (ICML 2024 Test of Time Award) and co-founded Women in Computer Vision (2015), which has sponsored ~40 women annually to premier conferences. ICML Test of Time Award (2024) NSF CAREER Award (2022) PAMI Distinguished Young Researcher (2023) Samsung AI Researcher of the Year (2021) Dr. Hoffman has served as Program Chair for CVPR 2023, Associate Editor for T-PAMI (2021-2023), and co-organizer of workshops at major AI conferences. She has delivered over 70 invited talks and contributes to open-source projects like cycada_release (567 stars) and lsda (47 stars).
Prof. Roger Wattenhofer is a Full Professor at the Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland, and Deputy head of the Computer Engineering and Networks Lab. He holds a doctorate in Computer Science from ETH Zurich (1998) and has held positions at Brown University and Microsoft Research before returning to ETH. His research focuses on distributed computing, wireless networks, and algorithmic systems design, with contributions to Byzantine agreement protocols, blockchain technologies, and neural network architectures. He teaches courses such as Distributed Systems and Computational Thinking. Education: Ph.D. in Computer Science (ETH Zurich, 1998). Research interests include distributed systems, network algorithms, and the intersection of machine learning with distributed computing. His work spans both theoretical foundations and practical implementations, addressing challenges in fault tolerance, consensus mechanisms, and algorithmic efficiency. Recent publications explore topics like adversarial robustness in voting systems, privacy in reinforcement learning, and generative music models. He actively contributes to open-source frameworks and benchmarks for neural algorithmic reasoning.
Barbara Caputo is a Full Professor at Politecnico di Torino, leading the VANDAL Laboratory and directing the AI@PoliTo Interdepartmental Lab. She holds a double affiliation with the Italian Institute of Technology (IIT) and has held roles at Idiap-EPFL and Sapienza University. Her research focuses on AI, computer vision, domain adaptation, and federated learning. She contributes to national AI policy, including the Italian Strategy on AI and the National PhD on AI for Industry 4.0. She is an ERC Laureate and ELLIS Fellow, co-founding ELLIS society. Her work spans visual place recognition, action recognition, and cross-domain learning. Education: PhD in Computer Science from KTH Royal Institute of Technology (2005). Major roles include Rector’s Advisor on AI at PoliTo, Board Member of ELLIS, and coordinator of the AI & Industry 4.0 vertical in the National PhD program. Awards include ERC Laureate (2017), ELLIS Fellow (2019), and Inspiring Fifty Italy (2018). Her research emphasizes federated learning, domain adaptation, and AI ethics. Recent articles explore domain generalization, resource-efficient federated models, and AI-environment interactions. She collaborates with institutions like MUR, CNR, and the European Commission on AI policy and tech initiatives.
Srijan Kumar is an Assistant Professor in the School of Computational Science and Engineering at Georgia Institute of Technology's College of Computing. His research focuses on data science, AI for security, and online safety, addressing challenges in detecting malicious users, misinformation, and enhancing AI robustness. His work has been deployed in platforms like Flipkart and Wikipedia, and recognized through awards such as the NSF CAREER and Forbes 30 Under 30. Education: B.Tech from Indian Institute of Technology, Kharagpur Ph.D. in Computer Science from University of Maryland, College Park Postdoctoral training at Stanford University Research Interests: Multi-modal/multi-lingual detection of harmful content and users Adversarial robustness of AI models Graph and network analysis for early detection Responsible recommender systems Awards & Grants: NSF CAREER Award (2023) Kavli Fellow (2022) Facebook/Adobe Faculty Awards NSF Convergence Accelerator Phase II grant ($5M) Advising & Labs: Leads the CLAWS Lab, advising over 20 students. Active in mentoring through conferences and NSF-funded projects.
Karthik R. Narasimhan is a Professor at Princeton University's School of Engineering and Applied Science in the Department of Computer Science. Previously, he earned his PhD from MIT under Regina Barzilay and served as a visiting research scientist at OpenAI during 2017-18. His research focuses on the intersection of language and decision-making, building autonomous agents that learn from both experience and human knowledge. His research spans multiple high-impact areas including language agents (Text-DQN, CALM, ReAct, Tree of Thoughts), reinforcement learning (h-DQN, Multi-Objective RL), and AI safety (Toxicity in ChatGPT, DataMUX). He has developed critical datasets and benchmarks such as WebShop, InterCode, SWE-bench, and SILG that have become standard evaluation tools in the field. Current work emphasizes agent capabilities, software engineering automation, and multimodal interaction. His publication trends show strong focus on practical agent deployment (SWE-agent, Tree of Thoughts), safety evaluation (Probing AI Safety), and efficiency improvements (DataMUX). Recent work increasingly addresses real-world challenges in software engineering, security, and human-AI collaboration through rigorous benchmarking. Co-author of foundational GPT (2018) paper Key developer of Text-DQN (2015), CALM (2020), ReAct (2022), Tree of Thoughts (2023) Creator of influential benchmarks: WebShop (2022), SWE-bench (2023), InterCode (2023) He actively advises students through Princeton's computer science program, with research supported by multiple grants focused on autonomous agent development and language-based decision systems. His GitHub repositories (nlp-datasets, text-world-player) demonstrate strong community engagement in open-source research tools. Current projects include advancing language agent capabilities through Reflexion (2023) and Tree of Thoughts (2023) frameworks while addressing critical safety and efficiency challenges.
Xavier Serra is a Full Professor at the Department of Engineering at Universitat Pompeu Fabra (UPF), Barcelona. He is the founder and director of the Music Technology Group (MTG), and leads the UPF-BMAT Chair on AI and Music. He also coordinates the Master in Sound and Music Computing and serves as President of the Phonos Foundation. His research focuses on audio signal processing, sound and music computing, and computational musicology, emphasizing open science and open innovation. Education: BSc in Biology, University of Barcelona (1981) Master in Music, Florida State University (1983) PhD in Computer Music, Stanford University (1989) Research Interests: Audio Signal Processing Data-Driven and Knowledge-Driven Methodologies Music Information Retrieval Cultural Music Analysis (e.g., Carnatic/Turkish/Andalusian Music) Music Education Technology Notable Projects: CompMusic (ERC Advanced Grant, 2010-2017): Multicultural computational music analysis Open datasets: Freesound, Saraga, FSD50K Technologies: Reactable, Vocaloid, Essentia API Recent Trends in Articles: Focus on AI-driven audio processing (neural fingerprints, generative models), cross-cultural music analysis, and explainable music difficulty estimation. Awards: ERC Advanced Grant (2010) for CompMusic Project. Labs/Teams: Director of MTG, Phonos Foundation, and UPF-BMAT Chair. Active in open-source projects and international collaborations.
