Dikai Liu is a Distinguished Professor and Strategic Research Director at the University of Technology Sydney (UTS), Australia, within the School of Mechanical and Mechatronic Engineering . His work spans field robotics and human-robot collaboration (HRC) , focusing on autonomous systems for infrastructure maintenance, construction automation, and underwater operations. Key research areas: Robotics, Human-Robot Interaction, Bio-Inspired Design, Infrastructure Maintenance Recent publications highlight innovations in trust modeling for HRC, stiffness control in continuum robots, and sociotechnical frameworks for AI-driven robotic systems. His 15 most recent articles emphasize applications in bridge maintenance, construction automation, and ethical AI integration. Awards include the 2019 UTS Medal for Research Impact, ASME DED Leonardo da Vinci Award (USA), and multiple engineering excellence recognitions. His research has generated over $22M in external funding, including 13 ARC grants and industry partnerships.
Marisa Exter serves as Associate Professor of Learning Design and Technology within Purdue University's Department of Curriculum and Instruction since 2020, following promotion from Assistant Professor (2013-2020). With 15+ years of software design and development experience, she holds dual expertise in Computer Science (BS/MS) and Instructional Systems Technology (PhD). Her educational foundation includes: PhD in Instructional Systems Technology, Indiana University (2011) MS in Computer Science, Illinois Institute of Technology (2003) BS in Computer Science, Elmhurst College (1999) Dr. Exter's research pioneers transdisciplinary educational innovation , focusing on how design processes transform technology-creation fields like Instructional Design, Computing, and Engineering. She investigates formal and non-formal learning experiences to enhance undergraduate education through interdisciplinary programs, while simultaneously examining online graduate program structures that support adult learners' connection to faculty and peers. Her work integrates heutagogical principles for lifelong learning and rigorously documents design successes/failures through scholarly design cases. Analysis of her 15 most recent publications reveals accelerating focus on dispositional development in computing professions , competency-based curriculum models, and transdisciplinary experience design. Her methodological evolution includes increased use of Q methodology and collaborative autoethnography to capture diverse perspectives in educational innovation. As Purdue PI for a $3M multi-institutional computing competencies grant and co-coordinator of AECT's Summer Research Symposium, she drives national curriculum reform. Her mentorship approach cultivates the Exploring Disruptive Education research family group, which investigates interdisciplinary design processes across technology-rich learning environments. Professional service spans Computing Curriculum 2020 Task Force leadership and ACM/AERA membership. The Exploring Disruptive Education research collective she co-leads operates as an interdisciplinary incubator for educational innovation. This team examines how design thinking transforms learning experiences through technology integration, with particular emphasis on creating connections between formal education and industry practices in computing and engineering fields.
Richard Born is a Professor of Neurobiology at Harvard Medical School , focusing on the circuitry of the mammalian cerebral cortex and its role in visual perception. His lab employs multi-species approaches, combining primate psychophysics and electrophysiology rodent 2-photon imaging and optogenetics hierarchical Bayesian modeling of perceptual inference to investigate cortico-cortical feedback, neural variability, and context-dependent visual processing. Research Interests span visual systems neuroscience, with emphasis on top-down modulation of sensory processing binocular rivalry and perceptual states gamma oscillations and neural synchrony input-gain control in V1/V2/V3 Bayesian brain frameworks neuroanatomical connectomics Recent work explores layer 1 dendritic interactions with somatostatin interneurons and collaborations with institutions like Boston University and the University of Rochester. Advising includes mentoring postdoctoral fellows (Ariana Sherdil, Camille Gómez-Laberge, Abhinav Grama) and students at Harvard Medical School. The lab utilizes advanced techniques including multi-electrode arrays laminar probes optogenetic perturbation DTI tractography validation for circuit analysis.
