UROP Research Mentor Project Submission Portal: Submission #806

Research Mentor Information
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Primary Research Mentor Name: Shibo Li
Research Mentor Preferred Pronouns: He/him
When potential research assistants are reaching out via email, what is your preferred honorific?: Dr.
Contact Email (FSU Email if affiliated): shiboli@cs.fsu.edu
Position Title: Faculty
FSU College (if applicable): Arts and Sciences
FSU Department or Non-FSU Organization Affiliation: Department of Computer Science
Headshot (optional): https://cre.fsu.edu/system/files/webform/urop_project_proposal_portal/14656/prof_pic_color.jpg

Additional Research Mentor(s)
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Overall Project Details
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Title of the Project: AI-Driven Approaches to Decipher Complex Physical Phenomena
Project Keywords: Machine Learning, Computational Physics, AI for Science
Are you currently looking for research assistants?: No
Number of Research Assistants Needed : 6
Relevant Research Assistant Major(s):
Computer Science, Statistics, Mathematics, Physics and broad studies of Engineering

Project Location:: On FSU Main Campus
If the project location is off campus, does the research assistant(s) need to provide their own transportation?: No, the project is remote
Please select the choice that most accurately describes your expectations for the research assistant(s):: Fully Remote
Approximately how many hours a week would the research assistant(s) need to work?: 5-8
Roughly what time frame do you expect research assistant(s) to work?: Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Overall Research Project Description:
The physical principles and laws governing our universe form the foundation of our understanding of the world. As the primary discipline, physics provides the basis for comprehending the cosmos. One of its central tasks is modeling physical phenomena through mathematical formulations and finding solutions to predict them across time and space— a field known as computational physics or physical simulations. However, deciphering the behavior of physical phenomena can be incredibly challenging. This pursuit has captivated generations of brilliant minds and has become the bedrock of various fields, including Engineering, Geology, Astronomy, Meteorology, and Economics. 

Artificial Intelligence (AI) and machine learning have revolutionized nearly every aspect of our lives in recent years. The core concept of AI is to discover and utilize statistical patterns within observed data. AI is efficient, capable of generalizing to unseen scenarios, and has transformed research and development across various fields, including computational physics. 

AI for Science, specifically AI for computational physics, represents a frontier of study in academia and industry. However, the current progress of machine learning in computational physics often involves oversimplifications of the physical systems of interest. In many cases, these approaches reduce the complexity of physical phenomena by assuming that only one set of physical laws governs the observed systems. In reality, the phenomena we observe are complex and influenced by multiple physical laws. These multi-physics systems involve the interaction of various physical processes or phenomena, often spanning different fields of physics. These interactions are complex due to the coupling between different physical behaviors, such as thermal, mechanical, electrical, fluid, and chemical processes. 

Empowering the study of complex physical systems with state-of-the-art machine learning research could have a significant impact, yet little existing work addresses this challenge. This project aims to develop flexible machine-learning frameworks that enhance our understanding of complex systems in our universe and enable data-driven solutions to predict their behaviors. 
  


Research Tasks:
Task 1: Search, scrap, and sort recent literature into a simple database (spreadsheet, CSV files) from top-tier machine learning venues and journals of scientific computing. 
Task 2:  Collecting open-source implementations, tools of the traditional studies for solving complex physical systems. Collecting some toy or synthetic problems as benchmarks for future studies
Task 3: Preliminary testing of simple and out-of-box data-driven approaches (such as linear models and convolutional neural networks) for solving those synthetic benchmarks.
Task 4: Establish a project website (github.io) with existing web development frameworks.


Skills that research assistant(s) may need::
(Required) interests in scientific studies and willing to spend time searching for papers
(Recommend) Familiar with Python and Linux
(Preferred) Experience with machine learning development frameworks (PyTorch, TensorFlow, Jax); Experience  with out-of-box machine learning tools (scikit-learn)
(Preferred) Students with a background in Computer Science, Statistics, Mathematics, Physics, and broad studies of Engineering


Mentoring Philosophy:
As a mentor, my philosophy is centered on empowering students to reach their full potential by fostering a supportive and collaborative learning environment. I believe in guiding students to think critically, explore new ideas, and develop confidence in their abilities. By encouraging open communication and mutual respect, I aim to create a space where students feel comfortable asking questions, taking risks, and learning from successes and challenges. I aim to inspire lifelong learning, resilience, and a commitment to ethical and inclusive practices, helping students navigate their academic and professional journeys confidently and purposefully.

Please provide a link to your publications, a video clip, or a website for your research project (if applicable):: https://imshibo.com/
Please add any additional information here (if applicable)::
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Are you interested in participating in the UROP Research Mentor Roundtable?: Yes
Roundtable times and Zoom links:
Session 1: 9/3, 2:00-2:30pm (https://fsu.zoom.us/j/7451048764?omn=94434911198)
Session 2: 9/5, 1:00-1:30pm (https://fsu.zoom.us/j/7451048764?omn=99595455910)

Roundtable Info:
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UROP Program Elements
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Mentor Handbook, FAQs, and Communication: Yes
UROP Performance Evaluation: Yes
Materials Grant: Yes
UROP Poster Presentation: Yes

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Year: 2024
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