Science Education Opportunities

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National Science Teachers Association Meeting in Chicago!

July 21-23 2022

Heading to NSTA Chicago in July?

Be sure to check out what folks from NSTA District I (CT, MA, RI) are contributing

via this link: https://tinyurl.com/DistrictOneChicago 

Sample sessions attached

Contact Susan Meath Kelly, NSTA District I Director, for information about an informal evening rooftop gathering for District I members

susan.kelly@uconn.edu

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National Astronomy Day Celebration in Rhode Island

For more information, please contact: Kathy Siok   kathys5@cox.net

Join us on Saturday, May 7, 2022, as we celebrate National Astronomy Day.  Events will be co-hosted by Skyscrapers, Inc. (Amateur Astronomical Society of RI), the Museum of Natural History  and Ladd Observatory. From 12:30 to 3:30 PM,  at the Museum of Natural History, Roger Williams Park and will run  planetarium shows (for a small fee) , public solar observing, and astronomy exhibits will take place at the museum, located in Providence at Roger Williams Park. Ladd Observatory, located on Hope Street in Providence, will conduct public solar observing  from 1:00pm-3:00 PM.  At 7PM, Seagrave Memorial Observatory in North Scituate invites everyone to attend a meeting with our featured speaker, Skyscraper member Richard Lynch.   He will transport us to Italy to visit the birthplace of Galileo and the museum dedicated to him.  Weather Permitting, observing will  follow using our 8” Alvin Clark, 12” Meade, and 16” Meade telescopes. 

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For more information, visit www.theskyscrapers.org or our Facebook page @skyscrapersinc

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Data Science in Action : Machine Learning for Self-Driving Cars

Program Co-directors: Tianxi Cai (Harvard), Aaron Sonabend (Google Inc), Jessica Gronsbell (University of Toronto)

A two-week day camp to introduce programming and machine learning to high school students. The program will be held online with Zoom.

The objective of the program is to introduce students to machine learning and programming through a project in which they program various machine learning algorithms to recognize images and make a self-driving toy car.

The course consists of lectures covering conceptual level statistical, machine learning and programming components. Students will be introduced to various machine learning methods and algorithms as well as their applications in different fields including biomedicine. In parallel they will be introduced to the Python programming language, which will allow them to implement the concepts they have studied.

Students will participate in exercises using different methods for classifying images, and will be shown tools to continue learning and programming on their own. They will take pictures of physical objects and train their own classification algorithms including neural networks to recognize these objects. Once they achieve high-quality performance, they will install their program into a toy car equipped with a camera which will self drive using their programmed neural network.

Lunch hours will include conversations with machine learning experts from academia and industry who will share their life experiences and perspectives on data science.

Program Dates:

• July 5-8, 2022 | Bootcamp: Introduction to Python; Car and Raspberry Pi Set-up and Practice

• July 11-15, 2022 | Introduction to Statistical and Machine learning, Programming

Tentative Daily Schedule:

July 5-8, 2022 | 10:00am – 3:00pm

• 10:00am – 11:00pm Bootcamp: Introduction to Programming/Python

• 11:30am – 12:00pm Practice + Q&A

• 12:00pm – 12:45pm Lunch Meeting with Guest Speakers

• 1:30pm – 3:00pm Troubleshooting and programming with Toy Car

July 11-15, 2022 | 9:00 am – 4:00 pm

• 9:00am – 9:45am Lecture on Statistics/ML

• 10:00am – 10:45am Programming Practice

• 11:00am – 11:45am Lecture on AI/ML

• 12:00pm – 12:45pm Lunch Meeting with Guest Speakers

• 1:30pm – 2:45pm Programming Practice

• 3:00pm – 4:00pm Q&A, Trouble Shooting w/ Toy Car

Registration Deadline: May 15, 2022

Tuition:

Tuition for the program is free, but students are responsible for purchase of materials. Scholarships including stipend are available for eligible underrepresented students and those in need of financial support as defined by the NIH. A separate application for financial aid is needed to request funding support.

Support the Community:

If you are able to sponsor purchase of materials for another student, please consider making a donation to the CELEHS Data Science Summer Camp Fund. We encourage those who can help to provide opportunities to other students facing financial hardship.

To support the CELEHS Data Science Summer Camp Fund, please see Harvard Medical School’s gift instruction page. If writing a check, please note ‘CELEHS Data Science Summer Camp Fund’ on your check. If paying with a credit card, under the drop down menu titled ‘Select a Fund’ please select ‘Other’, and for ‘Other Fund Name’, please type ‘CELEHS Data Science Summer Camp Fund’. Thank you for your support of future data scientists!

Equipment Needed: Tentative list of materials (Not Finalized)

Eligibility Requirements:

• High school student (rising freshman – senior)
• Students from underrepresented minorities & low-income backgrounds are encouraged to apply
• Interest in applying to college with a focus in STEM
• Basic algebra
• Completed application and release forms
• Resume

The program is hosted by the Translational Data Science Center for a Learning Health System (CELEHS) at Harvard Chan School of Public Health and Harvard Medical School, and co-sponsored by the Prediction Analytics Research Solution and Execution (PARSE), a non-profit research organization. It is taught by a team of data science researchers including faculty members from Harvard, the University of Toronto as well as data scientists from Paypal Global Data Science. Teaching assistants will provide hands-on programming and technical support throughout the camp.

For more information, please contact Amanda King (amking@hsph.harvard.edu).

Instructors:

• Jessica Gronsbell (University of Toronto)
• Andrew Beam (Harvard Chan School of Public Health)
• Junwei Liu (Harvard Chan School of Public Health)
• Aaron Sonabend (Google Inc)
• Nitin Sharma (Paypal Global Data Science)

Sponsors:

Support for the program is provided by CELEHS, NIH Aim-AHEAD consortium training CORE, PARSE and Paypal Global Data Science.