Summer 2026. Each Discovery Lab is a three-week introduction for students new to building with AI. Project Labs are five-week intensives where students contribute to research and engineering alongside practitioners in the field.
See how students apply what they learn through real projects and research.
Built an AI chatbot for conversational Chinese language learning, deployed as her senior project with her school's Chinese and CS teachers.
"SeqHub Academy inspired a passion for AI and coding… invaluable throughout job recruiting and developing personal projects."
Built Take Five, an AI mental health companion web app powered by the Gemini API.
"Building AI tools during the academy definitely helped me demonstrate prior experience during the hiring process."
Partnered with an international nonprofit to analyze 10 years of women's rights data across 20+ countries using AI.
"My confidence in using AI tools grew exponentially faster than had I been learning in a normal classroom setting."
Researched his great-grandfather, a Qing dynasty official, using LLMs — uncovering systematic AI bias patterns in historical Chinese translation.
"The program structure gave me the utmost flexibility — I was concert touring in rural China for my nonprofit while completing it."
Built a Buffett-inspired stock analyzer evaluating P/E ratio, debt-to-equity, dividends, and ROE to generate buy/sell/hold scores.
"The biggest takeaway I got from the Academy was perseverance…"
Researched whether accuracy gains in medical AI Q&A introduce demographic bias — testing LLM trustworthiness across patient groups.
"This experience gave me the confidence that I could contribute meaningfully to research at such an early stage."
Measuring the energy, water, and carbon footprint of large language models and building frameworks for reducing AI's environmental impact.
"They call them mentors, not teachers, because they really are there for you…"
"I went with Marina to her presentation in San Francisco, and I watched her hold a conversation about her research with graduate students doing sophisticated work, and defend it. She was sixteen. That's when I understood what the Academy had actually given her."
"I keep telling people how real the work was. Isabella didn't just build something for a class. She built something that opened a door. That's how she got her internship, and that's how she got her job at Emora Health. The Academy is the reason she had something to show."
"What I'm most grateful for isn't that the project ended up in Lucas's college applications. It's that the experience gave him real curiosity and real confidence. Those don't go away after the application is in. He carries them now."
"AI is a tool that rewards understanding and exposes the absence of it."
Julio Bernard guides the engineering team building SeqHub's AI co-teacher and has spent his career making coding accessible through years of building software, helping aspiring developers build real skills, and teaching computer science to younger students. This summer he leads Discovery, giving students not just the ability to use AI but a developer's understanding of what it actually is.
"The deep ocean is stranger and more alive than they imagined."
Dr. Jessica Sandoval designs deep-sea remotely operated vehicles and tagged sperm whales as a Harvard postdoc on Project CETI, the effort to decode whale communication. She leads an in-person Discovery Lab at Pierrepont School in Westport, CT, where middle and high school students build a working hydrophone to take home, then train AI to make sense of what it records. An online version covers the same ground, from how sound travels underwater to the AI that interprets it, without the hands-on build. She also mentors a few students one-on-one.
"What does this text reveal when you can finally read it as a collection?"
Dr. Kyle Sanders is a classicist who studies how ancient texts create relationships with the non-human world and has taught the Spiritual Meadow to intermediate Greek students at the secondary and university level for several years. This summer his students will use AI to produce the first clean digital edition of a 7th-century Greek manuscript, map where its stories take place, and analyze the collection as a whole for the first time.
"Does the model mean what it says, or has it just learned to look like it does?"
Adejumobi Joshua leads AI Evaluation research at SeqHub, where her work has taken her across the defining questions in the field: how models handle bias, whether they reason or perform, how safety and alignment get measured and gamed. Last summer one of her students presented at NeurIPS and her paper has since been cited. This summer students look inside the model, not just at its output.
"We won't see a human-like AI until we make one that can lose its mind."
Taylor Beck first encountered the tools we now call AI twenty years ago, decoding brain activity from fMRI in a neuroscience lab at Princeton. In labs from Kyoto to Washington University in St. Louis he studied memory, sleep, and aging. This summer his students work with real fMRI datasets to investigate how the brain and a language model represent the same things and what the differences reveal about both.
"Terabytes of audio, waiting for a pipeline that can make them searchable and analytically useful."
Dr. Andre Martin is an assistant professor at Notre Dame's Mendoza College of Business whose PhD in marketing opened a different question: what can AI reveal about human behavior, language, and persuasion in real business contexts? This summer students build a working audio intelligence pipeline, transcribing business conversations, detecting constructs in the text, and extracting vocal features from the moments that matter, developing real NLP and signal processing skills on actual data.
Friday sessions are Demo Days with extended time.
Ocean Engineering Discovery with Dr. Jessica Sandoval
June 15 – July 1, 2026 · Mon, Wed, Fri
Two sessions: 10:00–11:30 AM or 12:00–1:30 PM · Students join one session.
Scheduled directly around the student and mentor.
Time remaining
Cohorts are small. Project Lab seats are limited to 3 to 4 per mentor. Applications are reviewed on a rolling basis. Book a call to learn more or to discuss whether SeqHub is a fit for your student.
SeqHub AI Academy was founded by educators who are also AI practitioners and researchers. People who build AI applications, evaluate AI systems, and know both what these tools can do and where they fail. They also know how students learn. The Academy exists because of it.
AI without domain expertise produces output, not insight. AI in the hands of domain experts and practitioners changes what questions can be asked and answered. The Academy is where the two come together, and where motivated students belong.
We built the Academy as the next step for students who have already shown real depth in the classroom. It is where motivated students step from classroom work into real research and engineering, alongside the practitioners who run our labs. The work doesn't stay inside the program. It goes with them.
Taiwo holds a PhD in Computational Biology from Yale and a Master's in Technology Leadership from Brown, where his research explored conversational AI for educators. He now leads K–12 computer science at Pierrepont School and continues research in AI evaluation and algorithmic fairness at Brown's RISE Lab. Through the Academy he brings that work to AI learning systems, including the co-teacher platform he co-architected to power it.
Educated at Deep Springs and Stanford, J came to software development on his own and has spent over a decade teaching everything from ancient history to robotics while building systems that help schools run better. At the Academy, he combines that experience as developer, curriculum designer, and mentor advisor to shape how practitioners become great teachers, not just great researchers. He is co-architect of the AI co-teacher platform that powers the Academy.
What holds all of this together is more than a curriculum. It is a conviction, shared by the founders and carried by every mentor, that students given real problems, real data, real tools, and the right guidance will produce real things. Not approximations. Work that exists in the world and continues beyond the program.
