جست‌وجوی استاد و پژوهشگر

• Education
• Children's Literature
• Globalization in Education
• +۵ مورد دیگر

Matthew Zbaracki is an Associate Professor at the School of Education, Faculty of Education and Arts, Australian Catholic University. His research focuses on children’s literature, global education perspectives, teacher training, and the impact of societal changes (e.g., pandemics) on student well-being. He has contributed to journals such as the Journal of Teaching in Physical Education and British Educational Research Journal , as well as book chapters in international handbooks on globalization and educational policy. Key research areas include analyzing cultural representation in children’s literature, evaluating the effects of remote learning during the pandemic on student fitness, and exploring innovative methods for educator development through school-university partnerships. His authored books, such as Best Books for Boys and Books and Beyond , emphasize practical strategies for enhancing literacy and engagement in educational settings. No scientific awards or grants are explicitly mentioned in the provided text. He has advised no listed students, though his work on teacher education suggests involvement in mentorship programs. His research often intersects with global educational policy, multicultural narratives, and interdisciplinary approaches to pedagogy.

Farshid Hajatiمشاهده پروفایل

مدرس

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۱ مورد دیگر

Farshid Hajati is a Lecturer in Data Science at the University of New England's School of Science and Technology. He holds a PhD from Western Sydney University and has industry experience as a Senior Data Scientist at Australian government health agencies. His expertise spans machine learning, medical AI, and computer vision. Dr. Hajati's research develops deep learning solutions for medical applications including retinal disease detection, cardiac arrhythmia classification, and fungal infection diagnosis. He has secured significant funding including $433,000 for an intracranial pressure assessment device and $100,000 from Google Research. His publications demonstrate consistent innovation in multimodal medical AI, with recent advances in interpretable graph networks for biomedical data and handheld retinal imaging. Earlier foundational work established methods for 3D face recognition and dynamic texture analysis.

Chenjuan Guoمشاهده پروفایل

دانشیار

• Machine Learning
• Data Engineering
• Spatio-temporal Data Analysis
• +۴ مورد دیگر

Chenjuan Guo is an Associate Professor at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. She is affiliated with the Data Engineering, Science and Systems group and the AI for the People initiative, and is part of the Daisy - Center for Data-intensive Systems. Her research focuses on machine learning, data engineering, spatio-temporal data analysis, and time series forecasting. Key projects include the Villum Foundation-funded 'Explainable AI for Complex Microbial Community Interactions and Predictions' (2021-2024) and the Astra project on time series analytics in spatial networks (2018-2021). Her research interests span representation learning, autoencoders, path representation, outlier detection, trajectory data analysis, and time series modeling. She has supervised 3 PhD students and contributed to over 60 publications, with a recent emphasis on transformer-based forecasting, neural architecture search, and continuous learning frameworks for spatio-temporal data. Her work bridges theoretical advancements with practical applications in environmental science, cloud computing, and urban mobility systems. Key achievements include developing frameworks like AutoCTS++ for automated time series forecasting and LightGTS for lightweight models. She actively collaborates internationally, contributing to conferences like ECML PKDD and CVPR. Her research is supported by grants from the Villum Foundation and other institutions.

Qi Zhangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Reinforcement Learning
• Trustworthy AI Systems
• +۴ مورد دیگر

Qi Zhang is an Assistant Professor in the Department of Computer Science and Engineering, AI Institute at the Molinaroli College of Engineering and Computing, University of South Carolina. His research focuses on developing safe, reliable, and trustworthy AI systems through advancements in reinforcement learning and decision-making algorithms for uncertain environments. He holds a Ph.D. from the University of Michigan (2020) and a B.E. from Shanghai Jiao Tong University (2015). Research interests include artificial intelligence, reinforcement learning, multi-agent systems, and decision-making under uncertainty. Key themes involve leveraging domain knowledge for robust AI solutions and ensuring ethical, transparent, and risk-aware system designs. His work spans applications in autonomous systems, healthcare, robotics, and materials science. Current projects emphasize improving algorithmic trustworthiness, safety, and adaptability across diverse contexts. He is affiliated with the AI Institute at USC and maintains an active research lab focused on these areas.

