INF-8606 Generative AI for health and life sciences - 5 stp

The course can be taken as a singular Ph.D. course. The course can be organized for participants who are registered at NORA summer school, DLN transdisciplinary course participants, and UiT students, during summer/winter school or during the semester time as a block course. It will be conducted as a concentrated/block course in the style of summer/winter school/courses conducted under the NORA, DLN, and UiT.

NOTE: First lecture will be in the end of May and will be given digitally (online).

Part of the lectures will be uploaded as video lectures, which is a prerequisite before the onsite (at UiT) program begins in August. The details will be shared during the first digital introductory.

The principal aim of this course is to provide students with basic knowledge to adapt to the rapidly evolving realm of Generative AI, particularly for its applications in life sciences. This will enable participants in life sciences to apply generative models to visualize, synthesize, understand some complex biological phenomena so that they can employ these techniques in their studies. In short, this course forms a nexus between the explosive growth of artificial intelligence and the ever-evolving field of life sciences.

This course provides a friendly introduction to the preliminary concepts required to understand and employ generative AI to the life science community. Further, it explores the applications of generative AI in the context of life sciences. The aim of this initiative is to bridge the knowledge gap for those keen on delving deeper into the intersections of generative AI and life sciences. Recognizing the importance of generative AI techniques for life science research, and the future prospects of their collaborations, is central to this course's ethos.

PhD students enrolled in this course will have their knowledge and learning evaluated during an oral examination associated with their project presentations.

The course is specially designed for life science researchers and students. This course will cover different topics regarding important basic and fundamental concepts of generative artificial intelligence (AI) while skipping details of mathematical and technical derivations. Equipping the participants with generative AI approaches that can be used to enhance life science research. In addition, this course will equip them with practical skills in using multiple generative AI-empowered tools or methods that could be directly used in life science research to further cope with rapid pace of technology development in life science and computer science.

Introductory concepts [2 hours]

• Introduction to Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI
• Different applications of generative AI
• Impact of generative AI on life science research

Prerequisites topics [2 hours]

• Convolutional Neural Networks
• Natural Language Processing
• Large Language Models
• Generative AI

Application development requirements [5 hours]

• Programing language
• Frameworks
• Attention
• Transformer
• Synthetic image/video generation

Generative AI applications [6 hours]

• Synthetic data generation for life sciences
• Computational biology: visualization and prediction
• Language modeling for life sciences

Self-reading

• Two research articles - comparison, weaknesses, strengths, application domain
• Extensive lab work, self-exercises and groups are planned for competence development is also included.

PhD students or holders of a Norwegian master´s degree of five years (300 ECTS) or 3 (180 ECTS) + 2 years (120 ECTS) or equivalent may be admitted.

PhD students must upload a document from their university stating that there are registered PhD students. This group of applicants does not have to prove English proficiency and are exempt from semester fee.

Holders of a Master´s degree must upload a Master´s Diploma with Diploma Supplement / English PhD students at UiT The Arctic University of Norway register for the course through StudentWeb.

External applicants apply for admission through SøknadsWeb. All external applicants have to attach a confirmation of their status as a PhD student from their home institution.

Students who hold a Master of Science degree, but are not yet enrolled as a PhD-student have to attach a copy of their master's degree diploma. These students are also required to pay the semester fee.

Recommended prerequisites: Programming skills in python and / or INF-1400. Hands on knowledge of python programming for deep learning.

Application code: 9314 Application deadline: April 15th, 2024. Apply here: https://fsweb.no/soknadsweb/login.jsf

The course is limited to 25 places. Qualified applicants are ranked on the basis of a lottery if there are more applicants than available places.

Knowledge -The student:

• Understands the basic concepts of generative AI and its significance in advancing the field of life sciences.
• Learns about the capabilities of generative AI models in visualizing and generating biological data.
• Becomes aware of the potential of generative AI in accelerating drug discovery, disease diagnosis, structural biology, etc.
• Familiarizes with language modeling techniques to understand and analyze scientific literature pertinent to their studies.

Skills - The student can:

• Use generative AI tools/models that can help to visualize complex biological structures and phenomena.
• Apply language modeling tools to process and analyze life science scientific records.
• Use generative AI for creating synthetic image data.
• Use language modeling tools for innovative life sciences applications.
• Integrate their subjective knowledge with generative AI for designing life science related projects.

General competence - The student has developed:

• Confidence in employing the power of generative AI for diverse challenges in the life sciences sector.
• Capability to debate, assess, and use the advancements in generative AI for life sciences.
• A holistic perspective about the usefulness, potential risks, and ethics associated with using generative AI in life sciences.
• The capacity to extend life science research with the help of generative AI.
• Acumen to critically analyze and extract pertinent insights from the literature in their field of study using the power of generative AI tools.

Lectures: 15 hours

Guided hands-on session: 9 hours.

Project consultation session: 3 hours (offline) + 4 hours (online)

Self-study session: 30 hours

Project work: spread over 8 weeks - net time 50 hours.

Group / self-work session: 20 hours

Oral presentations/ presentation preparation: 4 hours

Net effort (~125 hours)

NOTE: First lecture will be in the end of May and will be given digitally (online).