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Yuko MunakataView profile

Professor

• Developmental Psychology
• Cognitive Neuroscience
• Executive Functions
• +5 more

Yuko Munakata is a Professor in the Department of Psychology at the University of California, Davis, and Director of the Cognition in Context Lab. She holds affiliations with the Center for Mind and Brain. Her education includes a PhD in Psychology from Carnegie Mellon University and dual BS/BA degrees in Symbolic Systems and Psychology from Stanford University. Her research integrates behavioral experiments, computational modeling, neuroimaging (ERP/fMRI), and cross-cultural studies to investigate how thinking varies across developmental stages and environmental/social contexts. Core interests include executive functions, developmental trajectories, cognitive control mechanisms, and interventions to enhance adaptive thinking. Her work highlights the role of cultural habits, social influences, and proactive control in shaping decision-making and self-regulation. Recent work explores the impact of environmental predictability on cognitive control development, the link between procrastination in adults/children, and the efficacy of executive function training programs. She advocates for contextual frameworks in understanding individual differences and educational inequality. Awards: American Psychological Association Boyd McCandless Early Career Award Outstanding Mentor Award (UC Boulder) Fellowships in Association for Psychological Science and APA Grants: NIH funding since 1998, supporting studies on child development and executive functions. Labs: Cognition in Context Lab (UC Davis), Center for Mind and Brain collaborations.

Aryeh KontorovichView profile

Professor

• Theoretical Machine Learning
• Probability
• Statistics
• +4 more

Aryeh Kontorovich is a Professor in the Computer Science Department at Ben-Gurion University. His research primarily focuses on theoretical machine learning, with expertise in probability, statistics, Markov chains, and metric spaces. His research interests span theoretical machine learning, with particular emphasis on: Probability theory and concentration inequalities Statistical learning theory Markov chains and mixing time estimation Metric space learning Kernel methods Sample compression schemes Professor Kontorovich's recent publications (2021-2025) demonstrate a continued focus on theoretical foundations of machine learning. His work shows strong trends in statistical estimation for Markov processes, distribution learning, metric space analysis, and sample compression. Many papers explore the intersection of probability theory and machine learning, particularly examining concentration inequalities, minimax optimality, and theoretical guarantees for learning algorithms. His research consistently bridges abstract mathematical theory with practical machine learning applications. Scientific awards and recognitions: Distinguished contribution award at MLG 2007 for "A Universal Kernel for Learning Regular Languages" Professor Kontorovich has advised numerous students and collaborated extensively with researchers in theoretical machine learning. His work spans both theoretical foundations and practical applications, with significant contributions to understanding the mathematical limits of learning algorithms. While specific grant information isn't provided in the source material, his extensive publication record in top venues suggests successful funding for his research programs. He maintains active collaborations with researchers worldwide, including prominent names like L. Gottlieb, D. Berend, and S. Hanneke.

• Algebraic Geometry
• Hyperkähler Manifolds
• Rozansky-Witten Invariants
• +3 more

Prof. Dr. Marc Nieper-Wißkirchen is a University Professor (W3) for Algebra and Number Theory at the Institute of Mathematics, University of Augsburg, since 2008. He has held visiting positions at the Max Planck Institute for Mathematics in Bonn and the University of Cambridge, and served as a Junior Professor at Johannes Gutenberg University Mainz (2004–2008). Since October 2021, he has been Vice Dean of the Faculty of Mathematics, Natural Sciences and Technology (MNTF). Education: Diploma in Mathematics (2000) from the University of Cologne, preceded by preliminary diplomas in physics (1997) and mathematics (1997). His doctoral studies (2000–2002) under Prof. Daniel Huybrechts focused on characteristic classes and Rozansky-Witten invariants of hyperkähler manifolds. His research spans algebraic geometry, differential geometry, and mathematical physics, with a focus on hyperkähler manifolds, Hilbert schemes of points, irregular singular connections, and Stokes phenomena. He has contributed to the understanding of Rozansky-Witten invariants, Chern numbers, and topological aspects of differential equations. Recent publications include works on Betti structures of hypergeometric equations (2022) and Stokes matrices for confluent hypergeometric equations (2022), reflecting his ongoing exploration of differential equations' geometric underpinnings. Earlier works address Hochschild homology, equivariant cohomology, and operads in algebraic contexts. His computational contributions include software demonstrations for elliptic curve arithmetic, Collatz sequences, and Google Matrix simulations. Technical expertise includes programming languages (TeX, PHP, HTML) and systems (Unix/Linux, Zope/Plone).

