Courses

Incoming ChemE grad students, advanced undergrads, and career transitioners

Chemical engineering is one subject. Most curricula teach it as five. This flagship program gives you the conceptual map that connects thermodynamics, transport, and reaction engineering into a single coherent framework.

• —Mathematical Language of Chemical Engineering
• —Thermodynamics from Molecules to Processes
• —Transport Phenomena
• —Reaction Engineering
• —Process Systems and Design

High schoolers, pre-med students, and curious adults

Not a summary. A full reconstruction from first principles — the kind of chemistry you'd get if you had a PhD walking you through the textbook chapter by chapter.

• —Atomic structure, periodic trends, and bonding — the real story
• —Stoichiometry and reaction types without shortcuts
• —Thermodynamics: enthalpy, entropy, and Gibbs free energy from scratch
• —Equilibrium, acids, bases, and buffers — one unified model
• —Electrochemistry and organic chemistry essentials

Students who passed physics but never understood it

Every physics course covers the formulas. This one covers why the formulas are true — and rebuilds your intuition from the ground up.

• —Newtonian mechanics: forces, energy, and momentum rebuilt
• —Waves, sound, and light — beyond the formulas
• —Electricity and magnetism from first principles
• —Modern physics: quantum basics and special relativity
• —Problem-solving architecture for hard physics problems

Anyone who survived calculus but couldn't explain it

Calculus is the study of change — but most courses never tell you that. This one starts with what calculus is actually doing, and builds up from there.

• —Limits and continuity — what calculus is actually doing
• —Derivatives: the geometry and the machinery
• —Integration: area, accumulation, and the fundamental theorem
• —Series, sequences, and convergence
• —Multivariable calculus and why it matters

Non-technical people who want to understand AI deeply

Not the hype version. The actual version — how transformers work, what training does, why hallucinations happen, and what that means for the technology reshaping every field.

• —From perceptrons to transformers: the honest history
• —How training works: gradient descent, loss, and backprop
• —What language models actually do when they 'understand' text
• —Why hallucinations happen and what that tells us
• —Evaluating AI systems: benchmarks, evals, and red-teaming

High school students aiming for Nationals or IChO selection

The national olympiad rewards a very specific kind of thinking — fast, precise, and built from first principles. Most prep courses teach the test. This one teaches the chemistry.

• —Thermodynamics & kinetics at exam depth
• —Electrochemistry, equilibrium, and acid-base systems
• —Organic mechanisms and synthesis
• —Lab practical skills and unknown identification
• —Full-length local and national exam walkthroughs

USNCO finalists seeking IChO selection

IChO problems live at the edge of undergraduate chemistry. This course bridges the gap — covering topics not taught in AP or most olympiad curricula, at the level of rigor needed to compete internationally.

• —Advanced coordination and inorganic chemistry
• —Physical chemistry: statistical mechanics and quantum basics
• —Spectroscopic problem-solving (NMR, MS, IR)
• —Reaction mechanism design and retrosynthesis
• —IChO preparatory problem deep-dives (2010–2025)

Working scientists, policy researchers, and technical professionals

Most AI safety content is written by CS people for CS people. This course approaches the problem from the scientist's perspective — what do frontier models actually know, where do they fail, and what does that mean for CBRN risk?

• —How LLMs encode and retrieve scientific knowledge
• —Red-teaming frameworks: what counts as meaningful uplift?
• —CBRN risk: chemical, biological, radiological, nuclear threat models
• —Adversarial evaluation design and prompt taxonomy
• —Regulatory landscape: AI Act, EO 14110, and biosecurity frameworks

Chemists, materials scientists, and engineers new to simulation

You don't need a CS degree to model molecules. This course teaches the intuition behind computational methods — DFT, MD, force fields — and gets you running simulations in Python by week two.

• —Quantum chemistry basics: Schrödinger, HF, and DFT
• —Force fields and molecular dynamics
• —Python for chemical data: RDKit, ASE, and pymatgen
• —Setting up and running DFT jobs (VASP / Gaussian overview)
• —Reading and interpreting simulation output

Working scientists transitioning into AI or technical roles

You already think rigorously. This course teaches you how to apply that to building with AI — APIs, evals, fine-tuning, and what the job market actually wants from scientist-engineers.

• —The scientist-to-engineer translation: what maps over and what doesn't
• —Python for AI: the libraries and patterns that matter
• —Working with LLM APIs: prompting, tool use, and evals
• —Building an AI pipeline: from prototype to production
• —Career paths: research engineering, AI safety, and applied science

Non-engineers with product ideas and the motivation to ship

AI coding tools have changed the game. You don't need to know how to code to ship a product anymore — but you need to think like a builder. This course teaches that.

• —Product thinking: from idea to spec in a day
• —AI coding tools (Cursor, v0, Copilot) — how to actually use them
• —Full-stack basics: what you need to know to ship
• —From prototype to deployed: Vercel, Supabase, and beyond
• —Launch, user feedback, and iteration cycle

Dental students, practicing dentists, and dental educators

AI is already in diagnostic imaging, treatment planning, and practice management. This course cuts through the noise and shows you what's real, what works, and what's actually coming next.

• —AI in dental imaging: detection, segmentation, and classification
• —AI-assisted treatment planning: current tools and clinical evidence
• —Practice management AI: scheduling, billing, and patient comms
• —Regulatory and liability landscape for AI diagnostic tools
• —How to evaluate and adopt AI tools in your practice

Dentists, practice managers, and dental students

Clinical training doesn't teach you to run a practice. This course does — overhead, team management, patient retention, and the numbers that separate a thriving practice from a stressful one.

• —Business fundamentals every dentist should know (and wasn't taught)
• —Overhead, margins, and the numbers that drive a healthy practice
• —Team management: hiring, onboarding, and performance systems
• —Patient experience: scheduling, communication, and retention
• —Growth levers: referrals, reviews, and scaling strategically

Dental students and early-career dentists who want to do research

You can be both a clinician and a scientist. Most dentists don't know this door exists. This course shows you how to find research opportunities, publish, and build a dual career without sacrificing either side.

• —The clinician-scientist identity: why you don't have to choose
• —Finding research opportunities as a practicing dentist
• —How dental school research pipelines work — and how to get in
• —Publishing your first paper: the practical guide for clinicians
• —Grant writing, academic affiliations, and clinical practice balance