I am a Ph.D student in Computer Science of Rensselaer Polytechnic Institute (RPI), working on complex networks and the applications in transportation, biomedical systems. Also, I am interested in Machine Learning (e.g. supervised or unsupervised learning, and reinforcement learning), Artificial Intelligence (AI), Information Retrieval (IR), and their applications in ranking problems (e.g. Rank Learning and Rank Aggregation).

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Contact

E-mail: jiangchunheng@gmail.com
Website: http://www.horsehour.com
GitHub: https://github.com/horsehour
Amos Eaton 123, 110 8th St. Troy NY, 12180, USA

Education

2016.09 - now: Ph.D, Computer Science, Rensselaer Polytechnic Institute
2011.09 - 2014.07: M.Sc, Applied Mathematics, Southwest Jiaotong University

Publications

Jiang, Chunheng, Sujoy Sikdar, Jun Wang, Lirong Xia, and Zhibing Zhao. Practical Algorithms for Computing STV and Other Multi-Round Voting Rules. In EXPLORE-2017: The 4th Workshop on Exploring Beyond the Worst Case in Computational Social Choice. 2017.

Jiang, Chunheng and Wenbin Lin. DEARank: A Data-envelopment-analysis-based Ranking Method. Machine Learning, 2015, 101: 415 – 435.

Pei, Zhongyou, Chunheng Jiang, and Wenbin Lin. Random Walks on the Bipartite Graph for Personalized Recommendation. In 2013 International Conference on Computer Science and Artificial Intelligence, Yuetong Lin and Gabriel Alungbe, Eds., Chengdu, China, 2013, 97 – 102.

Research Interests

Machine Learning and Artificial Intelligence

  • Machine Learning / Optimization
  • Deep Learning / Reinforcement Learning
  • Learning to Rank / Rank Aggregation
  • Information Retrieval (Extraction) / Recommendation

Work Experience

2014.07 - 2016.03: Software Developer, Antusuoji Network Technology Co., Ltd., Chengdu, CHINA

  • Collect web information (commercial products, job positions)
  • Build an information retrieval system based on Solr

Projects

Spring - Summer 2017 : Multiround Winner Determination, RPI

  • Devise heuristic strategies (sample, cache, prune) to search all tied winners
  • Train reinforcement learning models to simulate the voting procedure
  • Improve the baseline DFS in terms of running time & number of nodes

Fall 2016 : Matching Algorithm for OKCollege (now CollegeAI)

  • Design a bilinear model to match students and colleges’s preferences
  • Optimize a ranking-related loss function with SGD to train the model

Summer 2016 : Learning to Vote Fairly, RPI

  • Learn the fairness criteria in voting rules with machine learning approaches
  • Apply data augmentation to enhance the learning performance

Summer 2013 : Automating Data Collection

  • Crawl over 10,000 professors’ profiles from top Chinese universities
  • Semi-automate the inefficient and expensive manual collection procedure
  • Align the collected data and ouput with homogeneous content

Summer 2012 : Meta Extraction from PDF Papers

  • Crawl 5,000 research papers in PDF and related meta data from arXiv
  • Convert PDF documents to XML with pdf2xml and create training set
  • Recognize the meta information blocks (titles, authors, keywords, abstract, and references) with handcrafted rules and machine learning techniques

Teaching

Spring 2017 : Assistant, CSCI 4150: Introduction to Artifical Intelligence, RPI
Fall 2016 : Assistant, CSCI 4100/6100: Machine Learning from Data, RPI

Programming

Java, Python, C/C++, Matlab, Mathematical, Lingo
Eclipse, PyCharm, Git, LaTeX, Markdown, vim

Courses

A complete list of courses I have taken on-campus or online