Nick Roberts Student MS student in Machine Learning
@ Carnegie Mellon University.

Hi, I’m Nick!

I am an incoming Ph.D. student in CS at University of Wisconsin – Madison where I am advised by Fred Sala. Before that, I had the pleasure of working with Ameet Talwalkar and Zack Lipton during my MS at Carnegie Mellon University. As an undergraduate, I was extremely fortunate to work with both Sanjoy Dasgupta and Gary Cottrell at the University of California, San Diego. Before that, I was a community college student at Fresno City College, where I was lucky enough to learn calculus, linear algebra, AND C++ from Greg Jamison.

I am broadly interested in making machine learning more accessible and applicable to new domains. As of recent, I’ve been particularly interested in model selection and automation.

Other interests: caffeine (broadly), photography, pottery.

Extracurricular: I’m an ordained Dudeist priest so I’m pretty sure I can officiate weddings. I’m also the Head Researcher of Margarita Machine Lounge Therapy at Vacation Inc. - I encourage you to check out our awesome selection of luxury sunscreens. My Toyota Prius is unoffocially the fastest Prius at Bonneville Speedway.

Rethinking Neural Operations for Diverse Tasks

Nicholas Roberts*, Mikhail Khodak*, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar
> (under review)

Searching for Convolutions and a More Ambitious NAS

Nicholas Roberts*, Mikhail Khodak*, Tri Dao, Liam Li, Maria-Florina Balcan, Christopher Ré, Ameet Talwalkar
> AAAI 2021 Workshop on Learning Network
Architecture During Training
(plenary talk)

Decoding and Diversity in Machine Translation

Nicholas Roberts, Davis Liang, Graham Neubig, Zachary C. Lipton
> NeurIPS 2020 Resistance AI Workshop

A Simple Setting for Understanding Neural Architecture Search with Weight-Sharing

Mikhail Khodak, Liam Li, Nicholas Roberts, Maria-Florina Balcan, Ameet Talwalkar
> ICML 2020 AutoML Workshop

Weight-Sharing Beyond Neural Architecture Search: Efficient Feature Map Selection and Federated Hyperparameter Tuning

Mikhail Khodak*, Liam Li*, Nicholas Roberts, Maria-Florina Balcan, Ameet Talwalkar
> MLSys 2020 On-Device Intelligence Workshop

Deep Connectomics Networks: Neural Network Architectures Inspired by Neuronal Networks

Nicholas Roberts, Dian Ang Yap, Vinay U. Prabhu
> NeurIPS 2019 Real Neurons and
Hidden Units Workshop

Using Deep Siamese Neural Networks to Speed up Natural Products Research

Nicholas Roberts, Poornav S. Purushothama, Vishal T. Vasudevan, Siddarth Ravichandran, Chen Zhang, William H. Gerwick, Garrison W. Cottrell
> NeurIPS 2019 workshop on Machine Learning
and the Physical Sciences

Grassmannian Packings in Neural Networks: Learning with Maximal Subspace Packings for Diversity and Anti-Sparsity

Dian Ang Yap, Nicholas Roberts, Vinay U. Prabhu
> NeurIPS 2019 workshop on Bayesian Deep Learning,
> NeurIPS 2019 workshop on Information Theory and
Machine Learning

Model Weight Theft With Just Noise Inputs: The Curious Case of the Petulant Attacker

Nicholas Roberts, Vinay U. Prabhu, Matthew McAteer
> ICML 2019 workshop on Security and Privacy of
Machine Learning
(spotlight presentation)

Learning from Discriminative Feature Feedback

Sanjoy Dasgupta, Akansha Dey, Nicholas Roberts, Sivan Sabato
> NeurIPS 2018

Small Molecule Accurate Recognition Technology (SMART): A Digital Frontier to Reshape Natural Product Research [poster]

Chen Zhang*, Yerlan Idelbayev*, Nicholas Roberts (presenter), Yiwen Tao, Yashwanth Nannapaneni, Brendan M. Duggan, Jie Min, Eugene C. Lin, Erik C. Gerwick, Garrison W. Cottrell, William H. Gerwick
> Applied Machine Learning Days 2018
(best spotlight presentation award)

Small Molecule Accurate Recognition Technology (SMART) to Enhance Natural Products Research

