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Yakun Sophia ShaoView profile

Associate Professor

• Computer Architecture
• Specialized Accelerators
• Heterogeneous Architecture
• +4 more

Yakun Sophia Shao is an Associate Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She holds a Ph.D. (2016) and M.S. (2014) in Computer Science from Harvard University, alongside a B.E. in Electrical Engineering from Zhejiang University, China. Her research focuses on computer architecture , particularly domain-specific accelerators , heterogeneous systems , and agile VLSI design methodologies . Key research centers: Agile Design of Efficient Processing Technologies (ADEPT), Berkeley Emerging Technologies Research (BETR), Berkeley Wireless Research Center (BWRC), SpeciaLIzed Computing Ecosystems (SLICE) Her work explores hardware-software co-design for efficiency in AI and robotics, including projects like Simba (chiplet-based AI accelerators) and Virgo (GPU matrix units). Notable tools developed include WIICA (workload characterization) and Chipyard (SoC frameworks). Selected honors include: 2024 CRA-WP Anita Borg Early Career Award 2023 NSF CAREER Award 2022 IEEE TCCA Young Computer Architect Award 2022 Intel Rising Star Faculty Award She teaches courses including EECS 151 (Digital Design & ICs) and EECS 251A (Advanced Digital Design).

• Trustworthy AI
• Deep Learning Theory
• Decentralised Learning
• +10 more

Fengxiang He is a Lecturer in AI Driven Business Informatics and Financial Computing at the School of Informatics, University of Edinburgh, where he is affiliated with the Artificial Intelligence and its Applications Institute. He also holds fellowships and affiliations with the Generative AI Laboratory, Institute for Adaptive and Neural Computation, Edinburgh Future Institute, and the Edinburgh Centre for Financial Innovations. Prior to Edinburgh, he was the Founding Lead of the Trustworthy AI project at JD.com, Inc. His research centers on trustworthy AI, with core interests in deep learning theory, decentralised learning, privacy and fairness in machine learning, symmetry in machine learning, and algorithmic game theory, with applications in economics and finance. He leads the Edinburgh Ventures for Intelligence and Economics and is actively involved in shaping ethical and robust AI systems. The recent publications highlight a strong trend in theoretical and applied machine learning, particularly in decentralised learning, trustworthy AI, game theory, and financial modeling. His work bridges deep learning theory with real-world applications, focusing on safety, privacy, fairness, and robustness. The articles span top venues including ICML, NeurIPS, ICLR, IEEE TPAMI, and ACM, reflecting high impact and interdisciplinary reach. Area Chair, ICML Area Chair, NeurIPS Area Chair, UAI Area Chair, AISTATS Area Chair, ECAI Area Chair, ACML Associate Editor, IEEE Transactions on Technology and Society Fengxiang He supervises multiple PhD and MScR students and is currently recruiting for postdoctoral researchers and fully funded PhD studentships through Centres for Doctoral Training in Machine Learning Systems, Designing Responsible NLP, and AI for Biomedical Innovation. He welcomes visitors and collaborators. His team includes PhD students Johnny MyungWon Lee, Kejiang Brian Qian, Saimun Habib, Qian Zuo, and MScR student Ye Wang, along with several interns and visiting researchers. He leads the Edinburgh Ventures for Intelligence and Economics and is a key member of the Generative AI Laboratory and other interdisciplinary research centers, fostering collaboration across AI, finance, and societal impact domains.

Minna HuotilainenView profile

Professor

• Educational Neuroscience
• Music and Brain Research
• Cognitive Science
• +7 more

Minna Huotilainen is a Professor of Educational Sciences at the University of Helsinki , affiliated with the Faculty of Educational Sciences and Department of Education . She leads the team at the Centre of Excellence in Music, Mind, Body, and Brain and serves as the vice director of the Master's programme in Changing Education . Research Interests: Educational neuroscience, music-brain interactions, cognitive development, stress physiology in learning, early childhood language acquisition, and digital pedagogy. Projects: Active in long-term initiatives like the Centre of Excellence in Music, Mind, Body and Brain (2022–2029), NATUREACH (2023–2028), and WLIIP (2021–2025), focusing on preterm infant development, AI-enhanced learning, and nature-based stress recovery. Publications: Recent works examine neural mechanisms in music-trained adolescents, generative AI in VR education, and music’s role in language development for preterm infants. Activities: Regular speaker on topics like brain wellbeing, hybrid work neuroplasticity, and stress management through music. Supervises PhD/Master’s students including Anni-Lotta Hiirsalmi and Efthymios Papatzikis.

