Piotr Koniusz is a Principal Research Scientist at Data61/CSIRO and an Honorary Associate Professor at the Australian National University (ANU), with an Adjunct role at UNSW. He holds a PhD in Computer Vision from the University of Surrey (2013) and a BSc from Warsaw University of Technology (2004). His research focuses on Foundation Models, Representation Learning, and Few-shot Learning, with contributions to Graph Neural Networks and Adversarial Robustness. Key roles include Program Chair for NeurIPS’25, Senior Area Chair for ICML’25 and ICLR’25, and Workshop Co-Chair for WWW’25. Awards include the Sang Uk Lee Best Student Paper (ACCV’22) and recognition as an Outstanding Area Chair (ICLR 2021–2023). Research interests span Vision-Language Models (VLMs), Generative Adversarial Networks (GANs), and Domain Adaptation. He supervises PhD students at ANU and collaborates with industry on projects like traffic forecasting and ecotoxicology prediction.
Professor Brian C. Williams is a leading academic at the Massachusetts Institute of Technology (MIT) , holding the position of Professor of Aeronautics and Astronautics and directing the Autonomous Systems Laboratory (ASL) . He is also a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Space Systems Laboratory (SSL) . His work focuses on advancing model-based autonomy, enabling robots to operate independently in extreme environments such as space, underwater, and urban traffic. His contact details include email williams@csail.mit.edu and phone 253-2739 . Education : S.B., S.M., and Ph.D. in Computer Science and Electrical Engineering from MIT (1989). Williams' research spans risk-bounded decision making , collaborative robotics , and neural-symbolic learning . He has pioneered systems like Remote Agent, which demonstrated autonomous self-repair in NASA's Deep Space One mission, and Geordi, a risk-aware driver assistant. His work integrates symbolic reasoning, probabilistic methods, and machine learning to create resilient robotic systems for space exploration, manufacturing, and transportation. The most recent publications highlight trends in risk-aware planning , multi-agent robotics , and stochastic control . Key innovations include tube-based trajectory optimization, conflict-directed task allocation, and theory-of-mind guided interventions, reflecting his focus on robustness in uncertain environments. Scientific Awards : NASA Space Act Award (1999), AAAI Fellow, and multiple best paper awards from AAAI, IJCAI, ICAPS, CDC, HRI, and ECAI. Williams leads the Model-Based Embedded and Robotics Systems Group at CSAIL, collaborating with institutions like NASA's Jet Propulsion Laboratory. His projects include ASIST (Artificial Social Intelligence for Teams), RADMAX (Risk and Deadline Aware Planning), and Uhura (risk-aware personal assistants), addressing challenges in health care, manufacturing, and defense applications.
Bertram O. Ploog is a Professor at the College of Staten Island, City University of New York, and is affiliated with the CUNY PhD program in Cognition & Comparative Psychology. He teaches courses in behavior analysis and statistics at both undergraduate and graduate levels. Research Interests: His research focuses on autism spectrum disorders, with emphasis on selective attention, stimulus overselectivity, and emotion recognition. He employs behavioral animal models, including studies with squirrel monkeys and dogs, to explore cognition and Theory of Mind. His work extends to the development and evaluation of computer-assisted technologies (CAT) for enhancing social, communicative, and language skills in children with autism. Recent Research Trends: His recent publications reflect a strong focus on attention processes in autism, the use of educational technology and computer games in developmental disabilities, and cross-species cognitive studies. His work bridges experimental psychology, clinical applications, and technological innovation in autism research. Professional Recognition: He is a Board-Certified Behavior Analyst and a licensed psychologist and behavior analyst in New York State. Education and Training: He earned his Ph.D. from the University of California, San Diego, mentored by Laura Schreibman and Ben Williams. His academic foundation was shaped by George Reynolds and Edmund Fantino in experimental behavior analysis. Advising and Grants: While no specific students or grants are listed, his active research and doctoral program affiliation suggest involvement in mentoring graduate students and securing research funding. His ongoing publications and program affiliation indicate sustained scholarly engagement. Labs and Teams: Specific lab or research team names are not mentioned in the available text.
