Edderic Ugaddan

Senior machine learning engineer · Rumford, RI

From messy data
to useful products.

I build machine learning systems that solve practical problems—from making global place data cheaper to clean to helping people find respirators that fit their face.

Selected work / 01–03

Business outcomes. Research ownership. Products in use.

Cut the cost of cleaning
global place names.

Automated name cleaning with a production model that reduced inference cost by 98%.

Place records contain names in many languages, often mixed with promotional text and other clutter. The existing generative approach cost $70,000 per run and could introduce words that were never in the source name.

I trained a model to decide which parts of the original name to keep or remove. This made the task more focused and constrained the output to text already in the record.

Previous approach$70,000 / run
Production replacement$1,400 / run
$68,600 saved per run. 98% lower inference cost; figures are per run.

The business result: substantially lower operating cost for a recurring data-quality task, with a model designed around the actual cleanup problem.

Read about the model and design decisions →

Take a respirator-fit idea
all the way to production.

Secured a $50,000 Kanro Fund grant and led the research, machine learning, and product development behind MasqFit.

Finding a respirator that fits can involve considerable trial and error. MasqFit investigates whether facial measurements and fit-test evidence can help people choose better-fitting respirators.

I recruited participants, collected measurements and fit-test outcomes, developed ML algorithms, and built and released an open-source iOS app. My role spanned the research question, the evidence needed to study it, and the application people can use.

MasqFit iOS app guiding a user through facial captureMasqFit iOS app showing measured facial dimensions
A shipped product

From facial capture to respirator recommendations.

Actual MasqFit app screens: guided capture and facial measurements.

View MasqFit on the App Store ↗
Explore the research and engineering →

Turn attendance research
into guidance for school leaders.

Co-authored a white paper on student mindsets and chronic absenteeism whose findings were discussed on NBC News.

Understanding the Link: The Correlation Between Student Mindsets and Chronic Absenteeism connects attendance data with student survey responses to help school and district leaders understand patterns in absenteeism.

The study’s national attendance context spans 7,000+ schools and over 11 million student-year records. Its findings connect school safety, climate, relationships, and SEL topics with attendance patterns.

Value to school leaders

Helped leaders identify which social-emotional learning (SEL) and school-experience topics correlate with chronic absenteeism, with findings that vary by grade level.

Value to Panorama

Contributed to Panorama’s research leadership in education technology and gave the company evidence to bring into public conversations about student attendance.

The findings identify associations; they do not establish that changing a survey topic will cause attendance to improve.

Read about the study design →

More ways I build

A broader engineering foundation.

Full-stack performance

At Lingo Live, accelerated teacher–student matching 15×, reducing runtime from 45 seconds to 3 seconds.

See career experience ↗

Tools for cleaner classrooms

Lead engineering for an Inspired Air Fund school dashboard that tracks air-quality sensor and air cleaner data.

Clean Air for Schools ↗

Everyday AI products

Built PlateRhythm to turn recipes into meal plans and grocery carts, combining structured extraction, search, and product selection.

Explore PlateRhythm ↗

Let’s build something useful.

I bring experience across machine learning, data science, and full-stack engineering—from defining a problem to shipping a product.

Get in touch ↗