Elements of Forward-Deployed Engineer

Class Schedule

Course Meet Days Meet Time Location Instructor(s)
Elements of Forward-Deployed Engineer Rahul Katyayan

Instructional Team

Instructor: Rahul Katyayan

Office: To be announced

Office Hours: To be announced

Graduate Teaching Assistant: To be announced

Course Description

A capstone-style course designed to integrate design thinking, operational immersion, quantitative reasoning, and visual communication for the purpose of enabling learners to frame ambiguous problems and develop deployable solutions in real-world business and organizational settings.

Learning Outcomes

By the end of this course students should be able to:

  • Foundational Knowledge: explain the role of operational immersion, context engineering, and interdisciplinary reasoning in forward-deployed work.
  • Application: use design thinking, rough-order quantitative analysis, and rapid prototyping to structure and test solution ideas.
  • Integration: connect field observations, business constraints, data flows, and technical capabilities into a coherent deployment plan.
  • Human Dimension: collaborate with technical and non-technical stakeholders, translate across vocabularies, and lead conversations under ambiguity.
  • Caring: value responsible deployment, evidence-based judgment, and clear communication as core parts of engineering practice.
  • Learning How to Learn: build repeatable habits for framing unfamiliar problems, seeking feedback, and learning new tools quickly.

Teaching and Learning Practices

This course is experiential in design. Students move between studio workshops, field observation, case discussions, quantitative drills, critique sessions, and presentation rehearsals. The teaching design emphasizes operational immersion, reflective practice, and iterative improvement so that learners develop transferable habits for real-world problem framing and deployment.

There is no required textbook. Optional readings and software resources are provided to deepen weekly work and support independent practice.

Tentative Class Plan

This is a tentative course schedule. Minor changes may occur as the course evolves and will be communicated in advance.

