Zhao Xin is a dedicated researcher at the Language Technology Group (LLMC) within National Institute of Informatics, focusing on Natural Language Processing and Knowledge Representation. His academic journey includes a Doctoral Courser at The University of Tokyo, a Master Courser at Nara Institute of Science and Technology, and a Bachelor’s in Japanese at Xi'an Jiaotong University. Education: Doctoral Courser (2023–2026), Information Science and Technology, The University of Tokyo Master Courser (2018–2020), Computer Science, Nara Institute of Science and Technology Bachelor Courser (2013–2017), Foreign Languages and Literatures, Xi'an Jiaotong University His research interests span Natural Language Processing , Domain Adaptation , Cross-lingual Transfer , Model Interpretability , and Semantic Search . Recent projects include analyzing neuron-level controllability in language models and cross-lingual knowledge transfer for Japanese NLP tasks. Zhao’s publications (2024–2025) emphasize fact knowledge evaluation , neuron activation analysis , and cross-lingual entity alignment . He received the Young Investigator Award for his work on multilingual knowledge tracing. Proficient in Japanese and English , Zhao contributes to tools like llm-jp-eval and Megatron-LM , with expertise in LLM pre-training , domain adaptation , and Python/Machine Learning frameworks.
Hans Martin Kjer is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the UltraSound and Biomechanics group within the Visual Computing Center and the Center for Fast Ultrasound Imaging. His research bridges engineering and medical imaging, with a strong emphasis on developing and validating advanced ultrasound techniques for biomedical applications. Research Interests: His work focuses on super-resolution ultrasound imaging, microvascular analysis, 3D reconstruction of biological structures, and image registration. He applies computational methods to improve the resolution and accuracy of ultrasound, particularly in renal and lymph node vasculature imaging. His research contributes to the UN Sustainable Development Goals in health and well-being through innovative diagnostic tools. Publication Trends: Over the past several years, Kjer has consistently published in high-impact journals and conferences in biomedical engineering and imaging. His recent work emphasizes the validation of super-resolution ultrasound against micro-CT, realistic 3D blood flow simulation, and the application of AI in enhancing imaging resolution. These studies reflect a strong trend toward quantitative, reproducible, and clinically relevant imaging solutions. Scientific Contributions: While no specific awards are listed, his leadership in major research projects and frequent collaborations with leading experts in ultrasound (e.g., Jørgen Arendt Jensen) underscore his significant role in the field. Advising and Funding: Kjer serves as a supervisor and principal investigator in several funded research initiatives, including AI for Extreme Super-Resolution CT , 3DIM: 3D Imaging Center , and QIM: Center for Quantification of Imaging Data from Max IV . He mentors PhD students and collaborates across disciplines, contributing to both biomedical and materials science imaging projects. Laboratories and Teams: He is an integral member of the Center for Fast Ultrasound Imaging and the Visual Computing Center at DTU. These teams focus on cutting-edge ultrasound technologies, image processing algorithms, and multimodal imaging integration, positioning Kjer at the forefront of computational biomedical imaging in Denmark.
Professor Tony Jebara is a faculty member in the Department of Computer Science at Columbia University, where he chairs the Center on Foundations of Data Science and directs the Columbia Machine Learning Laboratory. His research focuses on machine learning with applications in vision, graphs, and spatio-temporal data. He holds a PhD from MIT (2002) and has advised startups including Sense Networks, Evidation Health, and Agolo. Notable awards include the NSF Career Award (2004), Best Paper at ICML 2009, and recognition as one of Esquire's Best and Brightest (2008). His work has been featured in major media outlets. Jebara's academic contributions span over 100 peer-reviewed papers and a textbook on machine learning. He served as General Chair for ICML 2017 and Program Chair for ICML 2014. His research explores generative and discriminative models, Bayesian inference, and optimization techniques. Current projects include neural ensemble analysis in neuroscience and robust learning algorithms for environmental modeling. Recent publications emphasize scalable methods for collaborative filtering, survival analysis in online experiments, and graphical model applications in neuroscience. His work bridges theoretical advancements with practical systems, including contributions to privacy-preserving algorithms and recommendation systems.
