Lecture series: Genetic sequence representation for machine learning
TIPs lecture series offers a free, remote session on genetic sequence representation for machine learning, aiming to inspire more Filipinos to engage in biomedical research innovation.
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TIPs launches a lecture series on genetic sequence representation for machine learning
The TIPs research group invites you to join an informal, remote, and free preliminary lecture on genetic sequence representation for machine learning, focusing on the TIPs-VF (Translator-Interpreter Pre-seeding for Variable Fragment) approach.
This session is part of our engagement program to inspire more students, researchers, and professionals in the Philippines to explore machine learning-driven biomedical research. By participating, you’ll help us refine our delivery, share feedback, and spark more innovation in the field.
Who is this for?
Students in life sciences, health, medicine, or engineering
Researchers and professionals in biomedical fields
Anyone curious about genetic data applications in AI
What to expect:
A beginner-friendly introduction to genetic sequence encoding for ML
Live demonstration of TIPs-VF in action
Open discussion on possible applications in Philippine biomedical research
A short Q&A session
Details:
Date: August 17, 2025
Time: 3:00 - 3:30 PM (Philippines Standard Time)
Online: access link will be provided after pre-registration
Fee: Free of charge
Limited slots: 5-10 people only for the first round
Open to all Filipinos
Instruction:
1. Pre-register using the form below
2. Wait for the confirmation
Please note that we can only accommodate a limited number of participants, for now. We'll do our best to inform all registrants about the results of the screening process. Selection will not be in a form of first come, first serve basis but we'll assess participants based on relevance of the lecture to the participant.
3. Attend the lecture
Pre-registration form
*required fields
TIPs
Translator-Interpreter Pre-seeding, a family of encoding schemes for augmenting the representation of genetic sequences in machine learning.
© 2025. Marvin De los Santos. All rights reserved.