جست‌وجوی استاد و پژوهشگر

• Biomedical Engineering
• Nanotechnology
• Microelectronics
• +۵ مورد دیگر

Professor Omid Kavehei is a Professor of Intelligent Microsystems in the Faculty of Engineering at the University of Sydney, serving as Deputy Head of the School of Biomedical Engineering. Previously, he held roles as a Research Fellow at the University of Melbourne and Lecturer at RMIT University. His research focuses on biomedical microsystems, nanotechnology, and brain-inspired hardware for healthcare applications, particularly epilepsy monitoring and seizure prediction. Education: PhD (specific institution not explicitly stated) His work bridges nanoelectronics and healthcare, aiming to develop low-power, brain-inspired devices for sensory perception and medical diagnostics. Key interests include neuromorphic engineering, wearable sensors, and AI-driven medical systems. Awards include the Ramaciotti Biomedical Research Award (2021), Microsoft AI for Accessibility Grant (2019), and multiple teaching/research excellence awards from the University of Sydney. His recent work explores neuromorphic cytometry for cell analysis, edge AI for ECG/EEG diagnostics, and closed-loop neurostimulation systems. Collaborations include the Sydney Nano Institute, Brain and Mind Centre, and industry partners like Microsoft. Current research students are advancing topics like bio-inspired algorithms for seizure detection, hardware-friendly machine learning models, and flexible sensor systems for aquatic environments. Grants include funding for neurophysiology platforms, quantum sensors, and semiconductor design. Labs/teams: Involved in the Centre for Drug Discovery Innovation and collaborations across engineering, neuroscience, and clinical domains.

Francesca Dalia Faraciمشاهده پروفایل

مدرس ارشد

• Biomedical Signal Processing
• Medical Data Analysis
• Wearable Technology
• +۵ مورد دیگر

Francesca Dalia Faraci is a Senior Lecturer and Researcher at the Department of Innovative Technologies (DTI) of SUPSI. She holds a PhD in Electronics from the University of York (UK) and a Laurea in Physics from the University of Genoa (Italy). Her research focuses on biomedical signal processing, statistical data analysis, and wearable technology applications in healthcare. She leads projects such as Cardio MIPA (arrhythmia monitoring) and AutoPlay_DD (child development analysis via smart toys). Key contributions include developing algorithms for sleep staging, arrhythmia detection, and synthetic ECG generation. Awards include the GOSPEL and DoE School Certificates. She coordinates research projects and teaches courses in Physics and Applied Statistics. Education: PhD in Electronics (University of York, 2006), Laurea in Physics (University of Genoa, 2002) Roles: Senior Lecturer, Researcher, Project Coordinator Key Projects: Tackling teleWorking Impacts, my Doctor's Lifestyle (lifestyle medicine), Telemonitoring Breast Cancer Her research bridges AI and clinical practice, emphasizing ethical frameworks and complementarity between clinicians and AI systems. Recent work includes causal inference for sleep dynamics and bias quantification in healthcare algorithms.

• Human-Computer Interaction
• Digital Health
• Rural Resilience
• +۱۲ مورد دیگر

Hilary Davis is a Senior Research Fellow at Swinburne University of Technology, affiliated with the School of Business, Law and Entrepreneurship and the Centre for Social Impact. She leads the Place-based Economies and Approaches research stream, focusing on digital technologies for social impact, particularly in health, rural resilience, and inclusive community development. Her research interests span Human-Computer Interaction (HCI) , digital health , rural mental health services , participatory design , and community resilience . She emphasizes co-design with marginalized groups and the use of digital storytelling to empower communities. Her work aligns with UN Sustainable Development Goals, especially SDG 3 (Good Health) and SDG 11 (Sustainable Cities and Communities). Hilary’s recent publications (2023–2025) explore digital interventions for internet addiction in children, aged care technologies, bushfire early warning systems, and participatory planning for sustainable development. These works reflect a strong trend toward community-led innovation , behavior change design , and technology for social equity . Special Award (for More Than Human Food Futures Cookbook), UMEA Food Symposium 2022 Hilary actively supervises PhD students and has led major research grants from the Australian Research Council (ARC), Department of Health, and community organizations. Her projects include evaluations of mental health outreach programs, digital health interventions (e.g., VerilyConnect), and national educational initiatives like the ELLA language app program. She is a member of the Australian Evaluation Society (AES) and the Association for Computing Machinery (ACM), and serves on program committees for international HCI conferences. She leads the SMART (Small Rural Health Services Team) network, which supports rural health innovation through digital storytelling and co-design. Her work bridges academia and community practice, fostering sustainable, equitable, and resilient solutions.