Santiago LombeydaView profile

Adjunct Assistant Professor

• Data Visualization
• Scientific Visualization
• Computational Art
• +7 more

Santiago Lombeyda is an Adjunct Assistant Professor in Interaction Design, focusing on the intersection of science, art, software, and design. His work spans data visualization, custom software development, and computational art, with applications in biological simulations, genomics, and seismology. Adjunct Assistant Professor, Interaction Design Developer of visualization tools: bio-ivview, iVu, shakemovie Creator of algorithmic and scientific art: Horizonte, Emergence, Expressive Maps Teaches IXD-255-01: Data Visualization His research interests lie at the crossroads of human-computer interaction, expressive rendering, and scientific data interpretation. He explores how visual perception can be manipulated through algorithmic design, and how fragile moments in scientific simulations can become powerful artistic statements. His work emphasizes the duality between order and chaos, serenity and violence, and the microscopic and the monumental. The recent articles and projects reveal a consistent trend in transforming complex scientific data into accessible, interactive, and aesthetically compelling experiences. His tools support both research and public engagement, particularly in genomics and earthquake visualization. The integration of session-based discovery in iVu highlights a forward-thinking approach to collaborative data exploration. While no formal awards are listed, his body of work demonstrates innovation in scientific visualization and digital art. Developed custom software for VolumePro, OpenInventor, Python, Processing, JavaScript/HTML5 Collaborated with Wold Lab on gene expression tools Designed public-facing earthquake visualization portals Lombeyda's work is deeply integrated into both artistic and scientific communities, operating at the edge of software, design, and discovery. His projects often function as both research tools and standalone artworks, blurring disciplinary boundaries.

Woldaregay, Ashenafi ZebeneView profile

Researcher

• Digital Health
• mHealth
• Artificial Intelligence in Healthcare
• +7 more

Woldaregay, Ashenafi Zebene is a researcher at UiT The Arctic University of Norway, affiliated with the Faculty of Health Sciences and the Department of Clinical Medicine. His work bridges digital health, artificial intelligence, and clinical applications, particularly in diabetes management and infectious disease surveillance. He is a key contributor to the EDMON (Electronic Disease Surveillance and Monitoring Network) project, which leverages self-recorded health data from people with Type 1 diabetes for early outbreak detection. PhD in Digital Health / Health Informatics (2021) Master’s in Electronic Disease Surveillance (2016) His research focuses on applying machine learning, data science, and AI to solve pressing healthcare challenges. Key areas include blood glucose pattern analysis in diabetes, anomaly detection, mHealth adoption, wearable technology, and syndromic surveillance. He has developed models for infection detection, surgical risk prediction, and deidentification of clinical text. His work emphasizes real-world applicability, patient engagement, and data privacy. The most recent articles show a strong trend toward transformer models, synthetic data, and instruction-guided NLP in clinical contexts, alongside continued work in mHealth usability, caregiver support, and personalized health monitoring. His publications span journals in health informatics, medical informatics, and digital health, reflecting interdisciplinary collaboration. Scientific contributions include: Development of the EDMON system for real-time infection monitoring Creation of datasets for wearables and mHealth motivation factors Systematic reviews on reinforcement learning in diabetes and AI in healthcare security Innovation in cluster detection algorithms (K-CUSUM) for outbreak detection While direct information on grants and advising is not provided, his role as a doctoral candidate and co-author on numerous student-led studies suggests involvement in research mentoring. He collaborates extensively with researchers such as Gunnar Hartvigsen, Eirik Årsand, and Karl Øyvind Mikalsen, indicating membership in a large, active digital health research group. He is involved in a multidisciplinary research lab focused on digital health innovation, mHealth systems, and AI-driven clinical decision support. The team works on real-time monitoring, data privacy, and user-centered design of health technologies, with applications in chronic disease management and public health surveillance.

