David Eigen - Resume and Curriculum Vitae

Email: de@deigen.net
Link to google scholar

Papers & Publications

Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space
Mohammad Saeed Abrishami, Amir Erfan Eshratifar, David Eigen, Yanzhi Wang, Shahin Nazarian, Massoud Pedram
ArXiv Preprint 2020 (pdf)
Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Hongyang Li, David Eigen, Samuel Dodge, Matthew Zeiler and Xiaogang Wang
CVPR 2019 (pdf) (github)
A Meta-Learning Approach for Custom Model Training
Amir Erfan Eshratifar, Mohammad Saeed Abrishami, David Eigen and Massoud Pedram
AAAI Student Abstract Track 2019 (pdf)
Gradient Agreement as an Optimization Objective for Meta-Learning
Amir Erfan Eshratifar, David Eigen and Massoud Pedram
NeurIPS Meta-Learning Workshop 2018 (pdf)
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
David Eigen and Rob Fergus
ICCV 2015 (pdf) (project page)
Predicting Images using Convolutional Networks: Visual Scene Understanding with Pixel Maps
PhD Thesis, 2015 (pdf)
Unsupervised Learning of Spatiotemporally Coherent Metrics
Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen and Yann LeCun
ICCV 2015 (pdf)
End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression
Li Wan, David Eigen and Rob Fergus
CVPR 2015 (pdf)
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
David Eigen, Christian Puhrsch and Rob Fergus
NIPS 2014 (pdf) (project page)
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and Yann LeCun
ICLR 2014 (pdf)
Learning Factored Representations in a Deep Mixture of Experts
David Eigen, Marc'Aurelio Ranzato and Ilya Sutskever
ICLR Workshops 2014 (pdf)
Understanding Deep Architectures using a Recursive Convolutional Network
David Eigen, Jason Rolfe, Rob Fergus and Yann LeCun
ICLR Workshops 2014 (pdf)
Restoring An Image Taken Through a Window Covered with Dirt or Rain
David Eigen, Dilip Krishnan and Rob Fergus
ICCV 2013 (pdf) (project page)
Nonparametric Image Parsing using Adaptive Neighbor Sets
David Eigen and Rob Fergus
CVPR 2012 (pdf) (code)
Method and Apparatus for Generating Dynamic Microcores
David Eigen, David Grunwald
US Patent 7783932 (filed 2007)
Visualizing Deep Brain Stimulation Settings in Obsessive Compulsive Disorder
David Eigen, Daniel Grollman, David Laidlaw, Benjamin Greenberg, Erin Einbinder
SIGGRAPH Poster 2004 (www) (abstract pdf)
Effects of Interaction on Human Memory
with David Laidlaw
Master's Project, 2004 (pdf)
Visualization for Differential Geometry
with Thomas Banchoff
Senior Thesis, 2003 (www) (pdf)


New York University, New York, NY
Computer Science Dept, Courant Institute
Ph.D. 2015
Thesis: Predicting Images using Convolutional Networks: Visual Scene Understanding with Pixel Maps
Brown University, Providence, RI
Sc.M. Computer Science, 2004
Brown University, Providence, RI
Sc.B. Mathematics - Computer Science, 2003

Work and Research Experience

Research Scientist
2015 - Present
Research Intern
Google Brain
PhD Student
New York University
Computer Science Dept, Courant Institute
2010 - 2015
Software Engineer
Cisco IronPort Systems
Research and Analysis Group, 2009 - 2010
Web Security Group, 2007 - 2009
Software Engineer
WAFL Filesystem Group
2005 - 2007
Research Assistant
Brown University
Scientific Visualization Group
with Prof. David Laidlaw, Computer Science Dept. 2004 - 2005
Software Engineer (part time)
Axon Labs (currently Zeo, Inc.)
2004 - 2005
Research Assistant
Brown University
with Prof. Thomas Banchoff, Mathematics Dept.
Summers 2000 - 2003
Teaching Assistant
Computer Graphics with Prof. John Hughes, Brown Univ.
Spring 2003
Teaching Assistant
Programming Languages with Prof. Shriram Krishnamurthi, Brown Univ.
Fall 2003

Selected Projects

Deep Training and Deployment in Cloud Environments
at Clarifai, 2018-20

Led efforts to integrate our training and experimentation system (see below) with our data platform to automatically create image-based object detection and classification models in multiple cluster environments. Handles data validation, model training, evaluation, and inference deployment. Defined benchmarks to measure accuracy and speed for different types of models, and found best price/performance points.

