Takeshi Onishi

Welcome

Hello! I am Takeshi Onishi, a Research Scientist at Toyota Central R&D Labs., Inc., specializing in Artificial Intelligence, Machine Learning, and Natural Language Processing. Previously, I was a Postdoctoral Research Scientist at Cedars-Sinai Medical Center in Los Angeles, where I conducted research in medical NLP. Earlier in my career, I worked at Toyota's Frontier Research Center, bridging advanced AI research with practical industry applications, including a visiting scholarship with Professor James Caverlee at Texas A&M University. I received my Ph.D. in Computer Science from Toyota Technological Institute at Chicago in 2020, under the supervision of David McAllester, and hold a B.A. in Engineering from the Toyota Technological Institute in Japan.

About me

My brief self-introduction.

• Research Scientist at Toyota Central R&D Labs., Inc.
• 文部科学省 科学技術・学術政策研究所(NISTEP) 専門調査員

• My research interests: Artificial Intelligence, Machine Learning, Natural Language Processing, Data Mining, and Information Retrieval.

Publications and talks

• Political Bias of Large Language Models in Few-shot News Summarization.
  Takeshi Onishi and James Caverlee.
  Presented at International Workshop on Algorithmic Bias in Search and Recommendation at SIGIR, July 2024.
  A collaborative research of Texas A&M University and Toyota Motor Corporation.
• Noisy-labeled Domain Adaptation under Generalized Label Shift
  Kazuhiko Shinoda, Takeshi Onishi, Masashi Sugiyama.
  In proceedings of the 37th Annual Conference of the Japanese Society for Artificial Intelligence, June 2023.
  A collaborative research of the University of Tokyo and Toyota Motor Corporation.
• Dealing with Medication Non-Adherence Expressions in Twitter
  Takeshi Onishi, Davy Weissenbacher, Ari Klein, Karen O’Connor and Graciela Gonzalez-Hernandez.
  SMM4H at EMNLP 2018
  
• Relation extraction with weakly supervised learning based on process-structure-property-performance reciprocity
  Takeshi Onishi, Takuya Kadohira, Ikumu Watanabe.
  Science and Technology of Advanced Materials, July 2018
  The source code is available HERE.
• Emergent Predication Structure in Hidden State Vectors of Neural Readers
  Hai Wang, Takeshi Onishi, Kevin Gimpel, David McAllester.
  A Best Paper in Repl4NLP: 2nd Workshop on Representation Learning for NLP, August 2017
  
• Who did What: A large-scale person-centered cloze dataset
  Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gimpel, and David McAllester.
  In Proceedings of Empirical Methods in Natural Language Processing, Austin, Texas, November 2016
  

Activities

Here is my activities!