2021.9 - present
Senior Algorithm Engineer, Data Intelligence Innovation Lab, Huawei Cloud
Responsible for buiding a LLM-driven copilot for data platform, question answering system for business analytics and decision.
2018.7 - 2021.8
Algorithm Engineer II, Decision Intelligence, Alibaba DAMO Academy.
Research interest includes causal inference, pricing/traffic/power grid optimization, item-user matching, CTR prediction.
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, 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.
Biography. I am currently an senior algorithm engineer at Huawei cloud working on building LLM-driven data copilot by RAG and Alignment for business analytics. Before that, I am a researcher at Alibaba DAMO Academy working on technologies for kinds of decision problems, such as causal inference, pricing optimization, traffic optimization, item-user matching, CTR prediction and power grid optimization. I received PhD from Zhejiang University under the supervision of Prof. Chunguang Li work on distributed variational inference , and BS degree from Zhejiang University of Technology.

Research interests LLM Inference Optimization, Reasoning with LLM, RAG, LLM Alignment, Augmented Analytics, Causal inference, Pricing and revenue optimzation, Bayesian learning, Distributed optimization.

Curriculum Vitae (out of date). My CV written in English can be found here, and that written in Chinese can be found here (中文简历).

On a side for fun I blog, weibo, and maintain several Projects (e.g. ActionRecognition, AgeEstimation, VBClusterings, UnixFileSystem).

News

2023年09月 华为云计算， 基于盘古大模型的新一代BI — 华为云智能数据洞察 DataArts Insight发布！ [链接]
2021年09月 杭州， 我离开了阿里巴巴达摩院，加入华为云计算
2021年11月 达摩智控 获得国家电网调控人工智能创新大赛“电网运行组织智能安排“赛道冠军
2021年5月 阿里巴巴达摩院， 一篇关于营销优化、促销定价论文Markdowns in E-commerce Fresh Retail: A Counterfactual Prediction and Multiple-Period Optimization Approach 被KDD 2021接收
2020年11月 阿里巴巴达摩院， 获得双十一卓越贡献奖（尖峰奖）
2029年5月 浙江大学， Distributed Variational Bayesian Algorithms for Extended Object Tracking 提交至Arxiv (未投稿).
2019年5月 阿里巴巴达摩院， 一篇关于营销优化，预算分配论文A Unified Framework for Marketing Budget Allocation 被KDD 2019接收
2018年7月 杭州， 从浙江大学毕业，加入阿里巴巴达摩院机器智能实验室
2018年11月 浙江大学， Distributed Robust Bayesian Filtering for State Estimation被IEEE TSIPN接收
2018年06月 杭州， 以优秀研究生毕业生身份从浙江大学毕业
2017年11月 浙江大学， 一篇关于量化通信的分布式贝叶斯学习论文Distributed Jointly Sparse Bayesian Learning with Quantized Communication被IEEE TSIPN接收
2016年12月 浙江大学， 获得博士研究生国家奖学金
2016月9月 浙江大学， 分布式多任务学习论文Distributed Learning of Predictive Structures from Multiple Tasks Over Networks 被IEEE TIE接收
2015月7月 浙江大学, 一篇分布式变分贝叶斯学习论文 Distributed Variational Bayesian Algorithms over Sensor Networks被IEEE TSP接收
2013年06月 杭州， 我以优秀本科毕业生身份从浙江工业大学毕业，并保研至浙江大学信电学院就读博士研究生
2010, 2011, 2012年 浙江工业大学， 一等奖学金一次(<5%)，二等奖学金两次 (<10%)
2011年11月 浙江工业大学， 获得高教社杯全国大学生数学建模竞赛本科组二等奖(<6.5%)
2011 & 2012年 浙江工业大学， 浙江工业大学数学建模竞赛一等奖两次(<5%)
2011年12月 浙江工业大学， 获得计算机86级圣诞杯ACM竞赛一等奖(<5%)
2011年05月 浙江工业大学， 获得浙江省大学生高等数学（微积分）竞赛工科类一等奖 (<5%)
2010年12月 浙江工业大学， 获得第二届全国大学生数学竞赛（非数学类）浙江赛区一等奖(<3%)

Publications

List of papers on Google scholar.


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.

Talks

2016 Nov: Talk at SIIP 2016 seminar, ZJU: Distributed variational Bayesian Algorithm in Networked System (in chinese) [slides].
2015 Jan: SIIP Group Talk: An Introduction to Transfer Learning [slides].
2014 Oct: SIIP Group Talk: Privacy Preserving Regression [slides].
2014 Oct: SIIP Group Talk: Vertically Partitioned Data [slides].
2014 Jul: SIIP Group Talk: Zero-Determinant Strategies [slides].
2014 Apr: Talk at Course of "Image & Video Analysis": Action Recognition & Categories via Spatial-Temporal Features [slides].
2014 Mar: Talk at csmath (2014-2015) course: A Tutorial on Variational Bayes [slides].
2013 Sep: SIIP Group Talk: Recursive parameter estimation and inference with incomplete data – Recursive EM & VB [slides].
2013 Jun: Undergraduate thesis defense: Brain MRI Segmentation based on Variational Bayesian methods [slides].
2012 Nov: SIIP Group Talk: Distributed Image Processing: Camera Networks, CV algorithms and Decentralized Multicamera Tracking [slides].

Projects

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.
show more
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.

Misc

Address: Hangzhou, Zhejiang, China.
Emails: huajh7 -at- gmail.com (replace -at- by @)
Last update:
  • 16 Nov. 2024

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