The Founding Team

Meet the founding team.

Founder portrait — replace

Harvard & Smithsonian CfA
Caltech
MIT
Carnegie Observatories
Columbia

Kiranjyot “Jasmine” Gill

Founder · Astrophysicist · Lead Mentor

Kiranjyot “Jasmine” Gill is an astrophysicist trained at the Center for Astrophysics | Harvard & Smithsonian and the founder of Great North AI. She was the inaugural Sikh graduate of Harvard University's Department of Astronomy.

During the breakthrough era of gravitational-wave discovery, she was the youngest member of the International Gravitational-Wave Network during the first detections of merging black holes — a scientific milestone connected to the 2017 Nobel Prize in Physics.

At Great North AI, she brings that same training directly into the classroom. Unless otherwise stated, every student at every tier works with her — not with a rotating roster of contract instructors.

The Record

Credentials, for those who
want to check.

Semper Veritas applies here too — every line below is a matter of public record, not a marketing claim.

Research Roles

Competitive appointments held at:

Center for Astrophysics | Harvard & Smithsonian
Caltech
MIT
Carnegie Observatories
Columbia University

Honors & Recognition

Tied to the gravitational-wave discovery era:

UK Royal Astronomical Society Group Achievement Award
Gruber Foundation Cosmology Prize
Milner Breakthrough Prize
Princess of Asturias Award
Bruno Rossi Prize
Forbes 30 Under 30, Science — 2024 Shortlist Nominee

Technical Domains

The range she teaches across:

Artificial intelligence & machine learning
Astrophysics & mathematics
Signal detection
High-dimensional inference
Scientific & research software engineering

Gravitational-wave detection of merging black holes — a milestone connected to the 2017 Nobel Prize in Physics.

The Standard, in Practice

From the observatory
to the classroom.

At Great North AI, Jasmine brings the discipline of scientific discovery into education. Her students learn to build real, defensible work — the same five habits, at every tier:

Clean datasets — before a model, before a chart: where did this data come from, and what is wrong with it?
Rigorous baselines — every result is compared against the simplest honest alternative, not just the most impressive one.
Honest validation — what the work shows, what it doesn't, and where it might be wrong.
Reproducible code — work that someone else — a teacher, a reviewer, a future employer — could run and verify.
Technical writing that withstands scrutiny — the ability to explain the work clearly to someone who will ask hard questions.

“The same rigor I used to search for signals in the noise of the cosmos is the rigor I bring into every session. Not because it makes the work harder. Because it makes the work real.”
— Kiranjyot Gill, Founder

Study with the person who did the work.

A free consultation is the simplest way to find out whether Great North AI is the right fit — for a student, a family, or an organization.

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Semper Veritas