Hy Truong Sonمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Drug Discovery
• +۶ مورد دیگر

Dr. Hy Truong Son is an Assistant Professor in the Department of Computer Science at the University of Alabama at Birmingham (UAB), affiliated with the College of Arts and Sciences. He holds a Ph.D. in Computer Science from the University of Chicago and has prior experience as a Lecturer and Postdoctoral Fellow at the Halicioglu Data Science Institute, UC San Diego. His research focuses on AI-driven solutions for science and engineering, particularly deep learning applications in drug discovery, repurposing, and biomedical problem-solving through his HySonLab group. Dr. Son’s educational background includes a Ph.D. from the University of Chicago, emphasizing foundational training in computer science. His postdoctoral work at UC San Diego further solidified his expertise in data science and interdisciplinary AI applications. Research interests span AI for drug discovery, generative AI, multimodal learning, and healthcare technologies. His lab’s work integrates AI with molecular biology, medical imaging, and wearable sensor data to address challenges in precision medicine and environmental science. Notable projects include DrugPipe for drug repurposing and SilVar-Med for explainable medical imaging analysis. His recent publications highlight advancements in generative models, speech synthesis, protein design, and scalable graph neural networks. These works reflect a focus on bridging AI with real-world biomedical and engineering applications. While no awards are explicitly listed, his prolific publication record and active lab indicate significant contributions to the field. He advises students and engineers in his team, fostering collaborative research environments. Ongoing projects include wearable device datasets for mental health and multimodal biomedical knowledge graph development. Dr. Son’s HySonLab group emphasizes translational research, aiming to deploy AI solutions in clinical and scientific settings. Current efforts include optimizing molecular interactions via large language models and enhancing drug discovery pipelines through interdisciplinary computational methods.

Ellen Rusheمشاهده پروفایل

استادیار

• Deep Learning
• Sign Language Recognition
• Domain Adaptation
• +۳ مورد دیگر

Dr. Ellen Rushe is an Assistant Professor at Dublin City University's School of Computing specializing in deep learning with limited supervision. Her research develops solutions for audio-visual data challenges including sign language recognition (SignOn Project), novelty detection, and domain adaptation for sports analytics. Previously a Research Fellow at Trinity College Dublin and Postdoctoral Fellow at UCD, she holds an MSc in Computer Science from UCD and BA in Music Technology from Maynooth University. Research focuses on: Limited-label learning paradigms Sign language recognition for low-resource languages Domain adaptation in sports video analysis Novelty detection in data streams

Igor Molybogمشاهده پروفایل

استادیار

• Artificial Intelligence
• Large Language Models
• Multimodal Modeling
• +۵ مورد دیگر

Igor Molybog is an Assistant Professor at the University of Hawai'i at Manoa, holding joint appointments in the Departments of Electrical and Computer Engineering and Information and Computer Sciences. His research focuses on advancing artificial intelligence, particularly through large language models (LLMs), multimodal modeling, and core machine learning optimization. He leads the HawAII research group, exploring applications like LLM alignment, efficient inference systems, and scaling properties of foundation models. Education: Ph.D. in Engineering from UC Berkeley (2022), specializing in optimization algorithms for complex systems. Previously worked at Meta AI on LLaMa model development. Research Interests: Efficient LLM development and evaluation frameworks Multimodal AI integration (video/audio + text) Scalable optimization for large models Computational efficiency in training/ inference Recent Work: Presented REAL alignment method (2024), developed long-context scaling techniques (2023), contributed to Llama 2 chat models (2023). Collaborates with organizations like Epoch AI on scaling challenges. Teaching: Offers courses in AI, machine learning, and optimization across ECE and ICS departments. Labs/Teams: Leads HawAII Initiative fostering AI collaboration at UH Manoa, organizes paper reading seminars, and hosts technical talks with industry experts.

Martha W. Alibaliمشاهده پروفایل

استاد

• Developmental Psychology
• Cognitive Psychology
• Mathematics Education
• +۳ مورد دیگر

Martha W. Alibali is a Professor in the Department of Psychology and Department of Educational Psychology at the University of Wisconsin-Madison. She directs the Cognitive Development & Communication Lab (CD&C Lab), which focuses on the intersection of developmental psychology, cognitive psychology, and mathematics education. Her research investigates mathematics learning and development with a special emphasis on the role of gesture in mathematical thinking and knowledge change. Her primary research interests include: The role of gesture in mathematical thinking and knowledge change Gesture in thinking and communication in educational settings Mathematics learning across developmental stages Embodied cognition and its implications for education Dr. Alibali's recent publications showcase her continued focus on gesture studies, mathematical cognition, and educational interventions. Her work spans from theoretical investigations of cognitive processes to applied educational research, with particular attention to visual representations, multimodal learning, and embodied cognition. She frequently collaborates with researchers across psychology, education, and cognitive science. Dr. Alibali has secured substantial funding from the National Science Foundation and Institute for Education Sciences for projects including intelligent tutoring systems for algebra, mathematical equivalence instruction, and visual representations in biology education. Her lab maintains an active research program with multiple graduate students, postdoctoral researchers, and undergraduate assistants. The CD&C Lab is committed to creating an inclusive research environment that values diversity of identities, backgrounds, and perspectives. The lab follows open science practices including pre-registration of studies and sharing research materials. Weekly lab meetings provide opportunities for all members to present their ongoing work and receive feedback.

