Fangju Wang is a retired Professor with expertise in artificial intelligence, machine learning, and intelligent systems. His research focuses on applying partially observable Markov decision processes (POMDP) and reinforcement learning to develop efficient algorithms for intelligent tutoring systems (ITS), emphasizing computing cost reduction and uncertainty management. He has contributed to interdisciplinary computational science education through collaborative academic program development. Key research interests include algorithm optimization, policy tree efficiency, Bellman equation solutions in POMDP frameworks, and adaptive teaching strategies in educational technology. His work bridges theoretical advancements with practical applications in speech recognition and user behavior modeling in dialogue systems. No academic awards, grants, or advising roles are explicitly documented in the provided materials. His publications span conferences like CSEDU and journals such as International Journal of Information and Education Technology, focusing on computational methods in education and AI.
Dr. Grace Wenling Cao is an Assistant Professor in Linguistics (Phonetics and Phonology) at the School of Languages, Cultures and Linguistics, University College Dublin. She holds a PhD from the University of York and an MSc from the University of Edinburgh. Prior to her current position, she served as a Hong Kong RGC Postdoctoral Fellow and lecturer at the Chinese University of Hong Kong and the Hong Kong University of Science and Technology. PhD in Linguistics – University of York MSc in Developmental Linguistics – University of Edinburgh Her research focuses on sociophonetics , forensic phonetics , and language attitudes in multilingual contexts, particularly in Hong Kong. She investigates phonetic convergence, cross-language speaker identification, the impact of visual cues on speech perception, and identity-related language changes in Cantonese and Hong Kong English. Her work bridges theoretical linguistics with real-world applications in forensic science and human-AI interaction. Recent publications span high-impact journals and conferences, showing a strong trend in using acoustic-phonetic analysis to understand social biases, speaker identity, and forensic challenges in trilingual environments. Her work increasingly integrates AI and machine learning approaches to speaker recognition, especially in cross-language settings using filled pauses as biometric markers. She has received multiple scientific awards and grants, including the prestigious Hong Kong RGC Postdoctoral Fellowship and funding from the Worldwide Universities Network. Her current research includes a major project on human-AI speech accommodation. Hong Kong RGC Postdoctoral Fellowship Worldwide Universities Network Research Mobility Program CUHK Global Scholarship Program Linguistic Association of Great Britain Conference Fund University of York Humanities Research Centre Grant Dr. Cao actively supervises graduate students, having guided 14 Master’s theses and one undergraduate intern. She coordinates key modules such as Phonology (MA), Phonology 2, and Sounds in Language at UCD. She has also taught a wide range of undergraduate and postgraduate courses at CUHK and HKUST, including sociolinguistics, phonetics, and bilingualism. She leads research on trilingual speech in Hong Kong, particularly focusing on forensic applications and sociolinguistic identity. Her team explores how filled pauses, accent perception, and visual cues affect intelligibility and speaker identification, with implications for both legal and technological domains.
Prof. Bettina Braun is a faculty member at the University of Konstanz, specializing in prosody and intonation. Her research explores intonational contrasts, listener processing, and second language acquisition. She teaches phonetics, phonology, and experimental methods, and was honored with the students' teaching prize. She holds regular office hours and is supported by a secretary, Barbara Werner. Research focuses on how prosody interacts with lexical meaning, particularly in questions and sarcasm. Cross-linguistic studies feature prominently, including work on German, Persian, Chinese, and Swiss dialects. She emphasizes experimental methods, including Active Learning systems for perception experiments. Her work also examines infant-directed speech and L2 acquisition challenges. Key contributions include analyses of rhetorical vs. information-seeking questions, the role of pitch accents, and the impact of native language on prosodic perception. Ongoing projects are detailed on her dedicated page. Scientific awards include the University of Konstanz student-recognized teaching prize. Her lab's work is supported by collaborations in corpus development, such as the Konstanz prosodically annotated infant-directed speech corpus (KIDS corpus). She actively publishes on intonation’s role in disambiguation and cross-cultural communication.