Joana Braga Pereira is an Associate Professor (Docent in Neurosciences) and Principal Researcher at the Department of Clinical Neuroscience, Karolinska Institutet, where she leads the Brain Connectomics research group. Her work focuses on brain connectivity measures derived from structural MRI, functional MRI, diffusion tensor imaging, and other neuroimaging modalities in patients with neurodegenerative disorders. Education: Docent in Neurosciences, Karolinska Institutet, 2021 PhD in Biomedicine, University of Barcelona, 2012 Master in Neurosciences, University of Barcelona, 2008 Postdoctoral Fellow in Neuroimaging, Karolinska Institute, 2017 Dr. Pereira's research focuses on understanding brain connectivity and network topology in neurodegenerative disorders, particularly Alzheimer's and Parkinson's diseases. Her work integrates multiple neuroimaging modalities with biomarker analysis to identify early signs of disease and track progression. She specializes in applying graph theory, deep learning, and novel imaging sequences to analyze complex brain networks and develop precision medicine approaches. Her research spans multimodal brain connectivity, dynamic brain connectivity, functional gradients, and proteomics to understand disease mechanisms. Her recent publications demonstrate a strong focus on computational approaches to understanding neurodegenerative diseases, with particular emphasis on the relationship between brain connectivity patterns, biomarkers, and clinical outcomes across aging and various neurological conditions. She has pioneered methods like delayed correlation-based approaches for dynamic connectivity analysis and developed BRAPH 2.0 software for brain connectivity analysis. Scientific Awards and Grants: Marie Curie Intra-European Fellowship Swedish Research Council Alzheimerfonden Hjärnfonden StratNeuro New Technologies Grant 2025 Swedish Foundation for Strategic Research Senior Research Faculty Position Dr. Pereira actively mentors the next generation of neuroscientists, currently supervising 3 PhD students and 3 postdoctoral researchers, with additional co-supervision of 4 PhD students from other institutions. She has successfully guided 4 PhD students to completion of their theses. She also organizes the yearly conference 'Emerging Topics in Artificial Intelligence' since 2020. She leads the Brain Connectomics research group, which is part of the Department of Clinical Neuroscience at Karolinska Institutet. The group is highly interdisciplinary, combining expertise from medicine, engineering, physics, and computer science to tackle complex questions in neuroscience. The group has developed BRAPH 2.0, a comprehensive software for brain connectivity analysis using graph theory and deep learning.
Dr. Amir-Homayoun Javadi is a Senior Lecturer in Cognitive Neuroscience at the School of Psychology, University of Kent . He holds additional roles as an Honorary Research Associate at the Institute of Behavioural Neuroscience, University College London and a Visiting Professor at the School of Rehabilitation, Tehran University of Medical Sciences . His research focuses on non-pharmacological interventions to enhance memory, learning, and mental health through methods like physical exercise, brain stimulation, music, and sleep studies. Key Research Themes: Memory consolidation and enhancement Neurostimulation (tDCS, tACS) Sleep and circadian rhythm effects Emotion processing Computational modeling The 15 most recent publications highlight his work in neurostimulation during memory tasks, music-induced emotional states, exercise neuroscience, and Alzheimer’s disease diagnostics. Scientific awards are not explicitly mentioned. He supervises PhD students in areas including memory enhancement, declarative memory, and procedural skill learning, while maintaining memberships in the Federation of European Neuroscience Societies (FENS) and British Neuroscience Association (BNA). The Javadi Lab investigates cognitive mapping, neural oscillations, and brain-body interactions using EEG, fMRI, and AI.
Fazl Barez is a Senior Research Fellow at the University of Oxford leading research on Technical AI Safety and Governance. He is also affiliated with Cambridge's CSER, NTU's Digital Trust Centre, Edinburgh's Informatics, and is a member of ELLIS. Previously, he was a researcher at Amazon and Huawei, and Co-director and Head of Research at Apart Research. He currently serves as an advisor to Martian and has worked with Anthropic's Alignment team (2024-2025). University of Oxford: Senior Research Fellow Cambridge CSER: Affiliate NTU Digital Trust Centre: Affiliate Edinburgh Informatics: Affiliate ELLIS: Member Anthropic: Alignment Team Collaborator (2024-2025) Martian: Advisor Dr. Barez's research focuses on ensuring AI systems remain safe, interpretable, and beneficial as they grow in capability. His work spans four interconnected areas: Interpretability (developing methods to reveal how AI models process information internally), Safety and Alignment (creating tools to detect and address deceptive behaviors), Technical Governance (translating technical insights into governance frameworks), and Societal Impact (examining broader implications of AI on society). His research is funded by OpenAI, Anthropic, Schmidt Sciences, Future of Life Institute, and NVIDIA. The trends in Dr. Barez's publications show a consistent focus on making AI systems more transparent and safer. His recent work explores mechanistic interpretability techniques like sparse autoencoders, investigates how language models relearn removed concepts, examines machine unlearning for safety applications, and develops frameworks for value alignment measurement. His publications appear in top venues including NeurIPS, ICML, ICLR, ACL, and EMNLP, reflecting his significant contributions to both theoretical and practical aspects of AI safety. Future of Humanity Institute PhD Affiliate (2022-2024) EPSRC PhD Student Scholarship (2019-2023) MSc Scholarship (2017-2018) BA (Hons) Sports Performance Scholarship (2013-2017) Dr. Barez has mentored numerous students who have gone on to prominent positions at organizations like Microsoft Research, DeepMind, and Martian. His research is generously funded by major AI organizations including OpenAI, Anthropic, Schmidt Sciences, Future of Life Institute, and NVIDIA. He has served as an Area Chair for ACL 2025 and on program committees for major conferences including ECAI 2024. His work has practical impact, with algorithms like N2G adopted by OpenAI to evaluate sparse autoencoders for interpretability. Dr. Barez leads research at the intersection of technical AI safety and governance. His work connects with multiple research groups including the UK AI Security Institute, Alan Turing Institute, and various university centers. He has co-organized workshops such as the first Mechanistic Interpretability workshop at ICML 2024 and actively collaborates with researchers across the AI safety ecosystem. His research bridges the gap between theoretical safety research and practical implementation in real-world AI systems.
