ByteDance
Top Seed Talent Program

Jiashi holds a bachelor's degree from the University of Science and Technology of China and a doctoral degree from the National University of Singapore. His expertise lies in computer vision and machine learning, as well as their application in multimedia.
Jiashi currently heads the Vision Research team at ByteDance Doubao, conducting cutting-edge research in visual and multimodal foundation models, AIGC, and 3D avatar/object reconstruction and generation, while also focusing on the wide application of these technologies. Before joining ByteDance, he was an assistant professor in the ECE department of the National University of Singapore, leading the Vision and Machine Learning Laboratory, and advising or co-advising nearly 20 doctoral students.

Liang Xiang graduated from the Department of Automation, University of Science and Technology of China in 2006. He was directly admitted to the Institute of Automation, Chinese Academy of Sciences, where he received his PhD in machine learning and recommendation systems. In 2009, he participated in the Netflix Prize competition and won second place. He is also the author of Recommendation System Practice and founded Resys China, a recommendation system community in 2009.
After joining ByteDance, he was a research scientist for video understanding in AILab before taking charge of the recommendation system. Liang joined AML in 2021 and is been at the helm of ByteDance's AML and Doubao Foundation teams now. He leads the charge in exploring foundational and advanced AI algorithms and engineering technologies, swiftly bringing a range of AI products from concept to market.

Mingxuan earned a PhD from the Institute of Computing Technology, Chinese Academy of Sciences. He currently heads the Large Language Model Research team at ByteDance Doubao. In the field of machine translation, he has authored more than 50 papers presented at leading conferences and has consistently achieved first place in the Workshop on Machine Translation (WMT) on several occasions.
Mingxuan has participated in multiple open-source projects, including lightseq, mrasp, and others, which are widely adopted in the industry. He also serves as the regional chairman and sponsorship chairman for conferences such as NeurIPS, ACL, EMNLP, and more.

Lin received a master's degree from the Institute of Computing Technology, Chinese Academy of Sciences. Currently, he leads the Large Language Model Alignment Team at ByteDance Doubao.
The Alignment team's work includes instruction tuning, reward models, RLHF, RLAIF, and self-learning models. Meanwhile, the team is also spearheading a series of avant-garde and challenging studies in key areas such as the generalizability, interpretability, and authenticity of large language models.

Zhi earned his PhD in Computer Science from the University of Adelaide, Australia, where he was mentored by Professor Chunhua Shen. In 2019, he was honored with the Google PhD Fellowship.
Zhi is currently a research scientist for Vision Generation Models at ByteDance Doubao, focusing on the development and comprehension of computer vision algorithms. He has authored multiple highly-cited papers presented at leading conferences and in prestigious journals, amassing more than 11,000 citations on Google Scholar.

Yuxuan obtained his PhD from The Ohio State University, specializing in deep learning, speech technology, and other related fields of research and application. He has led several groundbreaking efforts in the speech industry, with results widely applied in academia and the industrial world.
Currently, he heads the Speech team at ByteDance Doubao, leading the team in core research and applications of technologies focused on multimodal generation and understanding.

Zhuo obtained his PhD from Columbia University in 2017 and worked as a core application scientist at Microsoft after graduation.
He currently leads the Audio Generation Research team at ByteDance Doubao. With more than 100 research papers and patents, he pushed the frontier of multiple audio technologies and has made significant contributions in various areas including voice generation, recognition, and translation, voice separation and enhancement, speaker recognition and diarization, self-supervised learning for voice, multi-channel processing, as well as in the development of open-source voice data sets.

Chenggang obtained his bachelor's and master's degrees from the Department of Mechanical Engineering at Tsinghua University.
He served consecutively as the head of ByteDance's Toutiao Webpage Search team and TikTok Video Search technology team. He developed ByteDance's search system from scratch, making significant innovations and breakthroughs in ranking architecture and algorithms, multi-language, and multimodal relevancy. Thanks to his contributions, ByteDance was able to deliver the leading search experience for Chinese-based search. Currently, he leads the Large Language Model Pre-training team at ByteDance Doubao, focusing on areas including data cleaning/synthesis/blending, associative learning and curriculum learning, training algorithms, and scaling capability.

