Yohei KIKUTA, PhD
Tokyo, Japan
diracdiego[at]gmail.com
- Expert at machine learning, 9+ years experience in both research and practical application
- Solved machine learning/data analysis problems for business purposes: image recognition, recommendation, natural language processing, and so forth
- Published academic papers about deep learning and recommendation
- Made many external presentations about machine learning
- Contributed article to NikkeiBP and JSAI, and wrote a book for beginners in machine learning
- Core competencies: Machine learning theory and implementation, image analysis, recommendation, Python, R
- Team management and organization development
- Enthusiasm for creating valuable data
- Deep theoretical understanding of machine learning
- Image recognition, recommender system, natural language processing, marketing ROI optimization, time series analysis
- TensorFlow, PyTorch, scikit-learn, gensim, and so many more
- Basic knowledge of Linux (Ubuntu) and MINIX
- Git (GitHub), Docker (Docker Hub) and CI (GitHub Actions)
- AWS, GCP
- Japanese (native), English (business)
- Python (expert), SQL (expert), C (advanced), R (intermediate), Kotlin (beginner), Haskell (beginner), C++ (beginner)
Self-employment (202409 - Present)
- To Be Writtern
Ubie, Inc (202004 - 202408)
- Position: Vice President of Engineering (VPoE) (April 2023 - August 2024)
- Development and execution of management strategies.
- Optimization of software development productivity.
- Position: Machine Learning Engineer (April 2020 - March 2023)
- Developed and maintained machine learning algorithms.
- Focused on feature engineering.
Created and implemented algorithms to enhance services. - Managed production operations.
Wrote and maintained production code; oversaw the production environment.
- Focused on feature engineering.
- Data Strategy
- Planned and executed data-driven strategies.
- Organizational Development
- Communication
Improved communication within the workplace, encompassing both physical and virtual environments. - Scrum
Successfully implemented scrum methodologies within my team and across the organization.
- Communication
- Developed and maintained machine learning algorithms.
Self-employment (201902 - 202003)
- Entrusted project from a laboratory
- Quality assurance of machine learning applications
Surveyed and summarized recent progress on adversarial examples (+50 papers), and code implementations using PyTorch.
- Quality assurance of machine learning applications
- Self-improvement
- Theoretical topics
Read various machine learning papers, and studied information/measure theory. - Code constructions
Reimplementation of machine learning papers, made simple Android apps, competitive programming, etc. - Basics of computer science
CPU (ARM and X86), OS such as processes/threads and memory management (Linux and MINIX), interpreter/compiler (implementation of a PostScript interpreter in C), etc.
- Theoretical topics
Cookpad Inc. (201612 - 201901) position: machine learning engineer
- Research and Development
- Created new service using machine learning
Image recognition using deep learning: classification, object detection, attractiveness estimation, etc - Attended machine learning conference
PAKDD, IJCAI, NIPS (as a top conference reporter of JSAI in 2017), ...
- Created new service using machine learning
- Sponsoring conferences and organizing events
- Led company's exhibition, chaired a session of IPSJ, is one of the committee members of CEA
- Organized various events, see connpass page
- Hiring and team building
- Conducted interviews with candidates for machine learning positions in domestic and global
- Contributed to team building and communication strategy
Deloitte (201404 - 201611) position: data scientist/machine learning engineer
- Research and Development
- Investigated business applications through cutting-edge technologies and wrote papers
Image recognition using deep learning, recommendation
- Investigated business applications through cutting-edge technologies and wrote papers
- Data analysis service to a client (onsite project at a client's office)
- Built machine learning module for a recommender system
Learning to rank, Xgboost, Factorization Machines, Spark, Hive, AWS - Analyzed various topics: ROI optimization, time series analysis for budget planning, etc
ARIMA, price elasticity analysis, linear programming, genetic algorithm
- Built machine learning module for a recommender system
- Characteristic analysis of tourist spots and brand reputation analysis of a company
- Analyzed for reporting to clients
Text data processing, topic model (PLSA), bayesian network
- Analyzed for reporting to clients
- Joint research about business applications using deep learning
- SRGAN for Super-Resolving Low-Resolution Food Images (conference link)
- IJCAI-ECAI2018 WS CEA2018, poster, 20180715
- Improving SRGAN for Super-Resolving Low-Resolution Food Images (link)
- JSAI2018, in Japanese, 20180607
- ClassSim: Similarity between Classes Defined by Misclassification Ratios of Trained Classifiers (link)
- Approaches to Food/Non-food image classification using Deep Learning on cookpad (link)
- IJCAI2017 WS39 CEA2017, poster, 20170820
- Cookpad Image Dataset: An Image Collection as Infrastructure for Food Research (link)
- SIGIR2017, resource paper, 20170807
- Web-Scale Personalized Real-Time Recommender System on Suumo (conference link)
- PAKDD2017, long paper, 20170525
- Approaches to Food/Non-food image classification using Deep Learning on cookpad (link)
- JSAI2017, in Japanese, 20170523
- Proposing automated region extraction techniques from image data (link)
- JSAI2016, in Japanese, 20160606
- Inappropriate image detection based on Deep Learning (link)
- JSAI2016, in Japanese, 20160606
- Exploiting the Hidden Layer Information Toward the Understanding and Utilization of Feature Representations Obtained from Deep Learning (link)
- JSAI2015, in Japanese, 20150531
- Physics papers during my Ph.D. student years (link)
- Books & Articles
- Blogs
- 原理的には可能 (In Japanese)
- Ubie Blog (in Japanese)
- cookpad Developers Blog (in Japanese)
- 20181204, BERT with SentencePiece で日本語専用の pre-trained モデルを学習し、それを基にタスクを解く (link)
- 20180831, Cookpad Summer Internship 2018 5 DAY R&D を開催しました (link)
- 20180705, Firebase ML Kitで自作のカスタムモデルを使って料理・非料理画像を判定できるようにした (link)
- 20180328, 人工知能学会のトップカンファレンス派遣レポータとして NIPS2017 に参加しました (link)
- 20170914, 料理きろくにおける料理/非料理判別モデルの詳細 (link)
- 20170809, 2nd Hackarade: Machine Learning Challenge (link)
- Interviews
- Full list
- Speaker Deck
- Many presentations in private study groups
- Pattern Recognition and Machine Learning, Deep Learning, Python Machine Learning, Deep Learning with python, Information Theory Inference and Learning Algorithms, Categories types and structures, 深層学習, はじめてのパターン認識, 詳解ディープラーニング, 経済・ファイナンスデータの計量時系列分析, データ解析のための統計モデリング入門, etc
- hikifune.fm (in Japanese)
- Graduate University for Advanced Studies (200904 - 201403)
- Doctor of Philosophy (Ph.D.) in Elementary Particle Physics
- Ph.D. thesis: Higgs interactions in physics beyond the standard model
- JSPS Research Fellowship for Young Scientists (DC2)
- Tohoku University (200504 - 200903)
- Bachelor's degree in Physics