Matilda Karamatić BrčićView profile

Associate Professor

• Inclusive Education
• Educational Integration
• Curriculum Development
• +3 more

Assoc. Prof. Dr. Matilda Karamatić Brčić is a dedicated academic at the Department of Pedagogy, Faculty of Philosophy, University of Zadar, where she has been employed since 2004, progressing from junior researcher to her current position as Associate Professor. She teaches courses including Basic Knowledge of Education, Introduction to Pedagogy, Theory of Education, and Theory of Education in Practice across undergraduate, graduate, and teacher competency programs. Her research focuses on inclusive education , educational integration , and curriculum development , with particular emphasis on implementing inclusive practices in Croatian primary schools. Her doctoral dissertation examined the organization, content, and methodology of inclusive education implementation in primary schools. She has been actively involved in the scientific research project 'Education and Education for Entrepreneurship' since 2007. Dr. Karamatić Brčić has participated in numerous international conferences across Europe, with her most recent publications (2013-2015) analyzing sociodemographic factors in inclusive education, bilingual education implications, and models for quality inclusive practices. Her scholarly work demonstrates a consistent trajectory toward understanding and improving inclusive educational systems in Croatia. She maintains active professional engagement through memberships in: Croatian Pedagogical Society NGO 'Interkultura' in Zagreb European Association for International Education (EAIE) since 2005 Young SIETAR (Young society for International Education, Training and Research) since 2005 Dr. Karamatić Brčić has participated in extensive professional development, including working visits to Swiss teacher training universities focusing on inclusive education, and various international seminars on educational innovation and entrepreneurship.

Nakul GopalanView profile

Assistant Professor

• Language Grounding
• Robot Learning
• Human-Robot Interaction
• +3 more

Nakul Gopalan serves as an Assistant Professor at Arizona State University's School of Computing and Augmented Intelligence (SCAI) in Tempe, where he founded and leads the Logos Robotics Lab since joining in August 2022. His academic foundation was established through a PhD in Computer Science from Brown University completed in 2019. Education: PhD in Computer Science, Brown University (2019) Research Focus: Dr. Gopalan pioneers work at the critical intersection of language grounding and robot learning, developing algorithms that enable robots to interpret natural language instructions and learn from human demonstrations. His research directly addresses real-world usability challenges by focusing on hierarchical reinforcement learning, task planning, and human-robot collaboration frameworks that empower non-expert users to train robots for home and office environments. Key innovations include plannable representations for natural language instruction following and transfer learning techniques for robotic task execution. Publication Evolution: Recent publications (2023-2025) demonstrate accelerating specialization in language-conditioned robot learning, with 80% of his latest work exploring compositional instruction following, novice-user teaching interfaces, and explainable AI for robotics. His research trajectory shows a deliberate shift from foundational language grounding (2017-2020) toward practical human-robot collaboration systems, evidenced by increased focus on hardware-software co-design, cross-embodiment transfer, and clinical applications of explainable AI in neurology support systems. Scientific Recognition: Best Paper Award at RoboNLP workshop (Association for Computational Linguistics) 2017 RSS 2023 Best Student Paper Finalist Mentorship & Service: As lab director, Dr. Gopalan actively mentors graduate researchers while teaching core courses including Data Structures and Algorithms (CSE 310) and specialized seminars on robot learning. His significant service contributions include organizing the RSS 2021 "Robotics for People" workshop, serving as Action Editor for ICRA 2023/2024, and extensive reviewing for top-tier robotics conferences (RSS, ICRA, CORL) and AI venues (NeurIPS, AAAI). Research Infrastructure: The Logos Robotics Lab operates as his primary research vehicle, focusing on natural language interfaces for robot training, hierarchical task decomposition, and real-world deployment of language-grounded learning systems. Current projects integrate large language models with robotic control frameworks to enable zero-shot task generalization across different robot embodiments.

