GroqDec 2020–Present

Machine Learning EngineerMountain View, CA (Remote)

Development on hardware-level SOTA machine learning workloads incl. scaling very large natural language (NLP) and computer vision (CV) models on GroqChip, using both GroqAPI and Groq Compiler. Interfaced directly with customers.

AppleJun 2020–Aug 2020

Data Scientist, Machine LearningAustin, TX (Remote)

During my internship, I built a new ML system from unlabeled data to identify and categorize Apple Cash accounts. Included unsupervised analysis, data wrangling, label discovery, visualization, and plenty of collaboration. Culminated in a presentation on the concrete business applications of my work.

Robotic Embedded Systems Lab, FPGA Lab, CAIS++Aug 2019–May 2020

Hardware Machine Learning Research LeadUSC Viterbi, Los Angeles, CA

Research into the use of FPGAs as universal function approximators using evolutionary algorithms. The goal: evolve architectures that solve ML problems 1000x faster than neural networks. GitHub

Led a team of 5 and was advised by Viktor Prasanna.

Robotic Embedded Systems LabOct 2018–Jan 2020

ML Researcher/Software DeveloperUSC Viterbi, Los Angeles, CA

Research in reinforcement learning for multi-task learning and topological exploration, as well as the use of neuroevolution algorithms as an alternative to gradient descent for training neural networks. Developer on the next-generation platform for RL research, Garage, an open-source project.



Ablation of a Robot's Brain: Neural Networks Under a Knife2018

Peter Lillian, Richard Meyes, Tobias Meisen

It is still not fully understood exactly how neural networks are able to solve the complex tasks that have recently pushed AI research forward. We present a novel method for determining how information is structured inside a neural network. Using ablation (a neuroscience technique for cutting away parts of a brain to determine their function), we approach several neural network architectures from a biological perspective. Through an analysis of this method's results, we examine important similarities between biological and artificial neural networks to search for the implicit knowledge locked away in the network's weights.


University of Southern California

MS in Electrical Engineering (Machine Learning)2018–2020

        GPA: 3.8
        Rose Hills Research Scholarship
        Accepted to the Progressive Degree Program

BS in Computer Engineering and Computer Science2015–2019

        GPA: 3.7 (Graduated Magna Cum Laude)
        Full-Tuition Trustee Scholarship (Merit-based)
        Photography Minor



Python (numpy/sklearn/matplotlib/pandas), Tensorflow, Java, C, C++, HTML, JS, Vue, SQL, AWS, Spark, MapReduce, CUDA, OpenMP, Verilog, Docker, Ubuntu, Command Line, vim, tmux, Nginx, Linux Webserver


Adobe Suite (Photoshop, Illustrator, AfterEffects, Premiere), Cinema 4D, Cadence Virtuoso


Public Speaking, Eagle Scout, Writing (won CSPA’s Gold Circle for Best Nonfiction Article)


Artificial Intelligence, Photography, House music, Crème brûlée, Backpacking, History, Tea, Medieval Scandinavian Art, Sailing, Skiing