Dr. Sam Ferguson is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), with a multidisciplinary background in music performance, cognitive science, and psycho-acoustics. His research explores the intersection of sound, music, and human experience through creative coding, machine learning, and interactive systems. Key Research Areas: Sound and Music Computing, Human-Computer Interaction, Creative Coding, Cognitive Science, Installation Art, and Acoustics. Current Projects: ARC Linkage project on creative coding and multiplicitous media; industry collaborations on IoT-based audiovisual systems. Recent Publications: Focus on spatial audio complexity, gestural interaction with networked sound, music emotion recognition frameworks, and robotic performance through genre-based cultural platforms. Leadership Roles: Director of Teaching & Learning Engagement; former Deputy Head of School (Teaching and Learning); active in ACM Creativity and Cognition Steering Committee. Teaching: Courses like Digital Media Studio , Prototyping Physical Interaction , and Data Processing using R within UTS's interdisciplinary Software Development Studio.
Andrew W. Lo is the Charles E. and Susan T. Harris Professor and a Professor of Finance at the MIT Sloan School of Management. He serves as Director of the Laboratory for Financial Engineering and is a principal investigator at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His academic affiliations include the American Finance Association, Academia Sinica, American Academy of Arts and Sciences, Econometric Society, and Society of Financial Econometrics. As an external faculty member at the Santa Fe Institute and research associate at the National Bureau of Economic Research, Lo bridges finance, AI, and healthcare. BA in Economics, Yale University AM and PhD in Economics, Harvard University His research focuses on evolutionary models of investor behavior , AI in financial technology , healthcare finance , and impact investing . Recent work includes adaptive market frameworks, AI-driven financial advice systems, and statistical tools for clinical trial prediction. His 2025 articles explore applications of Bayesian analysis in healthcare, endowment risk management, and market innovation dynamics. Lo has received prestigious awards including the Guggenheim Fellowship , TIME 100 recognition, and multiple teaching and research honors. He co-founded asset management and biotech companies and serves on healthcare organization boards. MIT Sloan Executive Education courses highlight his leadership in AI and machine learning for business applications.
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.
Suren Jayasuriya is an Associate Professor at Arizona State University's The GAME School, with joint appointments in the School of Electrical, Computer and Energy Engineering (ECEE) and the Department of Arts, Media and Engineering (AME). He is also an Affiliate Faculty Member at the Mary Lou Fulton College for Teaching and Learning Innovation. His lab, the Imaging Lyceum, focuses on transdisciplinary research bridging computational imaging, computer vision, sensors, and STEAM education. Education Ph.D. Electrical and Computer Engineering, Cornell University (2017) M.S. Electrical and Computer Engineering, Cornell University (2015) B.S. Mathematics, University of Pittsburgh (2012) B.A. Philosophy, University of Pittsburgh (2012) Research Focus Dr. Jayasuriya's work integrates optics, computational photography, and machine learning to develop novel imaging systems. His research spans: Computational cameras and light transport analysis Atmospheric turbulence modeling and video restoration Neural volumetric reconstruction for sonar/radar STEAM education frameworks for K-12 teachers Philosophical aspects of imaging and representation His lab emphasizes interdisciplinary collaboration across engineering, arts, and humanities. Publication Trends Recent publications demonstrate strong focus on computational imaging (45%), AI/ML applications (30%), and educational technology (25%). Dominant themes include turbulence mitigation in videos, neural rendering for sonar/radar, sensor fusion, and AI curriculum development for middle schools. Work frequently appears in top venues like CVPR, SIGGRAPH, and IEEE Transactions. Awards Image Electronics Technology Excellence Award (IIEEJ, 2021) Best Demo Awards: IEEE ICCP 2019, MIRU 2018 Best Paper Award: IEEE ICCP 2014 ASEE Diversity Paper Finalist (2020) Teaching Honors: Fulton Top 5% Award (2019, 2021), ASU Game Changing Faculty (2021) Teaching & Advising Teaches graduate/undergraduate courses including Machine Vision (EEE 515), Minds and Machines (AME 400), and thesis supervision. Leads NSF-funded projects on computational imaging education and AI teacher training. Mentors students through the Imaging Lyceum lab with projects spanning optics, philosophy, and educational technology. Lab & Collaborations Directs the Imaging Lyceum, emphasizing Aristotle-inspired collaborative research. The lab works on: computational cameras, STEAM education, sensor development, and philosophical inquiries into imaging. Collaborates with Carnegie Mellon Robotics Institute and international partners. Funded by NSF, NEH, and industrial partners for projects in sonar imaging, heat resiliency sensing, and educational AI.
