RESEARCH: APPLY FOR OUR FALL PROGRAM BY SEPTEMBER 14TH HERE.
Improve state-of-the-art performance of large language models like GPT-4.
We are embarking on an ambitious goal of advancing the frontiers of large language model capabilities, rigorously evaluating their performance against industry-standard benchmarks. Leveraging open source LLMs such as Meta's Llama 2, our program is uniquely positioned to contribute to this cutting-edge field of research.
Fall 2023 Research Highlights
(New) NeurIPS 2024
In April 2024, NeurIPS, the most prominent AI conference in the world, announced their inaugural high-school conference due at the end of June.
Our NeurIPS Track
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Program Dates: May 12 - June 30.
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Lectures on Sundays 1-2:30 pm PT
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Two 20-30 minute check-ins per week on Tu/Th
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AI for Social Impact: Aligning with the conference scope, our projects will focus on AI for social impact.
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Learn AI fundamentals and publish a paper: Gain a foundation in AI/ML skills and conduct scientific research to be submitted to NeurIPS.
Program Overview
Objective
Research Experience: Immerse yourself in the process of real-world AI research by delving into literature review, formulating hypotheses, running experiments, communicating your results in a research publication, and submitting research to conferences.
Academic Contribution: Engage with the rapidly growing field of large language models by developing techniques that have the potential to make an actual impact.
Schedule
Weekly Structure: The program has two weekly meetings, with optional office hours available. We expect you to dedicate 5-10 hours per week in total, with flexibility for further exploration.
Weekend Instructor Lecture (1.5 hours): Learn LLM and ML fundamentals and review relevant literature for research inspiration.
Mid-week Progress Update (20-30 minutes, scheduled by group): Share your weekly progress with your mentor and explore research directions.
Office Hours and Slack: Receive support from mentors throughout the week if you run into roadblocks debugging, want to bounce ideas, or deep dive into technical topics.
Pedagogy
Hands-on Mentorship: Work in a close-knit team of 3-4, guided by a dedicated mentor who collaborates intimately with the team to facilitate progress and engages individually to enhance learning.
Streamlined Pedagogy: The program is tailored to allow you to engage in real AI research without prior research experience or AI expertise. We provide pre-structured code frameworks to minimize technical hurdles, and lessons on LLM fundamentals and meta-level research skills to ensure a solid foundation for all students.
Logistics
Class Format: Meetings are fully online and held over Zoom.
Program Dates (times listed in Pacific Time):
Summer B: Jun 23 - Sep 8. Lecture time: Sundays 10-11:30 am PT
Summer C: Jul 21 - Oct 6. Lecture time: Sundays 1-2:30 pm PT
Fall: Sep 22 - Dec 8. Lecture time: Sundays 10-11:30 am PT
Application Deadline: Admissions for all cohorts are currently on a rolling basis and will close as spaces fill. As of June 1st, Summer B is almost at full capacity.
Program Fee: The total cost of the program is $1725 (~$60 per instructional hour). We are priced at a fraction of other research programs; unlike many research programs, we are genuinely committed to accessibility and an authentic AI research experience.
Scholarships: Need-based scholarships and a limited number of merit-based scholarships are available.
Words from our Fall 2023 Research Alumni
Michael Naeim, Grade 12, Miami College Language High School
"I am thrilled to share my experience with the Algoverse research program. The lectures were exceptionally well-crafted, providing invaluable insights. The support I received throughout the program was nothing short of amazing. In comparison to other research programs, Algoverse stands out as I found myself learning a lot from lectures to office hours and weekly meetings. Embarking on a research project focused on BERT was a daunting task for me and my team, but the unwavering support from the program made it achievable. The mentors were not just knowledgeable, but also perfect in their guidance. Their friendliness and constant support made the learning journey truly enjoyable. One aspect that truly impressed me was the clarity of the plan provided and the abundance of resources at our disposal. The program's commitment to following up with teams and fostering a sense of community was outstanding. I not only gained valuable knowledge but also forged connections with like-minded individuals, creating a network of friends who share my interests. Above all, the mentors were the highlight of the program for me. Their daily support was instrumental in my success. I am grateful for the experience, the community, and the exceptional guidance I received from the Algoverse research program. Definitely, I am going to recommend this program to anyone who is both excited to learn about machine learning from amazing mentors and have a goal to publish his paper at a huge conference like NAACL but also interested in having fun and friendly experience. I would say mentors and the team you will have is the best part in the program. Support that you will get from the mentors and how much time they dedicate to helping you is definitely amazing."
Our Research Team
We are a dedicated team of graduate student researchers from leading AI universities and AI researchers in the industry, with an extensive background in teaching.
