
RESEARCH EXPERIENCE 2013.10Present Undergraduatepostgraduate
Student Intelligent Computing and Machine Learning
Lab, Beihang University, Beijing, China Advisor:
Prof. Zengchang Qin (Associate Professor) Topic: Machine
Learning, Music Information Retrieval, Uncertainty Modelling.
2010.102013.6 High School Student
Participant Mathematical Fuzzy Control Lab, Beijing
Normal University, Beijing, China Advisor: Prof. Ming Bai
(Associate Professor) Project: Digital MusicScore
Management, Display and Rehearsing System
PROJECTS

A Bayesian Model of Game Decomposition (2017) Direct link Intelligent Computing and Machine Learning Lab,
ASEE, Beihang University
In this paper, we propose a Bayesian probabilistic model to describe collective behavior generated by a finite number of agents competing for limited resources. In this model, the strategy for each agent is a binary choice in the Minority Game and it can be modeled by a Binomial distribution with a Beta prior. The strategy of an agent can be learned given a sequence of historical choices by using Bayesian inference. Aggregated microlevel choices constitute the observable time series data in macrolevel, therefore, this can be regarded as a machine learning model for time series prediction. To verify the effectiveness of the new model, we conduct a series of experiments on artificial data and realworld stock price data. Experimental results demonstrate the new proposed model has a better performance comparing to a genetic algorithm based decomposition model.
Published in: 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2017), Lecture Notes in Computer Science (LNCS), Volume 10350, pp 8291, Springer, 2017. 



Hybrid clustering of data and vague concepts based on labels semantics (2016) Direct link Intelligent Computing and Machine Learning Lab,
ASEE, Beihang University
Data clustering is the process of dividing data elements into clusters so that items in the same cluster are as similar as possible, and items in different clusters are as dissimilar as possible. One of the key features for clustering is how to define a sensible similarity measure. Such measures usually handle data in one modality, but unable to cluster data from different modalities. Based on fuzzy set and prototype theory interpretations of label semantics, two (dis) similarity measures are proposed by which we can automatically cluster data and vague concepts represented by logical expressions of linguistic labels. Experimental results on a toy problem and one in image classification demonstrate the effectiveness of new clustering algorithms. Since our new proposed measures can be extended to measuring distance between any two granularities, the new clustering algorithms can also be extended to cluster data instance and imprecise concepts represented by other granularities.
Published in: Annals of Operations Research, Volume 256, Issue 2, pp 393–416, Springer, 2017. 



A Theory of Modeling Semantic Uncertainty in Label Representation (2016) Direct link Intelligent Computing and Machine Learning Lab,
ASEE, Beihang University
A new theory of modeling the uncertainty associated with vague concepts is introduced. We consider the problem of quantifying an agents uncertainty concerning which labels are appropriate to describe a given observation. This can be regarded as a simplified model of natural language communication. Semantic meaning conveyed by highlevel knowledge representation is often inherently uncertain. Such uncertainty is referred to semantic uncertainty and dominated by fuzzy modeling. In this framework, from an epistemic point of view, labels are precise and uncertainty comes from the undecidable boundary between labels in agents conceptual space. In this framework the boundary is regarded as a random variable and it can be modeled by a probability distribution. We also propose a functional calculus to measure how appropriate of using a certain label to describe an observation. In this way, a vague concept can be represented by a distribution on the labels. The new theory is verified by applying it to the vague category game.
Published in: 5th International Symposium on Integrated
Uncertainty in Knowledge Modeling and Decision Making, Lecture Notes in Computer Science (LNCS), vol. 9978, pp. 6475, Springer,
2016. 



