Lecturer at Washington University in St. Louis

Ilan Goodman

I teach computer science by asking students to build real systems, explain their reasoning, and see where abstraction meets the messy world.

My work sits between teaching, theoretical computer science, data engineering, and assessment design. I like problems where the technical details matter, but the human structure around them matters just as much.

Explore my work Get in touch

2 WashU courses designed from scratch

8+ years in data and ML engineering

6.95/7 CSE 5114 helpful feedback

What I do

I build courses and systems that make hard ideas usable.

Data engineering education

I want data courses to feel like the work students will actually do: imperfect inputs, real tools, ambiguous tradeoffs, and enough theory to make good decisions.

AI-era assessment

I experiment with oral exams, multiplicative grading, and feedback structures that ask students to explain, revise, and defend what they understand.

Data and ML systems

Before WashU, I built data infrastructure, metrics, pipelines, and ML-adjacent tooling at Chan Zuckerberg Initiative, Robinhood, and Meta.

Selected work

Courses, systems, and public projects.

The work I am most proud of tends to have the same shape: start from a real problem, find the right abstraction, and then build something that helps other people reason or create more effectively.

Graduate course WashU CSE 5114

Data Manipulation and Management at Scale

A hands-on graduate course I created for students who need to work with large volumes of data, real-time streams, and the operational constraints that come with modern data systems.

6.81/7 homework relevance 6.95/7 helpful feedback 6.71/7 real-world applications

Course page Student advice Representative evaluation

Undergraduate course WashU CSE 3104 Intro to Data Engineering

Data Manipulation and Management

An introduction to data engineering that I designed from scratch, now numbered CSE 3104 and previously offered as CSE 314A and DCDS 510. The course helps data science students move from files and notebooks toward reliable workflows they can trust.

6.47/7 real-world applications 6.37/7 instructor availability 6.19/7 homework relevance

Teaching portfolio Course background

Core CS course WashU CSE 247 / CSE 2407

Data Structures and Algorithms

A large, co-taught undergraduate course where I have played a meaningful role reshaping CSE 247/2407 for the AI era. My focus has been helping students connect theoretical computer science ideas to implementation, problem solving, and durable habits of reasoning.

Algorithmic thinking Proofs and abstraction AI-era modernization

Evaluation context Teaching story

Side project Baseball + modeling

ABS Challenge Leaderboard

A public baseball analytics project that turns a niche rules experiment into a browsable data product. It is the sort of small, specific question I enjoy making legible.

Open project GitHub

Game project Strategy + AI Studio

Tic-Tac-Tic-Tac-Toe

A nested-grid strategy game that grows classic tic-tac-toe into a deeper tactical contest. It started as a chalkboard game between friends and has become a small playground for strategy, AI assistance, and web infrastructure.

Play game

Game project Daily logic + AI

Nerd Snipe Daily

A daily algorithmic-style logic puzzle built around real-world situations, elegant solutions, and AI-guided checking. The goal is a small problem that is easy to state and hard to stop thinking about.

Play daily challenge

Industry Data platforms

Data infrastructure and ML tooling

I have built data warehouses, deletion systems, telemetry, liquidity-risk models, Spark and Airflow frameworks, and analytics pipelines across education, finance, and XR.

Download CV

Teaching signal

I want rigor to feel useful, not arbitrary.

Across the WashU reports in my private evaluation archive, the clearest patterns are enthusiasm, availability, kindness, real-world context, and steady iteration. I publish one representative CSE 5114 evaluation for transparency, summarize the broader archive carefully, combine CSE 3104/CSE 314A/DCDS 510 evidence as the same course, and treat co-taught Data Structures and Algorithms evaluations as shared-course context rather than a clean single-instructor outcome.

6.32/7 CSE 5114 teaching quality

6.95/7 CSE 5114 helpful feedback

6.71/7 CSE 5114 real-world applications

How I read the evidence

I try to be honest about what the evidence can and cannot say. The strongest public signal comes from courses I designed from scratch, including CSE 3104, formerly CSE 314A and DCDS 510, and CSE 5114. The full evaluation archive stays private to avoid over-publishing student feedback, but the CSE 5114 report is linked as a representative raw sample. The larger CSE 247 and CSE 2407 reports still matter, especially when students comment directly on my teaching and on course modernization work, but the course structure and many student experiences were shared across multiple instructors.

"I appreciated the commitment to tying every concept to real-world examples."

CSE 5114 student evaluation

"Very enthusiastic and prepares you for a career in tech."

CSE 3104 / CSE 314A student evaluation

"Professor Goodman demonstrated a strong interest in the success of the class and each student."

CSE 247 co-taught course, instructor-specific comment

Conference ideas

Assessment should make understanding visible.

My iTeach talks focus on a practical question: if students have access to powerful AI tools, what kinds of assessment still help them learn and still tell us what they understand?

Scaling Oral Exams

Oral exams can mimic design interviews, adapt to student performance, provide quick personalized feedback, and evaluate understanding in a format where outsourcing the work is much harder.

20-25 minute exams with a short grading buffer
TA training, recordings, and sample probing questions
83% of responding students rated the oral midterm valuable at 6 or 7 on a 7-point scale

Open slides

Multiplicative Grading

A grading model that multiplies engagement by mastery, so homework and participation matter without letting completed work hide shallow understanding.

Designed around mastery, engagement, effort, and academic integrity
Unattempted homework dropped from 5.5% to 0.2%
Median grades stayed stable while the distribution better reflected mastery

Open slides

Background

From physics to CS classrooms to production data systems.

I came to computer science through physics and a few excellent teachers. That path still shapes how I teach: conceptually serious, practical where it counts, and attentive to what students are actually experiencing.

2011-2016

Stanford University

B.S. in Physics, M.S. in Computer Science, and teaching roles across CS, probability, algorithms, AI, data structures, and physics.

2016-2024

CZI, Robinhood, Meta

Senior data and machine learning engineering work across learning platforms, finance data, XR ground-truth pipelines, interviews, and infrastructure.

2024-present

Washington University in St. Louis

Lecturer in Computer Science and Engineering, course creator, advisor, AI tools committee member, and mentor for independent student projects.

Resources

Documents and profiles.

For hiring committees, collaborators, students, and curious readers who want the paper trail behind the short version.

Curriculum vitae Teaching portfolio CSE 5114 course page CSE 5114 student advice About Ilan Multiplicative grading white paper WashU faculty page LinkedIn GitHub

Available for education, data, and AI-aware assessment conversations.

Contact

Let's build things that help people reason better.

I am interested in computer science education, theoretical computer science, data engineering, AI-aware assessment, mentoring, and practical projects that are a little more interesting than they strictly need to be.

ilan@ilangoodman.dev Personal email ilang@wustl.edu WashU email igoodman@alumni.stanford.edu Stanford alumni email