Week # Dates Lecture/Activities PRA Activities/Assignments and Due Dates
1 Lecture 1: What is forward-deployed engineering? Operational immersion and problem context. Technique: interactive lecture and reverse briefing Activity: stakeholder map and problem inventory. Optional reading: Dan Heath and Chip Heath, Made to Stick: Why some ideas take hold and others come unstuck
1 Lecture 2: Observation, shadowing, and empathy interviews. Technique: paired interview lab Activity: field-note protocol and interview guide
2 Lecture 1: Problem framing, job stories, and needs statements. Technique: guided design studio Activity: problem framing brief
2 Lecture 2: Journey maps, service blueprints, and systems boundaries. Technique: collaborative mapping workshop Activity: system map critique
3 Lecture 1: Divergent ideation and concept generation under constraints. Technique: timed design sprint Activity: idea portfolio and concept clustering
3 Lecture 2: Rapid prototyping for workflows, agents, and decision support. Technique: critique-based studio Activity: low-fidelity prototype review
4 Lecture 1: User testing, iteration loops, and evidence from feedback. Technique: structured peer testing Activity: test script and observation log
4 Lecture 2: Design synthesis and handoff to quantitative reasoning. Technique: retrospective discussion Activity: design sprint reflection. Optional reading: Dan Heath and Chip Heath, Made to Stick: Why some ideas take hold and others come unstuck
5 Lecture 1: Fermi estimates and back-of-the-envelope reasoning. Technique: blackboard walkthrough Activity: estimation drills. Optional reading: Sanjoy Mahajan, Street-Fighting Mathematics
5 Lecture 2: Units, assumptions, and sanity checks. Technique: whiteboard clinic Activity: quantitative decision note 1
6 Lecture 1: Capacity, throughput, and queueing in operational systems. Technique: case-based problem solving Activity: operations sketch and bottleneck analysis
6 Lecture 2: Probability, uncertainty, and expected value in decisions. Technique: worked-example workshop Activity: scenario comparison worksheet
7 Lecture 1: Optimization under business and technical constraints. Technique: paired modeling session Activity: tradeoff table and recommendation draft
7 Lecture 2: Sensitivity analysis and model critique. Technique: debate and peer review Activity: quantitative decision note 2
8 Lecture 1: Communicating numbers to decision-makers. Technique: executive briefing rehearsal Activity: briefing deck outline
8 Lecture 2: From rough math to deployment choices. Technique: synthesis workshop Activity: problem-solving memo. Optional reading: Sanjoy Mahajan, selected chapters
9 Lecture 1: Narrative arcs for technical and business audiences. Technique: storyboard workshop Activity: audience map and story outline. Optional reading: Edward Tufte, The Visual Display of Quantitative Information
9 Lecture 2: Slide architecture, sequencing, and visual rhythm. Technique: live teardown and rebuild Activity: storyboard critique
10 Lecture 1: Writing for decisions, not just documentation. Technique: writing workshop Activity: one-page decision brief draft
10 Lecture 2: Revision, editing, and signaling uncertainty clearly. Technique: peer editing lab Activity: revision memo and line edit review
11 Lecture 1: Visual hierarchy, annotation, and chart choice. Technique: critique-driven seminar Activity: visualization draft
11 Lecture 2: Common visualization failures and ethical presentation. Technique: gallery walk Activity: visualization redesign
12 Lecture 1: Vector graphics workflow with Inkscape or Adobe Illustrator. Technique: software demonstration studio Activity: visual explainer build
12 Lecture 2: Integrating memo, figure, and verbal pitch. Technique: communication sprint Activity: visual story package. Optional reading: Tufte and selected Nature visualization essays
13 Lecture 1: Capstone scoping and field study design. Technique: project clinic Activity: capstone proposal and stakeholder plan
13 Lecture 2: Evidence collection, ethics, and risk logging. Technique: guided workshop Activity: field study checklist
14 Lecture 1: Capstone studio: synthesis, prototype refinement, and decision logic. Technique: team coaching Activity: field study check-in
14 Lecture 2: Executive narrative, evidence selection, and recommendation design. Technique: rehearsal and critique Activity: draft presentation deck
15 Lecture 1: Final capstone presentations. Technique: presentation forum Activity: final presentation
15 Lecture 2: Postmortem, transfer plan, and portfolio reflection. Technique: reflective dialogue Activity: capstone reflection and next-step plan

Required Materials and Technology

Readings

  • Optional reading for Weeks 1-4: Dan Heath and Chip Heath, Made to Stick: Why some ideas take hold and others come unstuck.
  • Optional reading for Weeks 5-8: Sanjoy Mahajan, Street-Fighting Mathematics: The Art of Educated Guessing and Opportunistic Problem Solving.
  • Optional reading for Weeks 9-12: Edward Tufte, The Visual Display of Quantitative Information and Envisioning Information; selected Nature visualization essays and points-of-visualization articles.
  • Software: Inkscape or Adobe Illustrator for diagramming and visual storytelling.

Other Materials

Item Notes / Comments Required Tool
Laptop Used for workshop exercises, field notes, and presentation development Required
Notebook or sketchbook Useful for observation logs, system sketches, and storyboard drafts Recommended Obsidian (Recommended)

Assessments & Activities

Component / Activity Date or Due Date Location / Submission Method Weight (%)
Problem Framing Brief 10
Design Sprint Reflection 10
Quantitative Decision Notes (2 x 10%) 20
Executive Briefing 10
Visual Story Package 15
Capstone Proposal 10
Capstone Field Study and Prototype 10
Final Presentation and Reflection 15

Assessment descriptions, due dates, and submission details will be announced.

Posted assessment descriptions provide general guidelines rather than exhaustive rules. Lectures, critiques, workshops, and field activities will provide the fuller context for expectations.

Active attendance and participation are strongly encouraged because much of the learning happens through studio discussion, peer feedback, and field-based observation.

Questions, clarification norms, revision opportunities, and any policy on AI-assisted work: to be announced.

Administrative Policy

Administrative policy details, including late work, revision process, attendance expectations, permitted use of AI tools, accessibility, and use of course materials: to be announced.