Laks V.S. Lakshmananمشاهده پروفایل

استاد

• Data Management
• Graph Computing
• Machine Learning
• +۵ مورد دیگر

Laks V.S. Lakshmanan is a Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. His research focuses on data management, graph computing, machine learning, and algorithms, with notable contributions to dense subgraph discovery, influence maximization, and healthcare informatics. He teaches advanced courses on databases and data management, including CPSC 404 (Advanced Relational Databases) and CPSC 534L (Topics in Data Management). His awards include the ACM SIGMOD Research Highlight Award, the IEEE Data Science Best Paper Award, and recognition as an ACM Distinguished Scientist (2016). His work bridges theoretical algorithm design with practical applications in social networks, bioinformatics, and healthcare. Key research themes include optimizing graph algorithms for large-scale data, combating misinformation through network analysis, and developing efficient methods for subgraph enumeration and influence propagation. His recent publications explore topics like clinical event prediction (TRACE), cost-effective LLM selection (ThriftLLM), and cross-modal consistency in AI systems. Education: Details not explicitly provided in sources. Grants & Funding: Recipient of NSERC Discovery Accelerator Supplements. Labs/Teams: Engaged in UBC's data management research groups and collaborative initiatives with industry partners.

Hangfeng Heمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Reasoning
• +۴ مورد دیگر

Hangfeng He is an Assistant Professor of Computer Science and Data Science at the University of Rochester , affiliated with the Hajim School of Engineering & Applied Sciences. He holds a PhD from the University of Pennsylvania and focuses on machine learning and natural language processing. His research emphasizes incidental supervision for natural language understanding, interpretability of deep neural networks, and reasoning in natural language. Education : PhD in Computer Science from University of Pennsylvania Research Interests : Machine Learning (especially deep learning theory, generalization, and optimization) Natural Language Processing (incidental supervision, language understanding, and multimodal reasoning) Reasoning (inductive biases, temporal data analysis, and model interpretability) Research Trends : His work spans foundational AI research and applied NLP, with recent focus on large language models, multimodal systems, and constraint-aware methods. He explores how models generalize from limited supervision and mitigate biases in real-world data. Key themes include improving model robustness, understanding inductive mechanisms, and applying AI to financial and social text analysis. Labs/Teams : While specific lab affiliations aren’t listed, his work aligns with the Department of Computer Science’s research clusters in AI, machine learning, and data science.

Magdalena Fuentesمشاهده پروفایل

استادیار

• Machine Listening
• Human-Centered Machine Learning
• Multimodal Representation Learning
• +۳ مورد دیگر

Magdalena Fuentes is an Assistant Professor of Music Technology and Integrated Design & Media at New York University (NYU), affiliated with the Music and Audio Research Lab (MARL) and the Integrated Design & Media (IDM) programs. She holds a Ph.D. from Université Paris Saclay (France) and a B.Eng. in Electrical Engineering from Universidad de la República (Uruguay). Her research focuses on Machine Listening, Human-Centered Machine Learning, and Multimodal Representation Learning, with applications to Music Information Retrieval and Environmental Sound Analysis. Previously, she served as a Postdoctoral Faculty Fellow at NYU’s MARL and Center for Urban Science and Progress (CUSP). Her work bridges technical innovation with cultural and societal contexts, particularly in underrepresented music traditions like Brazilian percussion and Candomblé rituals. She has developed open-source tools such as Soundata to ensure reproducible audio dataset usage. Her research outputs span audio-visual synchronization (e.g., SONIQUE), environmental sound monitoring (e.g., SONYC projects), and rhythm analysis frameworks (e.g., Carat toolbox). These contributions highlight her dual focus on advancing machine learning techniques while maintaining human-centric and culturally sensitive applications.

Jamie A. Leeمشاهده پروفایل

دانشیار

• Digital Culture
• Information and Society
• Archival Practice and Production
• +۳ مورد دیگر

Jamie A. Lee is an Associate Dean for Faculty Affairs and Associate Professor of Digital Culture, Information, and Society at the University of Arizona’s College of Information Science. They co-founded the Critical Archives & Curation Collaborative and the Digital Storytelling & Oral History Lab, focusing on participatory approaches with communities. Lee holds a Ph.D. in Information Resources and Library Science with a gender studies minor from the University of Arizona, an M.A. in the same field, and a B.S. in Communication and English from the University of Wisconsin-Superior. Lee’s research emphasizes archival theory, queer methodologies, and community-based practices. Their book *Producing the Archival Body* (2021) explores power dynamics in archives and embodied knowledge. They have received prestigious awards like the Agnese Nelms Haury Faculty Fellowship and an IMLS Early Career Grant for studying community archives’ politics of description. Lee’s work bridges archival studies, media, and digital humanities. Their articles address neoliberalism in LIS, queer/ed archival methodologies, and radical hospitality in archives. They actively advocate for inclusive, participatory archival practices through pedagogy and community collaboration. Awards : Agnese Nelms Haury Fellowship, IMLS Grant, multiple nominee recognitions. Grants : IMLS Early Career Grant (2019). Labs/Teams : Critical Archives & Curation Collaborative, Digital Storytelling & Oral History Lab.