Ga Wuمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Recommender Systems
• +۱۵ مورد دیگر

Ga Wu is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, where he leads the Applied Machine Learning Research Lab (DAMLR). His research spans machine learning, artificial intelligence, recommender systems, and large-scale data analytics, with strong industry and interdisciplinary collaborations. Research Interests: Machine Learning and Large-scale Data Analysis Recommender Systems and Personalization AI Safety, Fairness, Accountability, and Transparency Generative AI and Large Language Models Computer Vision (Object Detection on Low-power Devices) AI for Healthcare and Mental Health Physical Dynamic Modeling with ML Bioactive Peptides and Protein Property Understanding His recent publications have been accepted at premier venues such as SIGIR and ICLR, reflecting impactful contributions in information retrieval and deep learning. His work is increasingly focused on ethical, safe, and interpretable AI systems with real-world applications. Scientific Awards & Grants: NSERC RTI Award (2025) Mitacs Elevate Grant (awarded multiple times in 2024–2025) Transforming Climate Action Research Program (TCA) Support (2025) Advising and Funding: Dr. Wu actively supervises graduate students, including PhD candidates and Master’s students like Naihe Feng and Mahtab Sarvmaili. His lab is fully funded and currently recruiting PhD students for projects in computer vision and big data infrastructure in collaboration with industry partners. Labs & Teams: He founded and directs the Applied Machine Learning Research Lab (DAMLR) , which fosters collaboration between academia and industry to advance theoretical and applied AI research.

• Materials Science
• Solid-State Physics
• X-ray Diffraction
• +۳ مورد دیگر

Paul Evans is a Professor in the Department of Materials Science & Engineering at the University of Wisconsin-Madison, College of Engineering. His research focuses on nanoscale materials synthesis, ultrafast dynamics, and advanced X-ray characterization techniques. PhD, Harvard University (2000) MS, Harvard University (1996) BS, Cornell University (1994) Evans investigates solid-phase epitaxy of complex oxides, strain imaging in acoustic devices, and optically driven phase transitions. His work combines experimental and computational approaches, including deep learning for diffraction data analysis. His recent publications highlight breakthroughs in nanoscale crystallization, ultrafast magnetization dynamics, and hybrid magnon-phonon systems. Awards include the Bascom Professorship and Vilas Mid-Career Award. Surface Science and Technology Bascom Professorship (2022) Vilas Associate Award (2019) Polygon Engineering Outstanding Instructor Award (2006) Evans teaches courses in materials structure, advanced X-ray methods, and thesis research. His lab enables scalable synthesis of perovskites and defect-minimized oxide heterostructures.

Ramya Korlakai Vinayakمشاهده پروفایل

استادیار

• Machine Learning
• Statistical Inference
• Crowdsourcing
• +۲ مورد دیگر

Ramya Korlakai Vinayak is the Dugald C. Jackson Assistant Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. She also holds affiliations with the Department of Computer Science and the Department of Statistics at UW-Madison. Her research program bridges theoretical machine learning with practical applications in data science and crowdsourcing. Education: PhD in Electrical Engineering from California Institute of Technology (Caltech), advised by Prof. Babak Hassibi B.Tech in Electrical Engineering with minor in Physics from Indian Institute of Technology Madras (IIT Madras) Dr. Vinayak's research focuses on developing theoretically grounded machine learning tools for reliable inference using data from human sources. Her work spans machine learning theory, statistical inference, and crowdsourcing systems, with particular emphasis on preference learning, metric learning, and robust dataset construction. She has pioneered methods for learning from limited pairwise comparisons, auto-labeling systems, and human-in-the-loop out-of-distribution detection. Her recent publications reveal a consistent trajectory toward building practical machine learning systems that incorporate human feedback while maintaining theoretical guarantees. The pattern shows increasing focus on ethical considerations in AI, particularly regarding bias in generative models and reliable human-AI collaboration frameworks. Scientific Awards: Faculty for the Future fellowship (2013-2015) from Schlumberger Foundation NSF CAREER Award American Family Funding Initiative Award with Fred Sala Dr. Vinayak leads an active research group with multiple PhD students across ECE and CS departments. She has secured significant research funding including an NSF grant for "Uncovering the cognitive and neural fingerprints that make each of us unique" in collaboration with Tim Rogers, Rob Nowak and Brad Postle. She is also co-organizing the MidWest Machine Learning Symposium and NeurIPS tutorials on dataset construction. Her research group operates at the intersection of theory and practice, developing mathematically grounded frameworks that address real-world challenges in dataset construction, human-AI collaboration, and reliable machine learning systems.