Chaoqun DengView profile

Assistant Professor

• Data Science/Analytics
• Social Media
• Sharing Economy
• +6 more

Chaoqun Deng is an Assistant Professor at the Zicklin School of Business , Baruch College, City University of New York, affiliated with the Paul H. Chook Department of Information Systems and Statistics . His research bridges Data Science , Sharing Economy , and Digital Strategy . PhD in Management, Rensselaer Polytechnic Institute (2018) MS in Management Science and Engineering, Wuhan University of Technology (2012) BS in Information Systems Management, Wuhan University of Technology (2009) His research interests focus on: Machine Learning and Text Mining for analyzing online reviews and social media Digital Strategy in live streaming platforms and crowdfunding Trust mechanisms in sharing economy platforms like Airbnb His recent articles address topics such as: Impact of visual designs on digital entrepreneurship in live streaming Role of organizational memes in shaping consumer support AI response strategies for customer reviews and their emotional implications Scientific Awards : Teaching Excellence Award (2023) Eugene M. Lang Junior Faculty Research Fellowship (2022-2023) Premier Research Publication Award (2021, 2022, 2023) PSC-CUNY Research Award (2020) AMCIS Doctoral Consortium Fellow (2017) As an active academic , he serves as: Committee Member for Undergraduate Curriculum and Search Committees at Baruch College Faculty Advisor for Data Challenge Competitions Reviewer and Editor for journals like MIS Quarterly , Information Systems Research , and conferences including ICIS and AMCIS He has secured grants from PSC-CUNY (2020) and Eugene Lang Fellowship (2022) for studies on AI in live streaming and consumer feedback analysis .

Allmin SusaiyahView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Health Informatics
• +4 more

Allmin Susaiyah is a University Researcher at Eindhoven University of Technology (TU/e), affiliated with the Mathematics and Computer Science school and the Eindhoven MedTech Innovation Center (e/MTIC). Her work bridges Security and Process Analytics research groups, focusing on AI-driven health technology solutions that transform complex data into actionable behavioral insights for personalized health management. Dr. Susaiyah holds a Master of Science degree and completed her PhD at TU/e in 2024 with the dissertation "Insight Generation and Recommendation: Driving Behavior and Systemic Change," which explored unstructured/structured data integration for behavioral interventions. Her research expertise spans Artificial Intelligence, Machine Learning, and Health Informatics, with specialized focus on Wearable Computing and Natural Language Processing for health self-management systems. Her scientific contributions emphasize user-centered AI, particularly in developing neural network models that adapt to individual preferences and feedback mechanisms. Key innovations include smart insight selection from wearable devices, privacy-preserving text mining, and reinforcement learning frameworks for lifestyle simulation, all targeting practical health behavior change through computational methods. Analysis of her 15 most recent publications (2021-2025) reveals an evolving trajectory from foundational behavior insight mining frameworks toward advanced zero-shot learning for medical event logs and privacy-aware health analytics. This progression demonstrates increasing sophistication in handling high-dimensional health data while addressing critical challenges in user preference modeling and domain-specific adaptation. No scientific awards or fellowships are documented in available sources. Public information does not indicate active student supervision or specific grant funding details, though her e/MTIC affiliation suggests participation in multidisciplinary health technology initiatives. Her work contributes to UN Sustainable Development Goal 3 (Good Health and Well-being) through AI applications for preventive healthcare and personalized medicine. Within TU/e's ecosystem, Dr. Susaiyah collaborates extensively through the Eindhoven MedTech Innovation Center—a strategic partnership between TU/e, Catharina Hospital, and Philips—where she integrates computer science methodologies with clinical practice to advance wearable-based health monitoring systems and intelligent recommendation engines for real-world healthcare settings.