Dr. XiaoYue Cathy Liu is an Associate Professor in the Department of Civil & Environmental Engineering at the University of Utah. She holds a PhD in Transportation Engineering from the University of Washington and advanced degrees in Transportation Planning and Electronics Engineering. Her research focuses on sustainable transportation systems, shared mobility, public transit optimization, managed lanes, and intelligent transportation systems. She actively serves on committees including the Transportation Research Board's Highway Capacity Quality of Service (HCQS) Committee and chairs its Technology Transfer Subcommittee. She also advises the Utah Model Advisory Committee and previously served on Salt Lake City’s Transportation Advisory Board. Dr. Liu is a licensed professional engineer in Utah. Education: PhD, Transportation Engineering, University of Washington (2013) MA, Transportation Planning & Management, Texas Southern University BS, Electronics & Electrical Engineering, Beijing Jiaotong University Graduate Certificate, Global Trade & Logistics, University of Washington (2011) Research Interests: Electric vehicle infrastructure and charging networks Smart transportation systems and agent-based modeling GIS-based asset management for transportation infrastructure Snowplowing operations optimization and winter maintenance Managed lanes and high-occupancy toll (HOT) lane analysis Public transit equity and accessibility Her recent publications emphasize data-driven approaches to transportation challenges, including EV demand forecasting, drone delivery networks, and resilience in interdependent transit systems. She collaborates on projects addressing Utah’s unique transportation needs, such as optimizing snowplow routes and integrating renewable energy into public transport systems. Professional Contributions: Board member, Utah Model Advisory Committee Former Chair, Salt Lake City Transportation Advisory Board (2013-2016) TRB Managed Lane Committee member TRB Transit Capacity & Quality of Service (TCQS) Committee member (2016-2019) Dr. Liu’s work bridges transportation engineering with computational methods, focusing on equity, sustainability, and operational efficiency. She leads research initiatives involving large-scale modeling, geospatial analytics, and interdisciplinary solutions for modern mobility challenges.
Dominic Furniss serves as Professor of Plastic and Reconstructive Surgery at the University of Oxford's Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), concurrently holding an Honorary Consultant Plastic Surgeon position. His clinical expertise centers on hand surgery and supermicrosurgery for lymphoedema treatment. His educational background includes undergraduate medical studies at Trinity College, Cambridge (Junior and Senior Scholar, first-class degree in genetic pathology) followed by clinical training at Oxford University Clinical School. He completed basic surgical training in London before returning to Oxford's Plastic Surgery Department in 2003. Professor Furniss leads pioneering research into genetic and non-genetic causes of hand conditions through multiple initiatives including the Furniss Group, Centre for OA Pathogenesis, and Hand Research Group. His work spans molecular genetics of Dupuytren's disease, carpal tunnel syndrome epidemiology, and kidney stone disease mechanisms. Recent investigations extend to AI applications in health data through the PHAIR study and causal inference methods in genetic epidemiology. Analysis of his 2024-2025 publications reveals a strategic shift toward large-scale epidemiological studies of hand injuries, interdisciplinary genetic research, and AI integration in surgical practice. These works bridge orthopaedics, genetics, and medical informatics with significant real-world clinical implications. His major scientific recognitions include: Pushpa Chopra Award Plenary Prize of the MRS Wellcome Trust Intermediate Fellowship (first awarded to a plastic surgeon) Professor Furniss has secured substantial research funding including the NIHR Clinical Lectureship (2007) and Wellcome Trust Fellowship (2012). He directs multiple research streams: RAMBOH-1 studies, Molecular Genetics of Carpal Tunnel Syndrome project, and HAWAII initiative. His team actively investigates public perceptions of health data sharing for AI through the PHAIR study while advancing supermicrosurgical techniques for lymphoedema. He maintains active leadership in the Athena SWAN gender equality initiative as a Self-assessment team member, demonstrating commitment to inclusive academic culture.
