FROM
→A notebook experiment
TOA reliable intelligent application
Move beyond AI demos.
Build systems that earn trust.
Students learn to build, evaluate and operate intelligent systems across machine learning, computer vision, foundation models and agents. Every claim must survive evidence.
AI
OUR POINT OF VIEW
AI education is moving from model knowledge to systems judgment.
THE FIELD IS MOVING
What students must learn to see differently.
The durable skill is not familiarity with today’s tool. It is the ability to reason about the system, its constraints, and the evidence behind a decision.
FROM
→Can the model predict?
TOCan the system be trusted?
FROM
→Accuracy in isolation
TOEvaluation, safety, cost, latency, and operation
INDUSTRY-VETTED COURSE PORTFOLIO
A pathway from foundations to consequential work.
Representative courses can be configured as electives, honors pathways, minors, faculty-development modules, or an integrated specialization.
AI-3014 credits · lecture and systems lab
Building Reliable AI Systems
Design reproducible training, evaluation and inference pipelines that can survive outside a notebook.
01AI-3124 credits · field dataset studio
Computer Vision in the Wild
Build perception systems that account for noise, drift, edge deployment and imperfect data.
02AI-4014 credits · product engineering studio
Building with Foundation Models
Engineer grounded applications with retrieval, evaluation, observability and cost controls.
03AI-4154 credits · intensive build studio
Agentic AI Systems
Design tool using agents with memory, planning, approval boundaries and recovery paths.
04AI-4224 credits · evaluation lab
AI Evaluation and Red Teaming
Find failure before users do through adversarial tests, safety cases and model risk reviews.
05AI-4904 credits · reviewed capstone
Intelligent Systems Studio
Ship a working AI system through architecture, evaluation and deployment reviews.
06HOW WE TEACH THE DOMAIN
Practice that creates professional judgment.
Each learning experience is structured around how credible work is actually reviewed: assumptions are explicit, evidence is inspectable, and important tradeoffs must be defended.
Evaluation before demos
Students define datasets, baselines, failure taxonomies, and evaluation harnesses before they optimize presentation.
Systems around models
Data pipelines, retrieval, orchestration, observability, guardrails, and human workflows are treated as first-class engineering.
Evidence-based iteration
Experiments are versioned; claims are supported by measurements; regressions are made visible.
Responsible deployment
Safety, privacy, cost, latency, and misuse are assessed alongside functional performance.
APPLIED WORK
The kind of problems students can learn to own.
These briefs illustrate the project scope and engineering judgment we bring into programs, labs, and industry-supported capstones.
BRIEF / 01
Multilingual public-service copilot
Grounded retrieval and response evaluation across Indian languages.
BRIEF / 02
Vision-assisted quality inspection
Edge-aware detection pipeline with drift and false-positive analysis.
BRIEF / 03
Autonomous research workflow
Tool-using agent with source verification, budgets, and human approval.
CENTER OF EXCELLENCE BLUEPRINT
An AI Engineering Center of Excellence
A campus capability for building, evaluating and responsibly deploying intelligent systems, not a collection of GPU workstations.
01
FOUNDATIONSML, probability, data, optimization, software engineering
02
PLATFORMCompute, experiment tracking, model registry, evaluation suites
03
BUILDVision, language, generative AI, agents, intelligent applications
04
PROVEBenchmarks, safety reviews, demos, papers, open-source artifacts
CAPABILITYEVIDENCE
WHAT ACADEMIC LEADERS CAN INSPECT
Proof beyond completion.
Every program is designed to leave behind concrete evidence of what students can do and how well they can reason.
Reproducible experiment recordsEvaluation datasets and test harnessesModel and system cardsLive application demonstrations
WAYS TO BEGIN
Bring this domain into your institution at the right depth.
AI / START A CONVERSATION
What could AI Engineering look like at your institution?
We’ll help you choose the right academic format, faculty enablement model, infrastructure, and first set of student outcomes.