Stasha LauriaView profile

Lecturer

• Intelligent Robotics
• Neural Networks
• Pattern Recognition
• +6 more

Dr. Stasha Lauria is a Lecturer in the Department of Computer Science within the College of Engineering, Design and Physical Sciences at Brunel University London. With over 15 years of experience in intelligent robotics, she specializes in human-machine interactions, neural networks, and pattern recognition, leading the Brunel Robotics Laboratory for cognitive mobile robot experiments. Education: Laurea, University of Studies “Federico II” of Napoli, Italy Ph.D. in Cybernetics, University of Reading, UK Research Focus: Her work centers on modeling mobile robots through natural language interactions, converting human speech into robot actions, and AI-driven signal processing. Current projects investigate social media's impact on human-robot dialogue management and robotics as educational tools, with strong emphasis on neural networks and big data applications in vision systems. Publication Trends: Recent publications (2022-2009) reveal consistent innovation in computer vision (object detection, segmentation) and human-robot interaction, increasingly integrating deep learning for manufacturing and vision tasks. Educational robotics remains a parallel thread, with quantitative evaluations of programming pedagogy. Research Grants: A human-centred internet of things platform for the sustainable mine of the future (European Commission, May 2020 - April 2024) Intelligent data-driven pipeline for certified metal parts manufacturing (European Commission, October 2018 - March 2023) Big Data Learning-based QoS Analysis for Cloud-services (Royal Society, March 2016 - February 2018) Laboratory & Collaboration: Founder of the Brunel Robotics Laboratory, she collaborates extensively with Dr. Theodora Koulouri, Prof. Xiaohui Liu, and Dr. Stephen Swift on human-robot interaction, as evidenced by co-author networks spanning neural networks, dialogue systems, and multi-agent architectures.

• Numerical Mathematics
• Data Assimilation
• Hamiltonian Dynamics
• +5 more

Sebastian Reich is a Professor of Numerical Analysis at the University of Potsdam and holds an honorary Visiting Professorship at Imperial College London . He leads the Chair of Numerical Mathematics and serves as Editor-in-Chief of the SIAM/ASA Journal on Uncertainty Quantification since 2021. Research Interests Numerical methods for Hamiltonian systems Data assimilation in geoscience Stochastic particle filters Bayesian inference algorithms Molecular dynamics simulation Multi-scale modeling Collaborative Projects : Principal Investigator and former Speaker (2017-2024) of SFB 1294 Data Assimilation , a DFG-funded Collaborative Research Center Active participant in SFB 1114 Scaling Cascades in Complex Systems at Freie Universität Berlin Books Authored : Probabilistic Forecasting and Bayesian Data Assimilation (Cambridge UP, 2015) Simulating Hamiltonian Mechanics (Cambridge UP, 2005) Technical Contributions : Development of symplectic integration methods Innovations in ensemble Kalman filtering Regularization approaches for geophysical models Stochastic algorithms for molecular simulations

Jörn OstermannView profile

Professor

• Video Coding
• Computer Vision
• Machine Learning
• +9 more

Prof. Jörn Ostermann is a Full Professor and Head of the Institut für Informationsverarbeitung at Leibniz Universität Hannover since 2003, with prior roles at AT&T Bell Labs and AT&T Labs-Research. He served as Dean of the Faculty of Electrical Engineering and Computer Science (2011–2013) and member of the Senat (since 2020). His research spans video coding, computer vision, machine learning, 3D modeling, and computer-human interfaces , with applications in SAR imaging, predictive maintenance, children's speech analysis, and cochlear implants. Key projects include Next Generation Video Coding , Conditional Coding for Learned Compression , and GreenAutoML4FAS . Notable trends in his recent publications (2025–2023) include Neural network-based video compression Uncertainty estimation in speech recognition Zero-delay coding for cochlear implants Domain adaptation for aerial image segmentation 3D mesh compression standards Error concealment in VVC coding Scientific recognitions: AT&T Standards Recognition Award (1998) ISO Award (1998) IEEE Fellow (2005) Distinguished Lecturer, IEEE CAS Society (2002/2003) MPEG Convenor (2020–2023) He co-authored a graduate textbook on Video Communications , holds >30 patents, and has led >20 research projects. His work bridges academic research and industrial standardization, particularly in MPEG and IEEE committees.