Dr. Sheila Castilho is an Assistant Professor at the School of Applied Language & Intercultural Studies, Dublin City University (DCU). She holds a PhD from DCU (2016) and a Master's from the University of Wolverhampton and University of Algarve. Her expertise lies in machine translation (MT), post-editing, and translation technology evaluation. She co-leads the New Trends in Translation Technology (NeTTT’22) conference and chairs DCU's Master in Translation Studies and Master in Translation Technology programs. Education: Licenciatura em Letras Inglês/Português (UNIOESTE University, Brazil) Master in Natural Language Processing (University of Wolverhampton & University of Algarve) PhD in Translation Technologies (Dublin City University) Research: Focuses on document-level MT evaluation, post-editing strategies, and user-centric MT assessment. Leads the DELA project and contributed to TraMOOC/iADAATPA initiatives. Published over 40 articles and co-edited 'Translation Quality Assessment: From Principles to Practice' (Springer, 2018). Grants & Projects: DCU PI for DELA (Document-level Evaluation) PRINCIPLE project (EU Low-resource MT) ELE (European Language Equality) initiative Labs/Teams: Active in ADAPT Centre (DCU) and collaborates with international NLP/MT communities (ACL, EMNLP, WMT).
Bruno Echauri Galván serves as Associate Professor in the Department of Modern Philology at the University of Alcalá (UAH), Spain, teaching core translation courses including Introduction to Translation Theory and English-Spanish Translation across multiple degree programs in Alcalá de Henares and Guadalajara. His research centers on Translation Studies and Reception Theory, with significant contributions to intersemiotic translation (particularly in children's literature), healthcare interpreting, and film adaptation reception. As an active member of the Research Group on Interdisciplinary Reception Studies, he investigates cultural reception, literary reception, and translation through frameworks including cultural mediation, censorship studies, and myth reception. Recent publications (2021-2024) demonstrate consistent innovation in translation pedagogy, including color-based translation assessment tools, service-learning translation projects, and pandemic-era teaching adaptations. His work on collaborative writing projects, mental health communication, and Carver-Lish translation controversies reveals methodological diversity across literary, audiovisual, and healthcare domains. Dr. Echauri Galván has participated in multiple research initiatives including the JOB AND MOVE student network project (2018), Homopoly strategic partnership (2016), and InterMed healthcare mediation project (2011), where he contributed expertise in intercultural communication and translation standards. He maintains active research leadership through the Interdisciplinary Reception Studies group, examining reception phenomena across cultural, literary, musical, and translational contexts while addressing contemporary issues like censorship and myth criticism.
Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.
Vonda Jump is an Associate Professor in the Social Work department at Utah State University's College of Arts & Sciences , based in the USU Brigham Region (Brigham City, Kaysville, Tremonton). Her work bridges social work and early childhood development, emphasizing practical interventions for family support. Vonda Jump’s research focuses on parent-child interaction , home visiting programs , and language development . She specializes in creating and validating tools like the PICCOLO (Parenting Interactions with Children: Checklist of Observations Linked to Outcomes) and HOVRS-A+ (Home Visit Rating Scales—Adapted and Extended to Excellence) to assess and improve parenting behaviors and home visit quality. Her studies often target low-income and migrant families, including Latino communities, to address disparities in child outcomes. Her recent work highlights trauma-informed instruction for student resilience and technology-based design for inclusive education, particularly with deaf/hard of hearing populations . She has contributed to understanding how maternal warmth, responsiveness, and structured activities like family bookmaking enhance child attachment, vocabulary, and self-regulation. Her research spans longitudinal analyses of parenting stress , temperament , and maternal language strategies in early childhood settings.
