Faculty Profile

Wei Jin

Title
Associate Professor
Department
Computer Science and Engineering
College
College of Engineering

    

Education

PhD, University at Buffalo, State University of New York, 2008.
Major: Computer Science and Engineering
Degree Specialization: Information Retrieval; Text and Web Mining
Dissertation Title: Mining Hidden Associations in Text Corpora through Concept Chain and Graph Queries
MS, University at Buffalo, State University of New York, 2007.
Major: Computer Science and Engineering
Degree Specialization: Information Retrieval and Text Mining
MS, Institute of Computing Technology, Chinese Academy of Sciences, 2002.
Major: Computer Science
Degree Specialization: Multilingual Information Processing

Current Scheduled Teaching*

CSCE 6940.820, Individual Research, Fall 2024
CSCE 5200.404, Information Retrieval and Web Search, Fall 2024
CSCE 5200.461, Information Retrieval and Web Search, Fall 2024

* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching*

CSCE 6940.920, Individual Research, Spring 2024
CSCE 5200.004, Information Retrieval and Web Search, Spring 2024 Syllabus SPOT
CSCE 6940.820, Individual Research, Fall 2023
CSCE 5200.004, Information Retrieval and Web Search, Fall 2023 SPOT
CSCE 5200.601, Information Retrieval and Web Search, Fall 2023 SPOT
CSCE 5200.004, Information Retrieval and Web Search, Spring 2023 SPOT
CSCE 6940.820, Individual Research, Fall 2022
CSCE 6940.720, Individual Research, Spring 2022
CSCE 6950.820, Doctoral Dissertation, Fall 2021
CSCE 4380.001, Data Mining, Spring 2021 Syllabus SPOT
CSCE 5380.001, Data Mining, Spring 2021 SPOT
CSCE 6950.720, Doctoral Dissertation, Spring 2021
CSCE 6940.720, Individual Research, Spring 2021
CSCE 6950.820, Doctoral Dissertation, Fall 2020
CSCE 5200.001, Information Retrieval and Web Search, Fall 2020 SPOT
CSCE 5200.008, Information Retrieval and Web Search, Fall 8W2 2020
CSCE 5200.008, Information Retrieval and Web Search, Fall 2020
CSCE 4200.001, Web Search and Information Retrieval, Fall 2020 Syllabus SPOT
CSCE 6950.720, Doctoral Dissertation, Spring 2020
CSCE 6940.720, Individual Research, Spring 2020
CSCE 6940.820, Individual Research, Fall 2019
CSCE 6950.820, Doctoral Dissertation, Summer 10W 2019
CSCE 5950.820, Master's Thesis, Summer 10W 2019
CSCE 6950.820, Doctoral Dissertation, Spring 2019
CSCE 6940.820, Individual Research, Spring 2019
CSCE 5950.820, Master's Thesis, Spring 2019
CSCE 6950.820, Doctoral Dissertation, Fall 2018
CSCE 6940.820, Individual Research, Fall 2018
CSCE 5200.001, Information Retrieval and Web Search, Fall 2018 SPOT
CSCE 5950.820, Master's Thesis, Fall 2018
CSCE 6940.820, Individual Research, Summer 10W 2018
CSCE 6950.820, Doctoral Dissertation, Spring 2018
CSCE 6940.820, Individual Research, Spring 2018
CSCE 5200.001, Information Retrieval and Web Search, Spring 2018 SPOT
CSCE 5950.820, Master's Thesis, Spring 2018
CSCE 4200.002, Web Search and Information Retrieval, Spring 2018 Syllabus SPOT
CSCE 6950.820, Doctoral Dissertation, Fall 2017
CSCE 6940.820, Individual Research, Fall 2017
CSCE 5950.820, Master's Thesis, Fall 2017
CSCE 6950.820, Doctoral Dissertation, Spring 2017
CSCE 5200.002, Information Retrieval and Web Search, Spring 2017 SPOT
CSCE 5380.001, Data Mining, Fall 2016 SPOT

* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Published Publications

Published Intellectual Contributions

Book Chapter
Yang, W., Chowdhury, S., Jin, W. (2023). Finding New Connections between Concepts from Medline Database Incorporating Domain Knowledge. Research Advances in Data Mining Techniques and Applications. pp. 2-17.
Singh, A., Porwal, U., Bhardwaj, A., Jin, W. (2023). Multi-Scale Representation Learning for Biomedical Analysis. Deep Learning. 48, pp. 9-27. Elsevier.
Yan, P., Slator, B., Jin, W. (2015). Intelligent Tutors In Immersive Virtual Environments. E-Learning Systems, Environments and Approaches: Theory and Implementation.
Conference Proceeding
Xu, R., Li, G., Jin, W., Chen, A., Sheng, V. (2023). ACCD: An Adaptive Clustering-based Collusion Detector in Crowdsourcing.. The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023).
Xu, R., Li, G., Jin, W., Chen, A., Sheng, V. (2023). Adaptive Clustering-Based Collusion Detection in Crowdsourcing. Advanced Intelligent Computing Technology and Applications - 19th International Conference. pp. 261-275.
Yang, W., Chowdhury, S., Jin, W. (2022). Finding Hidden Relationships Between Medical Concepts by Leveraging Metamap and Text Mining Techniques. Advanced Data Mining and Applications - Proceedings of the 18th International Conference, ADMA 2022. pp. 41-52.
Ding, J., Jin, W. (2021). Evaluating Multiple-Concept Biomedical Hypotheses Based on Deep Sets. The 30th International Conference on Artificial Neural Networks (ICANN). 477-490.
Ding, J., Jin, W. (2021). Exploring Self-Supervised Graph Learning in Literature-Based Discovery. The 9th IEEE International Conference on Healthcare Informatics (ICHI). 53-62. IEEE.
Singh, A., Jin, W. (2020). On Using Composite Word Embeddings to Improve Biomedical Term Similarity. IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE). 281-287. IEEE.
Ding, J., Jin, W. (2019). OverlapLDA: A Generative Approach for Literature-Based Discovery. Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019). 1-8. IEEE.
Ding, J., Jin, W. (2019). A Prior Setting that Improves LDA in both Document Representation and Topic Extraction. Proceedings of 2019 international Joint Conference on Neural Networks (IJCNN 2019). 9-16. International Neural Network Society (INNS) in cooperation with the IEEE Computational Intelligence Society.
Ding, J., Jin, W. (2019). MDLDA: A New Multi-Dimension Topic Approach. Proceedings of 2019 international Joint Conference on Neural Networks (IJCNN 2019). 1-8. International Neural Network Society (INNS) in cooperation with the IEEE Computational Intelligence Society.
Shaik, A., Jin, W. (2019). Biomedical Semantic Embeddings: Using hybrid sentences to construct biomedical word embeddings and its applications. Proceedings of the 7th IEEE International Conference on Healthcare Informatics (ICHI 2019). 1-9. IEEE.
Singh, A., Blanco, E., Jin, W. (2019). Incorporating Emoji Descriptions Improves Tweet Classification. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2096--2101. Minneapolis, Minnesota: Association for Computational Linguistics. https://www.aclweb.org/anthology/N19-1214
Dharmavaram, S., Shaik, A., Jin, W. (2019). Mining Biomedical Data for Hidden Relationship Discovery. Proceedings of the 7th IEEE International Conference on Healthcare Informatics (ICHI 2019). 1-10. IEEE.
Florescu, C., Jin, W. (2019). A Supervised Keyphrase Extraction System Based on Graph Representation Learning. Proceedings of Advances in Information Retrieval - 41st European Conference on IR Research (ECIR 2019). 197-212. Lecture Notes in Computer Science, Springer.
Ding, J., Jin, W., Chen, H. (2018). Regression-Based Documents Reranking for Precision Medicine. Proceedings of the 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE 2018). 283-286. IEEE.