Sarah Xiao is a Professor of Marketing and Head of Department at Durham University Business School, with a dual role as Programme Director for the Doctorate in Business Administration (DBA) at Fudan University, China. As a consumer psychologist, her work bridges technology, marketing, and behavioral analysis in retail, tourism, and digital economies. Her research spans: Behavioral Change in consumer decision-making Consumer Analytics (AI, Big Data, Machine Learning) Illicit Consumption and consumer misbehavior Neuroscience in marketing and consumer choice Mobile Commerce and Digital Marketing strategies Recent publications focus on AI-driven consumer behavior, social media revenge typologies, and predictive modeling in tourism demand forecasting. She supervises PhD students including Bai Dan, Matthew He, Coco Li, and Shujun Xiao, while collaborating with global institutions and private-sector partners. Her work has attracted significant research funding and appears in leading journals like Journal of the Academy of Marketing Science and Annals of Tourism Research .
Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.
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.
Professor David Abbink is a Full Professor of Haptic Human-Robot Interaction at Delft University of Technology, holding a joint appointment between the Department of Cognitive Robotics in the Faculty of Mechanical Engineering and Industrial Design Engineering since November 2023. He founded the Delft Haptics Lab and co-founded the Cognitive Robotics Department in 2017. Abbink leads the transdisciplinary research and innovation centre FRAIM, which was awarded the prestigious NWO Stevin Premie (Dutch Nobel Prize equivalent) in June 2024. Trained as a mechanical engineer specializing in biomechanics, Abbink's research focuses on human behavior adaptations when interacting with autonomous systems. He has published over a hundred scientific articles on human-robot interaction, haptics, shared control, tele-operation, driver assistance systems, and sensorimotor control. His research has been funded by industry partners (Nissan, Boeing, Renault), RVO (Brightsky project 2022-2026), and the Dutch Science Foundation NWO through personal grants (VENI 2010-2014, VIDI 2015-2019). Abbink's recent work centers on worker-robot relations as an academic focus, collaborating with organizations like Erasmus Medical Centre for nursing work, Schiphol and KLM for baggage handling, and KLM Engine Repair Services for maintenance work. He also serves as scientific director for the Centre for Meaningful Human Control, launched in October 2024. His work bridges engineering, social sciences, and practical applications to responsibly shape the future of work with emerging robotic capabilities. NWO Stevin Premie (2024) Best IEEE SMC journal paper on Cybernetics (2019) Top 25 scientific talents according to New Scientist (2015) Best teacher of Faculty 3mE (2013, 2014) Best teacher of Department of BioMechanical Engineering (seven consecutive years) Abbink has supervised over 110 MSc students and 11 PhD students. His educational contributions include developing the Master Programme in Robotics at TU Delft and receiving international recognition for his course 'The Human Controller.' He is also a prominent science communicator, featured on national television, radio, and major Dutch newspapers, and has delivered lectures at venues like The Royal Institution and Lowlands Festival. Despite his academic commitments, Abbink maintains a drummer persona, having recorded four albums and performed over 400 shows across three continents between 1999-2014.
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 .
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.
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.
