An interview with Yonghong Tian, recipient of the 2025 Hans Karlsson Standards Award
Yonghong Tian stands as a global authority in the field of artificial intelligence and multimedia systems. Formerly serving as the Dean of the School of Electronics and Computer Engineering, now Vice-Dean of Peking University Shenzhen Graduate School and Dean of the new School of Science and Intelligence, and a Boya Distinguished Professor at Peking University, China, Professor Tian has made groundbreaking contributions to brain-inspired neural networks, distributed machine learning, and AI for Science. His visionary leadership as Chair of the IEEE P2941 Working Group led to the creation of the IEEE 2941-2021 standard, a milestone that bridges the gap between diverse computing architectures and algorithm frameworks. In this interview, he explains his journey, insights, and the impact of his work on shaping the future of AI and international standardization.
Your research in neuromorphic vision and brain-inspired computation is pioneering. What inspired you to explore these areas?
Our motivation grew out of two complementary forces: urgent practical demand and the intellectual pull of the scientific frontier. A decade ago, while working on video coding and conventional computer vision, my team and I noticed a critical gap. Frame-based cameras that capture 30 images per second simply discard most of the information that flows through the real world. For applications such as autonomous driving, that lost detail can spell the difference between safety and disaster. We asked ourselves how biology solves this problem, because the human retina never works in discrete frames and the brain operates with remarkable speed and efficiency on milliwatts of power. Beginning in 2014 we examined retinal signal transduction in detail, and by 2016 we built Vidar, our first retina-inspired visual processor. Vidar converts light intensity into one-bit spikes at forty thousand hertz, so it captures ultra-fast motion and copes naturally with extreme lighting conditions. However, traditional artificial neural networks cannot use spike streams directly, so we turned to spiking neural networks, the third generation of neural models that communicate through discrete events just like biological neurons. To accelerate research in this area we released SpikingJelly, an open-source training framework that is now used worldwide. In essence, the eye taught us how to sense and the brain taught us how to compute. By uniting neuromorphic sensors with spiking processors we hope to bring machines closer to the elegance, speed and frugality of natural intelligence, and that mission continues to inspire us every day.
As Dean of the School of Electronics and Computer Engineering at Peking University, how do you foster innovation and interdisciplinary research among faculty and students?
I actually concluded my term as Dean of the School of Electronics and Computer Engineering earlier this year and now serve as Vice-Dean of Peking University Shenzhen Graduate School and Dean of the new School of Science and Intelligence. The latter was established precisely to make cross-disciplinary work routine, because artificial intelligence is reshaping the way we pursue basic science, especially in materials and the life sciences. We encourage innovation on three fronts. First, our research grants follow a dual-PI model. Every “AI + Science” project is co-led by one principal investigator from a scientific domain and another from AI, who share resources and milestones. This structure ensures that deep domain problems and cutting-edge methods evolve together rather than in isolation. Second, our doctoral training adopts a dual-advisor system. Each student is guided by one mentor who defines the scientific question and a second who specialises in AI techniques that can answer it. For instance, a student working on AI for structural biology pairs a biologist who frames the problem with an AI researcher who designs the learning pipeline. The same approach drives our recent AI-materials collaborations, which have already produced joint papers in specialist journals. Third, we align incentives with this new workflow. Promotion guidelines, seed-fund competitions and annual awards now recognise shared publications, code releases and datasets that arise from genuine interdisciplinary partnership. Dedicated colloquia and matchmaking workshops help faculty spot complementary expertise and launch new teams quickly. By coupling shared leadership, shared mentorship and shared evaluation, we give both faculty and students clear reasons and concrete mechanisms to cross disciplinary boundaries and to turn AI advances into scientific breakthroughs.
With over 85 patents and 200 publications, how do you balance academic research with practical technological applications?
For me, scholarship and real-world impact form a single pipeline rather than two competing goals. Every project begins with a concrete need: an ability that industry, medicine, or national infrastructure still lacks. We then trace backward to the scientific unknowns that block progress. This need-first, frontier-second mindset keeps our work both relevant and ambitious. Once a project starts, we run it on two synchronized tracks. One team tackles the fundamental questions that yield journal papers, such as new learning-theory bounds or novel device physics. A second, often overlapping, team converts those advances into prototypes robust enough for field trials. As soon as a prototype shows a clear performance edge, we file a provisional patent to protect the core idea, and we publish the broader scientific framework so the community can build on it. This sequence (discover, patent, publish) has proved faster than the traditional publish-then-license path and keeps both our citation count and our technology-transfer metrics strong. We also measure success with a dual scorecard. Faculty and students earn recognition not only for high-impact papers but also for deployed systems and downstream revenue. Practical application therefore fuels basic research, and basic research feeds back into application.
Your involvement in developing retina-like visual sensing is groundbreaking for high speed imaging. Where have these been used and what potential do you see for these technologies in further real-world applications?
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