Marcelo SchmidtView profile

Assistant Professor

• Curriculum Development
• Program Assessment
• Educational Psychology
• +3 more

Marcelo Schmidt serves as Assistant Professor of Curriculum & Assessment in Texas Tech University's School of Veterinary Medicine and is an active STEM CORE member, focusing on educational psychology applied to veterinary medical contexts. His educational background includes: PhD in Educational Psychology from Texas Tech University Master's degree in Education from University of Texas - Pan American Research centers on curriculum development , program assessment , and data-use practices , with recent work examining communication barriers between English/Spanish-speaking animal professionals, psychological impacts of swine euthanasia on caretakers, and AI applications in veterinary training simulations. His studies frequently employ qualitative methodologies to address workforce challenges in agriculture. Publication analysis (2018-2025) shows evolving expertise from general educational assessment toward specialized veterinary medicine topics, particularly cross-cultural communication in animal health and technology-enhanced training solutions. No scientific awards were documented in source materials. As faculty, Schmidt advises veterinary students while leveraging prior administrative experience as Director of Assessment for TTU's College of Education. His grant work focuses on program evaluation for initiatives like Tech Teach across Texas, emphasizing data-driven educational improvements. Through STEM CORE, he contributes to K-12 outreach, undergraduate STEM support, and faculty development via the President's STEM Mentoring Academy.

Vladimir GolkovView profile

Researcher

• Deep Learning
• High-Dimensional Data
• Geometric Data
• +6 more

Dr. Vladimir Golkov is a Postdoctoral Researcher at the Technical University of Munich (TUM) within the School of Computation, Information and Technology, specifically in Informatics 9 (Computer Vision Group). He works under the supervision of Prof. Dr. Daniel Cremers and maintains an active research profile in deep learning applications for medical imaging and biomedical data analysis. His primary research interests focus on deep learning since 2014, with specialization in high-dimensional and geometric data structures, data-processing goals beyond supervised learning (including clustering and anomaly detection), and applications in biomedicine and physics. Dr. Golkov has successfully mentored students who have gone on to pursue PhD studies at prestigious institutions including TUM, LMU, Mila, ETHZ, Cambridge, and Stanford. Analysis of his recent publications reveals a strong trend toward medical imaging applications, particularly in MRI technology, where he combines deep learning approaches with traditional physics-based methods. His work frequently bridges the gap between theoretical machine learning advances and practical medical applications, with significant contributions to diffusion MRI, sequence optimization, and 3D data processing. He has also expanded into emerging areas including large language models for medical applications and equivariant deep learning architectures. Dr. Golkov receives grant support from the Deutsche Telekom Foundation and maintains an active publication record with numerous contributions to leading conferences including ISMRM, MICCAI, and NeurIPS workshops. His research demonstrates a consistent trajectory of innovation at the intersection of computer vision, deep learning, and biomedical applications. He is actively involved in teaching and mentoring, with students from his projects advancing to top PhD programs worldwide. His office is located at Boltzmannstrasse 3, 85748 Garching, Germany (Office: 02.09.061), and he can be contacted at vladimir.golkov@tum.de.

Irina NikishinaView profile

Researcher

• Semantics
• Argument Mining
• Taxonomy Enrichment
• +3 more

Irina Nikishina is a postdoctoral researcher at the University of Hamburg's Department of Informatics, working in the Language Technology Group under Prof. Chris Biemann. As a researcher in computational linguistics and natural language processing, she contributes to projects like ACQuA-2.0, focusing on semantics, argument mining, taxonomies, and knowledge graphs. PhD in Computational and Data Science and Engineering (2022), Skolkovo Institute of Science and Technology Bachelor's and Master's degrees from National Research University Higher School of Economics (NRU HSE) Her research spans taxonomy enrichment, comparative question answering systems, and biomedical concept representation. She organizes shared tasks like RUSSE’2020 and RuArg-2022, and co-founded the RusNLP semantic search engine for Russian NLP conferences. She chairs the Network Analysis track at the International Conference on Analysis of Images, Social Networks and Texts (AIST) and has served as secretary for AIST 2020 and 2021. Recent publications focus on large language models' performance in lexical semantics, multilingual comparative argumentation systems, and knowledge graph integration for QA tasks. Her work includes developing tools like TaxFree for candidate-free taxonomy enrichment and exploring cross-modal approaches for taxonomic graph expansion.

