Nick Locascio

Deep Learning Consultant

Booth AI

Co-Founder, CEO

Standard Cognition 

I joined Standard AI as employee #7 and wore many hats throughout our unicorn growth cycle. I wrote critical pieces of our algorithms and inference stack, led and grew our Machine Learning Teams, spearheaded our Synthetic Data initiatives, landed 10+ machine learning models into production, and drove key metrics and ml product launches.

Director Of Engineering

Symantec

Built deep learning systems for dynamic cyber threat and insurance modeling.

Massachusetts Institute of Technology

Published 2 Machine Learning Papers. Founded and lectured MIT's Deep Learning course, 6.S191.

Papers:
[1] High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision
Journal of Radiology '17

[2] Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge, EMNLP '16

Graduate Researcher

Perch

Developed deep learning computer vision models for early stage fitness technology startup.

Deep Learning Engineer

Pinterest

Worked on Machine Learning Recommendations team building big data prediction models with Hive+Hadoop.

Machine Learning Engineer

Fitbit

I worked on the iOS team doing various feature work.

Software Engineer

Mystery Snap

Mystery Snap was an ephemeral photo sharing iOS app.

Acquired +250,000 users. +3 million photos sent.

Mystery Snap held the #1 spot in the Norway Photo & Video Category for over two weeks.

Founder

Monster Cats

Monster Cats was a 2D action runner iPhone game.

Monster Cats reached #56 on the App Store
for Adventure Games and made the All Games Top 300 the first week of launch.
4.5 Star Rating. +150,000 downloads

Co-Founder / CEO at Booth AI (YC W23)

Scale AI

Currently leading our generative AI and vision ML teams. Created AFM-1, Scale's first vision foundation model. Led ML for human-AI collaboration in large language model data engines, including productionization of reward models and other quality & efficiency initiatives

Engineering Manager, Machine Learning

Led the CVML team during which we built Autoflag, the spam detection and quality assurance system used across all vision labeling projects at Scale.

Also developed some prelabeling models, co-authored a paper on OOD detection, developed segmentation calibration techniques, and started developing an autolabeling system.

Machine Learning Research Engineer

Standard Cognition

Technical leadership in machine learning, deep learning, and AI. I'm passionate about developing and refining ideas and turning them into useful products that work at scale.

Senior Staff Machine Learning Research Engineer

Tech lead in deep learning research and engineering

Staff Machine Learning Research Engineer

Explorer.ai (acquired by Standard Cognition)

Explorer.ai was acquired by Standard Cognition in January 2019.

While at Explorer, I worked on machine learning, computer vision, and data engineering for robotics. I researched, developed, tested, deployed, and scaled solutions to problems in autonomous vehicle mapping. Took deep learning ideation from conception, training, and experimentation to production at scale.

Software Engineer - Machine Learning

University of Arizona

Researcher and software developer of computer vision systems in the Interdisciplinary Visual Intelligence Lab. Work has focused on error metrics on object detection and shape fitting.

Graduate Research Assistant (Computer Vision)

Contributed development to natural language processing software to identify cancer signaling pathways as part of the Big Mechanism, DARPA project.  This technology reads research papers and produces causal, explanatory models.  

Worked with Dr. Clayton Morrison in the Computational Language Understanding lab.

Implemented machine learning methods in Scala applied to natural language features to automatically extract the biological context that biochemical events occur within. Set up evaluation protocols with custom k-folds cross-validation to evaluate the performance of the classifiers on unseen data.

Graduate Research Assistant (Natural Language Processing)

Research assistant in the ecology lab of Dr. Greg Barron-Gafford at the University of Arizona where my work was focused on applying computation to solving natural resource problems.

Projects included:
Developed and deployed large-scale machine learning models to quantify natural resources from computer vision-derived datasets.
Investigated natural resource dynamics using time series analysis, data visualization, and other quantitative techniques as part of a National Science Foundation project.

Graduate Research Assistant

Data analysis, research design, and experimental installation to determine ecosystem dynamics in response to climatic forcings.  Produced regression and time-series analyses.  Visualized large datasets to accelerate comprehension and produce actionable knowledge.

taplio

The Battle

Nick Locascio

Sean Hendryx

Followers

Posts

Engagements

Likes

Ready to grow your LinkedIn presence?