Dr. Jie Cao is an Assistant Professor at the School of Computer Science, University of Oklahoma, affiliated with the Gallogly College of Engineering and the Data Science and Analytics Institute. He holds a PhD from the University of Utah, and MS/BS degrees from Huazhong University of Science and Technology. His research focuses on Natural Language Processing (NLP), Machine Learning (ML), dialogue systems, structured prediction, and trustworthy AI applications in education and healthcare. Prior to OU, he was a postdoctoral researcher at the NSF AI Institute for Student-AI Teaming (iSAT) at the University of Colorado Boulder, and worked as a research scientist at WeChat AI, Amazon, and in software engineering at Alibaba and Baidu. Education: PhD in Computer Science, University of Utah MS in Computer Science, Huazhong University of Science and Technology BS in Information Security, Huazhong University of Science and Technology Research Interests: Dr. Cao’s work spans multi-party/multimodal dialogue analysis, LLM alignment, efficient structured prediction, and robust AI deployment. Notable projects include AI partners for collaborative classrooms, discourse analysis in psychotherapy, and database workload characterization. His research bridges foundational NLP/ML advancements with interdisciplinary applications in education, healthcare, and material science. Key Achievements: Recipient of the iSAT Trainee Grant (2023) and Alternative Textbook Grant (2025) Lead developer of MatterChat, a multi-modal LLM for material science Published over 20 peer-reviewed papers in top venues like ACL, COLING, and IEEE TVCG Co-instructor for NLP courses integrating LLM and agentic AI materials Labs & Teams: Leads the OUNLP lab at OU, focusing on advancing dialogue systems and trustworthy AI solutions.
Dr.-Ing. Stefan Hillmann is a researcher at the Quality and Usability Lab at Technische Universität Berlin, focusing on usability evaluation of spoken and multimodal dialogue systems. He holds a Doctor of Engineering degree and has been involved in various research projects since 2010, including the DFG-funded 'User Model' project and the Universal Home Control Interface@Connected Usability initiative. His work emphasizes dialogue management, machine learning, and reinforcement learning applications in conversational interfaces. He coordinates MOOCs on Communication Acoustics and teaches courses on topics like neural networks in language technology and multimodal interaction. Education: Diplom-Informatiker (2006), PhD candidate (since 2010) at TU Berlin. Research Projects: Smart home usability evaluation, reinforcement learning for dialogue systems, and medical dialogue system explainability. Research Interests: Prioritizes human-computer interaction, automatic usability prediction, and context-aware dialogue systems. His contributions span theoretical frameworks and applied systems, with a focus on practical usability in healthcare and education. Teaching & Mentorship: Coordinates edX courses and supervises study projects like 'Smart Picture Frame' and 'BC Stories VR.' Engages students in applied research through seminars and practical exercises. Publications: Over 15+ peer-reviewed papers since 2019, focusing on dialogue systems, explainability in AI, and crowdsourcing evaluation techniques. Recent work includes chatbot usability studies and multimodal interaction frameworks.
Neha Deshpande is a doctoral student and research associate at the Quality and Usability Lab, Technical University of Berlin. Her academic journey includes a Bachelor's in Electronics and Telecommunications Engineering from the University of Mumbai (2018), followed by a double-degree Master's in Human-Computer Interaction and Design (HCID) via EIT Digital Master School. She studied at the University of Twente (Netherlands) and TU Berlin, completing her thesis on machine learning methods for analyzing affective components of sign language. Her research focuses on fine-tuning convolutional neural networks for facial expression recognition in sign language contexts, published at the SLTAT conference (2022). She collaborates with DFKI (German Research Center for Artificial Intelligence) and teaches mobile UX/front-end development. Active in AI hackathons, she also pursues a doctorate in dialogue systems and NLP since April 2023.