Jiashi holds a bachelor's degree from the University of Science and Technology of China and a doctoral degree from the National University of Singapore. His expertise lies in computer vision and machine learning, as well as their application in multimedia.
Jiashi currently heads the Vision Research team at ByteDance Doubao, conducting cutting-edge research in visual and multimodal foundation models, AIGC, and 3D avatar/object reconstruction and generation, while also focusing on the wide application of these technologies. Before joining ByteDance, he was an assistant professor in the ECE department of the National University of Singapore, leading the Vision and Machine Learning Laboratory, and advising or co-advising nearly 20 doctoral students.

Liang Xiang graduated from the Department of Automation, University of Science and Technology of China in 2006. He was directly admitted to the Institute of Automation, Chinese Academy of Sciences, where he received his PhD in machine learning and recommendation systems. In 2009, he participated in the Netflix Prize competition and won second place. He is also the author of Recommendation System Practice and founded Resys China, a recommendation system community in 2009.
After joining ByteDance, he was a research scientist for video understanding in AILab before taking charge of the recommendation system. Liang joined AML in 2021 and is been at the helm of ByteDance's AML and Doubao Foundation teams now. He leads the charge in exploring foundational and advanced AI algorithms and engineering technologies, swiftly bringing a range of AI products from concept to market.

Mingxuan earned a PhD from the Institute of Computing Technology, Chinese Academy of Sciences. He currently heads the Large Language Model Research team at ByteDance Doubao. In the field of machine translation, he has authored more than 50 papers presented at leading conferences and has consistently achieved first place in the Workshop on Machine Translation (WMT) on several occasions.
Mingxuan has participated in multiple open-source projects, including lightseq, mrasp, and others, which are widely adopted in the industry. He also serves as the regional chairman and sponsorship chairman for conferences such as NeurIPS, ACL, EMNLP, and more.

Lin received a master's degree from the Institute of Computing Technology, Chinese Academy of Sciences. Currently, he leads the Large Language Model Alignment Team at ByteDance Doubao.
The Alignment team's work includes instruction tuning, reward models, RLHF, RLAIF, and self-learning models. Meanwhile, the team is also spearheading a series of avant-garde and challenging studies in key areas such as the generalizability, interpretability, and authenticity of large language models.

Zhi earned his PhD in Computer Science from the University of Adelaide, Australia, where he was mentored by Professor Chunhua Shen. In 2019, he was honored with the Google PhD Fellowship.
Zhi is currently a research scientist for Vision Generation Models at ByteDance Doubao, focusing on the development and comprehension of computer vision algorithms. He has authored multiple highly-cited papers presented at leading conferences and in prestigious journals, amassing more than 11,000 citations on Google Scholar.

Yuxuan obtained his PhD from The Ohio State University, specializing in deep learning, speech technology, and other related fields of research and application. He has led several groundbreaking efforts in the speech industry, with results widely applied in academia and the industrial world.
Currently, he heads the Speech team at ByteDance Doubao, leading the team in core research and applications of technologies focused on multimodal generation and understanding.

Zhuo obtained his PhD from Columbia University in 2017 and worked as a core application scientist at Microsoft after graduation.
He currently leads the Audio Generation Research team at ByteDance Doubao. With more than 100 research papers and patents, he pushed the frontier of multiple audio technologies and has made significant contributions in various areas including voice generation, recognition, and translation, voice separation and enhancement, speaker recognition and diarization, self-supervised learning for voice, multi-channel processing, as well as in the development of open-source voice data sets.

Chenggang obtained his bachelor's and master's degrees from the Department of Mechanical Engineering at Tsinghua University.
He served consecutively as the head of ByteDance's Toutiao Webpage Search team and TikTok Video Search technology team. He developed ByteDance's search system from scratch, making significant innovations and breakthroughs in ranking architecture and algorithms, multi-language, and multimodal relevancy. Thanks to his contributions, ByteDance was able to deliver the leading search experience for Chinese-based search. Currently, he leads the Large Language Model Pre-training team at ByteDance Doubao, focusing on areas including data cleaning/synthesis/blending, associative learning and curriculum learning, training algorithms, and scaling capability.