Education and Experience

• 2021.11 - present: Researcher (part-time) at FaiLab in Tohoku University, Sendai (remote). Research on machine learning and NLP.
• 2018.10 - 2021.9: Doctor of Information Science at Inui-Suzuki Lab in Graduate School of Information Sciences, Tohoku University, Sendai.
• 2016.10 - present: Researcher at Preferred Networks, Inc., Tokyo. Research and development on machine learning, 2D/3D computer vision, NLP, robotics, etc.
• 2014.10 - 2016.9: Master of Information Science at Inui-Okazaki Lab in Graduate School of Information Sciences, Tohoku University, Sendai.
• 2011.4 - 2014.9: Department of Information and Intelligent Systems, Tohoku University, Sendai. (Early graduation)
• 2008.4 - 2011.3: Suzaka High School, Nagano.

Internship and Part-time Work

• Part-time Work at Preferred Infrastructure, Inc., Neural Dialogue Response Generation and Multimodal NLP, Oct. 2015, Feb. and Mar. 2016.
• Internship at Preferred Infrastructure, Inc., Neural Dialogue Response Generation, Aug. and Sep. 2015.
• Part-time Work at Recruit Jobs Co., Ltd., Data Analysis and Development for Web Services, 2015.
• Internship at Recruit Holdings Co., Ltd., Data Analysis and Development for Web Services, Feb. 2015. (最優秀賞; Best Team Award)
• Collaborative Research with DENSO Corporation, Recognizing Potential Risks in Traffic Scenes, 2014--2015

Competition

• 20th/946, solo silver medal, at chaii - Hindi and Tamil Question Answering in Kaggle, Nov. 2021. (First time participating)

Review

• Paper review of NLP Conferences/Journals: ACL Rolling Review (2021-2022), ACL (2018-2021), EMNLP (2018-2020), NAACL (2019), COLING [Area Chair] (2022), 論文誌 自然言語処理 (2017, 2020-2022), [Editor] (2023)
• Paper review of ML Conferences: ICLR (2024), NeurIPS (2022-2023), ICML (2022)
• Paper review of Robotics Conference: ICRA (2019)
• Review and revision of Deep Learning Tutorial with Chainer, 2019.
• Review of Kaggleに挑む深層学習プログラミングの極意 (Essentials of Deep Learning Programming for Kaggle Challenge), 小嵜耕平, 秋葉拓哉, 林孝紀, 石原祥太郎, 講談社, 2023. [amazon]
• Review of 深層学習による自然言語処理 (機械学習プロフェッショナルシリーズ) (Natural Language Processing by Deep Learning, A Series of Machine Learning Professionals), 坪井祐太, 海野裕也, 鈴木潤, 講談社, 2017. [amazon]
• Review of ウェブデータの機械学習 (機械学習プロフェッショナルシリーズ) (Web Data and Machine Learning, A Series of Machine Learning Professionals), ダヌシカボレガラ, 岡﨑直観, 前原貴憲, 講談社, 2016. [amazon]
• Review of 深層学習 Deep Learning, 神嶌敏弘 (編), 麻生英樹, 安田宗樹, 前田新一, 岡野原大輔, 岡谷貴之, 久保陽太郎, ボレガラダヌシカ, 人工知能学会監修, 近代科学社, 2015. [amazon]

Research/Teaching Assistant

• Research Assistant of JST, CREST, 構造理解に基づく大規模文献情報からの知識発見, Dec. 2015--Sep. 2016.
• Teaching Assistant (Research Mentor) of Team-based Engineering Design Course (for underguraduates) of Tohoku University, Vector Representations of Emoji (Emoticons) on Microblogs, Autumn--Winter 2015--2016.
• Teaching Assistant of Information Communication Theory and Natural Language Processing (for guraduates) of Tohoku University, Autumn--Winter 2015--2016.
• Teaching Assistant of Programming Practice A (for undergraduates) of Tohoku University, Spring--Summer 2015.
• Teaching Assistant (Research Mentor) of Team-based Engineering Design Course (for underguraduates) of Tohoku University, Knowledge Extraction from Web for Question Answering, Autumn--Winter 2014--2015.