Vesa Lappalainen is a Senior University Lecturer at the University of Jyväskylä's Faculty of Information Technology. His work bridges quantum computing education, learning analytics, and programming pedagogy. He leads the Quantum Information and Computation Team, focusing on advancing quantum computational methods and scalable educational frameworks. Position: Senior University Lecturer University: University of Jyväskylä Research Group: Quantum Information and Computation Team Research interests include quantum computing pedagogy, predictive learning analytics for student retention in CS1, and innovative online learning environments. Notable contributions include developing interactive quantum circuit simulators and multi-functional digital course materials. His work emphasizes equity in education and scalable solutions for competency-based admissions. Publications span quantum education tools, learning analytics models, and comparative studies on programming education methods (e.g., game-based learning, TDD in CS1). Collaborations include international teams on projects like DEFA and KATTI, addressing challenges in educational technology and student identity development. Lappalainen has advised numerous collaborative projects and contributed to curriculum design in programming education. Current research trends focus on integrating multimodal emotion tracking in learning systems and generalizable predictive models for student success.
Professor Nirmalie Wiratunga is a leading academic at Robert Gordon University, focusing on artificial intelligence and its applications across diverse domains. Her work bridges theoretical advancements with practical implementations in areas requiring high explainability and domain-specific adaptation. Research Pillars : Explainable AI (XAI), Case-Based Reasoning (CBR), Legal AI, Healthcare Informatics, and Machine Learning. Methodological Focus : Retrieval-Augmented Generation (RAG), domain adaptation, dialogue understanding, and systematic review automation. Recent publications highlight her contributions to neonatal pain assessment transparency, legal question answering systems (SCaLe-QA and CBR-RAG), and multi-shot explanation platforms (iSee). Her work on hallucination detection and federated learning emphasizes robustness and privacy preservation. Collaborations span biomedical, legal, and energy sectors, including mini-grid optimization for rural communities. Her research integrates context-aware systems into data-to-text generation and dual-task dialogue understanding, pushing boundaries in human-AI collaboration. Projects like Agentic CBR demonstrate practical implementations for financial decision-making with counterfactual explanations.
Roman Taraban is a Professor of Psychological Sciences at Texas Tech University's College of Arts & Sciences, specializing in Cognition and Cognitive Neuroscience. He directs the Cognition Lab and co-leads the Ethical Engineer initiative. His research integrates cognitive psychology with engineering ethics, educational technology, and bilingual literacy, emphasizing empirical approaches to learning processes. Education includes a Ph.D. in Cognitive Psychology from Carnegie Mellon University (1988), an M.A. in Educational Psychology from the University of Chicago (1981), and post-doctoral training at the University of Massachusetts Amherst (1988-1989). Research interests focus on text analysis, early reading instruction, learning strategies, categorization, and engineering ethics. His work employs psycholinguistic methods to investigate language processing, ethical reasoning in cross-cultural contexts, and technology-enhanced pedagogy. Recent projects examine bilingual education frameworks and metacognitive development in engineering students. Publications reflect interdisciplinary themes: cognitive modeling (2024), engineering ethics (2022-2023), and educational technology (2020-2022), with consistent emphasis on empirical validation and cross-cultural applications across 40+ works. Awards: Chancellor's Council Distinguished Teaching Award (2019) President's Excellence in Teaching Professorship (2018-2021) Psychonomic Society Fellow (2015) Fulbright-Nehru Research Scholar (2010) Advises graduate students in cognition and education research. Leads NSF-funded initiatives like the Ethical Engineer platform and DREAM Project, fostering global ethics dialogues and arts-integrated engineering pedagogy. Directs the Cognition Lab (www.depts.ttu.edu/psy/cognition), developing tools for analyzing learning behaviors. Collaborates with STEM educators through CISER (Center for Integration of STEM Education & Research) to innovate curriculum design.
