Drawing on his volunteer and leadership experience within IEEE – including serving as the 2025 President of the IEEE Computer Society – Prof. Hironori Washizaki is honored to announce his candidacy for the 2027 IEEE Division V Delegate-Elect/Director-Elect: https://www.ieee.org/about/corporate/election/division5

I would be grateful for your support as we work together to advance IEEE’s mission of advancing technology for humanity, sustain the IEEE Computer Society’s leadership in computing, and strengthen IEEE’s role as the world’s premier professional community. The voting period runs from 17th August to 1st October. Thank you for your consideration and support.

My position statement: http://www.washi.cs.waseda.ac.jp/washizaki/
Election site: https://www.ieee.org/about/corporate/election

Foster innovation, global collaboration, and responsible technology: Strengthen partnerships among industry, academia, and government to accelerate research and practice, advance emerging technologies, and launch initiatives that deliver societal value in computing and engineering. Through standards, publications, conferences, and technical communities, provide evolving bodies of knowledge while ensuring innovation is pursued responsibly, with attention to ethics and sustainability in the era of digitalization, generative AI, and beyond.

Empower and grow the community: Expand mentorship, training, and professional development across all career stages, from pre-college to senior professionals, while encouraging broad participation across diverse regions and backgrounds. Drive membership and volunteer growth and foster continuous learning, networking, and leadership opportunities that create lifelong, impactful connections to broader society through both existing and new channels, leveraging innovations such as agentic AI responsibly.

Catalyze collaboration with integrity and transparency: Promote open, transparent, and agile collaboration across the IEEE Board of Directors, staff, volunteers, and organizational units, including the Computer Society, to address interdisciplinary opportunities and challenges with integrity. Strengthen connections and build an ecosystem where communication, ideas, resources, and communities are seamlessly integrated and synergistically coordinated.

鷲崎・鵜林研究室では以下のテーマに取り組むプロジェクト研究生を募集しています。

Washizaki-Ubayashi Laboratory is recruiting project graduate students to work on the following themes.

• AIおよび機械学習（ML）ソフトウェア工学： AIおよび機械学習システム・ソフトウェア開発のためのソフトウェア工学アプローチ。例：機械学習システム（MLS）開発支援モデリングフレームワーク、MLSの安全リスク評価、MLOpsワークフローパイプライン統合、機械学習および生成AIパターンの文書化・検出、IoT設計パターン、MLSにおける変更と欠陥に関する実証研究および開発者の実践。
• AI and machine learning (ML) software engineering: Software engineering approaches for AI and machine learning systems and software development, such as a modeling framework for supporting the development of machine learning systems (MLS)(e.g., FGCS’24, SQJ’24), safety risk assessment of MLS, MLOps workflow pipeline integration, documenting and detecting machine learning and generative AI patterns (e.g., Computer’22, APSEC’23 ERA, IT Pro’25), IoT design patterns (e.g., IoT-J’20), empirical study on change and defects in MLS as well as developers’ practices (e.g., SQJ’23)
• 機械学習、(生成)AI、自然言語処理(NLP)を用いたソフトウェア工学： 信頼性と効率性を備えたソフトウェアシステムの開発・運用へのNLPおよび（生成）AI/MLの応用。例：生成AI支援型要件定義・データ駆動型ペルソナと要件定義、セキュリティ知識と文書の追跡・構造化、バグ報告および関連する課題報告・チケットの分析、アジャイル開発とプロセス分析。
• Software engineering with machine learning, (generative) AI, and natural language processing (NLP): Application of NLP and (Gen)AI/ML to reliable and efficient software systems development and operation, such as GenAI-assisted requirements engineering (e.g., arXiv), data-driven persona and requirements engineering (e.g., ICSE’18 NIER, IJSEKE’21), security knowledge and document tracing and structuring (e.g., IEICE’25), bug reports and related issue reports and tickets  analysis (e.g., EASE’24, EASE’23), agile development and process analysis (e.g., EASE’23, Agile’12)
• ソフトウェア品質工学：ソフトウェア品質保証のための高度な工学的手法。自動プログラム修正や実証研究、プログラム修復の可視化、ソフトウェア信頼性評価と予測、プログラム品質測定・評価・改善、組み合わせテストと再利用など。
• Software quality engineering: Advanced engineering approaches for software quality assurance often involving the application of NLP/ML, such as automatic program modification and empirical research, visualization of program repair (e.g., SEKE’23), software reliability evaluation and prediction (e.g., Mathematics’21), program quality measurement, evaluation and improvement (e.g., ICSE’19, ENASE’20), combinatorial testing and reuse (e.g., PeerJ’21)
• プログラム解析：プログラムコードの静的・動的解析および教育分野への応用。例：コード最適化、コードクローン解析、プログラミング学習・教育支援
• Program analysis: Program code static and dynamic analysis as well as its application in education, such as code optimization (e.g., ACM TACO’25), code-clone analysis (e.g., IWSC’20), programming learning and teaching supports (e.g., SIGCSE’18)

• 卒業研究の進め方: 以下と同様。ただし年間を通してのより深い取り組み。
• プロジェクト研究の進め方: 研究グループに分かれて取り組み。最初の一か月ほどは基礎的な学びを深め、続いて二・三か月目に何らかの視点や検討を始め、四か月目に議論や実験などしつつ成果まとめ。
• 研究テーマの設定方法: 新規、あるいは、研究グループにおける継続発展課題。傾向としては新規の内容に一人で取り組むよりも、データや先輩の成果を引き継ぎ、あるいは共有して、先輩他とも相談しつつ取り組むケースが増加中。
• ゼミの曜日時限: 全体としては月曜日5限、他にはグループごとに主に木曜日3-5限。新配属生の科目履修状況によって適宜調整。
• Graduation Research Procedures: Same as below. However, a more in-depth approach throughout the year.
• Project research: Work in research groups. In the first month, students deepen their basic learning, then in the second and third months, they start to consider some viewpoints and studies, and in the fourth month, they summarize the results through discussions and experiments.
• Research theme setting: New or continuing development in the research group. The trend is that more and more students are taking over or sharing data and results of their seniors, and consulting with their seniors and others rather than working on new content by themselves.
• Seminar times: All international course students meet together on Mondays (around 5th period), and in groups on Thursdays (3rd, 4th, 5th periods), and so on. The schedule will be adjusted according to the course enrollment of newly assigned students.