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Intro
Courses
Materials
Philosophy
History
Students

Learning that lasts beyond the semester

Teaching & Mentorship

Rigorous foundations, generous feedback, and room to build real things—so students leave confident to reason about systems, communicate clearly, and keep learning on their own.

Courses taught

Distributed Systems & Cloud Platforms

Fall 2025 · Graduate

Consistency models, fault tolerance, and operational patterns for services at scale—with labs on observability and safe deploys.

Course details

Outcomes: students design for failure, reason about latency budgets, and document trade-offs clearly.

  • Weekly architecture critiques and code-adjacent labs
  • Team project with staged milestones and peer review

Software Architecture & Design

Spring 2025 · Upper undergrad

From requirements to interfaces: modularity, boundaries, and maintainability—with emphasis on reviewable design docs.

Course details

Outcomes: students produce ADRs, sketch sequence diagrams, and defend coupling choices.

  • Case studies from production systems
  • Iterative refactor exercises with tests

Data Engineering Pipelines

Summer 2024 · Professional

Ingestion, transformation, and quality checks—connecting batch and streaming patterns to measurable SLAs.

Course details

Outcomes: students build reproducible pipelines and explain idempotency and backfill.

  • Hands-on with schema evolution and data contracts
  • Observability for pipeline health

Research Methods for Engineers

Fall 2024 · Graduate elective

Framing questions, designing studies, and communicating evidence—without losing engineering pragmatism.

Course details

Outcomes: students write a short, reviewable research memo with clear threats to validity.

  • Peer workshops on clarity and reproducibility
  • Ethics and responsible deployment discussion

Course materials

Architecture

Lecture deck — boundaries

Data Engineering

Lab starter & tests

Teaching philosophy

“Clarity is kindness. I teach so students can explain ideas to a teammate at a whiteboard—and know what to measure when things break.” — Mehedi Hasan

Courses blend conceptual maps with hands-on work: short lectures, guided labs, and frequent feedback loops. I emphasize intellectual honesty—naming assumptions, comparing alternatives, and writing so the next reader can continue the work.

Mentorship extends beyond grades: I help students build portfolios they are proud of, practice technical communication, and connect classroom themes to internships and research opportunities.

Rigor with empathy High standards paired with actionable feedback and multiple ways to demonstrate mastery.
Collaboration that teaches Structured peer review so students learn to give and receive technical critique.
Evidence over vibes Metrics, experiments, and postmortems—so opinions become testable claims.

Teaching history

2023 — present

Graduate instructor & project mentor

Led seminars and capstone-style projects in distributed systems and reliability engineering.

2020 — 2023

Guest lectures & workshops

Industry-aligned modules on cloud-native architecture, observability, and incident response drills.

Earlier

TA & undergraduate mentoring

Office hours, grading with rubrics, and guiding first research experiences for junior students.

Student supervision

PhD

Aisha Rahman

Doctoral candidate

Workload scheduling under bursty traffic and SLO-aware autoscaling.

MSc

Sofia Nguyen

Master’s thesis

Alert design and cognitive load in on-call tooling.

MSc

Marcus Chen

Graduate project

Benchmark harness for comparing streaming join strategies.

Undergrad

Lina Torres

Honors research

Visualization of comparative load-test results for coursework projects.

Undergrad

Jordan Lee

Independent study

Introduction to OpenTelemetry in a small microservice stack.

Research intern

Priya Nair

Summer cohort

Chaos experiments and postmortem templates for student teams.