Juhua Hu

Juhua Hu, Ph.D.

Assistant Professor

Computer Science and Systems
School of Engineering and Technology
University of Washington | Tacoma

Director

Center for Data Science

Contact

Box 358426, 1900 Commerce Street
Tacoma, WA 98402-3100
Office: MDS / 203A
Phone: +1 253-692-4625
Email: juhuah [at] uw [dot] edu

Biography [CV][My LinkedIn] [MyCFP] [My Google Scholar Citations] [My DBLP]

Juhua Hu is currently an Assistant Professor of Computer Science and Systems in the School of Engineering and Technology at the University of Washington Tacoma and the director of Center for Data Science. She obtained her Ph.D. degree in Computer Science from School of Computing Science, Simon Fraser University in December 2017, under the supervision of Dr. Jian Pei. Juhua received her B.Sc. and M.Sc. degrees in Computer Science from Nanjing University in June 2009 and June 2012, respectively. As a master student, Juhua joined the LAMDA group and worked with Dr. Yuan Jiang and Dr. Zhi-Hua Zhou.


News

July 2020: My undergraduate student Tucker R. Stewart receives Outstanding Undergraduate Research Award.
June 2020: My undergraduate student Tucker R. Stewart graduates with CSS Honors.
Feb. 2020: Our paper "Hierarchically robust representation learning" got accepted by CVPR'20.
Jan. 2020: My undergraduate student Tucker R. Stewart is awarded the Mary Gates Research Scholarship.
July 2019: Our paper "SoftTriple loss: Deep metric learning without triplet sampling" got accepted by ICCV'19.
Sept. 2018: Juhua joined UW Tacoma as an Assistant Professor.
Jun. 2018: Our paper "Exact and consistent interpretation for piecewise linear neural networks: A closed form solution" got accepted by KDD'18.
Dec. 2017: Juhua successfully defended her thesis "Subspace Clustering Methods for Understandable Information Organization".
July 2017: Our survey paper "Subspace multi-clustering: A review" got accepted by KAIS.
Oct. 2016: Our extended version of ICDM'15 paper "Finding multiple stable clusterings" got accepted by KAIS.
July 2016: Computing Science Graduate Student Story: Juhua Hu.

Research Interests

Juhua's primary research interest is in the areas of machine learning, data mining, and data science. She is especially interested in understandable information organization that facilitates human end users to efficiently and effectively interpret or understand machine learning or data mining outputs as follows, where the applications span over Computer Vision, Networking, Cybersecurity, Healthcare, Human Computer Interaction, and Smart City.