Talk

• Talk about Machine Learning for Language and the World at NLP若手の会 (YANS) 第18回シンポジウム (18th Symposium of Young Researcher Association for NLP Studies), Aug. 2023.
• Talk about Efficient Estimation of Influence of a Training Instance (best paper invited talk of JNLP) at the 28th Annual Meeting of the Association for Natural Language Processing (remote), Mar. 2022.
• Talk about an efficient estimation of machine learning models trained without an instance at the 22nd Doctoral Student Presentations of GSIS, Tohoku University, Dec. 2019. (Best Presentation Award)
• Talk about ビジョン＆ランゲージによる意図理解と曖昧性解消 (Vision and Language Help Intent Understanding and its Disambiguation) at 第25回 画像センシングシンポジウム (SSII2019) オーガナイズドセッション『ビジョン & ランゲージ 「意図」をどのようにモデリングするか？』, Jun. 2019
• Talk about Learning Communication with Neural Networks at PFN Seminar, Mar. 2017.
• Talk about NAACL-HLT 2016 at NLP若手の会 (YANS) 第11回シンポジウム (11th Symposium of Young Researcher Association for NLP Studies), Aug. 2016.
• Talk about Recent Progress in RNN and NLP at NLP-DL Study Group, Jun. 2016.

Organizer

• Organizer of 言語処理技術セミナー2021 (NLP Technology Seminar 2021), Oct. 2021.
• Organizer of 第11回 最先端NLP勉強会 (11th NLP Paper Reading Circle), Sep. 2019.
• Organizer of 第10回 最先端NLP勉強会 (10th NLP Paper Reading Circle), Aug. 2018.
• Organizer of 第9回 最先端NLP勉強会 (9th NLP Paper Reading Circle), Sep. 2017.
• Organizer of 第8回 最先端NLP勉強会 (8th NLP Paper Reading Circle), Sep. 2016.

• Research: Reproduction
  • Interval Bound Propagation - "Scalable Verified Training for Provably Robust Image Classification" (Goyal et al., 2019)
  • Meta-learning LSTM Optimizer - "Learning to learn by gradient descent by gradient descent" (Andrychowicz et al., 2016)
  • BERT - "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" (Devlin et al., 2018)
  • Crawler of BookCorpus - "Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books" (Zhu et al., 2015)
  • Finetuned Transformer Language Model - "Improving Language Understanding by Generative Pre-Training" (Radford et al., 2018)
  • ELMo - "Deep contextualized word representations" (Peters et al., 2018)
  • CapsNet - "Dynamic Routing Between Capsules" (Sabour et al., 2017)
  • Transformer - "Attention is All You Need" (Vaswani et al., 2017)
  • Convolutional seq2seq Model - "Convolutional Sequence to Sequence Learning" (Gehring et al., 2017)
  • Neural Image Caption - "Show and Tell: A Neural Image Caption Generator" (Vinyals et al., 2015)
  • Skip-Thought Vectors and Sentence-level RNN Language Model - "Skip-Thought Vectors" (Kiros et al., 2015)
  • Document-level RNN Language Model - "Recurrent Neural Network Regularization" (Zaremba et al., 2014)
  • SRU (Simple Recurrent Unit) Language Model and Proposed Variants - "Training RNNs as Fast as CNNs." (Lei and Zhang, 2017)
  • Efficient Softmax Approximation: Adaptive Softmax and BlackOut - "Efficient softmax approximation for GPUs" (Grave et al., 2017) and "BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies" (Ji et al., 2016)
  • Weight Normalization - "Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks" (Salimans and Kingma, 2016)
  • Variational Dropout - "Variational Dropout Sparsifies Deep Neural Networks" (Molchanov et al., 2017)
• Research: Original
  • Turn-over Dropout - (My work at SustaiNLP 2020)
  • Contextual Augmentation - (My work at NAACL 2018)
  • PFN Picking Instructions for Commodities Dataset (PFN-PIC) - (Our work at ICRA 2018)
  • Emergence of Language Using Discrete Sequences with Autoencoder - (My work not published, in 2017)
  • Next Event Predictor - (Related to our work at LSDSem 2017)
  • Sentence Generater Using Deep Convolutional Generative Adversarial Network (DCGAN) - (My work not published, in 2016)
  • Dynamic Entity Representation Network - (Our work at NAACL 2016)
  • Dynamic Neural Text Model - (Our work at IJCNLP 2017)
• Others
  • ArxivLeaks - Read secret comment of TeX in arXiv papers
  • Contributions for Chainer, a python-based Deep Learning framework. e.g., Layer normalization, Tree LSTM, seq2seq example and text classification example.