Chen Weiمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Generative AI
• +۳ مورد دیگر

Chen Wei is an Assistant Professor of Computer Science at Rice University (starting Fall 2025) and a Postdoctoral Researcher at Meta AI's FAIR division. She holds a PhD from Johns Hopkins University (2024) and a B.Sc. from Peking University (2019). Her research focuses on advancing visual intelligence through generative AI, multimodal learning, and self-supervised learning. She has been recognized with awards such as EECS Rising Stars (2023) and Most Influential CVPR Papers (2023). Education: PhD, Computer Science, Johns Hopkins University (2024) BSc with Honors, Computer Science, Peking University (2019) Research Interests: Chen's work addresses core challenges in AI by transforming visual data into structured knowledge. Key areas include: Generative Understanding: Building world models via generative systems Multimodal Learning: Integrating visual data with LLMs for higher-order cognition Self-Supervised Learning: Discovering structures in raw visual data Awards: EECS Rising Stars, 2023 Most Influential CVPR Papers, 2023 Lab Openings: Recruiting PhD students (2025) and Rice undergraduates. Focus areas include generative models, multimodal systems, and scalable self-supervised learning. Contact via email for advising inquiries.

Michael L. Austinمشاهده پروفایل

مدرس ارشد

• Sound and music in interactive media
• Ludomusicology
• Sound studies
• +۷ مورد دیگر

Dr. Michael L. Austin is a Senior Lecturer in Music and Sound at Edge Hill University, serving as Programme Leader in Music Production since January 2023. He previously held academic roles at Louisiana Tech University, Howard University, and others. His research focuses on sound and music in interactive media, including video games, social media, and smart devices. He is Executive Director of the Society for the Study of Sound and Music in Games and a Research Associate at the Library of Congress's Radio Preservation Task Force. Education: PhD in Humanities (Aesthetic Studies) from the University of Texas at Dallas (2011), MA in Music Theory from UT Austin (2007), and BA in Music Composition from UT San Antonio (2005). Research interests include ludomusicology, cultural representation in media, sound design for UX, and algorithmic bias in emerging technologies. His 2024 monograph Audiovisual Alterity explores identity representation in music videos, while earlier works like Music Video Games: Performance, Politics, and Play (2016) analyze music's role in digital games. Publications span video game music analysis, algorithmic bias, and sound art. His talks include topics like 'Sounding Drag Identities in Video Games' and 'Algorithmic Bias in Emerging Media.' As a sound artist, his work has been exhibited at the Smithsonian and Dallas Museum of Art.

• Digital Twins
• Smart Cities
• Blockchain Technology
• +۵ مورد دیگر

Noel Crespi is a Professor and Director of Studies at Telecom SudParis, part of Institut Polytechnique de Paris. He leads research in the NeSS group and is affiliated with SAMOVAR, a prominent research laboratory focusing on networks, systems, and services. His work spans multiple domains in telecommunications, networking, and smart systems. His primary research interests include digital twins for smart cities, blockchain technologies, Internet of Things (IoT) security and applications, 5G/6G networking, and machine learning applications in network management. He has published extensively on these topics, with over 100 publications in top-tier journals and conferences. His recent work shows a strong focus on digital twin applications for urban management, particularly in traffic and air quality monitoring. He has developed modular frameworks for smart city digital twins that integrate real-time data from multiple sources. His research also explores blockchain applications for network security, access control, and service provisioning in next-generation cellular networks. Dr. Crespi has received significant recognition for his work on digital twins, with his 2020 paper "Digital twin in the IoT context" in Proceedings of the IEEE being highly cited. His current research explores the integration of AI with digital twin technologies for sustainable urban development. He has supervised numerous PhD students and collaborates extensively with researchers across Europe and internationally. His work often involves interdisciplinary approaches, bringing together computer science, networking, urban planning, and environmental science. Dr. Crespi has been instrumental in developing frameworks for network digital twins, with applications in traffic management, air quality monitoring, and resource allocation in smart cities. His research demonstrates practical implementations in cities like Madrid, showing measurable improvements in urban management.