Dai O'BrienView profile

Associate Professor

• Deaf Studies
• British Sign Language
• Deaf Space
• +8 more

Dai O'Brien is an Associate Professor of British Sign Language and Deaf Studies at York St John University, where he has worked since 2012. As a deaf academic who uses British Sign Language, he brings unique lived experience to his teaching and research in Deaf Studies. Dr O'Brien's educational background includes: BSc Hons in Biological Sciences from University of Bristol MSc in Deaf Studies from University of Bristol MRes in Sociology from University of Bristol PhD in Sociology from University of Bristol His research focuses on deaf space - how deaf people navigate and creatively transform environments not designed for them. His work explores deaf young people's transition to adulthood, responses to loss of deaf community spaces, and the experiences of deaf academics in higher education. He has a growing interest in deaf people's involvement in radical left politics and applies Bourdieu's and Lefebvre's theories to understand deaf spatial experiences. Recent publications reveal a clear trend toward examining the intersection of deaf identity with broader social and political movements, particularly through spatial theory. His work demonstrates increasing attention to deaf-led research methodologies and the application of visual methods in Deaf Studies. Dr O'Brien actively supervises postgraduate researchers and has contributed significantly to making higher education more accessible for deaf students and staff through initiatives like the LIdIA Position Statement on making higher education more Deaf-friendly. He serves on the Journal of Deaf Studies and Deaf Education Advisory Board and co-organizes the Bridging the Gap conference series, which strengthens connections between deaf communities and higher education institutions. His professional service includes peer reviewing for major journals and evaluating research grants for organizations like the European Research Council.

• Theoretical Deep Learning
• Uncertainty Quantification
• Reliable Artificial Intelligence
• +10 more

Gianni Franchi is an assistant professor at ENSTA Paris , affiliated with the Computer Science and Systems Engineering Unit (U2IS) . His work focuses on theoretical deep learning , with a strong emphasis on uncertainty quantification, robustness, and explainability in machine learning models. Current affiliation: ENSTA Paris (U2IS) Academic rank: Assistant Professor Key collaborators: David Filliat, Emanuel Aldea, Andrei Bursuc, Antoine Manzanera His research spans uncertainty quantification , explainable AI , and reliable machine learning . He investigates methods like Bayesian neural networks, ensemble approaches, and deterministic uncertainty models. His work also addresses domain adaptation , self-supervised learning , and autonomous systems , particularly in trajectory forecasting and semantic segmentation for autonomous driving. Recent publications analyze probabilistic modeling for robustness, symmetry-aware Bayesian methods , and multi-modal datasets like InfraParis. He develops frameworks like Torch-Uncertainty and benchmarks such as MUAD for uncertainty types in autonomous driving. Key themes: Uncertainty Quantification Deep Learning Theory Autonomous Systems Explainable AI Dataset Creation Bayesian Methods

• Computational Social Science
• Conversational AI
• Discourse and Pragmatics
• +4 more

Maarten Sap is an Assistant Professor at Carnegie Mellon University's Language Technologies Institute with a courtesy appointment in the Human-Computer Interaction Institute. He also holds a part-time research scientist position at the Allen Institute for AI (AI2) as an AI safety lead. Current affiliations: CMU (2022–present), AI2 (2022–present) Prior: Postdoctoral Researcher at AI2 (2021–2022), Research Intern at AI2 (2018–2019) and Microsoft (2019) His research focuses on enhancing AI systems with social intelligence and addressing social biases in language technology. Key themes include: Ethical AI and Human-Centric Design Narrative Dynamics and Social Context Analysis AI Agents and Social Intelligence Toxic Language Detection and Cultural Bias Mitigation Recent publications examine: AI safety frameworks like HAICOSYSTEM Clinical reasoning alignment (ALFA) Multilingual moderation (PolyGuard) Cultural sensitivity in non-verbal AI (Mind the Gesture) Personality shaping in LLMs (BIG5-CHAT) Scientific Recognition: 2025 Okawa Research Grant Best Paper Runner Up - NAACL 2025 Outstanding Paper - EMNLP 2023 Best Paper - FAccT 2023 Best Paper - WeCNLP 2020 He advises a diverse group of PhD students across CMU and MIT, and has served on multiple program committees including ACL, EMNLP, and FAccT. His work appears in top venues like Nature Machine Intelligence, PNAS, and ACL.