Renata Borovica-Gajicمشاهده پروفایل

دانشیار

• Database Systems
• Machine Learning
• Artificial Intelligence
• +۱۲ مورد دیگر

Renata Borovica-Gajic is an Associate Professor in Data Analytics and an ARC DECRA Fellow at the School of Computing and Information Systems (CIS), University of Melbourne. She also serves as Associate Dean (Diversity and Inclusion) for the Faculty of Engineering and IT, demonstrating leadership in both research and academic community development. Her research lies at the intersection of database systems, machine learning, and artificial intelligence, with a vision of creating adaptive, self-driving database engines that optimize query execution in real-time. Her work spans learned indexes, query optimization, data quality, and data-driven traffic optimization, aiming to reduce costs and improve performance in data analytics. The recent publications reflect a strong trend toward integrating machine learning into core database operations—particularly through learned indexes, bandit-based tuning, and reinforcement learning for traffic systems. These works emphasize automation, provable guarantees, and real-time adaptation, showcasing a cohesive research agenda focused on intelligent, self-optimizing data systems. Her scientific excellence is recognized by numerous awards, including: L'Oréal-UNESCO for Women in Science Fellowship (2023) Victorian Young Tall Poppy (2024) Test of Time Award at SIGMOD 2022 Multiple Research and Teaching Excellence Awards from the University of Melbourne Google Research Inclusion Award (2021) She actively mentors PhD students and leads significant research projects funded by the Australian Research Council, Google, and Telstra. Her service includes roles as Associate Editor for SIGMOD Record, conference organization (e.g., aiDM, ADC, VLDB), and leadership in diversity and inclusion initiatives. She has also contributed to influential publications such as a chapter in the 7th edition of Database System Concepts . Her research lab focuses on AI-powered databases, traffic optimization via reinforcement learning, and self-healing data systems, positioning her at the forefront of next-generation data management.

Daniel Fürstenauمشاهده پروفایل

استاد

• Digital Transformation
• IT Infrastructures
• Digital Platforms
• +۱۲ مورد دیگر

Daniel Fürstenau is a Professor of Business Administration with a focus on IT Management and Digital Transformation at Freie Universität Berlin’s School of Business & Economics, and is affiliated with the Einstein Center Digital Future (ECDF). He also holds positions as Researcher at Charité – Universitätsmedizin Berlin and previously served as Associate Professor at IT University Copenhagen and Assistant Professor at Copenhagen Business School. He earned his doctorate from Freie Universität Berlin in 2014 and completed postdoctoral research with affiliations at UC San Diego, Copenhagen Business School, and the University of British Columbia. His academic journey is complemented by professional experience as a consultant at BOC Information Technology Consulting, where he guided digital transformation in banking and public sectors. Fürstenau’s research centers on digital transformation, IT infrastructures, and digital platforms, with a strong emphasis on healthcare applications. He investigates how digital systems influence organizational structures, innovation, and patient care, using methods such as network analysis and simulation. His work spans AI in nursing, digital therapeutics, health data governance, and low-code development. He leads interdisciplinary projects involving Charité and ECDF, focusing on digital health ecosystems and fall prevention using AI. His recent publications reflect a trend toward interdisciplinary digital health research, with a focus on AI, data platforms, governance, and sustainability. He frequently publishes in top-tier journals such as Information Systems Research , Journal of Management Information Systems , and Business & Information Systems Engineering . 2020 Best Paper Award, Academy of Management (OCIS) Best Associate Editor, International Conference on Information Systems (Healthcare track) Spokesperson, Division Digital Health, Gesellschaft für Informatik Cluster Spokesperson (since 2024) Member, Association for Information Systems (AIS) Fürstenau leads a research team of assistants based at FU Berlin and Charité, and is involved in major projects like DIPA-SCALE, CCA, and SoKIP. He is committed to socially relevant research with impact on medical care and financial systems. His leadership at ECDF underscores his role in shaping digital future research in Berlin.

