Tutorial
Dive into the exciting world of nanoscience and nanotechnology at the dedicated Tutorial Session of the IEEE-NANO 26, designed exclusively for students and young professionals seeking invaluable insights into these cutting-edge fields. This unique opportunity offers a dynamic platform for participants to interact with experts from around the globe. Renowned professionals will offer a series of tutorials, providing a comprehensive overview of key aspects of various advancing technologies. This immersive experience aims to bridge the gap between theoretical knowledge and practical applications, offering a deepened understanding of the latest advancements. Whether you are a novice or a seasoned enthusiast, this tutorial day promises to inspire, educate, and connect students and young professionals with the forefront of innovation in nanotechnology. Don’t miss this chance to broaden your horizons and engage with leading minds during IEEE-NANO 2026.
Confirmed Tutorial Speakers
Hengjie Yu
Assistant Researcher
Westlake University, China
Biography: Click to View
Dr. Hengjie Yu is an Assistant Researcher in the Department of Artificial Intelligence at Westlake University. He received his Ph.D. from the Department of Biosystems Engineering, Zhejiang University, in 2024. During his doctoral studies, he completed a one-year joint training program in the Department of Chemistry at the National University of Singapore. He subsequently completed his postdoctoral training in the Department of Artificial Intelligence at Westlake University from 2024 to 2026.
His research lies at the intersection of Artificial Intelligence for Science, nano-bio interfaces, and biomacromolecular analysis and design. As one of the early researchers to introduce explainable AI methods into nano-bio interface research, he bridges wet-lab experimentation and AI-driven modeling to enhance scientific understanding and discovery. His interdisciplinary work has been published in journals spanning environmental science, nanotechnology, and chemical engineering, including Environmental Science & Technology, Chemical Engineering Journal, Environmental Science: Nano, and Nanoscale, as well as AI and computational venues such as Artificial Intelligence Review, MICCAI and IJCNN. Driven by a passion for interdisciplinary innovation, he is dedicated to leveraging advanced AI methodologies to address important scientific challenges.
Topic: Click to View
Artificial Intelligence in Nanomaterials and Nanobio Interface Research: A Tutorial on Prediction, Discovery, and Design
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Artificial intelligence (AI) is transforming nanoscience by enabling data-driven prediction, discovery, and design of nanomaterials and nanobio interfaces. Because nanobio interactions involve complex physicochemical and environmental factors, experimental exploration is often costly and time-consuming. AI methods provide powerful tools to model these multidimensional relationships and predict nanomaterial properties and biological interactions. Recent advances in machine learning (ML), representation learning, and large language models (LLMs) have further expanded AI capabilities in nanotechnology. Classical ML models remain widely used for structured datasets, while encoding models and LLM-based systems enable knowledge extraction, reasoning, and automated research workflows. Although some of these approaches have already been applied in nanomaterial and nanobio interface research, others remain emerging technologies with significant potential for future applications. This tutorial reviews AI methodologies for nanomaterial and nanobio interface research across three paradigms: AI for Prediction, AI for Discovery, and AI for Design. We also discuss practical considerations such as model explainability, generalization, and data quality to guide responsible and effective applications of AI in nanoscience. This tutorial aims to provide practical guidance for leveraging AI to accelerate innovation in nanomaterial and nanobio interface research, while fostering a balanced understanding of both the strengths and limitations of current AI methodologies.