Olaf Wiest serves as the Grace-Rupley Professor of Chemistry & Biochemistry at the University of Notre Dame's College of Science, holding office in McCourtney Hall. With continuous academic service since 1996—from Assistant Professor (1996-2001) to Associate Professor (2001-2005), Professor (2005-2024), and current endowed chair position—he maintains active research and teaching responsibilities. His educational background includes a Dr. rer. nat. (1993) and Diplom (1991) from the University of Bonn, Germany, followed by postdoctoral work at UCLA (1993-1995). Key research interests span catalysis, computational chemistry, drug design for rare diseases (particularly Niemann-Pick Type C), epigenetic modulators targeting histone deacetylases, and machine learning applications in chemical reaction prediction. His interdisciplinary work bridges organic chemistry, biophysics, and artificial intelligence, frequently involving collaborations across synthetic chemistry, biology, physics, and medical research. Wiest's publication trends reveal a strong focus on computational-experimental integration, with recent work emphasizing AI-driven chemistry (2023-2025), including transfer learning for reaction prediction, large language models for molecular analysis, and machine learning potentials for catalytic reactions. His group maintains dual expertise in traditional organic synthesis (catalysis, enzyme mechanisms) and cutting-edge computational methods (Q2MM, virtual screening). Research Achievement Award, University of Notre Dame (2025) Fellow, American Association for the Advancement of Science (2012) John Kaneb Award for undergraduate teaching (2004) Camille Dreyfus Teacher-Scholar Award (2001) NSF CAREER Award (1997) NIH First Award (1997) His research group actively mentors graduate students in organic and biophysical chemistry projects, with significant grant support evidenced by NIH/NSF awards and industry collaborations. Current work includes developing computational tools for stereoselective catalysis (CatVS, Q2MM) and therapeutic strategies for rare diseases. The Wiest Lab operates at the intersection of experimental synthesis and computational modeling, utilizing advanced techniques like time-resolved crystallography and machine learning for reaction prediction.
Aurina Arnatkeviciute is a Research Fellow at Monash University's Turner Institute for Brain & Mental Health within the Faculty of Medicine, Nursing and Health Sciences. Her work focuses on the intersection of neuroscience, genetics, and psychiatry, with particular emphasis on brain connectivity, neuroimaging, and the genetic underpinnings of psychiatric disorders. She actively contributes to large-scale international collaborations including the ENIGMA consortium. Dr. Arnatkeviciute's research interests span multiple domains of cognitive neuroscience and psychiatric research. She investigates how genetic factors influence brain connectivity and network organization, with applications to understanding conditions like schizophrenia, ADHD, and autism spectrum disorders. Her work bridges molecular neuroscience with systems-level brain organization, utilizing advanced neuroimaging techniques combined with genetic and transcriptomic data. She has made significant contributions to imaging transcriptomics - the integration of brain-wide gene expression data with neuroimaging findings. Her publication record demonstrates expertise across multiple methodologies including genome-wide association studies, diffusion MRI connectomics, and large-scale international collaborations. Her recent work examines how socioeconomic factors like gender inequality impact brain structure, how genetic variations affect inhibitory control, and the neuroanatomical signatures of schizotypy across diverse populations. Australasian Cognitive Neuroscience Society (ACNS) Emerging Researcher Award (2020) Australasian Neuroscience Society Paxinos-Watson Award for the most significant neuroscience paper published by a member of the Society (2022) Discovery Early Career Researcher Award (DECRA) (2022) Monash University Dean's Award for Doctoral Thesis Excellence for "Genetics of brain network hubs" (2020) Monash University Early Career Researcher Publication Prize for "A practical guide to linking brain-wide gene expression and neuroimaging data" (2020) Dr. Arnatkeviciute serves as a supervisor for Honours students at Monash University, teaching units including PSY4215: Advanced Data Science and PSY4130: Developmental Psychology and Clinical Neuroscience. She is a Chief Investigator on the NHMRC-funded project "Neuropharmacology of decision-making: causal brain network modelling across species" (2022-2026), demonstrating her leadership in securing competitive research funding. Her supervisory activities extend through 2025, indicating ongoing commitment to mentoring the next generation of neuroscience researchers. As a member of the Turner Institute for Brain & Mental Health, Dr. Arnatkeviciute collaborates with a multidisciplinary team of neuroscientists, clinicians, and data scientists. Her work within the ENIGMA consortium connects her with researchers across more than 30 countries, facilitating large-scale analyses of brain structure and function across diverse populations. This collaborative environment supports her research at the intersection of genetics, neuroimaging, and psychiatric disorders.
