Nearly every high schooler faces the inevitable, daunting challenge of college applications. With extensive personal essays, standardized testing, and teacher references, and coupled with the challenge of identifying the programs best fit to one’s interests, completing the college application process is undoubtedly hard. However, this task has become even more difficult during the COVID-19 pandemic. Without on-campus tours, interactive open-houses, and chances to get to know a major, both applying to and deciding on a university is now harder than ever. In October, AInsight, a Bay-Area based organization founded by Arhana Aatresh and Joyce Yang, inspired by their summer experience at the 2020 UCSF AI4all program, hosted a virtual AI student panel event where Harvard, Stanford, and UCSF graduate and undergraduate students spoke about artificial intelligence, research and work experiences, academic challenges, and much more. Twenty-five high school student attendees had the opportunity to ask their own questions, from how to choose a major to starting an AI project.
The workshop began with introductions from us, Joyce and Arhana, followed by a crash course presentation about artificial intelligence, where we presented the different applications and interdisciplinary nature of AI. To bring those concepts to life, the students successfully completed a Python programming exercise and discussed the role of Python in AI algorithms and data cleaning
We then proceeded to perhaps the most anticipated part of the workshop — the panel! Our panelists were Aishani Aatresh, Tianna Grant, Snow Naing, Alice Tang, and Janice Yang.
Aishani Aatresh is a freshman at Harvard, a computational immunoengineer at Distributed Bio and Centivax, and a writer and occasional editor for SynBioBeta. Her interests lie at the intersection of biology, computation, and policy. Her love for science stems from food allergies; the computational piece cropped up when being young meant you weren’t getting anywhere near a wet lab; the policy interest comes from a recognition that science cannot serve the world without an effective framework rooted in this (at times) seemingly but unnecessarily orthogonal world.
Tianna Grant is a native of New York City, where she attended St. Francis College and obtained an Honors BSc, with a specialization in Biology. Tianna continued to a NIH funded Post-Baccalaureate Research Education Program (PREP) at UC Davis. During her Post-Baccalaureate she pioneered a longitudinal pilot study on gut microbial composition correlated with disease severity using a Rett Syndrome, Mecp2e1 deficient mouse model. Currently, Tianna is a graduate student at UCSF, where she has pivoted to studying single-cell genomics, computational biology, and immunology under Dr. Chun Jimmie Ye. She hopes to utilize her experience as a first-generation, underrepresented minority to highlight the disproportionate ethnicities in STEM and provide support for these groups.
Snow Naing earned her undergraduate degree in Biochemistry and Physics at Simmons University, Boston, MA; her thesis was on understanding physics principles behind medical applications. For her post-undergraduate degree, Snow worked as a lab technician for two and a half years at Brigham and Women’s Hospital. There, she worked very closely with bioinformaticians in validating their findings. That was the beginning of Snow’s interest in bioinformatics, which led her to pursue a Ph.D. degree at UCSF. She is now a fourth year Ph.D. student in bioinformatics at Krogan Lab, studying protein-protein interactions in autism spectrum disorder.
Alice Tang is an MD/PhD Student at UCSF. She is currently doing graduate training with the UCSF/UC Berkeley Bioengineering Ph.D. program in Marina Sirota’s lab. After graduating from UCLA with a B.S. in Bioengineering, her interests expanded to sensors, data, and AI for understanding and treating human diseases, with the goal of bridging the gap between research discoveries and clinical care!
Janice Yang is a freshman at Stanford studying computer science, and is particularly interested in the applications of computer vision in healthcare.
We prepared questions to start the conversation, which focused on the panelists’ work and experience and their thoughts on their respective fields. The panelists began by explaining their work, which ranged from computational biology to computer vision and clinical applications of AI. Janice explained, “Now that I’m an undergrad, I’m looking forward to exploring more areas of AI, because there are so many applications, and by limiting myself, I would be missing out on what AI is.” They also spoke about bridging various interests, such as medicine and computer science. Snow emphasized, “You don’t necessarily have to have majored in certain areas to work in [AI].” She explained that because AI is so interdisciplinary, people from multiple fields, such as bioinformatics or medicine, always have something to contribute. One of the final panel questions was about advice for high schoolers who want to pursue a career in artificial intelligence, and the panelists drew from their various experiences to provide some insight. As Tianna succinctly stated, “You are the driver of how you get introduced to the field.” She explained that not everyone may have accessibility to resources or connections, but reaching out to professors and simple Google searches can advance one’s journey, a sentiment everyone expressed. Alice said, “The best way to learn is by doing. Work on a project!” She emphasized jumping into what you want to pursue is the best way to learn. Aishani stated, “Cultivate self-awareness — what are you passionate about, how do you work best, what challenges inspire you?” Like the others, she stressed that professionals in the field are always willing to help young people if they reach out and demonstrate interest.
We then opened the floor open to the high schoolers’ questions, which centered around getting started in the field of AI, and the panelists truly delivered with a plethora of resources, such as Kaggle Datasets, online courses, and sample projects, and advice on how to reach out to professionals and how to read papers.
We felt truly gratified when we received overwhelmingly positive feedback from the workshop participants, who reported that they felt inspired by the panelists, truly grasped some AI and Python concepts, and learned about the direction of the field, because of our panelists’ enthusiasm and time. We realized that with the growth of the Internet, the number of resources available to students has skyrocketed, but they often do not know where to begin. With our workshop, we were able break down this barrier and inspire the students by showing them the doors AI leads to through the panelists’ experiences and real-life examples. We hope to continue our work and bring our insight to the Bay.
Contact us at joycebyang@gmail.com and arhana@aaresh.net
