Junhao Hua Academic Website

2021.9 - present

Principal Engineer, Data Intelligence Innovation Lab, Huawei Cloud

Building a trustworthy, controlable LLM-driven data copilot, which is really really usefully for business analytics and decision making.

2018.7 - 2021.8

Algorithm Engineer II, Alibaba DAMO Academy.

Solving decision and prediction problems by Bayesian/causal inference, pricing/traffic/power grid optimization, such as sales forecasting, dynamic pricing, traffic control.

2013.9 - 2018.6

Ph.D, Colleage of ISEE, Zhejiang University

statstical machine learning, variational Bayesian inference, distributed optimization. My adviser is Chunguang Li.

Summer 2012

C/C++ Engineer Internship, State Street Corp, Hangzhou

Maintenance and Development for Princeton Financial Systems.

2009.9 - 2013.6

Bachelor's Degree, Computer Science & Automation, Zhejiang University of Technology

Double major in Computer Science and Automation. My adviser is Shengyong Chen.

Junhao Hua, Ling Yan, Huan Xu, Cheng Yang, Markdowns in E-commerce Fresh Retail: A Counterfactual Prediction and Multiple-Period Optimization Approach, 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21). [arxiv]

Kui Zhao, Junhao Hua, Ling Yan, Qi Zhang, Huan Xu, Cheng Yang, A Unified Framework for Marketing Budget Allocation, 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19). [arxiv]

Junhao Hua, Chunguang Li, Distributed Variational Bayesian Algorithms for Extended Object Tracking, unsubimitted, 2019. [arxiv]

Junhao Hua, Chunguang Li, Distributed Robust Bayesian Filtering for State Estimation, IEEE Transactions on Signal and Information Processing over Networks, vol. 5, no. 3, pp.428-441, Sept 2019. [link]

Junhao Hua, Chunguang Li, Distributed Jointly Sparse Bayesian Learning with Quantized Communication, IEEE Transactions on Signal and Information Processing over Networks, Vol. 4, no. 4, Dec 2018.

Junhao Hua, Chunguang Li, Hui-Liang Shen, Distributed Learning of Predictive Structures from Multiple Tasks Over Networks, IEEE Transactions on Industrial Electronics, vol. 64, no.5, pp.4246-4256, May 2017.

Junhao Hua, Chunguang Li, Distributed Variational Bayesian Algorithms over Sensor Networks, IEEE Transactions on Signal Processing, vol.64, no.3, pp.783-798, Feb. 2016.

Distributed Learning of Predictive Structures from Multiple Tasks Over Networks

Junhao Hua, Chunguang Li, Hui-Liang Shen

IEEE Transactions on Industrial Electronics(TIE, ZJU-TOP100), vol. 64, no.5, pp.4246-4256, May 2017.

We concerned with the problem of distributed multitask learning over networks, which aims to simultaneously infer multiple node-specific parameter vectors in a collaborative manner. In this work, we implicitly model the similarity of parameter vectors by assuming that the parameter vectors share a common low-dimensional predictive structure on hypothesis spaces, which is learned using the available data in networks. A distributed structure learning algorithm for the in-network cooperative estimation problem is derived based on the block coordinate descent method integrating with the inexact ADMM technique.

Distributed Variational Bayesian Algorithms over Sensor Networks

Junhao Hua, Chunguang Li

IEEE Transactions on Signal Processing (TSP, SCI-TOP), vol.64, no.3, pp.783-798, Feb. 2016.

We propose two novel distributed VB algorithms for general Bayesian inference problem, which can be applied to a very general class of conjugate-exponential models. In the first approach, the global natural parameters at each node are optimized using a stochastic natural gradient that utilizes the Riemannian geometry of the approximation space, followed by an information diffusion step for cooperation with the neighbors. In the second method, a constrained optimization formulation for distributed estimation is established in natural parameter space and solved by ADMM. An application of the distributed inference/estimation of a Bayesian Gaussian mixture model is then presented, to evaluate the effectiveness of the proposed algorithms.

Action Recognition & Categories via Spatial-Temporal Features

Author: Junhao Hua, Shangyao, Lin

2014 April

This project consider this problem of recognizing and localizing multiple actions in long and complex video sequences containing multiple motions. Inspired by the previous works by Juan Niebles et al, 2008, we use the "bag of word" represetations for action recogntion. We first extract the spatio-temporal features (interest points), then construct the codebook (a set of words) by clustering of interest points using k-means algorithm. Then, the action categories can be infered by using unsupervised learning such as pLSA or LDA learned by the MCMC/variational inference. The hierarchal structure can be written as: document (video) - words (by clustering of interest points) - topic( types of actions). In this project, we simply use the supervised algorithms (such as KNN, SVM) to classify each word in every frame. For classifying multiple types of actions in a single video, we propose a simply algorithm called 'voting', to vote the top-N topics each frame/image is likely to have. This simple method can achieve the aim of multiple actions recogintion. Thanks to Piotr's Computer Vision Matlab Toolbox, the project is implemented by MATLAB.

Action Recognition & Categories via Spatial-Temporal Features

Author: Junhao Hua, Shangyao, Lin

2014 April

This project consider this problem of recognizing and localizing multiple actions in long and complex video sequences containing multiple motions. Inspired by the previous works by Juan Niebles et al, 2008, we use the "bag of word" represetations for action recogntion. We first extract the spatio-temporal features (interest points), then contruct the codebook (a set of words) by clustering of interest points using k-means algorithm. Then, the action categories can be infered by using unsupervised learning such as pLSA or LDA learned by the MCMC/variational inference. The hierarchal structure can be written as: document (video) - words (by clustering of interest points) - topic( types of actions). In this project, we simply use the supervised algorithms (such as KNN, SVM) to classify each word in every frame. For classifying multiple types of actions in a single video, we propose a simply algorithm called 'voting', to vote the top-N topics each frame/image is likely to have. This simple method can achieve the aim of multiple actions recogintion. Thanks to Piotr's Computer Vision Matlab Toolbox, the project is implemented by MATLAB.

Academic Blog

Address: Hangzhou, Zhejiang, China.

Emails: huajh7 -at- gmail.com (replace -at- by @)

Last update:

16 Nov. 2024

Vistors Since Apr 2017

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