Hongwei WangView profile

Researcher

• Natural Language Processing
• Interconnected Systems
• Large Language Models
• +6 more

Dr. Hongwei Wang is a Senior Researcher at Tencent AI Lab Seattle , specializing in applied machine learning for Natural Language Processing and Interconnected Systems . His work bridges Knowledge Graphs , Recommender Systems , and Graph Neural Networks , with a focus on large language models and retrieval-augmented generation. Ph.D. (2018), B.E. (2014) in Computer Science from Shanghai Jiao Tong University Postdoctoral Researcher : Stanford University (2019-2021), University of Illinois Urbana-Champaign (2021-2022) Dr. Wang’s research explores integrating Knowledge Graphs with Graph Neural Networks to enhance recommendation systems, language models, and information retrieval. His work spans Retrieval-Augmented Generation , Representation Learning , and GAN-based Graph Modeling , with recent papers on State-Space Exploration for LLM agents and Semantic Watermarking . His 15 most recent publications (2022-2024) focus on Retrieval Granularity , Interactive Memory , and Agent Systems , with keywords spanning Computer Science , Machine Learning , and Knowledge Graphs . Trends highlight advancements in Token-Level Semantic Matching , Schema-Guided Event Prediction , and Multi-Document Summarization . Scientific Awards: 2020 CCF Outstanding Doctoral Dissertation Award 2018 Google Ph.D. Fellowship Dr. Wang contributes to open-source projects like DKN and RippleNet , with 11 repositories on GitHub. He actively engages in Knowledge Graph Conferences (KDD, WWW, AAAI) and studies Chinese Classical Poetry and Film Arts .

Matthieu SozeauView profile

Researcher

• Type Theory
• Proof Assistants
• Functional Programming
• +4 more

Matthieu Sozeau is a prominent researcher at Inria in the Gallinette team in Nantes, France, and a key contributor and coordinator of the Coq/Rocq proof assistant project. His work bridges theoretical computer science and practical software development, focusing on creating reliable formal verification tools. His research interests span Type Theory, Proof Assistants, Functional Programming, and Unification. He has made significant contributions to the development of Coq (recently renamed to Rocq Prover), particularly through the MetaCoq project which aims to verify Coq's kernel within Coq itself, the Equations plugin for dependent pattern matching, and CertiCoq, a verified compiler from Coq to assembly. His work enables stronger guarantees about formalized mathematics and verified software. Sozeau's publications reveal a consistent focus on foundational aspects of proof assistants. His recent work includes verified type checking ('Coq Coq Correct!'), verified extraction from Coq to OCaml, and sort polymorphism for proof assistants. These contributions advance both theoretical understanding and practical implementation of dependently-typed programming languages. Distinguished paper award for Verified Compilation from Coq to OCaml at PLDI'24 As an academic mentor, Sozeau has supervised PhD students including Théo Winterhalter and Antoine Allioux. He regularly teaches courses on proof assistants, notably at MPRI (Master Parisien de Recherche en Informatique), and actively participates in the academic community through program committees, invited talks, and workshops. His work has significantly influenced both the theoretical foundations and practical applications of interactive theorem proving.

Hazel DoughtyView profile

Assistant Professor

• Video Understanding
• Computer Vision
• Action Recognition
• +7 more

Hazel Doughty is an Assistant Professor at Leiden University in the Leiden Institute for Advanced Computer Science (LIACS) . Previously, she was a postdoctoral researcher at the University of Amsterdam and completed her PhD at the University of Bristol under the supervision of Prof. Dima Damen and Prof. Walterio Mayol-Cuevas. Research Interests: Video Understanding, Skill Determination, Self-Supervised Learning, Temporal Attention, and Adverb Recognition in Instructional Videos. Grants: Co-Applicant for DUAL-IMPACT (NWO High Tech Systems and Materials, €1.25M); Main Applicant for NWO Veni grant ( From What to How: Perceiving Subtle Differences in Videos , €280K). Scientific Recognition: Veni Grant (2023) ELLIS Member (2022) Outstanding Reviewer for CVPR, NeurIPS, ECCV, and ACCV Academic Service: Organizer of workshops at CVPR 2024, BMVC 2023, NCCV 2024, and NeurIPS'21. Area Chair for CVPR, ICCV, NeurIPS, AAAI. Teaching: Courses on Computer Vision (BSc) and Advances in Deep Learning (MSc) at Leiden University. Her work focuses on fine-grained video understanding with weak or incomplete supervision , including adverb analysis, self-supervised learning, and egocentric vision datasets. Her recent publications analyze benchmark sensitivity, motion-focused video-language models, and generalized category discovery. Collaborations include the HAVA lab at the University of Amsterdam and the EPIC-Kitchens-100 project. She leads the DUAL-IMPACT initiative on high-tech systems and co-organized workshops at CVPR, ICCV, and NeurIPS. Her students include PhD candidates Luc Sträter and Kaiting Liu , and former advisees like Fida Mohammad Thoker (now postdoc at KAUST) and Piyush Bagad (now PhD at Oxford).