Dobrik Georgiev is a Lecturer in the Department of Computer Science and Technology within the School of Technology at the University of Cambridge. His research centers on bridging algorithmic reasoning with neural architectures, focusing on how neural networks can execute and generalize algorithmic processes. His primary research interests include: Neural algorithmic reasoning and its applications to combinatorial problems Graph neural networks and hypergraph learning systems Explainable AI through concept-based interpretability Deep equilibrium models for algorithmic execution Biological data analysis using neural architectures Georgiev's publication record demonstrates consistent innovation in neural execution models, with recent work exploring bottlenecks in algorithmic reasoning (2025), multi-solution reasoning frameworks (2024), and generalization beyond synthetic graph models (2023). His research shows strong interdisciplinary connections between theoretical computer science, machine learning, and computational biology. While no formal awards are documented in available sources, his work has established significant contributions to neural algorithmic reasoning frameworks. Georgiev maintains active research collaborations through the Department of Computer Science and Technology's initiatives, particularly in the areas of machine learning and neural architectures. His technical leadership is evident in software contributions like the LENs library for logic-explained networks.
Minjeong Kim is an Associate Professor and Interim Department Head in the Department of Computer Science at The University of North Carolina at Greensboro (UNCG), serving as a CAS Dean's Fellow. She holds a Ph.D. from Ewha Womans University, Korea, with postdoctoral research at the Biomedical Research Imaging Center (BRIC) at UNC Chapel Hill and the University of Pennsylvania. Her research focuses on biomedical image analysis, deep learning applications in neurodegenerative diseases, and computational neuroscience. Education includes a Ph.D., M.S., and B.S. in Computer Science and Engineering from Ewha Womans University. Professional experience includes roles as a Research Fellow at Ewha Womans University and visiting researcher at the University of Pennsylvania's Department of Radiology. Research interests emphasize graph neural networks for brain connectivity analysis, Alzheimer’s disease diagnosis via machine learning, and multimodal medical imaging techniques. Her work bridges computational methods with clinical applications, particularly in uncovering disease mechanisms through advanced imaging analysis. Her recent articles highlight innovations in graph representation learning, tau protein propagation modeling, and functional MRI analysis. She maintains a lab at Moore Building 301A and teaches graduate courses in software engineering and computer vision.
Rosalba Garcia-Millan is a Lecturer in Disordered Systems at King’s College London, part of the Faculty of Natural, Mathematical & Engineering Sciences and the Department of Mathematics. She holds a PhD in Mathematical Physics from Imperial College London (2020). Prior to joining King’s in 2023, she was a postdoctoral researcher at the University of Cambridge and held an independent Research Fellowship at St John’s College, Cambridge. Her research focuses on non-equilibrium physics, active matter, and stochastic thermodynamics. She applies microscopic field theories to study active matter systems and agent-based models, with applications to biological physics, including DNA organization in cell nuclei, branching growth, and neuronal avalanches. Her work bridges theoretical approaches with interdisciplinary applications in biophysics and complex systems. Recent publications highlight contributions to entropy production in nonreciprocal systems, liquid-state theories for active matter, and chromatin dynamics. Her research emphasizes understanding collective behavior, nonequilibrium dynamics, and thermodynamic principles in both synthetic and biological systems. Rosalba is affiliated with the Disordered Systems group at King’s, known for its leadership in statistical mechanics of complex systems. No scientific awards are explicitly listed in the provided materials. She currently has no listed advisees or grants in the available data.
