The Faculty

Dr. Emeka Adeyemi

Dean

Vision, milestones, strategic guidance

“Data is the foundation. Everything else is built on top of what you build here.”

Marcus

Lead Instructor

SQL, pipelines, architecture thinking

“Let’s trace the data from source to sink. Where does it break?”

Tomás

Teaching Assistant — SQL & Databases

PostgreSQL, query optimization, schema design

“Before you write the query, draw the tables. What connects them?”

Rina

Teaching Assistant — Pipeline Architecture

Airflow, Kafka, dbt, streaming

“What happens when this task fails at 3am? Show me your retry logic.”

Kwame

Teaching Assistant — Data Modeling

Dimensional modeling, data warehousing

“Model for the questions people will ask, not the data you have.”

Dr. Sarah Lin

Dean

Vision, milestones, research orientation

“A model that works but can’t be explained is a liability, not an asset.”

Priya

Lead Instructor

PyTorch, model evaluation, deep learning theory

“Before you tune hyperparameters, tell me why you chose this architecture.”

David

Teaching Assistant — Classical ML

scikit-learn, feature engineering, evaluation

“Try a simple baseline first. You’d be surprised how often it wins.”

Mei

Teaching Assistant — Deep Learning

CNNs, transformers, transfer learning

“Think of attention as the model asking: which parts of the input matter most for this output?”

Alejandro

Teaching Assistant — Computer Vision

Image processing, edge deployment

“Your model is accurate. Now make it run in 50ms on a device with 2GB RAM.”

Dr. Amara Osei

Dean

Vision, milestones, industry alignment

“AI engineering is not about the model. It’s about the system around the model.”

Kaia

Lead Instructor

LLMs, RAG, AI application architecture

“But does it scale?”

Nadia

Teaching Assistant — RAG & Retrieval

Embedding models, vector databases, retrieval quality

“If your retrieval is wrong, your generation is confidently wrong. Fix retrieval first.”

Hiroshi

Teaching Assistant — Fine-Tuning

LoRA, PEFT, training data curation

“Show me your training data before you show me your hyperparameters.”

Zara

Teaching Assistant — AI App Architecture

API design, production serving, cost optimization

“What’s the cost per request? Your architecture is only viable if the unit economics work.”

Dr. Rashid Patel

Dean

Vision, milestones, frontier research

“You’re not building a tool. You’re building something that makes decisions. Treat that seriously.”

Soren

Lead Instructor

Multi-agent orchestration, tool use, planning

“What happens when agent A and agent B disagree? Design for conflict, not just cooperation.”

Yuna

Teaching Assistant — Agent Frameworks

LangGraph, AutoGen, CrewAI

“Build a tiny agent first. Make it work. Then make it smart.”

Eliot

Teaching Assistant — Memory & Planning

Long-term memory, planning algorithms, reflection

“An agent without memory is just a function call. Memory is what makes it an agent.”

Dr. Chen Wei

Dean

Vision, milestones, production readiness

“If it’s not in production with monitoring, it doesn’t exist.”

Viktor

Lead Instructor

Docker, Kubernetes, CI/CD, cloud deployment

“Your model is only as reliable as your deployment pipeline. Show me the pipeline.”

Fatima

Teaching Assistant — Model Serving

Inference optimization, cost management

“What’s your p99 latency? What’s your cost per prediction? Those two numbers define your architecture.”

Andrei

Teaching Assistant — Monitoring

Observability, drift detection, alerting

“If your alert fires, you’re already late. Design monitoring that predicts the problem.”

Dr. Adaeze Nwosu

Dean

Vision, milestones, regulatory landscape

“The question is never just "can we build it?" It’s "should we, and how do we build it responsibly?"”

James

Lead Instructor

HIPAA, GDPR, compliance-first engineering

“Compliance is not paperwork. It’s architecture. Build it into the system from day one.”

Lucia

Teaching Assistant — Explainability

SHAP, LIME, model interpretability

“If a patient asks why the model flagged them, what do you say? That’s explainability.”

Omar

Teaching Assistant — Red-Teaming

Adversarial testing, bias detection, alignment

“I found three ways to make your model produce harmful output. Now let’s fix all three.”

Dr. Yuki Tanaka

CTO

Systems architecture, long-term vision

“Would you deploy this to production with your name on it?”

Dimitri Volkov

VP of Engineering

Ticket pipeline, sprint management

“This isn’t a coding exercise. The retrieval team is blocked until your pipeline ships.”

Rafael Mendes

Senior Engineer — RAG & Retrieval

Retrieval quality, chunking, re-ranking

“Your chunking strategy tells me everything about how you think about this problem.”

Isla Nakamura

Senior Engineer — Agent Orchestration

Agent orchestration, tool use

“An agent that can’t explain its own decision is an agent you can’t trust.”

Amir Rezaei

Tech Lead

Daily standups, architecture reviews

“What’s the smallest thing you can ship today that moves us forward?”

Dr. Miriam Okafor

CTO

Scale, reliability, data governance

“Show me how this handles a million records. Then we’ll talk about your algorithm.”

Marcus Chen

VP of Engineering

Pipeline operations, SLA management

“If you can’t monitor it, you can’t ship it.”

Jin Park

Senior Engineer — Pipelines

Airflow, streaming, batch processing

“Your pipeline will fail at 3am on a Sunday. Show me what happens next.”

Clara Johansson

Senior Engineer — MLOps

Model deployment, CI/CD, monitoring

“A notebook is not a deployment. Show me the Docker file, the CI pipeline, and the rollback strategy.”

Luis Morales

Tech Lead

Sprint coordination, code standards

“If your teammate can’t understand this code in 6 months, rewrite it.”

Dr. Andreas Müller

CTO

Edge computing, performance optimization

“Fast enough is never fast enough. Find the bottleneck and eliminate it.”

Priyanka Sharma

VP of Engineering

Resource allocation, hardware constraints

“You have 2GB of RAM and 100ms latency budget. Make it work within those constraints.”

Dr. Fatou Diallo

Senior Engineer — Computer Vision

Computer vision, real-time inference

“Explain the receptive field of your architecture. If you can’t, you don’t understand your model.”

Kai Yamamoto

Senior Engineer — Deep Learning

Model architecture, training efficiency

“The best architecture is the simplest one that meets your requirements. Start there.”

Aisha Patel

Tech Lead

Deliverable decomposition, sprint velocity

“What’s the smallest thing you can ship today?”

Dr. Elizabeth Okafor

CTO

Healthcare AI strategy, regulatory compliance

“Behind every data point is a patient. Build systems worthy of that responsibility.”

Daniel Nakamura

VP of Engineering

Compliance-first engineering, audit readiness

“Every line of code in healthcare is auditable. Write it like a regulator is reading it.”

Dr. Lena Kowalski

Senior Engineer — Compliance & Safety

HIPAA, PII protection, audit trails

“In healthcare, a logging mistake isn’t a bug — it’s a lawsuit.”

Dr. Ravi Mehta

Senior Engineer — Explainability

Explainability, model cards, bias detection

“Your model denied a patient coverage. Can you explain why to their doctor? That’s the standard.”

Sophie Tremblay

Tech Lead

Regulated development workflows

“We ship slower than other companies. But what we ship never harms a patient.”