Jaynie Yang, PhD, is a full Professor in the Department of Physical Therapy, Faculty of Rehabilitation Medicine at the University of Alberta, where she has served since January 1990. She additionally holds adjunct appointments in the Department of Biomedical Engineering and is an active member of the Neuroscience & Mental Health Institute and the Women and Children’s Health Research Institute. She has previously acted as Graduate Coordinator for the thesis-based MSc and PhD programs and as Acting Chair of the Department of Physical Therapy. Education Post-doctoral Fellowship, Neuroscience, University of Alberta (1987–1989) PhD, Kinesiology, University of Waterloo (1987) BSc, Physical Therapy, Queen’s University (1978) Research Interests Dr. Yang’s research centres on how the nervous system controls human walking and how this control is altered following injury to the central nervous system. She investigates three inter-related themes: (1) neural mechanisms underlying gait control in healthy humans and how these are disrupted by spinal cord injury or perinatal brain injury; (2) optimization of task-specific training paradigms—such as intensive early therapy in infants with perinatal stroke or powered exoskeleton training in adults with spinal cord injury—to drive neuroplasticity and improve walking; and (3) developmental aspects of motor learning, comparing how children and adults acquire and retain novel walking patterns on split-belt treadmills. Recent Publication Trends Over the past decade her team has produced a high-impact portfolio that blends mechanistic studies of neural plasticity with pragmatic clinical trials. Common keywords across recent papers include “perinatal stroke,” “cerebral palsy,” “spinal cord injury,” “powered exoskeleton,” “functional electrical stimulation,” and “neuroplasticity.” The work spans bench-to-bedside translation, from rodent studies of critical periods through multi-centre randomized controlled trials evaluating early intensive rehabilitation protocols in infants and gait-retraining paradigms in adults. Current Studies & Funding Multi-centre RCT (Edmonton & Calgary) examining early, intensive leg training in children Cohort studies investigating cortical and spinal neuroplasticity induced by powered exoskeleton (ReWalk, Ekso) training in adults with chronic SCI. Split-belt treadmill studies comparing motor learning retention across children, young adults, and older adults. Laboratory & Team Dr. Yang leads an interdisciplinary group that integrates neurophysiology, biomechanics, and clinical rehabilitation. The lab is embedded within the University of Alberta’s Neuroscience & Mental Health Institute and has active collaborations with the Glenrose Hospital, Alberta Children’s Hospital, and Children’s Hospital of Eastern Ontario. At present she mentors one graduate student and is not accepting additional trainees for the upcoming cycle. Teaching She is the instructor for PTHER 500 – Movement Analysis, a core course in the MScPT curriculum covering mechanical and analytical concepts essential for physical therapy practice (scheduled for Fall Term 2025).
Professor Banu Bayar is a distinguished faculty member at Muğla Sıtkı Koçman University's Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation with specialization in Musculoskeletal Rehabilitation. With over two decades of academic and clinical experience, she has established herself as a leading researcher in physical therapy and rehabilitation sciences. Education: Bachelor's Degree: Hacettepe University - School of Physical Therapy and Rehabilitation (1990-1994) Master's Degree: Hacettepe University - Institute of Health Sciences - Physical Therapy and Rehabilitation (MSc) (1996-1998) Doctorate: Hacettepe University - Institute of Health Sciences - Physical Therapy and Rehabilitation (1998-2002) Professor Bayar's research spans multiple critical areas in rehabilitation science, with particular emphasis on stroke rehabilitation, musculoskeletal disorders, pain management, and geriatric rehabilitation. Her work integrates clinical practice with scientific research to develop evidence-based rehabilitation protocols. She has extensively studied proprioception, balance disorders, and functional recovery in neurological conditions, contributing significantly to the understanding of how physical interventions can improve patient outcomes. Her publication record demonstrates consistent scholarly output with a notable increase in high-impact research in recent years. The trend shows a strategic focus on developing assessment tools, validating measurement instruments, and investigating innovative rehabilitation techniques. Her work bridges clinical practice and scientific validation, with particular attention to culturally adapted assessment tools for the Turkish population. Recent publications reveal growing interest in technology-assisted rehabilitation, cognitive-motor interactions, and the psychological aspects of chronic conditions. Professor Bayar has served as an editor for multiple journals including the Journal of Karya Health Science (2020-2022) and has contributed to numerous book chapters on physiotherapy and rehabilitation. Her collaborative research approach is evident in her extensive co-authorship network, working with colleagues across various institutions to advance rehabilitation science. While specific grant information isn't detailed in the available records, her sustained publication output suggests successful research funding throughout her career.
Christopher Mlynski is a researcher at the Department of Occupational, Economic and Social Psychology, Faculty of Psychology, University of Vienna. His work focuses on self-control mechanisms, cognitive fatigue, and environmental psychology, particularly how lay beliefs influence pro-environmental behavior and motivational shifts. His research spans psychophysiological responses to behavioral challenges, ego depletion, and the interplay between cardiovascular metrics and psychological effort. Recent publications highlight collaborations with Veronika Job and colleagues on disengagement strategies and reward systems in mental labor. Key trends in his 7 publications (2016–2025) include empirical investigations into inhibitory control, task disengagement, and the physiological correlates of motivation. His work bridges experimental psychology with practical applications in environmental behavior and self-regulation.