Guixiang Li
Professor
Southeast University, China
Biography: Click to View
Guixiang Li is a Professor at Southeast University. He is a recipient of a national-level young talent program, a Marie Skłodowska-Curie Fellowship awardee, and a member of the Global Young Academy (GYA). He received his Ph.D. from Helmholtz-Zentrum Berlin for Materials and Energy (HZB), Germany, and subsequently conducted postdoctoral research at the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, and Northwestern University, USA. His research focuses on hybrid semiconductor materials and optoelectronic devices, with particular emphasis on fundamental scientific problems and key technological challenges in photovoltaic energy conversion. His research spans functional material design, perovskite photovoltaics and tandem solar cells, photovoltaic-energy storage integration, and emerging semiconductor optoelectronic devices. He has published over 100 papers in international journals, including more than 40 as first or corresponding author in leading journals such as Science, Nat. Rev. Mater., Nat. Photonics, Nat. Energy, Nat. Commun., Adv. Mater. and Angew. Chem. Int. Ed. Several of his publications have been recognized as ESI Highly Cited or Hot Papers. He has also co-authored one English monograph. He a member of the American Chemical Society (ACS), serves on the editorial boards of Discov. Sustain., Sci. Rep., Adv. Mater. Res., and regularly reviews manuscripts for leading journals including Nat. Energy, Nat. Photonics, Nat. Commun., Adv. Mater., Sci. Adv., and JACS. He has led projects funded by the National Natural Science Foundation of China, the Jiangsu Basic Research Program, and the European Union research framework programs. He is also selected for the Jiangsu “U35 Young Science and Technology Talent Program” and has received the Second Prize of Provincial Natural Science Award.
Topic: Click to View
Structural Engineering and Stability Mechanisms of Perovskite Photovoltaic Devices
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Halide perovskite solar cells have emerged as a promising next-generation photovoltaic technology due to their outstanding power conversion efficiency and low-cost potential. However, their long-term operational stability under multiple environmental stressors, including temperature, humidity, and illumination, remains a critical challenge for practical deployment. This talk presents a systematic study on the intrinsic structural stability and interfacial/transport layer regulation mechanisms in perovskite photovoltaic devices. Particular emphasis is placed on the structural response and phase transition dynamics of perovskites under thermal cycling and coupled photo-thermal conditions, revealing the interplay among ion migration, interfacial stress, and defect evolution. Furthermore, strategies including compositional engineering, interface construction, and strain-relief design are discussed to achieve stable interfaces and optimized energy level alignment between charge transport layers and perovskite absorbers. Additionally, the crystallization dynamics of lead-free, low-toxicity perovskites and their implications for device stability are explored. By integrating approaches from intrinsic material structure to interfacial energy-level engineering, our work aims to provide fundamental insights and design principles for achieving long-term reliable operation of perovskite photovoltaic devices under complex environmental conditions.
Tingting Zhang
Tenure-track Professor
Nanjing University of Aeronautics and Astronautics, China
Biography: Click to View
Tingting Zhang is a Tenure-track Professor at the College of Integrated Circuits, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China. She received her Ph.D. degree in Integrated Circuits and Systems from University of Alberta (UA), Alberta, Canada, in 2024. Prior to joining NUAA in November 2025, she served as an Assistant Lecturer at UA and completed a Postdoctoral Fellowship at McGill University. Her research focuses on emerging computing chips for the post Moore era, with an emphasis on new computing architectures, approximate computing, Ising computing, combinatorial optimization, nanoelectronic circuits and systems. She was the recipient of the Best Paper Award Candidate at the Design, Automation and Test in Europe Conference (DATE) 2022. She has led or substantially participated in multiple research initiatives funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) and Huawei Technologies Co., Ltd.. She is an active member of the global research community, serving as the Publication Chair for the IEEE International Conference on Nanotechnology (IEEE-NANO) 2026, Session Chair for the Asia and South Pacific Design Automation Conference (ASP-DAC) 2026, and a Technical Program Committee member for the International Conference on Computer-Aided Design (ICCAD), DATE, and other flagship conferences. She has also been recognized as a Best Reviewer by the IEEE Circuits and Systems Society.
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Approximate and Stochastic Ising Machines
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The Ising model is useful in searching for (sub)-optimal solutions of combinatorial optimization problems (COPs). CMOS implementations of Ising model-based solvers, commonly referred to as Ising machines, provide reliable and accurate solutions with flexible and dense connectivities. However, they incur a significant hardware overhead. Approximate computing, as a low-power technique, offers a way to reduce hardware complexity, while stochastic computing is efficient in simulating the dynamics of the Ising model. The approximations introduced by these techniques may be beneficial in helping the system escape from local minima. In this tutorial, we discuss the potential of using approximate and stochastic computing to improve the performance of Ising machines.
Tutorial Agenda
Will be announced soon, please check back later.