Santiago Barreda is an Associate Professor in the Department of Linguistics at the University of California, Davis, specializing in speech perception and phonetic analysis. His research examines how acoustic properties of speech convey speaker characteristics including age, gender, and physical attributes. Education: Ph.D. in Linguistics (Phonetics), University of Alberta, 2013 M.A. in Hispanic Studies (Language and Linguistics), University of Western Ontario, 2008 B.A. in Linguistics and Spanish Language and Literature, University of Western Ontario, 2006 Research Focus: Dr. Barreda employs behavioral experiments and statistical modeling to investigate perceptual mechanisms in speech recognition. His work bridges theoretical phonetics with practical applications, particularly in vowel normalization techniques and formant tracking algorithms. Key questions address how listeners extract speaker identity from acoustic cues and interpret social characteristics through vocal signals. Publication Trends: Recent publications (2020-2025) reveal three dominant themes: computational phonetic tools (FastTrack, phonTools), perception of social/physical speaker characteristics from children's voices, and interdisciplinary public health research on speech-related aerosol transmission. His work demonstrates strong methodological consistency in combining acoustic analysis with perceptual validation. Scientific Awards: No scientific awards were mentioned in the source material. Advising and Grants: The provided documentation does not specify graduate student advising roles or external grant funding. Technical Contributions: Dr. Barreda develops open-source phonetic analysis software including FastTrack (Praat-based formant tracking) and the phonTools R package, which have become standard resources in acoustic phonetic research.
Max Pellert is a computational social scientist and cognitive scientist with faculty appointments at multiple institutions. Since 2022, he has served as an Assistant Professor at the Chair for Data Science in the Economic and Social Sciences at the University of Mannheim . He previously held an interim Professor position at the University of Konstanz and an Assistant Researcher role at Sony Computer Science Laboratories Rome. His work bridges computational methods with social science theory. Education M.Sc., University of Vienna (2017) in Middle European interdisciplinary Master's program in Cognitive Science (with distinction) Ph.D., Medical University of Vienna (2022) in Medical Informatics, Biostatistics & Complex Systems Research interests center on Computational Social Science , Digital Traces , and Natural Language Processing for emotion and sentiment analysis. He develops Temporal Adapters for tracking longitudinal emotional patterns and FAULTANA pipeline for polarization studies. His AI Psychometrics framework assesses personality-like traits in large language models. Recent publications include: (1) 2025 ACL work on political bias in LLMs; (2) 2025 ICWSM study of temporal emotion analysis; (3) 2024 Perspectives on Psychological Science paper on LLM psychometrics; (4) 2024 PNAS Nexus polarization analysis; (5) 2023 Emotion cross-cultural study of pandemic emotions. Scientific Awards Habilitation candidate status at University of Mannheim Teaching includes IS 616: Large Scale Data Analysis , IS 809: Advanced Text Mining Lab , and IS 723: Data Science Seminar at master’s and PhD levels. His Barcelona Supercomputing Center role focuses on principal investigator duties for computational social science projects.
Jason Smith is a Postdoctoral Scholar at Northwestern University , affiliated with the Interactive Audio Lab . He earned his PhD in Music Technology from the Georgia Institute of Technology . Research Focus Human-AI collaboration in creative domains Interactive music systems AI-driven accessibility solutions Creative autonomy and neural audio generation Recommender systems for music libraries Publication Trends His work spans 2019–2025, emphasizing AI applications in music education, accessibility (especially for blind/visually impaired users), and immersive environments like AI holodecks. Key methodologies include co-design, hybrid recommendation algorithms, and automated creativity assessment. Lab Affiliation He contributes to the Interactive Audio Lab, exploring intersections of sound, code, and AI.