Jin, W., Florescu, C. (2018). Improving Search and Retrieval in Digital Libraries by Leveraging Keyphrase Extraction Systems. Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries (JCDL 2018). 419-420. ACM/IEEE.
Florescu, C., Jin, W. (2018). Learning Feature Representations for Keyphrase Extraction. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). 8077-8078. The Association for the Advancement of Artificial Intelligence and The MIT Press.
Zhu, Y., Jin, W., Zhang, Y. (2017). Identifying Implicit Features in Sentiment Analysis with Conditional Random Fields and Pattern Analysis. Proceedings of the The 30th International Conference on Computer Applications in Industry and Engineering. 185-192. International Society for Computers and Their Applications (ISCA).
Li, X., Jin, W. (2016). Cross-Document Knowledge Discovery Using Semantic Concept Topic Model. 108-114. The 15th IEEE International Conference on Machine Learning and Applications (ICMLA'16).
Jha, K., Jin, W. (2016). Mining Novel Knowledge from Biomedical Literature using Statistical Measures and Domain Knowledge. 317-326. The 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics.
Chen, D., Edstrom, J., Chen, X., Jin, W., Wang, J., Gong, N. (2016). Data-Driven Low-Cost On-Chip Memory with Adaptive Power-Quality Trade-off for Mobile Video Streaming. 188-193. The 2016 IEEE/ACM International Symposium on Low Power Electronics and Design.
Jha, K., Jin, W. (2016). Mining Hidden Knowledge from the Counterterrorism Dataset Using Graph-Based Approach. 310-317. The 21st International Conference on Applications of Natural Language to Information System.
Singh, A., Jin, W. (2016). Ranking Summaries for Informativeness and Coherence without Reference Summaries. 104-109. The Twenty-Ninth International Florida Artificial Intelligence Research Society Conference.
Gopalakrishnan, V., Jha, K., Zhang, A., Jin, W. (2016). Generating Hypothesis: Using Global and Local Features in Graph to Discover New Knowledge from Medical Literature. 23 – 30. The 8th International Conference on Bioinformatics and Computational Biology (BICoB 2016).
Yan, P., Jha, K., Jin, W. (2016). Discovering Semantic Relationships between Concepts from MEDLINE. 370- 373. The 10th IEEE International Conference on Semantic Computing (ICSC 2016).
Jin, W., Ho, H. H., Srihari, R. K. (2009). OpinionMiner: a novel machine learning system for web opinion mining and extraction. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. 1195–1204.
Journal Article
Ding, J., Jin, W. (2021). COS: A new MeSH term embedding incorporating corpus, ontology, and semantic predications. PLOS One. PLoS One. 2021; 16(5): e0251094., pp. 1-16. PLOS.
Gopalakrishnan, V., Jha, K., Jin, W., Zhang, A. (2019). A survey on literature based discovery approaches in biomedical domain. Vol. 93, 141-159. Elsevier.
Yan, P., Jin, W. (2017). Building semantic kernels for cross-document knowledge discovery using Wikipedia. 51(1), 287-310. Springer.
Yan, P., Jin, W. (2015). Improving Cross-Document Knowledge Discovery through Content and Link Analysis of Wikipedia. Other. 21, 161-184. Springer.

Awarded Grants

Contracts, Grants and Sponsored Research

Contract
Jin, W. (Principal), "EAGER: Data-Mining Driven Power-Efficient Intelligent Memory Storage for Mobile Video Applications," Sponsored by National Science Foundation, Federal, $28831 Funded. (September 22, 2016June 30, 2018).
Grant - Research
Jin, W. (Principal), "CAREER: Creation, Visualization, and Mining of Domain Textual Graphs: Integrating Domain Knowledge and Human Intelligence," Sponsored by National Science Foundation, Federal, $498432 Funded. (February 1, 2015August 31, 2023).
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Overall
Summative Rating
Challenge and
Engagement Index
Response Rate

out of 5

out of 7
%
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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