Curriculum Vitae

Principal Scientist

Amazon AGI - Realtime & Multimodal, Pittsburgh, PA.

2022 - Present

• Lead Principal Scientist driving scientific strategy and innovation for real-time AI and human-AI interaction initiatives, pioneering foundation models capable of live streaming and interactive engagement across multiple modalities (speech, text, vision, action). See our Nova Sonic Speech-to-Speech Model.
• Leadership and influence for cross-functional teams spanning multiple organizations, driving collaboration between Science, Engineering, Product, Data Science, and Technical Program Management to deliver breakthrough real-time AI solutions.
• Architect of organization-wide scientific roadmap integrating diverse AI specialties (pre-training, post-training, conversational fine-tuning, live synthesis) into cohesive strategies for next-generation interactive AI products.
• Strategic technology leader overseeing development of scalable, real-time AI systems reaching massive customer bases through both first-party products and third-party partnerships, with focus on solving novel challenges in latency, efficiency, and performance optimization.

Principal Scientist

Amazon Alexa, Pittsburgh, PA.

2018 - 2022

• Building the Alexa Pittsburgh organization (from 5 to 50+ ML scientists and engineers).
• Delivering science for local voice control - enabling Alexa inference on device.
• Developing new modeling techniques for AI on the edge.
• End-to-end neural methods for speech recognition, natural language understanding, and text-to-speech.

Machine Learning Scientist

Amazon.com, Seattle, WA.

2014 - 2017

• Research Lead - Amazon Consumer Payments.
• Advanced ML platform for targeting and recommendations of advertisements.
• Focused on various theoretical and applied questions related to sampling, online matching, policy optimization, and fairness.
• Large-scale computational methods for model building.

Software Development Internship

Amazon.com, Seattle, WA.

Summer 2013

• Project: ''Genetic Banners and Advertisements''.
• Investigated genetic algorithms and online machine learning models and their interactions for content optimization and advertisement targeting.
• Increased German division clicks by >7% over three month period.

Software Development Internship

ANSYS Inc., Pittsburgh, PA.

Summer 2011

• Used OpenCL to parallelize algorithms across GPUs used for scene graph processing.
• Implemented custom data structures similar to R-trees and Kd-trees for engineering applications.

Ph.D. in Computer Science - Incomplete

Carnegie Mellon University, Pittsburgh, PA.

Withdrew to grow Amazon AI group in Pittsburgh, PA.

2017

M.S. in Computer Science

Carnegie Mellon University, Pittsburgh, PA.

Advisors: Manuela M. Veloso, Avrim Blum.

2013 - 2014

B.S. in Computer Science

Carnegie Mellon University, Pittsburgh, PA.

Minor in Robotics.

University Honors.

2013 - 2014

Grant P. Strimel, Jing Liu, Mingzhi Yu, Sunwoo Kim, Ross William McGowan, Kanthashree Mysore Sathyendra, Andreas Stolcke, Ariya Rastrow. US12531056B1.

Grant P. Strimel, Eli Joshua Fidler, Pavel Kveton, Markus Mueller, Benjamin Li, Pavlo Stelmakh, Srikanth Subramaniam, Nathan Anthony Susanj, Sunwoo Kim, Athanasios Mouchtaris. US12482465B1.

Grant P. Strimel, Bjorn Hoffmeister, Ariya Rastrow. US12412567B1.

Grant P. Strimel, Jonathan Jenner Macoskey, Ariya Rastrow. US11887583B1.

Grant P. Strimel, Kai Wei, Thanh Dac Tran. US12033618B1.

Grant P. Strimel, Jonathan Jenner Macoskey, Ariya Rastrow. US12205574B1.

Grant Strimel and Sachin Grover. US10558738B1.

Amazon Science Blogs. 2021.

Amazon Science Blogs. 2018.

Beyond Big Data AI/ML Summit Panelist

Pittsburgh Technical Council.

Jan. 2020

TVR: Beyond Big Data - Amazon Alexa (link)

TechVibe Radio Podcast.

Jan. 2020

Traffic Scheduling Under Eligibility Constraints

CP Tech Talks, Amazon.com.

Feb. 2016

Machine Learning on Distributed Data

CP Tech Talks, Amazon.com.

Dec. 2015

Olympic Games

Amazon.com

2015 - 2019

• Developed biannual (Summer and Winter) programming competition for engineering organization.
• Educational programming and theory puzzles.
• Autograded submissions and live leaderboard.
• Taught participants how to, amongst others, develop parallel algorithms using Java8 functionality, interface with optimization suites to solve LP’s and mixed integerprograms, write scalable algorithms to process large graphs, design communication efficient protocols, etc.

APP Learning Lecture Series

Amazon.com

2014 - 2017

• Began learning series for engineers and analysts interested in data science, ML, and algorithms.
• Planned and gave 32 biweekly lectures, with course notes and recordings.

CS Teaching Assistant

Carnegie Mellon University

Spring 2012

• CS 15-210: Parallel and Sequential Data Structures and Algorithms.
• Taught recitation, wrote and graded homework and tests.
• Tutoring.