Ganesan worked as a scientist for the software development platform GitHub.[5][4] In 2017, she brought the first machine learning (ML) pipeline to GitHub with the launch of GitHub Topics.[6][7] While working at GitHub, Ganesan founded the Utah-based AI and machine learning company Opinosis Analytics.[4][8][9]
In 2021, Ganesan was nominated for the Rising Star Award at VentureBeat’s Women in AI Awards.[10]
In 2022, Ganesan published the book The Business Case for AI: A Leader’s Guide to AI Strategies, Best Practices & Real-World Applications.[11]
In 2023, her book The Business Case for AI was the runner up in the San Francisco Book Festival’s Technology category[12] and also won Gold Medal in Business Life Category in the2023 Global Book Awards.[13] The book also won an award in the 2nd Quarter 2023 Firebird Book Awards.[14]
Research
Ganesan’s background is in natural language processing (NLP), search technologies, and machine learning.[15] Her work has been cited in over 1500 papers.[1]
Ganesan has researched applications for artificial intelligence in fields such as knowledge management[16] and search engines.[17][14]
In addition to her research on artificial intelligence, Ganesan has also researched topics such as record linkage,[18] data analysis for clinical notes[19] and written opinions,[20][21] and data mining for e-commerce.[22]
Ganesan has patents for software systems and methods used for visualizing reputation ratings,[23] activity-based recommendations,[24] and search clustering.[25]
Selected publications
Ganesan, K. (2022). The Business Case for AI: A Leader's Guide to AI Strategies, Best Practices & Real-World Applications. United States: Opinosis Analytics Publishing. ISBN9781544528717
Ganesan, K., Zhai, C., & Han, J. (2010, August). Opinosis: A graph based approach to abstractive summarization of highly redundant opinions. In Proceedings of the 23rd international conference on computational linguistics (Coling 2010) (pp. 340–348).
Ganesan, K., & Zhai, C. (2012). Opinion-based entity ranking. Information Retrieval, 15, 116-150.
Ganesan, K., Zhai, C., & Viegas, E. (2012, April). Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions. In Proceedings of the 21st International Conference on World Wide Web (pp. 869–878).
Ganesan, K. (2018). Rouge 2.0: Updated and improved measures for evaluation of summarization tasks. arXiv preprint arXiv:1803.01937.
^ abGanesan, Kavita; Zhai, Chengxiang (2013). "FindiLike: A preference driven entity search engine for evaluating entity retrieval and opinion summarization". Proceedings of the 2013 workshop on Living labs for information retrieval evaluation. pp. 19–22. doi:10.1145/2513150.2513163. ISBN978-1-4503-2420-5. S2CID16400707.
^Gordon, Andrew S.; Ganesan, Kavita (2005). "Automated story capture from conversational speech". Proceedings of the 3rd international conference on Knowledge capture. pp. 145–152. doi:10.1145/1088622.1088649. ISBN1-59593-163-5. S2CID1003623.
^Sundaresan, Neel; Ganesan, Kavita; Grandhi, Roopnath (2007). "Multi-factor clustering for a marketplace search interface". Proceedings of the 16th international conference on World Wide Web. pp. 1181–1182. doi:10.1145/1242572.1242755. ISBN978-1-59593-654-7. S2CID9546591.
^Ganesan, Kavita (16 January 2014). Opinion driven decision support system (Thesis). hdl:2142/46628.[page needed]
^Ganesan, Kavita; Zhai, Chengxiang; Viegas, Evelyne (2012). "Micropinion generation: An unsupervised approach to generating ultra-concise summaries of opinions". Proceedings of the 21st international conference on World Wide Web. pp. 869–878. doi:10.1145/2187836.2187954. ISBN978-1-4503-1229-5. S2CID16144861.
^Ganesan, Kavita A.; Sundaresan, Neelakantan; Deo, Harshal (2008). "Mining tag clouds and emoticons behind community feedback". Proceedings of the 17th international conference on World Wide Web. pp. 1181–1182. doi:10.1145/1367497.1367716. ISBN978-1-60558-085-2. S2CID21505177.