Programs for Fellows

 Travel Awards

Fellows are encouraged to apply for travel grants of up to $500 for presenting MIP-related research at conferences and workshops as well as for company/university visits (domestic travel only). Apply through the website application portal (inset) with information about the opportunity and how it relates to BioPACIFIC MIP. Awards will be made on a rolling and first-come first-served basis, with priority given to first-time applicants.

 Outreach Opportunities

Teacher Workshops

Through CNSI, 2-3 BioPACIFIC MIP Fellows are invited to participate in a one-day training workshop for elementary, middle, and high school teachers. Teachers learn about biopolymers and receive a kit for conducting experiments with their class.

2023 Teacher Workshops schedule:

At UCLA:

• Elementary School teacher workshop - "Biogels" October 7, 2023
• Middle/High School teacher workshop - "Plasmonics: Color from Gold" October 28, 2023
• Middle/High School teacher workshop - "Biotoxicity" December 2, 2023
• Elementary School teacher workshop - "Liquid Crystals" January 20, 2024
• Middle/High School teacher workshop - "Magnetic Liquids" February 10, 2024
• Middle/High School teacher workshop - "Emulsions" March 9, 2024
• Middle/High School teacher workshop - "Fast Batteries" April 6, 2024 
• Middle/High School teacher workshop  - "Nanoscale Wetting or Biosensors" May 4, 2024

At UCSB:

• UCSB Nanoscience Workshop for Teachers, (date TBD)

2022 Teacher Workshops schedule:

• UCLA Nanoscience Workshop for Teachers: “High School Biopolymers”, Saturday, November 12
• UCSB Nanoscience Workshop for Teachers: “High School Biopolymers”, Spring 2023 (Saturday, date TBD)

2021 Workshops: Nanoscience Workshop for Teachers on "Elementary Biopolymers" November 13, 2021, at the UCLA BioPACIFIC MIP.

Middle and high school teachers from across Southern California participated in a free, one-day biopolymers training workshop.

School for Scientific Thought (High School)

The School for Scientific Thought (SST) is a Saturday program for high school students in grades 9-12. SST classes introduce students to concepts in science that extend beyond the typical high school science classroom – from nanotechnology to reverse engineering – and relate these concepts to “the real world”. BioPACIFIC MIP Fellows are invited to participate as instructors.

2022 SST Schedule: Saturdays, January 21, 28, & February 4, 2023, from 10am-2pm. Contact Wendy Ibsen if you're interested in participating.

2021 SST: “From Biomedical Devices to Your Trendy Boba Beverage: How Hydrogels Give Us Leverage”, February 26 and March 5, 2022, at UCSB.

Students learned about both synthetic and naturally derived hydrogels, their prevalence in our daily lives, and their potential in next-generation biomaterials and therapeutics. Students engaged in hands-on activities (making hydrogels from biopolymers), studying hydrogel properties such as swelling, stiffness and decomposition, and toured the BioPACIFIC MIP facilities at UCSB.

Family Ultimate Science Exploration (Junior High)

FUSE is an evening event at local junior high schools for 8th grade students and their families. The event is structured so that each family participates in 3 workshops. BioPACIFIC MIP will be hosting a GAK activity and BioPACIFIC MIP Fellows are invited to participate as facilitators. Register here.

2023 FUSE Schedule Facilitator Training:

October 10, 6:00-8:00pm

2023 FUSE In-school sessions (5:15pm-8:00pm):

• October 19 @ La Colina Junior High
• October 23 @ Santa Barbara Junior High
• November 7 @ Goleta Valley Junior High
• November 16 @ Carpinteria Middle
• TBA @ La Cumbre Junior High

 Python and Data Science Training

This independent training module is intended to help BioPACIFIC MIP Fellows develop basic skills in Python, data visualization, and machine learning. The module provides a condensed list of free (**) and paid ($$) courses and exercises to aid in skills development. Participants can pick and choose courses from this list that best suit their current skill level or interests. The paid courses are accessible through BioPACIFIC MIP Fellows training accounts. Apply for an account via the link in the inset or contact Tal Margalith or Chris Dunham for more information.

Core Content

1. Scientific Computing with Python (**) - An introductory course that will introduce you to fundamentals of the Python programming language, including variables and expressions, conditional execution, functions, objects, iterators, API interactions, and more.
2. The Modern Python3 Bootcamp ($$) - Another introductory course, this resource is a much lengthier and robust course that can serve as an effective complement to the introductory course above. You’ll learn how to use anonymous functions, decorators, generators, and delve further into object oriented programming (OOP) principles. If you find too much repetition with the previous course, we encourage you to pick and choose those lessons/modules within this course that best suit your needs.
3. Data Analysis with Python (**) - This course expands upon the fundamentals you learned earlier and delves deeper into widely utilized Python tools and libraries, including Jupyter Notebooks, NumPy, Pandas, and Matplotlib.
4. Python Data Analysis Visualization ($$) - Much like before, this course serves as a strong complement and reinforcement tool to the previous course. It covers additional Pandas features and also introduces you to Seaborn, a high-quality visualization library built upon Matplotlib.
5. Complete Machine Learning and Data Science, Zero to Mastery ($$) - This course provides a comprehensive overview of machine learning techniques and data visualization in Python, using both supervised and unsupervised methods. You’ll learn how to analyze datasets with SciKit Learn and TensorFlow in order to perform classification and regression modelling.
6. Machine Learning with Python (**) - Once again, this course has some strong overlap with the previous course and will reinforce your knowledge of machine learning techniques. However, this course has a greater focus on TensorFlow and neural network design and implementation. You will also learn how to utilize neural networks for natural language processing.

Extras

If you would like to further reinforce what you’ve learned and dig deeper into the mathematics underlying machine learning, or simply to experience additional exercises, we recommend the courses below.

1. https://developers.google.com/machine-learning/crash-course/ (**) - Google’s open-access machine learning course.
2. https://www.coursera.org/learn/machine-learning (*$) - A very popular and long-running MOOC maintained by Andrew Ng at Stanford University. Free to audit, paid if a certificate is desired.
3. http://www.databookuw.com (*$) - Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. This resource (referred to as the “Databook”) is offered by the University of Washington. They provide several medium-length (20-30 minute) videos covering a wide range of topics in the space of machine learning (supervised and unsupervised) and deep learning techniques for both Python and MATLAB packages. The videos are available on YouTube for free. There is also a textbook (2nd edition) available on Amazon for ~$60, should you wish to learn more about the underlying mathematical and other concepts.

In addition to the above, there are many excellent (and free!) sources of knowledge and support for Python. We recommend the following:

• https://realpython.com/
• https://www.tutorialspoint.com/python/index.htm

Library documentation will one day be your best friend in programming, but at the beginning of your journey, you may find documentation to be overwhelming. Some good examples of library documentation include the following:

• https://pandas.pydata.org/docs/reference/index.html
• https://numpy.org/devdocs/reference/index.html
• https://seaborn.pydata.org/api.html

Finally, if you find yourself particularly stumped regarding a programming problem or implementation, there’s always Google, StackOverflow, and Reddit (yes, Reddit – there are many good programming subreddits out there!). Good luck!