Gül Varol is a permanent researcher at École des Ponts ParisTech's IMAGINE group, an ELLIS Scholar, and Guest Scientist at Max Planck Institute. She holds a PhD from Inria Paris/ENS with awards from ELLIS and AFRIF. Her academic service includes Program Chair at ECCV'24 and Area Chair roles at major conferences. Current affiliations: IMAGINE group (École des Ponts ParisTech), Max Planck Institute Previous roles: Postdoctoral researcher at University of Oxford Her research focuses on vision-language applications, particularly in 3D human motion synthesis, sign language technology, and audio description generation. Key techniques include text-conditioned diffusion models, temporal context modeling, and synthetic data utilization. Scientific contributions recognized through: Google Research Scholar award (2023) ELLIS PhD Award (2020) AFRIF PhD thesis award (2020) Best application paper at ACCV'20 Recent publications demonstrate expertise in: Text-driven 3D motion editing (MotionFix, 2024) Cross-dataset generalization studies (TMR++, 2024) Temporal action composition frameworks (TEACH, 2022) Sign language dense annotation methods (BOBSL, 2022) Zero-shot audio description generation (AutoAD-Zero, 2024) She actively contributes to dataset development including BOBSL (British Sign Language corpus) and SURREACT synthetic action dataset, while pioneering new evaluation metrics for audio description quality and motion retrieval benchmarks.
Muhammad Ali Gulzar is an Assistant Professor in the Computer Science Department at Virginia Tech and an Amazon Scholar at Amazon Web Services. His research focuses on improving developer productivity through automated debugging and testing for applications in emerging domains, including data-intensive software such as dataflow programs, ML/AI applications, and computational notebooks. Education Ph.D. in Computer Science from University of California, Los Angeles (Google Ph.D. Fellow 2017-2020) Research Interests Gulzar's research spans three primary areas: (1) automated tracking-code localization techniques in web applications, (2) re-engineering testing and debugging for data-intensive applications, and (3) advancing current testing and debugging practices in Federated Learning Applications. His work addresses the challenges of debugging in complex systems where traditional approaches fail due to the scale and distributed nature of modern applications. His research has significant implications for improving software quality, developer productivity, and accessibility in web applications. Research Trends Recent publications demonstrate a strong focus on debugging and testing challenges in emerging application domains. His work bridges traditional software engineering with machine learning, data-intensive systems, and web technologies. Notably, he has made significant contributions to Federated Learning debugging (FedDebug), accessibility challenges in ad-driven web applications, and semantic caching for Large Language Models. His approach often combines novel algorithmic insights with practical implementations that address real-world challenges in software development and maintenance. Scientific Awards Google Ph.D. Fellow (2017-2020) $1.1 million NSF award for Federated Learning research ACM CCS 2024 Distinguished Artifact Award Advising and Grants Gulzar leads a productive research group with multiple students contributing to publications in top-tier venues. His NSF-funded research on Federated Learning demonstrates his ability to secure competitive funding for innovative projects. His advising style appears to emphasize practical impact alongside theoretical contributions, with students often taking lead roles in publications. Current research directions include debugging techniques for Large Language Models, accessibility challenges in modern web applications, and novel testing approaches for distributed data processing systems.
Shih-Chii Liu holds the rank of Privatdozent (Associate Professor) in the Department of Information Technology and Electrical Engineering at ETH Zürich. He is affiliated with the Institute of Neuroinformatics , a joint institute between the University of Zurich and ETH Zurich. His research focuses on neuromorphic engineering, bio-inspired neural hardware, and edge computing systems, emphasizing energy-efficient algorithms and sensor technologies. Key research areas include neuromorphic sensors for real-time data processing, sparsity-aware neural networks, and adaptive computing architectures for edge devices. His work spans applications such as speech enhancement, wearable health monitoring, and bio-inspired keyword spotting systems. He leads the Sensors Research Group, which develops neuromorphic systems integrating novel sensors, spiking neural networks, and low-power hardware accelerators. Recent projects include the DeltaKWS low-power keyword spotting IC, EFLOP computational cost metrics for spiking networks, and NeuroBench benchmarking frameworks for neuromorphic systems. His contributions emphasize bridging biological neural principles with practical engineering solutions for IoT and embedded systems. Liu teaches courses such as Neuromorphic Engineering I and collaborates on cross-disciplinary projects involving neuroprosthetics, smart wearables, and multimodal sensor fusion. His work is characterized by hardware-software co-design approaches to tackle challenges in real-time, low-latency, and energy-constrained computing environments.