Robert Nowak holds dual distinguished professorships as the Keith and Jane Morgan Nosbusch Professor in Electrical and Computer Engineering and the Grace Wahba Professor of Data Science at the University of Wisconsin–Madison. Based at the Discovery Building (330 N Orchard Street), he leads interdisciplinary research at the Wisconsin Institute for Discovery, bridging engineering with data science applications. His academic foundation includes: BS, MS, and PhD from the University of Wisconsin–Madison Post-doctoral Fellowship at Rice University Nowak's research program spans artificial intelligence, machine learning, and optimization with dual emphases on AI-driven health applications and systems optimization. His work integrates theoretical rigor with practical implementations, particularly in large language model fine-tuning, active learning frameworks, and neural network theory. Recent publications demonstrate strong focus on improving model efficiency, humor comprehension in AI systems, and theoretical bounds for retrieval-augmented generation. Analysis of his 15 most recent publications reveals dominant trends in large language model advancement (particularly humor understanding and task diversity), theoretical neural network analysis (including sparse architectures and multi-task learning), and novel active learning methodologies for open-world scenarios. His work consistently bridges theoretical machine learning with real-world applications in health and recommendation systems. While specific named awards aren't documented in the source material, his appointment to two endowed chairs (Nosbusch and Wahba professorships) represents exceptional institutional recognition of his scholarly impact. Nowak advises graduate students in the Electrical and Computer Engineering department and secures significant research funding, including NSF grants such as CIF: Small: Advanced Understanding and Applications of Deep Learning. His group operates within the collaborative ecosystem of the Wisconsin Institute for Discovery, fostering cross-disciplinary projects that integrate AI with health sciences and engineering systems. Current projects indicate strong momentum in human-AI collaboration frameworks and optimization of language model training pipelines.
Anna Vilanova is a Full Professor in Visual Analytics at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e), and is associated with the Electrical Engineering department's Signal Processing Systems. Previously, she served as Associate Professor at TU Delft (2013-2019) and Assistant Professor at TU/e (2002-2013). Her research focuses on Visual Analytics for high-dimensional data , explainable AI , and biomedical applications including Diffusion Weighted Imaging, 4D Flow, and Pangenomics. Education: Doctorate in Computer Graphics & Visualization (2001) Master in Computer Science (1997), Universitat Politècnica de Catalunya Research Highlights: Vilanova leads work on Visual Analytics systems for biomedical data, with recent publications in Diffusion MRI modeling , Tractography visualization , Explainable AI frameworks , and Pangenomic variant analysis . Her work bridges dimensionality reduction , uncertainty visualization , and medical imaging applications. Scientific Contributions: NWO-Veni grant (2005): "Visualization of global tensor information for diffusion tensor imaging" NWO-Aspasia grant (2013) Best Poster Award EuroVis (2025) Best Demo/Poster Awards (2022) Leadership & Service: Vilanova serves on the IEEE VIS Steering Committee , was EUROGRAPHICS President (2019-2022), and contributes to conferences like IEEE Visualization and EG-EuroVis . She co-founded the EAISI Health research initiative at TU/e.
Daniel Hershcovich is a Tenure-Track Assistant Professor at the Natural Language Processing section of the Department of Computer Science, University of Copenhagen. His research focuses on cross-cultural adaptation of language models, integrating human values into AI systems, and evaluating AI's real-world impact in domains like law, literature, and food culture. Research Themes Cultural value alignment in LLMs Multimodal models for accessibility Cross-cultural recipe and food knowledge Historical Scandinavian text analysis Ethical AI and bias mitigation Scientific Recognition SAC Highlight Award (ACL 2025) Outstanding Paper Award (ACL 2017) Advising & Grants : Mentions collaborations on multiple EMNLP/ACL/CoNLL papers. Leads Independent Research Fund Denmark project ALIKE (2025-2027) and contributes to Innovation Fund Denmark's XHAILe (2025-2028). Co-organized SemEval 2019 and CoNLL 2019/2020 shared tasks. Labs & Teams : Leads the CoAStaL research group. Collaborates with teams at IBM Research Haifa, University of Manchester, and Wuhan University of Science and Technology.