Mikhail Gelfand is a Full Professor and Director of the Center for Molecular and Cellular Biology at Skolkovo Institute of Science and Technology (Skoltech), where he also serves as Vice President for Biomedical Research. His distinguished career spans multiple prestigious institutions including Lomonosov Moscow State University and the Higher School of Economics. His educational background includes: 1985: MSc in mathematics (functional analysis) 1993: PhD in physics-mathematics (biophysics) 1998: DSc in biology (molecular biology) 2007: full professor (bioinformatics) Professor Gelfand's research focuses on molecular evolution, comparative genomics, systems biology, and metagenomics. His work examines eukaryotic processes including alternative splicing, mRNA editing, and chromatin structure, as well as bacterial genome evolution and transcription regulation. His lab combines data on three-dimensional chromatin structure, epigenetic states, and gene expression to obtain an integrated view of genome functioning across diverse organisms from humans to amoebae. One major research direction focuses on the evolution of transcript splicing and editing, while comparative analysis of bacterial genomes yields functional annotations of novel enzymes, transporters, and transcription factors. His recent publications demonstrate a strong focus on RNA editing in cephalopods, bacterial genome analysis, and computational approaches to understanding chromatin structure. The work spans molecular biology, evolutionary biology, and bioinformatics, with particular emphasis on how RNA editing contributes to adaptation and molecular evolution across metazoans. His research shows how edited adenines are more frequently substituted with guanine in evolution than their unedited counterparts, suggesting RNA editing may enhance adaptation. His notable awards include: The President of Russian Federation's Award for Young Doctors of Science (2000) The "Best Scientist of the Russian Academy of Sciences" award (2004) A. A. Baev Prize in Genomics and Genoinformatics (2007) Member of Academia Europaea (2010) As Director of the Center for Molecular and Cellular Biology, Professor Gelfand leads a research group that combines computational and experimental approaches to study genome function and evolution. His lab's work has significant implications for understanding molecular mechanisms of evolution and adaptation across diverse biological systems, from bacteria to complex eukaryotes. His research on metagenomics extends to practical applications in areas including coral disease, aphids, and oil wells.
Koray Tahiroglu is a University Lecturer at Aalto University's School of Arts, Design and Architecture, specializing in Sound and Music Computing. His work bridges artificial intelligence, digital musical instruments, and embodied interaction, with a focus on deep learning applications in audio synthesis and human-AI creative collaboration. His research explores New Interfaces for Musical Expression (NIME), sonic interaction, and physical computing. He collaborates with SOPI Research Group and Google Brain Team (Magenta) on AI-driven artistic innovation. Recent Publications : 2024 studies on dance-sound cross-correlation and intra-action frameworks; 2023 work on AI-terity and deep learning syllabi; 2022 explorations of GAN synthesis, musical expectations, and lifeworld sonification. Scientific Awards : Co-Creative Artificial Intelligence of Music (2022) 2010 grant for scientific publications and artistic activities 2017 Honorable Mention for mobile cultural heritage research Tahiroglu contributes to digital art education and leads projects at Media Lab Helsinki, advancing sonic interaction and generative audio systems.
Richard Futrell is an Associate Professor at the University of California, Irvine (UCI), affiliated with the Department of Language Science. He leads the Language Processing Group, focusing on computational models of human and machine language processing. His work bridges information theory, Bayesian cognitive modeling, and natural language processing (NLP) interpretability. University of California, Irvine Department of Language Science Language Processing Group leader His research examines how linguistic structures emerge from cognitive and communicative pressures. Key areas include dependency locality, surprisal theory in sentence processing, and efficiency-driven language evolution. He investigates how memory constraints, predictability, and information density shape syntactic and morphological patterns across languages. Recent publications analyze code-switching efficiency, syntactic priming, ERP component modeling, and agent-based language contact simulations. His work frequently employs Bayesian modeling, neural network analysis, and cross-linguistic corpora to uncover universal principles in language processing. ACL Best Paper Award (2024) Best Paper Award for Computational Modeling of Language (2023) Marr Prize for Best Student Paper (2017) He has developed datasets like SPACER for error repair analysis and contributed to phonotactic learning frameworks. His collaborations span cognitive scientists, computational linguists, and neuroscientists, advancing understanding of language production, comprehension, and structural optimization.
Prof. Dr.-Ing. Stefan Kopp is a faculty member at Bielefeld University's Faculty of Engineering and serves as Research Group Leader of the Cognitive Systems and Social Interaction Group . He also holds administrative roles as Vice Dean and Deputy CITEC Coordinator . His work focuses on Artificial Intelligence , Cognitive Systems , and Socio-Technical World research areas. Research Group Leader: Cognitive Systems and Social Interaction Group Vice Dean: Faculty of Engineering Deputy Coordinator: Center for Cognitive Interaction Technology (CITEC) Project Manager: TRR 318 "Constructing Explainability" subprojects His research explores human-agent interaction , multimodal conversational agents , and social AI through projects like 39-Inf-11 Human-Machine Interaction and 39-M-Inf-VKI Virtual Humans and Conversational Agents . Publications address topics including adaptive explanation generation , gesture synthesis , and social cognition in dynamic environments. Current research topics span cooperative AI , explainable decision-making , and sensorimotor grounding in artificial systems.