Saptarshi Dasمشاهده پروفایل

دانشیار

• Two-Dimensional Materials
• Semiconductor Devices
• Field-Effect Transistors
• +۵ مورد دیگر

Saptarshi Das is an Associate Professor in the Department of Engineering Science and Mechanics within the College of Engineering at Pennsylvania State University. His research focuses on cutting-edge semiconductor technology, particularly in the realm of two-dimensional materials for next-generation electronic devices. Das maintains an active research program with numerous publications in high-impact journals including Nature, Nature Electronics, and Nature Communications. Dr. Das's research interests center around two-dimensional materials, semiconductor devices, and nanoelectronics with applications in AI hardware and computing systems. His work explores the fundamental properties and applications of materials like transition metal dichalcogenides (MoS 2 , WSe 2 ), graphene, and other 2D materials for creating novel electronic devices that can overcome limitations of traditional silicon-based technology. His research spans from fundamental material science to practical device implementation, with particular emphasis on developing energy-efficient computing solutions. Analysis of Dr. Das's recent publications reveals a strong focus on 2D material-based transistors, 3D integration techniques, and novel computing architectures. His work demonstrates significant advancements in p-type 2D transistors, monolithic 3D integration, and biomimetic sensor systems. The research shows a clear trajectory toward developing non-silicon computing platforms that could revolutionize AI hardware and low-power electronics. Dr. Das leads an active research group with multiple PhD students and collaborators working on semiconductor device fabrication, characterization, and integration. His laboratory likely has strong connections with Penn State's Materials Research Institute and other interdisciplinary research centers focused on advanced materials and semiconductor technology. His research has attracted significant attention, with multiple mentions in university news and science media outlets highlighting breakthroughs in 2D computing and electronic tongue technology.

• Electron Microscopy
• Atomic Resolution Microscopy
• Low-Voltage Electron Microscopy
• +۷ مورد دیگر

Dr. Rolf Erni is a leading expert in electron microscopy and materials science. Since 2009, he has served as Head of the Electron Microscopy Center at Empa (Swiss Federal Laboratories for Materials Science and Technology) and became a Titular Professor at the Department of Materials, ETH Zurich, in 2022. His research spans atomic-scale characterization techniques, including aberration-corrected TEM, low-voltage electron microscopy, in-situ imaging, and valence electron energy-loss spectroscopy. PhD in Materials Science from ETH Zurich (1999-2003) Postdoctoral work at UC Davis and Lawrence Berkeley National Laboratory Former faculty at the University of Antwerp's EMAT Institute Rolf's research bridges fundamental physics of electron scattering with applied studies in nanostructured materials , functional oxides , and energy storage systems . His recent work focuses on operando liquid-phase TEM for battery interfaces, catalysis under reaction conditions, and AI-driven nanoparticle analysis. He has taught advanced microscopy courses at ETH Zurich since 2013, including High Resolution TEM and Diffraction Physics . Rolf actively contributes to scientific governance through memberships in the Scientific Committee of the Fondation pour la recherche en biologie et médecine , COST Action committees, and review panels for international facilities like ePSIC and KNMF. His methodological innovations include correlative microscopy techniques and multislice simulation models that enhance atomic-scale analysis precision.

Jie Dingمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Statistics
• Scientific Computing
• +۶ مورد دیگر