Srirupa Chakraborty is an Assistant Professor of Chemical Engineering and Chemistry and Chemical Biology at Northeastern University, with an affiliation in the Department of Physics. She leads the SimBioSys Lab, focusing on computational modeling of biomolecular systems to address biomedical challenges. Her research integrates theoretical biology, biophysics, and computational simulations to study viral glycoproteins and design therapeutic strategies. Education: PhD in Biophysics (SUNY Buffalo, 2016); M.S. in Physics (IIT Guwahati, India); B.S. in Physics (Presidency College, University of Calcutta). Postdoctoral research at Los Alamos National Laboratory, recognized with the Wiley Award (2020) and CHAVD Award (2019). NIH MIRA Award (2023) supports her work on mucin-inspired biomaterials for cystic fibrosis treatments. Research interests include glycoprotein dynamics, rational drug design, and biomaterials. Current projects involve computational models for mucosal glycopeptide mesh and non-viral gene delivery systems. She mentors students like Natesan Mani and leads grants totaling $2.0M from NIH. Awards: NIH MIRA, Wiley Award, CHAVD Award Lab: SimBioSys Lab (computational biophysics and biomaterials) Grants: $1.99M (MIRA), $100K (NIH SBIR for CF therapies) Her work bridges theoretical models with experimental data, emphasizing interdisciplinary approaches in biochemistry, physics, and computer science.
Laura Astolfi is an Associate Professor at the Department of Computer, Control, and Management Engineering , Sapienza University of Rome, and a Researcher at Fondazione Santa Lucia Hospital, Italy. She leads the Bioengineering and Bioinformatics Laboratory and is a Junior Fellow at the Sapienza School for Advanced Studies. Ph.D. in Biomedical Engineering, University of Bologna Master's in Electronic Engineering, University of Rome Sapienza Her research spans brain connectivity , high-resolution EEG source reconstruction , neurorehabilitation , hyperscanning , and social neuroscience . Key applications include disorders of consciousness and motor recovery post-stroke. Recent publications focus on deep learning for EEG localization , functional ultrasound imaging , and multi-subject brain network analysis . Awards include World's Top 2% Scientists (2021–2024) and the Best Under-40 Researcher Award at Sapienza (2010). Associate Editor, Brain Topography , Medical & Biological Engineering & Computing , and IEEE Open Journal of Engineering in Medicine and Biology 261 peer-reviewed papers, 8933 citations, H-index 45
Nitin Williams is a Visiting Professor in the Department of Neuroscience and Biomedical Engineering. His research focuses on brain connectivity, aging, and neuroimaging techniques like MEG and fMRI. He has contributed to understanding functional connectivity dynamics, age-related neural changes, and computational models of brain networks. Recent work includes studies on TMS-evoked potentials, phase synchronization in resting-state networks, and discrete Ricci curvatures to analyze brain networks. He has collaborated internationally, including a visiting research stint at the Institute of Mathematical Sciences in India. Williams is an active member of academic communities, serving on editorial boards for journals like Frontiers in Computational Neuroscience and Network: Computation in Neural Systems . His datasets, including connectome analyses and biophysical models, highlight computational approaches to neuroscience challenges. Key research themes include: (1) Aging’s impact on neural plasticity and connectivity, (2) Biophysical modeling of phase synchronization, (3) Multimodal neuroimaging integration, and (4) Applications of graph theory in brain network analysis.
Bin Gu is a professor at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), specializing in machine learning and artificial intelligence. Previously affiliated with institutions including Nanjing University of Information Science and Technology (former position) and Nanjing University of Aeronautics and Astronautics (PhD 2011). His research focuses on optimization algorithms, spiking neural networks, federated learning, kernel methods, adversarial robustness, and neuromorphic computing. Education: PhD in Computer Science (2011) from Nanjing University of Aeronautics and Astronautics. Prior affiliations include Tianjin University, Boston University, University of Science and Technology of China, and Southeast University. Research Interests: Extensive work on machine learning theory and applications, including robust learning, federated systems, neural architecture design, and privacy-preserving techniques. Over 200 publications in top venues such as AAAI, NeurIPS, ICLR, ICML, KDD, and IEEE journals. Publications Trends: Recent focus on spiking neural networks (SNNs), federated learning frameworks, and optimization methods for handling adversarial attacks and privacy constraints. Notable contributions include scalable algorithms for kernel-based learning, robust SVM formulations, and neuromorphic computing architectures. Labs/Teams: Active in AI research groups focused on neural networks, optimization, and distributed learning systems. Collaborates with industry and academic partners on applied AI solutions.