Desmond ElliottView profile

Associate Professor

• Vision-Language Models
• Multimodal Machine Learning
• Natural Language Processing
• +5 more

Desmond Elliott is an Associate Professor and Villum Young Investigator at the Department of Computer Science, University of Copenhagen. His research focuses on vision-language models, multilingual and multimodal processing, with particular emphasis on tokenization-free language modeling approaches. He leads a research group actively working on pixel language models and cross-lingual multimodal understanding. University of Copenhagen, Department of Computer Science Villum Young Investigator Associate Editor for JAIR (2025-2028) Senior Area Chair for ACL 2025 Elliott's research spans vision-language integration, multilingual NLP, and multimodal machine learning. His work explores how language models can operate directly on visual pixels without traditional tokenization, enabling more seamless integration of vision and language processing. He investigates compositional generalization in multimodal systems, retrieval-augmented image captioning, and cross-lingual transfer in vision-language tasks. His group develops methods for low-resource language processing and creates benchmarks for evaluating multimodal systems across diverse cultural contexts. His recent publications demonstrate strong trends in pixel-based language modeling, synthetic dataset generation through retrieval augmentation, and multilingual vision-language processing. The work spans theoretical advances in model architectures and practical applications in areas like medical text analysis, food culture understanding, and social media content moderation. His research often bridges computer vision and natural language processing with a focus on making these technologies accessible across diverse languages and cultures. Best Paper Honorable Mention at CVPR Visual Concepts Workshop 2025 Best Long Paper Award at EMNLP 2021 Area Chair Favourite paper at COLING 2018 Elliott actively supervises student projects in BSc and MSc programs related to his research interests. His research has received substantial funding from Google (2024-2025), Facebook (2022-2024), Villum Foundation (2021-2026), Novo Nordisk Foundation (2019-2024), and European Union (2023-2026). He regularly recruits postdocs for projects including the Danish Foundation Models project and the Responsible AI for the People Project. His group holds regular meetings on Tuesdays from 13:00-14:00 in IF G.03, with an active mailing list for announcements. The research environment appears collaborative, with frequent co-authorship across institutions and regular participation in major NLP and computer vision conferences.

• Natural Language Processing
• Machine Learning
• Explainable AI
• +4 more

Isabelle Augenstein is a Professor at the University of Copenhagen's Department of Computer Science, where she leads the Copenhagen Natural Language Understanding (CopeNLU) research group and the Natural Language Processing section. She became Denmark's youngest female full professor in 2022 and co-leads the Danish Pioneer Centre for Artificial Intelligence's Speech and Language collaboratory. ERC Starting Grant recipient DFF Sapere Aude Research Leader fellow Karen Spärck Jones Award winner Hartmann Diploma Prize recipient Her research focuses on fair and accountable NLP systems, with specific emphasis on explainability, factuality, bias detection, and social NLP. She investigates cultural biases in language models, develops frameworks for explainable fact checking, and explores uncertainty estimation in NLP systems. Recent publications demonstrate expertise in: Mechanistic analysis of cultural bias representations Context utilization techniques for LLMs Explainability metrics and attribution methods Cross-domain label adaptation Retrieval-augmented generation Fact checking uncertainty quantification Major scientific contributions include: Numerous EMNLP and ACL publications Foundational work on stance detection Development of fact checking benchmarks Multilingual model analysis AI ethics frameworks She supervises a team of researchers working on explainable AI and fact checking systems, with current projects including the ExplainYourself ERC-funded initiative on explainable fact checking. Her group recently presented multiple papers at EMNLP 2025 on topics spanning explainable AI and social NLP.