Tatjana Tchumatchenko is a Group Leader at the Max Planck Institute for Brain Research in Frankfurt and affiliated with the University of Bonn Medical Center. She leads the Theory of Neural Dynamics group, focusing on computational models of neural coding, synaptic plasticity, and dendritic computation. Her work integrates mathematics, physics, and computer science to understand how neurons and networks process information. Institution: Max Planck Institute for Brain Research, Frankfurt Secondary Affiliation: University of Bonn Medical Center Group: Theory of Neural Dynamics Research Focus: Computational Neuroscience, Neural Coding, Synaptic and Dendritic Dynamics Her research spans from molecular-level processes like mRNA and protein distribution in dendrites to network-level phenomena such as information transmission, oscillations, and learning. She develops theoretical models and computational tools to analyze neural data and predict novel effects testable by experiments. Her interdisciplinary approach bridges theoretical neuroscience with experimental biology, often in close collaboration with experimental groups worldwide. The 15 most recent publications highlight a strong trend toward integrating molecular, structural, and functional aspects of synaptic and dendritic computation. Key themes include competitive synaptic plasticity, energy constraints on molecular localization, astrocyte involvement in learning, and the development of novel analytical methods for imaging and electrophysiology data. Her work increasingly connects computational principles with biological realism, influencing both neuroscience and artificial intelligence. Heinz Maier-Leibnitz-Prize (2016) ERC Starting Grant (2020) Boehringer Ingelheim FENS Research Award (2022) Young Academy of Europe Fellow (2019) Focus Magazine: 25 Young Innovators Shaping Germany’s Future (2017) Tchumatchenko has mentored over thirty students and postdocs, many of whom have received prestigious fellowships. Her research is supported by the Max Planck Society, DFG, and Hessian funding agencies. She actively contributes to the neuroscience community through organizing workshops, serving on program committees (Bernstein Conference, CNS, FENS), and promoting women in science. She currently chairs the Bonn Center for Neuroscience and co-organizes international workshops on dendritic computation and synaptic plasticity. Her lab operates at the intersection of theoretical modeling and experimental collaboration, with members shared across scientific groups. She emphasizes training the next generation of computational neuroscientists and fostering interdisciplinary dialogue.
Prof. Jacques de Swart is a Professor of Applied Mathematics at Nyenrode Business University, where he is part of the Faculty Research Center for Accounting, Auditing & Control. He holds a unique part-time role, dedicating one day per week to academia while serving as a Partner at PwC’s Consulting practice for the remaining four days. His expertise bridges mathematical rigor with business applications, focusing on audit processes, Bayesian statistics, and strategic decision-making aligned with ESG principles. Prof. de Swart’s academic journey includes a Mathematics degree from Utrecht University, with studies at La Sapienza University in Rome. He completed his PhD in Computational Science at the University of Amsterdam under the Centre for Mathematics and Computer Science (CWI). Later, he earned an MBA from Rotterdam School of Management, Erasmus University. His research interests revolve around Bayesian statistical methods, responsible AI integration, and strategic decision-making frameworks that prioritize environmental, social, and governance (ESG) considerations. He actively develops and teaches modules in machine learning, statistics, and programming, emphasizing their practical applications in auditing and business analytics. His publications highlight the application of Bayesian approaches to audit sampling and transparency, advocating for open-source tools to bridge auditors and statisticians. Recent work explores strategic decision-making frameworks that align business goals with sustainability and ethical practices. Earlier contributions include foundational research on computational methods for orbit calculations and Fekete point configurations. Prof. de Swart supervises students in their master’s thesis projects, fostering the next generation of analytical thinkers. His work reflects ongoing engagement with industry partnerships and data-driven innovation. He also chairs the Steering Group for Statistical Audit and the Watersportpark Ouderkerkerplas Foundation, advocating for accessibility in water sports for people with disabilities.