Xiaobo Li is a Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology. Holding a Ph.D. in Computer Aided Geometric Design from the University of Birmingham and a B.S. in Automation from Nanjing University of Aeronautics, their research bridges computational methods with neuroimaging and psychiatric disorder analysis. Ph.D., University of Birmingham (Computer Aided Geometric Design, 2004) B.S., Nanjing University of Aeronautics (Automation, 1999) Dr. Li’s work focuses on applying machine learning and graph theory to understand brain network abnormalities in conditions like ADHD , schizophrenia , and traumatic brain injury . Their studies analyze structural-functional connectivity , reward processing , and gut-brain axis interactions using fMRI , fNIRS , and diffusion tensor imaging . Recent publications highlight their development of tools like the GAT-FD MATLAB toolbox for brain network analysis and their exploration of multimodal MRI in schizophrenia diagnosis. They also investigate the neurobiological effects of photobiomodulation and vision therapy interventions.
Dominik Stammbach is a Postdoctoral Research Associate at Princeton University's Center for Information Technology Policy (CITP) and Polaris Lab, applying Natural Language Processing to enhance access to justice, detect climate misinformation, and combat corporate greenwashing through data-centric methodologies. His educational background includes: Dr. Sc. in Computer Science, ETH Zurich (2024) Master's in Language Science and Technology, Saarland University, Germany Stammbach's research pioneers NLP applications for societal impact, focusing on automated fact checking (evidence extraction from legal documents), AI tools for public defenders, and detection of climate denial narratives. He integrates high-quality data practices with transformer-based models to address real-world challenges in legal accessibility and environmental communication, emphasizing user-centered design through nationwide interviews with public defenders. His publication trajectory reveals accelerating specialization in climate-NLP intersections and legal AI, with 2023-2024 works dominating his output. Key themes include knowledge-base optimization for fact verification, political bias mitigation in LLMs, and environmental claim detection—showcasing methodological rigor through ACL/EMNLP publications and interdisciplinary journal contributions. Stammbach actively shapes research communities as organizer of ClimateNLP workshops (ACL 2024/2025) and keynote speaker at IEEE ICDM 2025, driving collaboration between NLP researchers and climate scientists while developing practical AI tools for public sector agencies.
Tim Rocktaschel is a Professor of Artificial Intelligence in the Department of Computer Science at University College London (UCL), where he has been working since 2018. He was promoted to Professor in October 2023, having previously served as an Associate Professor (2021-2023) and Lecturer (2018-2021) at the same institution. His educational background includes a Doctorat from University College London (2017) and a Diplom Informatiker from Humboldt-Universitat Berlin (2012). Rocktaschel's research focuses on the cutting edge of artificial intelligence, with particular emphasis on reinforcement learning, evolutionary computation, and open-ended learning systems. His work explores how AI systems can learn more efficiently through better exploration strategies, environment design, and the integration of language models with reinforcement learning frameworks. His recent publications reveal a strong trend toward developing more efficient and generalizable AI systems. The research spans unsupervised environment design, prompt engineering for self-improving systems, exploration strategies in reinforcement learning, and the application of language models to enhance policy learning. His work often bridges theoretical AI concepts with practical implementations, as evidenced by tools like GriddlyJS for reinforcement learning development. Rocktaschel maintains an active presence in the AI research community with numerous publications in top venues including NeurIPS, ICML, and the Journal of Artificial Intelligence Research. His work on zero-shot generalization, pragmatic understanding in language models, and open-ended learning environments has garnered significant attention in the field. He is actively involved in developing tools and datasets for the AI community, such as the large-scale NetHack dataset, which provides a complex environment for testing reinforcement learning algorithms. His research continues to push the boundaries of what's possible in artificial intelligence, particularly in creating systems that can learn and adapt in complex, open-ended environments.