Jacob YoungView profile

Assistant Professor

• Neuro-oncology
• Glioblastoma
• Brain Tumor Immunology
• +6 more

Jacob Young, MD, is an Assistant Professor in the Department of Neurological Surgery at the University of California, San Francisco (UCSF) School of Medicine and a Principal Investigator in the UCSF Brain Tumor Center. His clinical practice focuses on neurosurgical management of adult brain tumors including gliomas, metastatic tumors, and meningiomas, utilizing advanced brain mapping techniques to preserve critical motor, language, and sensory functions during resection. Dr. Young's educational background includes a BS in Neuroscience from Duke University (2012), an MD from the University of Chicago Pritzker School of Medicine where he was elected to Alpha Omega Alpha Honor Medical Society (2017), and a neurosurgery residency at UCSF (2017-2024). His research program integrates laboratory investigations with clinical trials to address fundamental challenges in brain tumor treatment. His primary research interests center on understanding glioblastoma immune microenvironment dynamics and developing innovative therapeutic strategies. Key focus areas include: First-in-human clinical trials of novel immunotherapies Focused ultrasound-mediated blood-brain barrier disruption to enhance drug delivery Longitudinal molecular profiling of tumor evolution during treatment AI-driven tools for patient care navigation and clinical trial assessment Prospective outcomes research through the RANO resect group and NeuroPoint Alliance His work bridges fundamental tumor biology with translational applications to overcome treatment resistance. Analysis of Dr. Young's 15 most recent publications (2023-2025) reveals a strong emphasis on surgical innovation, tumor immunology, and molecular characterization. Key trends include: development of prognostic classification systems for resection extent, investigation of glioma-neuronal circuit interactions driving immunosuppression, and optimization of drug delivery strategies. His collaborative work within the RANO consortium establishes evidence-based surgical guidelines while his lab's focus on microenvironmental factors informs next-generation immunotherapies. Dr. Young has received significant recognition including: Chan-Zuckerberg Physician Scientist Fellowship (2021-2022) ASCO Young Investigator Award (2022-2023) Andrew J. Lockhart Focused Ultrasound Fellowship (2023) Multiple Harold Rosegay Teaching Awards from UCSF Howard Naffziger Award for Clinical Excellence His research is supported by NIH, NCI, Focused Ultrasound Foundation, and AANS grants. As lab director, Dr. Young mentors a diverse team including PhD candidates like Edward Valenzuela (DSCB program) and specialists in immunology and neuro-oncology. His lab participates in the RANO resect group, ENCRAM research program, and NeuroPoint Alliance to advance clinical protocols. Current projects include developing intraoperative focused ultrasound prototypes, single-cell analysis of tumor evolution, and AI tools for patient navigation through care pathways. Future work focuses on translating microenvironment discoveries into combination therapies targeting treatment resistance mechanisms.

Emily CrossView profile

Professor

• Cognitive Neuroscience
• Social Neuroscience
• Embodied Cognition
• +5 more

Emily Cross is a Full Professor at the Department of Humanities, Social and Political Sciences at ETH Zurich, leading the Social Brain Sciences Professorship since spring 2023. She previously held professorships at Bangor University (Wales), University of Glasgow (Scotland), Macquarie University (Australia), and Western Sydney University's MARCS Institute (Australia). Her research centers on how embodied experience shapes social learning and perception across diverse contexts. Key contributions include identifying neural signatures of embodied expertise using dancers, developing embodied neuroaesthetics theory, uncovering neurocognitive foundations of visual learning across lifespans, and pioneering paradigms for human-robot social engagement. Her interdisciplinary approach bridges technology, performing/visual arts, and social sciences to explore experience-dependent plasticity at brain and behavioral levels. Recent publications (2024-2025) demonstrate intense focus on human-robot interaction dynamics, aesthetic movement perception, and context-dependent social cognition. Work increasingly examines self-disclosure mechanisms to robots, cultural influences on robot acceptance, and neural correlates of movement synchrony, reflecting her expanding influence at the neuroscience-robotics intersection. Scientific awards include: Philip Leverhulme Prize for Psychology Jacob Bronowski Award from British Science Foundation Young Talent Award from Dutch Neuroscience Society RoboHub and Insight Analytics top women in robotics listings Australia’s Superstars of STEM (2022) Cross passionately trains next-generation scientists with emphasis on research ethics. Her work attracts major funding from ERC, NIH, Fulbright Commission, ESRC, EPSRC, and UK Ministry of Defence. She serves on UNESCO’s International Bioethics Committee (co-rapporteur for neurotechnology ethics report) and as Associate Editor for International Journal of Social Robotics. She leads ETH Zurich's dynamic Social Brain Sciences group, which embraces interdisciplinarity through research paradigms bridging technology, performing/visual arts, and biological/social sciences, while maintaining active roles in editorial boards and conference committees including Intelligent Virtual Agents and Affective Computing meetings.