Jie Ding is an Associate Professor at the University of Minnesota's School of Statistics with graduate faculty appointments in Electrical Engineering, Computer Science, and the Data Science Program. He serves as a core faculty member of the Data Science and AI Hub and holds an Amazon Scholar position with the Amazon AGI Team focusing on foundation model training. His educational background includes a Ph.D. in Engineering Sciences from Harvard University (2017), postdoctoral work at Duke University (2018), and a B.S. from Tsinghua University where he participated in both the Math & Physics Academic Talent Program and Electrical Engineering program. Ding's research sits at the intersection of artificial intelligence, statistics, and scientific computing, with focus areas including Agentic AI for autonomous data science workflows, AI Foundations for interpretability and trustworthiness, Scalable Modeling for broader AI accessibility, Decentralized and Collaborative AI systems, and AI Safety addressing privacy and security concerns. He developed the STAT 8931 Generative AI course with open-source materials available at genai-course.jding.org . His recent publications demonstrate strong activity across multiple AI subfields, particularly in value alignment (MAP framework), AI safety mechanisms, federated learning innovations, and statistical foundations for modern AI systems. The breadth of venues (ICML, ICLR, NeurIPS) indicates significant impact across the AI research community. NSF CAREER Award (2024) Army Early Career Program (Young Investigator) Award (2023) Cisco Research Award (2022-25) AWS Cloud Credits for Research (2021-22) Meta/Facebook Faculty Research Award (2021-22) UMN Thank-A-Teacher Teaching Award (2019-20) Ding leads the Agentic AI for Data Science Benchmark initiative, collaborating with University of Minnesota colleagues and Minnesota industry partners to evaluate AI agent capabilities across healthcare, insurance, retail, energy and other sectors. His research group actively recruits PhD students interested in AI/Statistics intersections, with focus on developing theoretically grounded yet practically impactful AI systems.

• Computer Graphics
• Virtual Reality
• Cultural Heritage Visualization
• +۷ مورد دیگر

Carlos Antonio Andújar Gran is a faculty member at the Faculty of Informatics of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), where he is affiliated with the Department of Computer Science. He actively contributes to the academic community through teaching and supervising numerous student projects, particularly in advanced computing domains. His research interests center on the application of computer graphics, virtual reality, and artificial intelligence to real-world problems. Key areas include the 3D visualization and virtual reconstruction of cultural heritage, physically accurate rendering, intelligent segmentation of images, and the use of machine learning and reinforcement learning in sports analytics, particularly for padel. His work bridges technical innovation with practical applications in cultural preservation and sports technology. The recent publications under his supervision reflect a strong trend in digital cultural heritage, with multiple projects focused on tools for virtual visits, reconstruction, lighting simulation, and web-based 3D visualization of heritage models. Another significant trend is the application of AI to padel, including ball and player tracking, virtual coaching, and training virtual agents using reinforcement learning. These projects demonstrate a consistent focus on computer vision, interactive systems, and data-driven modeling. Carlos Antonio Andújar Gran has supervised a substantial number of students, guiding both undergraduate and master's theses. While specific grant information is not available in the text, his sustained supervision across many years suggests active involvement in research projects. He plays a key role in mentoring the next generation of computer scientists at UPC. His work is closely tied to the Department of Computer Science at FIB, where he collaborates with other faculty members such as Imanol Muñoz Pandiella, Núria Pelechano Gómez, and Mohammadreza Javadiha on various student projects, indicating a collaborative research environment focused on graphics, AI, and VR applications.

• Artificial Intelligence
• Blockchain
• Digital Humanities
• +۵ مورد دیگر

Anthony Masure is an Associate Professor at Geneva University of Art and Design (HEAD – Genève) , part of the University of Applied Sciences and Arts Western Switzerland (HES-SO) . His work bridges Artificial Intelligence , Blockchain , and Digital Humanities through critical design lenses. He explores tensions between creative automation and human agency , questioning how machine learning and smart technologies reshape authorship, value systems, and ecological practices in design. Key research areas: AI ethics , NFT implications , post-digital design , and technological accountability Projects like "Design et machine learning : l'automatisation au pouvoir?" (2021-2023) and "Post digital Graphic Design" (2019-2022) demonstrate his focus on critical technological engagements Advocates for open access and non-traditional research formats through initiatives like HEAD-Publishing His recent articles analyze generative AI in design, NFT governance , and circular design frameworks. Collaborations with researchers like Guillaume Helleu and Yves Citton emphasize socio-technical implications. Current projects include "L'organisation autonome automatisée (OAA)" (2024), exploring future enterprise models under climate constraints.