Tianxing HeView profile

Assistant Professor

• AI Safety
• AI-driven Simulation
• Large Language Models
• +5 more

Tianxing He is an Assistant Professor at Tsinghua University's Institute for Interdisciplinary Information Sciences (IIIS), also known as the Yao Class, which he joined in September 2024. He also works part-time at Shanghai Qi Zhi Institute where he recruits for research-oriented positions and video game development roles. His academic journey includes a postdoc at the University of Washington under Yulia Tsvetkov, a PhD at MIT supervised by James Glass, and bachelor's and master's degrees at Shanghai Jiao Tong University in the ACM honored class under Prof. Kai Yu. Dr. He's research spans AI safety and AI-driven simulation with particular focus on large language models. His work examines LLM capabilities in cryptography (AICrypto benchmark), security vulnerabilities (jailbreaking techniques), multi-agent system integrity, and socially impactful applications like vaccine hesitancy simulation (VACSIM framework). He has developed visualization tools like MiniAgents-Pro using Unity for generative agent simulation and created watermarking techniques such as SemStamp and k-SemStamp for detecting machine-generated text. His publication record shows consistent contributions to top-tier conferences including ACL, EMNLP, NeurIPS, and ICLR, with recent work focusing on evaluating LLM capabilities across diverse domains. The research demonstrates sophisticated understanding of both the technical aspects of language models and their broader societal implications, particularly in security and ethical considerations. Scientific Awards: The Best Paper Award at the Efficient Natural Language and Speech Processing Workshop (NeurIPS ENLSP 2022) for "PCFG-based Natural Language Interface Improves Generalization for Controlled Text Generation" Dr. He actively mentors students and recruits research interns, PhD candidates, and post-bachelor researchers. He plans to teach an undergraduate NLP course in Fall 2025 and organizes the IIIS AI Safety Lunch, a monthly gathering in Beijing for those working on AI safety. His recruitment efforts target students interested in LLM social simulation, AI+game development, and AI safety research, emphasizing the need for strong self-motivation and research passion. He leads research initiatives including the IIIS AI Safety Lunch series and has developed multiple visualization and simulation frameworks. His work sits at the intersection of AI safety, language modeling, and practical applications, with team collaborations spanning multiple institutions including MIT, University of Washington, and Shanghai Jiao Tong University.

• Cognitive Psychology
• Experimental Psychology
• Cognitive Neuroscience
• +7 more

Dr. János György Horváth is a Professor at the Department of General Psychology and Methodology within the Faculty of Humanities and Social Sciences at Károli Gáspár Reformed University. He also serves as a Scientific Advisor and Group Leader at the Experimental Psychology Research Group within the Institute of Cognitive Neuroscience and Psychology at HUN-REN Natural Sciences Research Center. His academic journey includes positions at the Hungarian Academy of Sciences' Institute of Cognitive Neuroscience and Psychology (2008-present), University of Leipzig (2006-2008), and the Hungarian Academy of Sciences' Psychological Research Institute (1998-2006). Horváth completed his PhD in Cognitive Psychology at Eötvös Loránd University in 2002, with his dissertation focusing on acoustic environmental regularities in auditory memory using event-related potential studies. He holds dual MA degrees in Physics and Psychology from the same institution (1997, 2002). His research primarily investigates attention and distraction mechanisms, particularly in the auditory modality, along with action regulation, intentional action, and perception-action interactions. He employs experimental psychology and cognitive neuroscience methods, with a strong emphasis on event-related potential analysis. Horváth has been principal investigator on numerous significant research grants including NKFIH projects on proprioceptive and motor functional brain connectivity during stroke rehabilitation (K 134787), sensory-motor reflections of intentional action (K 128083), and action-induced changes in perceptual processing (K 108783). His publication record shows consistent high-impact output, with research focusing on motor adaptation, action-effect integration, auditory distraction, and the neural correlates of perception-action interactions. Bolyai plakett, Hungarian Academy of Sciences, 2015 János Bolyai Research Scholarship of the HAS (2015-2016) János Bolyai Research Scholarship of the HAS with Bolyai plakett award (2011-2014) Horváth serves as co-editor of Psychophysiology (since 2016) and was associate editor of the Hungarian Journal of Psychology (2012-2017). He has supervised doctoral students including Márta Volosin (ELTE, 2019), Bence Neszmélyi (BME, 2020), Sámuel Varga, and Fanni Dóra Kovács. His research group focuses on experimental approaches to understanding the neural mechanisms underlying attention, distraction, and action-perception integration.