Chen Zhang*, Yerlan Idelbayev*, Nicholas Roberts, Yiwen Tao, Yashwanth Nannapaneni, Brendan M. Duggan, Jie Min, Eugene C. Lin, Erik C. Gerwick, Garrison W. Cottrell, William H. Gerwick
> Scientific Reports 2017

Carnegie Mellon University

M.S. Machine Learning
August 2019 - May 2021
  • MSML Student Committee Leader

University of California, San Diego

B.S. Computer Science
Mathematics minor
CSE Honors Program
September 2015 - March 2019
Magna Cum Laude
CSE Highest Distinction

Fresno City College

Computer Science
Leon S. Peters Honors Program
August 2013 - May 2015
  • Tutor for CIT 65 (Android Application Development)
  • Mathematics Tutor
  • Computer Science Tutor
  • President/Founder, Google Developer Group Fresno City College
  • Treasurer, Science and Engineering Club


research assistant
  • Conducted research related to Neural Architecture Search supervised by Ameet Talwalkar
  • Technologies used: Python, PyTorch, AWS

Amazon AI

applied scientist intern
  • Researched and developed methods for hypothesis rescoring in ASR systems using neural language modeling
  • Identified areas for improvement in many existing ASR systems when recognizing rare or zero shot entities
  • Technologies used: Python, PyTorch, RWTH ASR, Kaldi, AWS


ai fellow
machine learner intern
  • Researched various ways in which research from network neuroscience could be applied to deep learning
  • Developed a novel model extraction attack against deep learning models for computer vision using just noise inputs
  • Technologies used: Python, Keras, PyTorch, TensorFlow, MATLAB, AWS

Intuit Futures

software engineering intern
  • Researched and implemented a novel deep learning model for controllable text generation as a service within Intuit
  • Developed a system for proposing alternative candidate sentences for Intuit content writers using deep learning
  • Investigated the use of dynamic topic models for customer support tickets to gain actionable insights over time
  • Technologies used: Python, PyTorch, TensorFlow, Gensim, Keras


applied scientist intern
  • Developed language model to extract NLP features from text data regarding cryptocurrency trading
  • Investigated unsupervised learning techniques for extracting sentiment data in real time from online forums
  • Technologies used: Python, PyTorch

The Cottrell Lab

machine learning researcher
  • Published, Scientific Reports:
    • "Small Molecule Accurate Recognition Technology (SMART) to Enhance Natural Products Research"
  • Presented SMART research at the Applied Machine Learning Days 2018 conference at EPFL, Lausanne, Switzerland
    • Won an award for Best Spotlight Presentation
  • Analyzed performance of deep learning system for natural products research with Scripps Institute of Oceanography
  • Explored the effects of artificial experimental noise added to the dataset and showed resistance to gaussian noise
  • Improved quality of image dataset by identifying and handling noisy outliers using principal component analysis
  • Technologies used: Python, Tensorflow, Lasagne, Theano, SciPy


software engineering intern
  • Developed open source Spark-Teradata connector forked from Databricks’ connector for AWS Redshift in Scala
  • Designed and implemented Teradata stored procedures in Java to mimic Redshift’s UNLOAD and COPY using S3
  • Improved training methodology and architecture of deep learning time series model used internally
  • Implemented system for updating the time series dataset and fine tuning the deep learning model
  • Technologies used: Scala, Java, Maven, Teradata SQL, AWS, Tensorflow, Flask

The Comeback Community

volunteer full stack developer
  • Developed site in Go, gohtml, and CSS on Google App Engine
  • Mentored new developers in web development
  • Technologies used: Go, Google App Engine, gohtml, HTML5, CSS3, JavaScript


software engineering intern
  • Developed web crawler to compile needfinding and product data using Scrapy and Selenium
  • Designed and implemented an extensible product search solution designed to handle future user search needs
  • Technologies used: Python, Scrapy, Selenium, Django, MySQL, JavaScript


software engineering intern
  • Implemented new user account, edit profile, and login designs in Objective-C for iOS application
  • Refactored analytics code for gathering statistics on app usage, helping designers make more informed choices
  • Technologies used: Objective-C, Cocoa Touch, Flurry Analytics

nick.roberts. [the at character] gmail [the dot character] com