Tingting Mu is a Senior Lecturer in Machine Learning at the University of Manchester's Department of Computer Science. She holds a B.Eng. from the University of Science and Technology of China (2004) and a Ph.D. from the University of Liverpool (2008). Prior to joining Manchester in 2016, she was a lecturer at Liverpool’s Department of Electrical Engineering and Electronics. Her research focuses on advanced mathematical modeling and large-scale optimization in machine learning and data analytics. Key areas include developing algorithms for human intelligence simulation, analyzing complex data (e.g., text, images, signals), and applications in text mining and scientific data analytics. Teaching includes courses like COMP24112 Machine Learning and COMP34111 Natural Language Systems. Her work contributes to UN Sustainable Development Goals related to Digital Futures and Sustainable Futures. Notable projects include MCAIF (Centre for AI Fundamentals) as a researcher and a Knowledge Exchange Partnership with Fusion21 Ltd. Over 60 research outputs span topics like neural networks, adversarial learning, and ontology-based systems. Current projects emphasize AI fundamentals, surrogate models for engineering systems, and interdisciplinary applications. Her lab explores techniques to enhance model interpretability, scalability, and robustness across vision, NLP, and multimodal domains.
Kelsey Allen is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC) and a Senior Research Scientist at DeepMind. Her research bridges cognitive science, machine learning, and robotics, focusing on understanding and replicating human-like problem-solving, tool use, and physical reasoning. She holds a PhD from MIT (2016) under Josh Tenenbaum and a B.Sc. in Physics from UBC (2010). Research Interests : Allen investigates computational mechanisms underlying human complex behaviors, particularly tool use and design. Her work emphasizes endowing machines with flexible problem-solving abilities. Key themes include lifelong learning, embodied cognition, and integrating symbolic and neural approaches. Awards & Recognition Best Paper Award at Robotics: Science and Systems (RSS) 2018 Oral Presentation at Cognitive Science Society 2019 Spotlight at NeurIPS 2018 and ICLR 2019 Key Projects : Includes developing graph network simulators for rigid body dynamics, tools for physical design optimization, and studies on human tool-use learning. Her work often combines empirical experiments with machine learning models to bridge human and artificial intelligence.
Ehsan Tavakoli-Nabavi is a Senior Lecturer in Technology and Society at the Australian National University (ANU), leading the Responsible Innovation Lab within the Australian National Centre for the Public Awareness of Science. His career transition from civil engineering to sociology and AI research positions him uniquely to bridge technical and social dimensions of innovation. He holds adjunct roles at Harvard Kennedy School and University of Bonn, and has held visiting positions at SOAS, University of London. Research focuses on Responsible AI , ethical computing , and addressing 'wicked problems' through transdisciplinary approaches. Key areas include governance frameworks for emerging technologies, water resource modeling, and sustainable development. He pioneered the Responsible Innovation Lab, emphasizing experimental methods in innovation ethics. Prior roles include Research Fellow at ANU School of Cybernetics (2018-2020) and Harvard Kennedy School (2016-2017). Current projects include the Water Policy Innovation Hub (2018-2022), addressing policy challenges in water scarcity via innovation ecosystems. His work integrates methodologies from systems dynamics, environmental impact assessment, and participatory modeling. Publications span journals like Nature Humanities and Social Sciences Communications , IEEE Transactions on Technology and Society , and Water Alternatives . He critiques AI development's societal impacts, advocating for equity-centered technical design and global South perspectives in innovation governance.
John M. Pauly is the Reid Weaver Dennis Professor in the Department of Electrical Engineering at Stanford University, with affiliations in the Wu Tsai Neurosciences Institute, Stanford Cancer Institute, Cardiovascular Institute, and Bio-X program. His research focuses on medical imaging, particularly MRI acceleration and reconstruction techniques for applications like cardiac imaging and interventional guidance. He holds a PhD from Stanford University (1990) and has received notable awards including the ISMRM Gold Medal (2012) and IEEE Fellow distinction (2022). His academic appointments include teaching courses such as Medical Image Reconstruction (EE 369C), Signals and Systems II (EE 102B), and The Wireless World (EE 100). He advises numerous PhD and master's students in MRI hardware, reconstruction algorithms, and clinical applications. Research highlights include innovations in deep learning for MRI quality assessment, coil design for pediatric imaging, and non-contact motion sensing via Doppler radar. His work bridges engineering and clinical needs, emphasizing practical translation of compressed sensing and machine learning methods into clinical MRI workflows.