Sean O'Brien
AI Research Director
AI Research at UCSD | Former AI Resident at Meta | Berkeley AI Research
Sean conducts research on large language models like GPT-4 as a PhD researcher at UCSD. While an AI resident at Meta, he researched language model decoding methods and co-authored Shepherd, a small language model that generates critiques matching the quality of ChatGPT. Previously, at Berkeley AI Research (BAIR), he specialized in transformer architectures for strategy learning. Sean was also a 7-time GSI at Berkeley, teaching introductory programming, discrete mathematics, and upper-division machine learning, while triple majoring in EECS, math, and cognitive science.
Kevin Zhu
Program Director
Former UC Berkeley Instructor | Software Engineer at Palantir | Quant at Citadel
Kevin taught 3000+ Berkeley students during his tenure as a lecturer for CS198-112 and 5-time Head GSI, specializing in upper-division algorithms. He has also taken software engineering roles at Palantir and various startups, and ML research roles at Citadel, Goldman Sachs, and Berkeley RISE Lab, where he applied traditional machine learning techniques to the stock market and researched techniques for improving convolutional neural network inference efficiency. Kevin now serves as the lead director for the Algoverse programs, as well as an instructor.
Celine Lee
AI Research Director
AI Research at Cornell | Former AI Resident at Intel | Harvard AI Research
Celine is a PhD candidate at Cornell Tech in New York City, where she researches neurosymbolic approaches to language reasoning, especially in coding tasks. Celine has held various research and development roles at IBM TJ Watson, Intel, and VMware. Her excitement for teaching shows through her TA positions while pursuing her bachelor’s / master’s degrees at the University of Pennsylvania and her PhD at Cornell University; as a head instructor with Break Through Tech AI and through external mentorship programs, Celine continues to give back to and learn from other students.
Read more about Celine at her website: https://celine-lee.github.io/
Kevin Han
Research Mentor
AI Research at CMU | Lawrence National Berkeley Laboratory
Kevin is a PhD student researcher at Carnegie Mellon University studying AI for materials and drug discovery. He did his undergraduate at UC Berkeley in majoring in CS and was Head GSI for CS61A, Berkeley's 2000 student intro course, creating LLM-based infrastructure. He has previously researched at Lawrence Berkeley National Laboratory for 2 years and interned on the AI team at JP Morgan Commercial Bank for a summer.
Andy Chung
AI Research Director
AI Research at UMich | Former Software Engineer at Amazon
Andy Chung conducts research on large language models as a PhD researcher at the University of Michigan. His research focuses on leveraging large language models to build autonomous agents. Previously, he worked as a software engineer at Amazon. As the tech lead of Amazon Made for You, featured on TechCrunch, Vogue, CNBC, and other major news outlets, he designed the machine learning architecture and implemented the infrastructure needed to deploy the model at scale in a production environment. Andy received his Bachelors in Computer Science from Georgia Tech.
Thomas Lu
Research Mentor
AI Research at CMU | Former AI Research at Tiktok | Berkeley AI Research
Thomas conducts AI research at Carnegie Mellon University as a Master's student in machine learning. He is a co-author of "Learned Incremental Representations for Parsing", which earned the highest distinction of Best Paper at ACL 2022, the premier NLP conference (reference). He has previously researched at Berkeley AI Research, MDI, and Tiktok. Thomas completed his bachelor's at UC Berkeley, triple majoring in CS, data science, and linguistics with a 4.0 GPA.
Chris Chankyo Kim
Research Mentor
AI Research at Stanford Engineering and Stanford Medicine
Chris is a CS Master's student at Stanford University with a specialty in AI. He has extensive experience applying AI to medical fields, working on AI-assisted care, molecular imaging, cardiovascular biomechanics, and immunology. Chris has also conducted independent research in RL and NLP. Chris graduated with honors for his BS at Stanford, and has experience in software engineering at big tech.
Michael Lam
Research Mentor
Research Engineer at aiXplain | Former AI Research at Berkeley
Michael is an AI/ML research engineer at aiXplain and holds his Masters and Bachelors degrees from Berkeley. He was one of seven Berkeley recipients of the highly selective Siebel Scholarship for his research modeling cancer populations using generalized Lotka-Volterra equations. Michael has extensive research experience applying machine learning to computational biology and medicine, and has also served on the course staff for Berkeley's algorithms course.
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Jonathan Lu
Research Mentor
AI Research at UC Berkeley | Former Stanford Section Leader | Berkeley ML GSI
Jonathan is a Master's student at UC Berkeley, doing research on large language model safety and security. His work focuses on new approaches to object detection and segmentation based on a transformer architectures. He has also served as a GSI for upper division machine learning and introductory programming at Berkeley and led remote discussions for an introductory programming course at Stanford. He's worked on products using LLMs as a software engineering intern at Deepgram and previously worked as a software intern for Meta.