Projects Tagged

Google Cloud

Mobile Apps

Social Events App

Created, Designed, and Deployed a social events application. Built in Flutter, it provided a system for groups of people to create and organize events through natural language. Application processed over $100K in transactions before it was shutdown.

Genetic Algorithms

Construction Company AI Task & Staff Scheduler

Leveraged Genetic Algorithms to schedule optimal task orderings across all construction staff and projects. This system solved a more complicated version of the Job Shop Scheduling Problem, an NP-Hard problem that is “clearly harder than the Traveling Salesman problem.” The system optimized over individual staff skillsets, drive time, site visit ordering (traveling salesman), task ordering, resource availability, wage spend, and more.

Recommender Systems

Social Event Time Recommendation Engine

This time recommendation engine takes an arbitrary number of calendar schedules as input and understands the context of the event based on features such as NLP embeddings of event name and description, as well as history of users past events. It output times that worked well for everyone and also made the most sense for the event in question.

Recommender Systems

Location Recommender for Social Events

Our location recommender utilized natural language embeddings of massive databases of location details to match locations to groups of users and their respective social events. This engine allowed for curated social events to be built automatically with everyone’s preferences in mind.

Computer Vision

Computer Vision Receipt Parser

Computer vision model that takes in an image of a receipt and outputs an itemization of all charges, each tagged with relevant item categories. To make this work properly, we needed to create a custom pipeline comprised of many deep neural networks. The first model performed object recognition, passed the output to an OCR model, then a model that we trained to understand receipt geometry, and a finally an NLP model that performed classification on the types of charges.


Search Engine for Audio Records

Utilized ASR to transcribe 20+ years of cataloged audio records from a multimedia company, then created a semantic search engine via natural language embeddings to retrieve relevant recordings and pinpoint the most relevant audio segments down to the second level.


NLP Event Detail Extractor

Trained a model to reliably extract event details such as title, date, time, participants, and location from blocks of unstructured text and automatically create standardized event entries. This enabled event extraction from both the internet and from conversations, as well as instant event creation via voice.

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