Selected Publications

Journal Articles
J. Hu and J. Pei. Subspace multi-clustering: A review. Knowledge and Information Systems (KAIS), 2018, 56(2): 257-284. DOI: 10.1007/s10115-017-1110-9. [Online PDF]
J. Hu, Q. Qian, J. Pei, R. Jin and S. Zhu. Finding multiple stable clusterings (An extended version of ICDM 2015). Knowledge and Information Systems (KAIS), 2017, 51(3): 991-1021. DOI: 10.1007/s10115-016-0998-9. [Online PDF][Code]("Bests of ICDM 2015")
J. Hu, D.-C. Zhan, X. Wu, Y. Jiang and Z.-H. Zhou. Pairwised specific distance learning from physical linkages. ACM Transactions on Knowledge Discovery from Data (TKDD), 2015, 9(3): Article 20. [Pre-Print PDF]
J. Hu, Y. Jiang and Z.-H. Zhou. A co-training method based on teaching-learning model. Journal of Computer Research and Development (in Chinese with English abstract), 2013, 50(11): 2262-2268. [PDF] (This paper won the Best Student Paper Award at 2012 National Conference on Agent Theory and Applications, Changchun, China)
Conference Papers
Q. Qian, J. Hu, and H. Li. Hierarchically robust representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'20, acceptance rate: 1,470/6,656=22%), Seattle, WA, 2020, pp.7336-7344. [Pre-Print PDF] [Concepts in ImageNet]
B. Yu, G. Graciani, A. Nascimento, and J. Hu. Cost-adaptive neural networks for peak volume prediction with EMM filtering. In: Proceedings of the IEEE International Conference on Big Data (BigData'19), Los Angeles, CA, 2019, pp.4208-4213. [Pre-Print PDF]
Q. Qian, L. Shang, B. Sun, J. Hu, H. Li, and R. Jin. SoftTriple loss: Deep metric learning without triplet sampling. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV'19, acceptance rate: 1,077/4,303=25%), Seoul, Korea, 2019, pp.6450-6458. [Pre-Print PDF][Code]
L. Chu, X. Hu, J. Hu, L. Wang, and J. Pei. Exact and consistent interpretation for piecewise linear neural networks: A closed form solution. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18, acceptance rate: 107/983=10.9%), London, UK, 2018, pp.1244-1253. [Pre-Print PDF]
J. Hu, Q. Qian, J. Pei, R. Jin and S. Zhu. Finding multiple stable clusterings. In: Proceedings of the 15th IEEE International Conference on Data Mining (ICDM'15, acceptance rate: 68/810=8.4%), Atlantic City, NJ, 2015, pp.171-180. [Pre-Print PDF][Slides][Fruit Data] (Invited to KAIS SI on "Bests of ICDM 2015", Student Travel Award)
Q. Qian, J. Hu, R. Jin, J. Pei and S. Zhu. Distance metric learning using dropout: A structured regularization approach. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'14, acceptance rate: 151/1,036=14.6%), New York, NY, 2014, pp.323-332. [Pre-Print PDF]
J. Hu, J. Pei and J. Tang. How can I index my thousands of photos effectively and automatically? An unsupervised feature selection approach. In: Proceedings of the 14th SIAM International Conference on Data Mining (SDM'14, acceptance rate: 60/389=15.4%), Philadelphia, PA, 2014, pp.136-144. [Pre-Print PDF][Poster] (Student Travel Award)
Y.-F. Li, J. Hu, Y. Jiang and Z.-H. Zhou. Towards discovering what patterns trigger what labels. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI'12, acceptance rate: 294/1,129=26%), Toronto, Canada, 2012, pp.1012-1018. [Pre-Print PDF] [Code]
Ph.D. Thesis
J. Hu. Subspace Clustering Methods for Understandable Information Organization. School of Computing Science, Simon Fraser University, Canada, December 2017. [PDF]

Teaching

Instructor, TCSS 422 [Computer Operating Systems], Spring 2019, Winter & Spring 2020, Autumn 2020, Winter 2021, University of Washington | Tacoma.
Instructor, TCSS 551 [Big Data Analytics], Autumn 2018, Spring 2019, Winter & Spring 2020, Spring 2021, University of Washington | Tacoma.
Instructor, CMPT 454 [Advanced Database Systems], Fall 2017, Simon Fraser University (Burnaby).
TA, CMPT 454, Spring 2017, Simon Fraser University (Surrey).
TA, CMPT 454, Summer 2016, Simon Fraser University (Burnaby).
Part-time Instructor, CMPT 354 [Database Systems], Fall 2015, Simon Fraser University (Burnaby).
Instructor, CMPT 354 [Database Systems], Fall 2014, Simon Fraser University (Surrey).
TA, CMPT 354, Fall 2013, Simon Fraser University (Burnaby).
TA, Theory of Compiling, Spring 2010, Nanjing University.

Students

Current
Tucker R. Stewart, PhD 2020
Yiming Gan, Capstone, Master 2019
Ziqing Ying, Capstone, Master 2019
James Haines-Temons, Independent Study, Master 2019
Past
Christine Allen, Thesis (co-supervisor), M.Sc. 2020, now at KenSci
Ghazaleh Jowkar, Thesis (co-supervisor), M.Sc. 2020, now at Oracle
Zhongyu Jiang, Thesis (co-supervisor), M.Sc. 2019, now Ph.D. student at UW Seattle
Amandeep Puri, Capstone, M.Sc. 2020, now at Washington State Patrol
Giovanna S. Graciani, Capstone published at Proc. of IEEE BigData (BigData'19), M.Sc. 2019, now at Intel
Tongjue Wang, Independent Study, M.Sc. 2019, now at Amazon
Tucker R. Stewart, Thesis, B.Sc. 2020, now Ph.D. student at UW Tacoma
Nicole Guobadia, Directed Research, B.Sc. 2020, now at Progeny Systems

Family Members

Q. Qian, Ph.D., Staff Engineer at Alibaba Group.
M. Qian, born in 2013.
H. Qian, born in 2019.

Privacy | Terms Last modified on June 15, 2020 by Juhua Hu.