Clustering Data and Vague Concepts Using Prototype Theory
Interpreted Label Semantics (2015) Direct link Intelligent Computing and Machine Learning Lab,
ASEE, Beihang University
Clustering analysis is wellused in data mining to group a set of
observations into clusters according to their similarity, thus, the
(dis)similarity measure between observations becomes a key feature
for clustering analysis. However, classical clustering analysis
algorithms cannot deal with observation contains both data and vague
concepts by using traditional distance measures. In this paper, we
proposed a novel (dis)similarity measure based on a prototype theory
interpreted knowledge representation framework named label
semantics. The new proposed measure is used to extend classical
Kmeans algorithm for clustering data instances and the vague
concepts represented by logical expressions of linguistic labels.
The effectiveness of proposed measure is verified by experimental
results on an image clustering problem, this measure can also be
extended to cluster data and vague concepts represented by other
granularities.
Published in: 4th International Symposium on Integrated
Uncertainty in Knowledge Modeling and Decision Making,Lecture Notes in Computer Science (LNCS), vol. 9376, pp. 236246, Springer,
2015. 



A Bagofphonemes Model for Homeplace Classification of
Mandarin Speakers (2015) Direct link
Intelligent Computing and Machine Learning Lab,
ASEE, Beihang University
Mandarin, also known as Standard Chinese is the official language
of China and Singapore, there are certain differences when mandarin
is spoken by people from different homeplaces. The homeplace
classification is important in speech recognition and machine
translation. In this paper, we proposed a novel model named
Bagofphonemes (BOP) for homeplace classification of mandarin
speakers, which follows the conceptually similar idea of the
Bagofwords (BOW) model in text processing. The lowlevel
Melfrequency cepstral coefficients (MFCC) speach features of each
homeplace are clustered into a set of codewords referred to as
phonemes. With this codebook, each speech signal can be represented
by a feature vector of distribution on phonemes. Classical
classifiers such as support vector machine (SVM) can be applied for
classification. This model is tested by
RASC863 database, empirical studies show that
the new model has a better performance on the RASC863 database
comparing to previous works
Published in: 7th Iberian Conference Pattern Recognition and
Image Analysis, Lecture Notes in Computer Science (LNCS), vol. 9117,
pp. 683690, Springer, 2015. 



TuneRank Model for Main Melody Extraction from MultiPart
Musical Scores (2014)
Direct link
Intelligent Computing and Machine Learning Lab,
ASEE, Beihang University
An algorithm for extracting
the main melody from multipart musical scores. This model is
referred to as the TuneRank model that has the conceptually similar
idea of the PageRank model. If each musical note can be considered
like a web page in the Internet, and the dissonance value between
two notes is like the quantity of links between two web
pages. The TuneRank (rank
of becoming main melody) of each note is calculated using Markov
transition probability. This model is tested on the
ECPK4 database. This notebased model is more effective
for processing scores containing main melody in multiple parts.
Also, the accuracy does not change with the increase of the number
of parts. In general, this model can be used for extracting the
singlepart main melody of digital musical scores.
Published in: Proceeding of Sixth International Conference on
Intelligent HumanMachine Systems and Cybernetics (IHMSC), vol. 2,
pp.176180, IEEE, 2014.




Digital MusicScore Management, Display and
Rehearsing System (2011)
Mathematical Fuzzy Control Lab, Beijing Normal
University
A digital music score management and rehearsal supporting system
that combine and coordinate music score display, management,
modification, distribution and turning collaboration. The system
consists of a conductor equipment and several performers' terminal
electron music stands with the composition of a star network
topology. It gives paperless viable solutions to digitalization,
intellectualization in modern music group.
Published in: Chinese Patent
No.201220110920.5.

AWARD
 Outstanding Graduates of Colleges and Universities in Beijing (top 5%), Beijing, China, 2017.
 First Prize in Beijing Contest District in China Undergraduate Mathematical Contest in Modeling, Beijing, China, 2016.

“SinoFrench Medal” of École Centrale de Pékin (the highest honor of Centrale Pékin student), Beijing, China, 2016.
 Second Prize (Honorable Mention) in the Interdisciplinary Contest In
Modeling (ICM), 2015.
 First Prize in 24th Fengru Cup in Beihang University (Highest
Academic Award in Beihang Univ.), Beijing, China 2014.
 Mayor's Award for Beijing Youth in Science and Technology 2013,
Beijing, China 2013.
 First Prize in the Danish International Competition for Young
Scientists, Aarhus, Denmark 2012.
 Second Prize in the 12th Awarding Program for Future Scientists,
Beijing, China 2012.

