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Research Fellow 葉彌妍 研
究
Mi-Yen Yeh 人
員
Ph.D., Electrical Engineering, National Taiwan University, Taiwan Faculty
T +886-2-2788-3799 ext. 1412 E miyen@iis.sinica.edu.tw
F +886-2-2782-4814 W www.iis.sinica.edu.tw/pages/miyen
・ Joint Assistant Professor (2014), Joint Associate Professor (2015), Joint Professor
(2019-present), Dept. of Computer Science and Information Engineering, National Cheng
Kung University
・ Associate Research Fellow (2014-2018), Assistant Research Fellow (2009-2014), Institute of
Information Science, Academia Sinica
Research Description
My research mainly focuses on Data Mining, in particular on designing e ective and e cient algorithms to discover knowledge from Big
Data, where we need to tackle the challenges bringing by the 4V characteristics: volume, velocity, veracity and variety. In the direction of
tackling data volume and velocity, my research team and I have representative works such as considering how to e ciently search similar
patterns among streaming time series in a central or distributed environment, how to design e cient algorithms for mining large-scale social
network data, and how to accommodate the large-scale data over non-volatile memory such as ash while providing e cient and reliable
tree index designs for data manipulation. In the direction of tackling data veracity and variety, we have representative works such as building
a winning price model in the real-time bidding environment of online display advertisement when usually the historical winning price
information is unobservable, building a recommender system while the social link information between users is either explicit or implicit,
and dealing with the heterogeneous data in all the above applications at the same time. The rise of Big data era has empowered the deep
learning techniques and the related AI applications. Our team will further explore the possibility of utilizing the deep learning models to the
Big Data applications in consideration of those V-characteristics.
1. Yi-An Lai, Chin-Chi Hsu, Wen-Hao Chen, Mi-Yen Yeh, and Publications Brochure 2020
Shou-De Lin, "DeepRank: improving unsupervised node ranking
via link discovery," Data Mining and Knowledge Discovery , 6. Su-Chen Lin, Mi-Yen Yeh, and Ming-Syan Chen, "Non-overlapping
volume 33, number 2, pages 474-498, March 2019. Subsequence Matching of Stream Synopses," IEEE Transactions
on Knowledge and Data Engineering, volume 30, number 1,
2. Ming-Han Feng, Chin-Chi Hsu, Cheng-Te Li, Mi-Yen Yeh and pages 101-114, January 2018.
Shou-De Lin, "Multi-relational Network Embeddings with
Relational Proximity and Node Attributes," The Web Conference 7. Yi-An Lai, Chin-Chi Hsu, Wen-Hao Chen, Mi-Yen Yeh, and Shou-
2019 (WWW-2019), May 2019. De Lin, "PRUNE: Preserving Proximity and Global Ranking
for Node Embedding," The 31st Annual Conference on Neural
3. Chin-Chi Hsu, Mi-Yen Yeh, and Shou-De Lin, "A General Information Processing Systems (NIPS-2017), December 2017.
Framework for Implicit and Explicit Social Recommendation,"
IEEE Transactions on Knowledge and Data Engineering, volume 8. Chun-Yen Kuo, Mi-Yen Yeh, and Jian Pei, "Principal Pattern Mining
30, number 12, pages 2228-2241, December 2018. on Graphs," The 2017 IEEE/ACM International Conference
on Advances in Social Networks Analysis and Mining
4. Wush Chi-Hsuan Wu, Mi-Yen Yeh, and Ming-Syan Chen, (ASONAM-2017), July 2017.
"Deep Censored Learning of the Winning Price in the Real Time
Bidding," 24th ACM SIGKDD International Conference on 9. Wush Chi-Hsuan Wu, Mi-Yen Yeh, and Ming-Syang Chen,
Knowledge Discovery and Data Mining (KDD-2018), August "Predicting Winning Price in Real Time Bidding with Censored
2018. Data," 21st ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining (KDD-2015), August
5. Chien-Wei Chang, Mi-Yen Yeh, and Kun-Ta Chuang, "Node 2015.
Reactivation Model to Intensify Influence on Network Targets,"
Knowledge and Information Systems, volume 54, number 3, 10. Hua-Wei Fang, Mi-Yen Yeh, Pei-Lun Suei, Tei-Wei Kuo, "An
pages 567-590, March 2018. Adaptive Endurance-Aware B+-Tree for Flash Memory Storage
Systems," IEEE Transactions on Computers, volume 63, number
11, pages 2661-2673, November 2014.
