In 2022, from June to August, 65 high school students attended the STEM to SHTEM (Science, Humanities, Technology, Engineering and Mathematics) summer Engineering and Mathematics) summer program hosted by Prof. Tsachy Weissman and the Stanford Compression Forum. During this summer program, the high schoolers pursued fun research projects in various domains under the supervision of 13 mentors, where the entire collection of the high schoolers’ reports can be found below.
A YouTube playlist of all of the talks can be found below.
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Misinformation and/or Disinformation on Climate Change By Internet Influencers
Mentor: Amy Dunphy By: Alejandro Macias, Krish Dev, Nhi Huynh, Corina Chen, Michael Sun Abstract: Disinformation and misinformation on climate change have been present early on as the discovery of human cast-offs led to the rise of carbon dioxide emissions and thus global warming. As early as 1980, oil industry companies, such as the American…
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Implementation and Evaluation of Ultra-Low Bandwidth Video Conferencing Platform Versus Traditional Compression Techniques
Arjun Barrett, Arz Bshara, Laura Gomezjurado González, Shuvam Mukherjee Abstract In the wake of the SARS-COV-2 pandemic, video conferencing has become a critical part of daily life around the world and now represents a major proportion of all internet traffic. Popular commercial video conferencing services like Zoom, Google Meet, and Microsoft Teams enable virtual company…
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A Tangible Solution to Closing the Gender Gap in Engineering
Iris Li; Jefferson High School, Daly City, CA; 1128irisli@gmail.com Atma Joshi; Chadwick School, Palos Verdes, CA; atmajoshi05@gmail.com Charley Walsh; San Marcos High School, San Marcos, CA; charsemail@icloud.com Taliyah Huang; Johns Hopkins University, Baltimore, MD; thuang57@jhu.edu Abstract Despite decades of effort to improve the gender disparity in engineering, women represent only 13% of engineers today, as…
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Improving Children’s Social and Emotional Literacy Using NLP-Based Emotional Detection
JASMINE LIU1, VALENTINA JURICEK, ALLISON SU Abstract Social and emotional recognition are fundamental aspects of children’s development, namely their ability to regulate their own emotions and properly understand those of others. However, while children’s literature can aid in developing their emotional competence, many children struggle with emotional expression through literacy; unlike in verbal communication where…
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Exploring the Intersection between Astronomy and Art through Abstract Creative and Analytical Processes
Maya Moseley, Alexander Herman, Casey Chang, Afra Ashraf, Laia Balasubramanian, Carrie Lei Abstract Science, Technology, Engineering, and Math make up a well-known acronym known as STEM. However, the acronym STEM has room to expand. SHTEAM better encompasses different disciplines that can come together for a common goal. The addition of the “H” and the “A”…
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Implementing and Analyzing Three Machine Learning Models For Brain Tumor Recognition
Vivian Chang, Ashwin Chintalapati, Chessa Park, Artem Tesov, and Keying Zhang Abstract Magnetic resonance imaging is a medical imaging technique that uses a magnetic field and computer generated radio waves in order to create images of the human body’s organs and tissues. Radiologists use these images to determine what condition your body is in, detect…
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Machine Learning and Truncated Monte Carlo data shapely to Predict Stages of Cirrhosis
Ethan Daniel Taylor*, Joseph Chai*, Joyce Zheng*, Juliana Maria Gaitan*, Paulos Waiyaki*, Tara Maria Salli*, David Jose Florez Rodriguez** *These authors contributed equally to this work **Mentor Abstract Cirrhosis is a common and deadly disease that requires the time and experience of a doctor to diagnose. We hypothesized that we could use machine learning and…
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Algorithms on Stock Performance
Kevin Xiao, Anahita Vaidhya, Nathan Pao, Audrey Kuo Stanford University and New York University Abstract One important approach of systematic trade is to make decisions based on the predicted price. To predict accurate results, statistics and machine learning models are normally used. We seek to develop different models for stock price prediction, comparing the feasibility…
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“AI is Smart Technology!”: Analyzing Expert-Novice Conceptions of Artificial Intelligence, Machine Learning, and Mathematics
Elly Kang, Sean Sehoon Kim, Sarah Porter, Taylor Torres Abstract There has been widespread enthusiasm for artificial intelligence and machine learning (AIML) curricula and instruction. Yet, integrating these fields into schools remains challenging. One underexplored avenue, presented in this paper, involves integrating AIML curricula with mathematics. To explore this approach, we conducted interviews to illustrate…
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Biofeedback in Performance
Jai Bhatia, Yui Hasegawa, Gabriele Muratori, Stasia Vaituulala, Farangiz Akhadova, Nikko Boling Abstract The purpose of our research is to investigate whether there are physical, quantifiable differences between an actor’s portrayal of emotions and the real-life sensation of those emotions. There is a lack of research surrounding the physiological changes that actors undergo as a…
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Creating Multi-Platform Immersive Narratives to Illustrate the Effects of AI Algorithms on Human Behavior and Thought Patterns
Ritali Jain, Alexander Nguyen, Houda Miftah, Julian Reed, Kepler Boyce, Nina Franz, Cecilia Colberg, Audrey Edwards, Ayushman Chakraborty Abstract Algorithms generated by artificial intelligence on online platforms have dominated our lives and our time, affecting our behavior and opinions without user awareness. The more these intelligent algorithms are able to paint a clearer picture of…
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Designing for Authenticity
Adanna Taylor, Chloe Zhu, Jared Rosales, Lilah Durney, Ryan Brunswick, Samip Phuyal, Selina Song Abstract We have entered an age of information disorder; with the current design of the internet, it has become increasingly difficult for users to access, identify, and trust authentic information. Editing tools have made the alteration or fabrication of image and…
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A Novel Approach for Generating Customizable Light Field Datasets for Machine Learning
Authors Julia Huang, Aloukika Patro, Vidhi Chhabra, Toure Smith (High School Students) Mentor: Manu Gopakumar, Stanford Electrical Engineering PhD Student Abstract To train deep learning models, which often outperform traditional approaches, large datasets of a specified medium, e.g., images, are used in numerous areas. However, for light field-specific machine learning tasks, there is a lack…