Yishu Miao

I'm the Founder & CEO of Haiper, a startup for building the largest GenAI video community. 

Previously, I was leading the London ML team for building LMs for Global Trust & Safety at TikTok.  And I was Research Associate at Imperial College London working with Prof. Lucia Specia, focusing on developing Large Multi-modal Models

I founded a startup MO Intelligence, developing Robotics technologies for construction industries.

I completed my PhD with Prof. Phil Blunsom at University of Oxford where I worked on Generative Models for Language

Prior to Oxford, I did my master on Data Mining, working with Prof. Chunping Li at Tsinghua University.

Email: yishu.miao [-at-] haiper.ai

Past Work Experiences:

Tech Lead Manager, Trust & Safety. TikTok London. 2020 - 2021

Research Associate. Imperial College London. 2020 - 2021

Founder & CEO, MO Intelligence. 2018 - 2020

Research Intern, DeepMind. 2017

Research Intern, DeepMind. 2016

Selected Publications:

Kubric: A scalable dataset generator.

Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J. Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam Laradji, Hsueh-Ti (Derek)Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, Cengiz Oztireli, Etienne Pot, Noha Radwan, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, Andrea Tagliasacchi. CVPR 2022

A Generative Framework for Simultaneous Machine Translation.

Yishu Miao, Phil Blunsom, Lucia Specia. EMNLP 2021

"Poisson prior for balancing the quality and latency in simultaneous machine translation"

Discovering Topics in Long-tailed Corpora with Causal Intervention.

Xiaobao Wu, Chunping Li, Yishu Miao. ACL Findings 2021

"A causal inference framework to explain and overcome topic modeling on long-tailed corpora"

Cross-Modal Generative Augmentation for Visual Question Answering

Zixu Wang, Yishu Miao, Lucia Specia. BMVC 2021.

"Variational generative framework for multimodal data augmentation"

Exploiting Multimodal Reinforcement Learning for multimodal Simultaneous Machine Translation

Julia Ive, Andy Mingren Li , Yishu Miao, Ozan Caglayan, Pranava Madhyastha, Lucia Specia. EACL 2021.

"Reinforcement learning for multimodal simultaneous machine translation (SiMT)"

"Short text topic modelling with quantization and contrastive learning"

Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation

Zhenghua Xu, Cheng Chen† Thomas Lukasiewicz, Yishu Miao. ICASSP 2020.

"A hybrid deep-semantic matrix factorization for tag-aware personalized recommendation"

TextPlace: Visual Place Recognition and Topological Localization Through Reading Scene Texts

Ziyang Hong, Yvan Petillot, David Lane, Yishu Miao, Sen Wang. ICCV 2019.

"Read scene texts in the wild for visual place recognition"

Selective Sensor Fusion for Neural Visual Inertial Odometry

Changhao Chen‚ Stefano Rosa‚ Yishu Miao‚ Chris Xiaoxuan Lu‚ Wei Wu‚ Andrew Markham and Niki Trigoni. CVPR 2019.

"Neural VIO with discrete latent variables"

MotionTransformer: Transferring Neural Inertial Tracking Between Domains

Changhao Chen, Yishu Miao, Chris Xiaoxuan Lu, Linhai Xie, Phil Blunsom, Andrew Markham, Niki Trigoni. AAAI 2019.

"Generative Adversarial Networks for domain transformation"

Learning with Stochastic Guidance for Navigation

Linhai Xie, Yishu Miao, Sen Wang, Phil Blunsom, Zhihua Wang, Changhao Chen, Andrew Markham, Niki Trigoni. NIPS workshop 2018.

"Efficient and effective way for training DDPG"

Neural Allocentric Intuitive Physics Prediction from Real Videos

Zhihua Wang, Stefano Rosa, Yishu Miao, Zihang Lai, Linhai Xie, Andrew Markham, Niki Trigoni. NIPS workshop 2018.

"Intuitive Physics from video inputs"

Memory Architectures in Recurrent Neural Network Language Models.

Dani Yogatama, Yishu Miao, Gabor Melis, Wang Ling, Adhiguna Kuncoro, Chris Dyer and Phil Blunsom. ICLR 2018.

"Language model with stacks"

Discovering Discrete Latent Topics with Neural Variational Inference.

Yishu Miao‚ Edward Grefenstette and Phil Blunsom. ICML 2017. 

"Neural interpretation of Bayesian non-parametrics"

Latent Intention Dialogue Models.

Tsung−Hsien Wen*Yishu Miao*‚ Phil Blunsom and Steve J. Young (*equal contribution). ICML 2017.

"Neural dialogue model with discrete latent variable for capturing intention"

Tag−Aware Personalized Recommendation Using a Hybrid Deep Model.

Zhenghua Xu‚ Thomas Lukasiewicz‚ Cheng Chen‚ Yishu Miao and Xiangwu Meng. IJCAI 2017. 

"Deep Recommendation system with reconstruction learning signal"

"Discrete variational auto-encoder for text sequence"

Neural Variational Inference for Text Processing.

Yishu Miao, Lei Yu and Phil Blunsom. ICML 2016.

"Variational auto-encoder as neural topic model", 

Default NTM algorithm on Amazon Sagemaker.

Tag−Aware Personalized Recommendation Using a Deep−Semantic Similarity Model with Negative Sampling.

Zhenghua Xu‚ Cheng Chen‚ Thomas Lukasiewicz‚ Yishu Miao and Xiangwu Meng. CIKM 2016.

"Deep neural model for recommendation system"

Bayesian Optimisation for Machine Translation.

Yishu Miao‚ Ziyu Wang and Phil Blunsom. Bayesian Optimisation Workshop‚ NIPS 2014.

"BO for statistical machine translation system"