Dr. Eric T. Reifenstein is a neuroscience researcher at Humboldt-Universität zu Berlin's Faculty of Life Sciences, Institute of Biology, specializing in neural mechanisms of spatial navigation and memory. As a key contributor to SFB 1315 (Entorhinal Cortex as Interface between Memory and Space), his work bridges computational modeling and human electrophysiology. His research focuses on egocentric spatial mapping and sequence learning mechanisms , with particular expertise in human single-neuron recordings during virtual navigation tasks. Reifenstein investigates how the brain encodes self-centered spatial representations and how synaptic learning rules enable temporal sequence memorization. His publication trends reveal a strong emphasis on translational neuroscience , connecting rodent studies to human cognition through innovative virtual reality paradigms. Key methodological approaches include single-neuron recordings, computational modeling of neural networks, and behavioral analysis of spatial memory. Scientific contributions include: Identification of egocentric bearing cells in human parahippocampal cortex Mathematical analysis of phase precession in sequence learning Vectorial representation models of egocentric space Reifenstein actively collaborates with leading researchers including Prof. Richard Kempter and Joshua Jacobs, contributing to major neuroscience journals like Neuron and eLife. His work has implications for understanding memory disorders and developing neural prosthetics.
Jie M. Zhang is an Assistant Professor in the Department of Informatics at King's College London, specializing in the intersection of software engineering and artificial intelligence. Her research focuses on two main directions: AI for Software Engineering (leveraging AI technologies to automate software tasks) and Software Engineering for AI (applying SE principles to enhance AI system trustworthiness). Her educational background includes a PhD in Computer Science from Peking University, where she was supervised by Professors Lu Zhang and Dan Hao. Prior to joining King's College London, she was a Research Fellow at University College London working with Professor Mark Harman and Professor Federica Sarro. Dr. Zhang's research interests center on software testing, machine learning trustworthiness, fairness testing, bias mitigation in AI systems, and program analysis. Her work particularly examines how large language models can be utilized for code generation, test case creation, and program repair, while also developing techniques to detect and fix issues within AI models. Her recent publications demonstrate strong trends in evaluating and enhancing the trustworthiness of AI-generated code, with specific emphasis on fairness testing across various domains including autonomous driving systems, machine translation, and decision-making software. Her research increasingly focuses on the efficiency of generated code and detecting hallucinations in large language models. 2025 ACM Sigsoft Early Career Researcher Award for pioneering contributions to software engineering for AI IEEE TSE 2024 Best Paper Award for 'Stealthy Backdoor Attack for Code Models' FSE 2025 Distinguished Paper Award Royal Society International Exchange Grant recipient NMES Enterprise & Engagement Partnerships Fund recipient Dr. Zhang has served in numerous leadership roles across major software engineering conferences including as General Chair for AIware 2025, Area Chair for ASE 2025, and Steering Committee Member for ICST. She has advised multiple PhD students and received significant research funding for her work on LLMs and software engineering. Her research group collaborates with industry partners including Huawei and Facebook, and she leads projects such as ITEA GENIUS and ITEA GreenCode. She is actively involved with King's College London research hubs including the Trusted Autonomous Systems Hub, Security Hub, and Software Systems group, where her work contributes to developing trustworthy AI systems across multiple domains.
Prof. Dr. Jens Eisert is a Professor at the Free University of Berlin, where he leads the Quantum Many-Body Theory, Quantum Information Theory, and Quantum Optics research group (Eisert AG) within the Institute of Theoretical Physics at the Dahlem Center for Complex Quantum Systems. His office is located at Arnimallee 14, Room 1.3.06 in Berlin-Dahlem. His research focuses on the intersection of quantum information theory and condensed matter physics, specifically exploring what information processing tasks are possible using individual quantum systems as information carriers. His group develops mathematical-theoretical foundations of quantum information, particularly in entanglement theory and tomography, while also investigating quantum optical implementations using light modes or cold atoms in optical lattices. A major emphasis of their work is on quantum many-body systems, including static properties, efficient numerical simulation methods like tensor networks, and non-equilibrium quantum dynamics. Recent publications highlight significant contributions in thermalization of quantum systems (Communications Physics 2025), quantum thermodynamics (Nature Physics 2025), and quantum error correction (PRX Quantum 2025). The group's work is characterized by combining the rigor of mathematical physics with physically motivated applicability, frequently leading to direct collaborations with experimental groups. Quantum Information Theory Quantum Many-Body Theory Quantum Optics Entanglement Theory Tensor Networks Quantum Error Correction Prof. Eisert maintains active supervision of numerous PhD students and postdoctoral researchers, with research positions regularly available in areas including quantum error correction, quantum information theory, tensor networks, and quantum simulation. His group has published extensively in top journals including Nature Physics, PRX Quantum, and Physical Review series.