Sarika Khushalani-Solankiمشاهده پروفایل

دانشیار

• Smart Grids
• Power Systems
• Electrical Engineering
• +۶ مورد دیگر

Sarika Khushalani-Solanki is an Associate Professor in the Lane Department of Computer Science and Electrical Engineering at West Virginia University , affiliated with the Benjamin M. Statler College of Engineering and Mineral Resources . She holds a Ph.D. in Electrical and Computer Engineering from Mississippi State University (2006), an M.E. in Electrical Engineering from Mumbai University (2000), and a B.E. in Electrical Engineering from Nagpur University (1998). Education: Ph.D., Electrical and Computer Engineering, Mississippi State University, 2006 M.E., Electrical Engineering, Mumbai University, 2000 B.E., Electrical Engineering, Nagpur University, 1998 Her research interests focus on smart grids , microgrid modeling and control , data mining , and AI applications in power systems . She has pioneered work in cyber-physical microgrids , resilient frequency control , and intelligent power system restoration , with a strong emphasis on integrating renewable energy and electric vehicles into modern grids. Her recent publications highlight advancements in microgrid control under cyber disruptions , AI-driven disturbance identification , and stochastic optimization for resilient power systems. She actively employs machine learning , dynamic time warping , and neuroscience-based control to address challenges in smart grid technologies . Scientific Awards: NSF CAREER Award Honda Fellowship As an IEEE Senior Member and former IEEE PES Subcommittee Chair , she contributes to grid resilience standards and workforce development . She serves as an Editor for IEEE Transactions on Smart Grid and MDPI Energies , and has taught courses like Electric Distribution Systems , Power Markets , and Smart Grid Technologies .

• Self-regulated Learning
• Learning Analytics
• Online Education
• +۴ مورد دیگر

Dr. Jacqueline Wong is an Assistant Professor in the Department of Education and Pedagogy at the Faculty of Social and Behavioural Sciences, Utrecht University. Her academic work focuses on enhancing educational practices through evidence-based research in self-regulated learning and learning analytics, with particular applications in higher education contexts. Dr. Wong earned her Ph.D. (cum laude) from Erasmus University Rotterdam with a thesis entitled "Enhancing Self-Regulated Learning Through Instructional Supports and Learning Analytics in Online Higher Education," which received the Best Thesis Award 2021 from the Erasmus Graduate School of Social Sciences and the Humanities. She completed her Master's degree (cum laude) in Educational Psychology, where she examined the use of movements to support text comprehension. Prior to joining Utrecht University, she was a postdoctoral researcher at Delft University of Technology (2020-2022) working with the Program of Innovation in Mathematics Education (PRIME). Dr. Wong's research program spans several interconnected areas: the application of learning analytics to support self-regulated learning, the design of instructional interventions that promote metacognitive awareness, and the investigation of how emerging technologies like generative AI can enhance educational experiences. Her work frequently examines how technology can be leveraged to support students' metacognitive processes and goal-setting behaviors in both traditional and online educational settings. She has made significant contributions to understanding how conversational agents and learning analytics can provide personalized support for students' self-regulated learning processes. Analysis of her recent publications reveals a clear trajectory toward integrating emerging technologies with established educational theories. Her 2024-2025 work increasingly focuses on the intersection of artificial intelligence and educational psychology, particularly examining how generative AI tools can enhance academic text accessibility and provide adaptive support for learning. She also continues to investigate cognitive aspects of learning in educational games and augmented reality environments, with implications for both educational practice and learning theory development. Among her notable achievements, Dr. Wong received the Best Thesis Award 2021 from the Erasmus Graduate School of Social Sciences and the Humanities. This recognition highlights the significant contribution her doctoral work has made to the field of educational psychology and learning sciences. Her systematic reviews, including examinations of student engagement in mathematics education and gamification in MOOCs, have provided valuable syntheses of research in growing areas of educational technology. Dr. Wong has been an active research member of the Leiden-Delft-Erasmus Centre for Learning and Education (LDE-CEL) and the network for Self-Regulated Learning in Digitalised Schools (SeReLeDiS). Her collaborative approach to research is evident in her extensive co-authorship network across multiple institutions. Before joining Utrecht University, she worked with lecturers to promote student motivation and self-regulated learning in blended service mathematics courses and examined the effectiveness of technology tools for learning mathematics. Dr. Wong's work bridges theoretical frameworks with practical implementations, often involving close partnerships with educators to ensure research relevance to real-world classroom challenges. Her current research appears to be moving toward deeper integration of AI technologies with educational theory, particularly examining how generative AI can support metacognitive processes and self-regulated learning across diverse educational contexts.