Machine Learning Summer School for Scientists

Applications open for the July-August 2025 Program

Women in Science Japan is launching the Machine Learning Summer School for Scientists to help address the AI gender gap in Japan.

This selective six-week course will teach women and gender minority scientists applied machine learning for science through lectures, hands-on tutorials, and mentor-guided projects. The program aims to equip participants with the technical knowledge and skills needed to continue their machine learning journey in their respective scientific domains.

Program Schedule

  • July 12th, 2025 - Building Models with Tabular Data

    Dr. Marco Visentini-Scarzanella

  • July 19th, 2025 - Audio Processing

    Dr. Meishu Song

  • July 26th, 2025 - Computer Vision

    Dr. Rebeka Sultana

  • August 2nd, 2025 - Physics-informed Machine Learning

    Dr. Guido Cossu

  • August 9th, 2025 - Collaborative Software Engineering

    Dr. Rousslan Dossa

  • August 23rd, 2025 - LLMs for Science

    Dr. Indra Priyadarsini and Dr. Lisa Hamada

Target Audience

Below are the minimum requirements for participation in the program:

  • Woman, non-binary, and other gender minority person in STEM in Japan

  • Must have at least a bachelor’s degree in a STEM field

  • Must be comfortable using Python

  • Must be familiar with common machine learning concepts

  • Must be comfortable with the mathematics required for machine learning, such as linear algebra and calculus

  • Must be proficient in English

  • Must be able to attend the program in-person in Tokyo during July and August 2025.

The ML Summer School is open to non-WISJ members as well as WISJ members (WISJ membership is free)

Additionally, participants are expected to come with an idea for a machine learning project they would like to develop during the program. With guidance from their mentors, they will refine and build their project throughout the course, culminating in a final presentation on the last day. We seek participants who are eager to apply machine learning in their field of expertise, excited to develop their own project, and committed to continuing their learning journey beyond the summer school.

Meet the Instructors

  • Dr. Marco Visentini-Scarzanella

    Senior Manager, Applied Science at Amazon Japan

  • Dr. Meishu Song

    Postdoctoral Researcher at the University of Tokyo

  • Dr. Rebeka Sultana

    Specially Appointed Assistant Professor at the Institute of Global Innovation Research, Tokyo University of Agriculture and Technology

  • Dr. Guido Cossu

    Technology and Creative Director at Braid Technologies

  • Dr. Rousslan Dossa

    Chief Researcher at Araya

  • Dr. Indra Priyadarsini

    Research Scientist at IBM Research - Tokyo

  • Dr. Lisa Hamada

    Research Scientist at IBM Research - Tokyo

Venue

 

Code Chrysalis

〒106-0046 Tokyo, Minato City, Motoazabu,

3 Chome−1−35 Vort元麻布 B2F

The Summer School will be held in-person only.

Thank you to Code Chrysalis for generously providing a venue for the Summer School.

Detailed Daily Schedule

Throughout the program, participants will be exposed to many methods and many fields of science beyond their own domain of expertise. We believe that the multidisciplinary nature of the course will lead to rich conversations and spark ideas for participants and instructors alike. The program is designed to open communication between students and instructors such that deeply-technical conversations can occur organically.

11:00 - 12:00: Sync with Mentors

Mentors will provide personalized guidance to participants throughout the program, supporting their projects and development. Participants are expected to work on their personal projects throughout the week to make the most of their time with their mentor.

12:00 - 13:00: Lunch

Lunch will be provided for participants and volunteers thanks to the generous support of our sponsors.

13:00 - 15:00: Lecture

Experts across a range of machine learning disciplines will deliver a lecture introducing the theory and applications.

15:00 - 16:00: Tutorial

After the lecture, participants will engage in a hands-on tutorial to apply what they’ve learned in the lecture. Teaching assistants will be available to clarify content from the lecture and help debug code.

Application Process

1) Submit the initial application via Google Forms by April 30th.

Applications must complete the initial application form, which includes a short personal statement and a summary of the project idea you would like to develop during the program. The initial application must be submitted no later than Wednesday, April 30th at 11:59 pm. Applicants can expect to hear a response within 3-4 days of submission.

2) Complete the technical assignment on Google Colab by May 2nd.

For applications who pass the initial stage, they will be invited to complete a short technical assignment that tests their knowledge of Python, mathematics, and machine learning. The assignment is done on the applicants’ own time and should take 30 - 60 minutes.

Applicants must submit the technical assignment via email to admin[at]womeninsciencejapan.com no later than Friday, May 2nd at 11:59 pm. Applicants can expect to hear a response within 7 days of submission.

3) Participate in a short 15-20 minute interview by mid-May.

For applicants who pass the technical assignment, they will be invited to participate in a short interview with one or both of the program organizers. This final step is to ensure your readiness and commitment to the program. Interviews will be conducted on a rolling basis as participants proceed through the application process. Applicants can expect to hear a final decision within 3-4 days of their interview.

Program fees

There is a non-refundable 5,000 JPY participation fee to help cover the costs of the program. Only accepted participants need to pay the fee; the application is free. Participants who are accepted to also volunteer as a teaching assistant or mentor can participate for free.

We can accommodate up to 30 participants in this program, and admissions will be granted on a rolling basis. Therefore, the application may close before April 30th if all spots are filled.

Volunteer

Teaching assistants

Teaching assistants should have 2+ years of experience with ML/DL in either academia or industry, including current PhD students in machine learning, computer science, or closely-related fields. Teaching assistants should be comfortable helping participants apply the lecture content, including explaining concepts and debugging code. Teaching assistants must volunteer for at least 3 sessions.

Mentors

Mentors should have completed a PhD in a STEM field or have equivalent industrial experience, as well as 2+ years of applying ML/DL in a professional setting. Mentors are expected to provide technical and project management support for mentees’ projects. Mentors must be able to attend all six sessions in order to support their mentees.

Transportation reimbursement

Transportation within or near Tokyo will be reimbursed for the days that you volunteer. We will reimburse public transportation (train, subway, or bus) costs directly to and from the venue. Please submit receipts after your session. Taxi fares will not be reimbursed. No honorarium will be provided for volunteers.

About the organizers

Elizabeth Oda

Elizabeth is a Technical Project Manager at Braid Technologies, where she uses scientific machine learning and other AI methods to accelerate the advanced engineering design process. She has presented on AI and gender at events such as TEDxTIU in 2023 and the 2024 Asia-Pacific Economic Cooperation (APEC) Senior Officials' Meeting in Peru in 2024.


Her career history is diverse, ranging from working as an AI Engineer at a Japanese start-up to serving as the Director of Research at an education company. She earned her B.S. in International Agriculture and Rural Development from Cornell University and her M.S. in Molecular Biosciences and Bioengineering from the University of Hawaii at Manoa. Since co-founding the organization in 2019, Elizabeth has been leading Women in Science Japan.

Kai Arulkumaran

Kai is a Research Team Leader at Araya, where he works on brain-controlled robots as part of the JST Moonshot R&D program. He received his B.A. in Computer Science at the University of Cambridge in 2012 and his Ph.D. in Bioengineering at Imperial College London in 2020. He has previously worked at DeepMind, Microsoft Research, Facebook AI Research, Twitter and NNAISENSE.

He was a lecturer on Deep Learning at the International Machine Learning Summer School 2019, and also a mentor for the 2019 OpenAI Winter Scholars program.

Sponsors & Partners

We extend our heartfelt gratitude to our valued sponsors for their support. Your generosity fuels our mission for a brighter, more inclusive future for AI in Japan.

Interested in sponsoring this program? Please send us a short message via our Sponsorship page.