177
究
Mi-Yen Yeh 人
員
Ph.D., Electrical Engineering, National Taiwan University, Taiwan Faculty
T +886-2-2788-3799 ext. 1412 E miyen@iis.sinica.edu.tw
F +886-2-2782-4814 W www.iis.sinica.edu.tw/pages/miyen
・ Joint Assistant Professor (2014), Joint Associate Professor (2015), Joint Professor
(2019-present), Dept. of Computer Science and Information Engineering, National Cheng
Kung University
・ Associate Research Fellow (2014-2018), Assistant Research Fellow (2009-2014), Institute of
Information Science, Academia Sinica
Research Description
My research mainly focuses on Data Mining, in particular on designing e ective and e cient algorithms to discover knowledge from Big
Data, where we need to tackle the challenges bringing by the 4V characteristics: volume, velocity, veracity and variety. In the direction of
tackling data volume and velocity, my research team and I have representative works such as considering how to e ciently search similar
patterns among streaming time series in a central or distributed environment, how to design e cient algorithms for mining large-scale social
network data, and how to accommodate the large-scale data over non-volatile memory such as ash while providing e cient and reliable
tree index designs for data manipulation. In the direction of tackling data veracity and variety, we have representative works such as building
a winning price model in the real-time bidding environment of online display advertisement when usually the historical winning price
information is unobservable, building a recommender system while the social link information between users is either explicit or implicit,
and dealing with the heterogeneous data in all the above applications at the same time. The rise of Big data era has empowered the deep
learning techniques and the related AI applications. Our team will further explore the possibility of utilizing the deep learning models to the
Big Data applications in consideration of those V-characteristics.
1. Yi-An Lai, Chin-Chi Hsu, Wen-Hao Chen, Mi-Yen Yeh, and Publications Brochure 2020
Shou-De Lin, "DeepRank: improving unsupervised node ranking
via link discovery," Data Mining and Knowledge Discovery , 6. Su-Chen Lin, Mi-Yen Yeh, and Ming-Syan Chen, "Non-overlapping
volume 33, number 2, pages 474-498, March 2019. Subsequence Matching of Stream Synopses," IEEE Transactions
on Knowledge and Data Engineering, volume 30, number 1,
2. Ming-Han Feng, Chin-Chi Hsu, Cheng-Te Li, Mi-Yen Yeh and pages 101-114, January 2018.
Shou-De Lin, "Multi-relational Network Embeddings with
Relational Proximity and Node Attributes," The Web Conference 7. Yi-An Lai, Chin-Chi Hsu, Wen-Hao Chen, Mi-Yen Yeh, and Shou-
2019 (WWW-2019), May 2019. De Lin, "PRUNE: Preserving Proximity and Global Ranking
for Node Embedding," The 31st Annual Conference on Neural
3. Chin-Chi Hsu, Mi-Yen Yeh, and Shou-De Lin, "A General Information Processing Systems (NIPS-2017), December 2017.
Framework for Implicit and Explicit Social Recommendation,"
IEEE Transactions on Knowledge and Data Engineering, volume 8. Chun-Yen Kuo, Mi-Yen Yeh, and Jian Pei, "Principal Pattern Mining
30, number 12, pages 2228-2241, December 2018. on Graphs," The 2017 IEEE/ACM International Conference
on Advances in Social Networks Analysis and Mining
4. Wush Chi-Hsuan Wu, Mi-Yen Yeh, and Ming-Syan Chen, (ASONAM-2017), July 2017.
"Deep Censored Learning of the Winning Price in the Real Time
Bidding," 24th ACM SIGKDD International Conference on 9. Wush Chi-Hsuan Wu, Mi-Yen Yeh, and Ming-Syang Chen,
Knowledge Discovery and Data Mining (KDD-2018), August "Predicting Winning Price in Real Time Bidding with Censored
2018. Data," 21st ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining (KDD-2015), August
5. Chien-Wei Chang, Mi-Yen Yeh, and Kun-Ta Chuang, "Node 2015.
Reactivation Model to Intensify Influence on Network Targets,"
Knowledge and Information Systems, volume 54, number 3, 10. Hua-Wei Fang, Mi-Yen Yeh, Pei-Lun Suei, Tei-Wei Kuo, "An
pages 567-590, March 2018. Adaptive Endurance-Aware B+-Tree for Flash Memory Storage
Systems," IEEE Transactions on Computers, volume 63, number
11, pages 2661-2673, November 2014.
177
