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Beijing, February 5, 2025 — Fan Shukai, a distinguished Machine Learning Engineer at ByteDance, is revolutionizing the digital advertising landscape through groundbreaking applications of artificial intelligence and machine learning technologies. With his philosophy that "AI should not only optimize performance but create tangible business value," Fan has consistently pushed the boundaries of what's possible in advertising technology.
Currently leading critical initiatives in ByteDance's Global Monetization Product and Technology division, Fan has demonstrated exceptional expertise in developing and implementing sophisticated machine learning solutions. His innovative work on sample selection bias optimization has yielded remarkable results, achieving a 3% increase in advertiser value while significantly improving ranking system consistency. This breakthrough has redefined how digital advertising platforms approach sample space alignment between serving and training environments.
Fan's technical acumen is particularly evident in his work on computational resource optimization, where he designed and implemented a revolutionary traffic value estimation model. This innovation has led to substantial resource savings - approximately 400,000 CPU cores and 4,000 GPU cards - while simultaneously improving advertiser value by 5%. His ability to balance technical efficiency with business performance has become a benchmark in the industry.
One of Fan's most significant contributions has been his pioneering work in Large Language Model (LLM) token design and optimization. By developing an innovative offline construction of LLM token expression for advertising inventory, he has enhanced model generalization capabilities and solved critical challenges in material cold start scenarios. This breakthrough initiative resulted in a 0.5% lift in advertiser value, demonstrating the practical impact of applying cutting-edge AI technologies to real-world business challenges.
Fan's approach to innovation is grounded in his exceptional academic background, holding a Master's degree in Information with a focus on Data Sciences from the University of Michigan and a Bachelor's degree in Engineering from the prestigious Shanghai Jiao Tong University's Joint Institute with a near-perfect GPA of 3.97/4.0. His academic excellence is matched by his remarkable research contributions, including first-author publications in IEEE JSAC and IEEE INFOCOM, with his works being cited dozens of times by peers in the field.
Beyond his core work in global monetization, Fan has demonstrated remarkable versatility in solving complex industry challenges. At ByteDance's Douyin (TikTok China) platform, he spearheaded the development of contrastive learning approaches in recommendation systems, successfully addressing the long-standing challenge of long-tail content optimization. His innovative application of self-supervised learning in MMOE models has become a benchmark solution for handling sparse data scenarios in large-scale recommendation systems. The implementation of Show Send Rate modeling in fine-sort ranking marked another significant breakthrough, maximizing revenue from ad show events to ad send events and substantially improving overall system accuracy.
In the e-commerce sector, Fan's contributions have been equally impactful. His development of an independent CVR model and optimization of the billing ratio achieved significant improvements in conversion rates. By introducing a high-low cross structure into CTR and strengthening the model's understanding with landing page features, he successfully enhanced the system's performance across various business metrics. His innovative solution to the repeated reach problem, through the introduction of creative dimension features and soft frequency control strategy, led to notable optimization of CTR/CVR differentials.
Fan's leadership in the Qianchuan project at ByteDance showcases his ability to drive substantial business impact. He led the development of an automated creative generation system that achieved remarkable results, generating daily revenue of 200 million RMB. This success demonstrates his exceptional ability to transform cutting-edge AI technology into tangible business outcomes, bridging the gap between technical innovation and commercial success. The project's success has made it a benchmark case study in the industry for automated content generation and monetization.
Prior to his role at ByteDance, Fan's professional journey included significant contributions at some of China's leading technology companies. At Alibaba Ant Financial, he developed sophisticated deep learning algorithms for financial risk control, utilizing advanced data analysis techniques to enhance the reliability assessment of small and micro loans. At Alibaba Cloud, he created innovative time-series prediction frameworks for industrial applications, specifically tailored for local power grid optimization. His early work at Philips Electronics demonstrated his ability to solve complex engineering challenges, where he developed a novel acoustic-optic interaction method that became a key product feature and significantly boosted market performance.
Fan's research work has garnered significant recognition in the academic community. His groundbreaking paper on terahertz indoor localization, published in IEEE JSAC, achieved remarkable accuracy with a mean distance error of just 0.25 meters under challenging NLoS environments. This work, along with his conference paper at IEEE INFOCOM, has garnered significant attention, collectively receiving over 80 citations. These publications demonstrate his ability to contribute meaningful advancements to both academic research and practical applications.
Looking ahead, Fan Shukai continues to push the boundaries of AI and machine learning applications in digital advertising. His current focus on developing next-generation ranking systems and exploring advanced applications of large language models positions him at the forefront of advertising technology innovation. By combining deep technical expertise with practical business acumen, Fan is not just solving today's challenges but actively shaping the future of digital advertising technology.
His achievements represent more than individual success; they mark significant progress in the field of AI-driven advertising optimization. As the industry continues to evolve, Fan's innovative approaches and methodologies are setting new standards for how technology can enhance digital advertising effectiveness while improving computational efficiency. His work exemplifies the perfect blend of theoretical knowledge and practical application, making him a leading figure in the
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