Object Detection for Aerial Video
at Clarifai, 2017-19

Led ML work to create object detector for near-realtime detection on aerial videos in a government contract. Developed further improvements to detection methods, data cleaning, and measurement, resulting in significant performance gains. Top-performing system in comparison to other contract competitors.

Few-Shot Learning Research Projects
at Clarifai, 2017-18

Mentored interns on projects in few-shot learning. Published works at CVPR 2019 and NeurIPS Meta-Learning Workshop 2018.

Object Detection Neural Network and Code Framework
at Clarifai, 2016-18

Wrote object detection code framework for use with in-house neural network library and tensorflow. Created object detection models performing at state-of-the-art accuracy and ~1.5x faster compared to concurrently developed opensource object detectors.

Experiment and Training Infrastructure
at Clarifai, 2016-18

Built job scheduler and experiment tracking system targeted for ML model building, comparison, reproducibility and change tracking. Allowed team members to independently run modified codebases in our cluster, encapsulating most dependencies and automatically tracking changes for reproducibility.

Fast Image Classifier Training
at Clarifai, 2015-17

Developed fast classifier training system used by customer end users and internal company teams to quickly build thousands of classifiers in diverse applications. Includes work on frozen classifier embeddings, quantization, data balancing, and neural network architecture and settings. Developed benchmarks based on first customers' uses in beta deployment to rework key components for general release.

Logo Recognition from Synthetic Data
at Clarifai, 2016

Built a system to train detection models to recognize logos in images based on synthetic data; filed patent application.

Face Detection and Recognition
at Clarifai, 2016

Created industry-competitive face detection and recognition system, based on data collected from publicly available sources. Initial labeling for the detector based on combinations of open-source detectors with data filtering, and refined with hard example mining.

Image Content Moderation using Weak Labels
at Clarifai, 2015

Created industry-leading classifier for image-based content moderation and filtering, using target labels created automatically from a word-based classifier applied to weak labels and user-supplied text.

Depth, Surface Normals and Semantic Labels Prediction

Extended our depth-prediction network (below) also to surface normals and semantic segmentation, as well as higher resolution outputs. ICCV 2015.

Depth Prediction from a Single Image

Predicts depth from a single image using a multi-scale convolutional network that first predicts the depth coarsely looking at the full image, then refines using local information. State-of-the-art single-image depth estimation. NIPS 2014.

OverFeat: ImageNet Classification, Localization and Detection

Part of team to develop then-state-of-the-art ImageNet object localization and detection system. My contributions concentrated on developing the location regressor and localization task (winning entry 2013), and helping integrate this with the object detection system. ICLR 2013.

Removing Localized Corruption from Natural Images

Removed structured corruption from images, such as those in pictures taken through a layer of dirt or water droplets using a convolutional network. ICCV 2013.

Convolutional LISTA Autoencoder

Trained a network model to learn image features from unlabelled or partially labeled data, using a feed-forward convolutional sparse coding approximation. Inspired by Deconvolutional networks, our model produces similar features while requiring 1-2 orders of magnitude fewer coding iterations. Unpublished work.

Nonparametric Image Parsing using Adaptive Neighbor Sets
(pdf) (code)

In this project, we explore two ways of customizing nearest-neighbor results for individual queries in the context of a kNN image classifier. First, we learn per-descriptor weights that minimize classification error, using backprop through the NN lookups and calculations. Second, we adapt the training set used for each query based on image context; in particular, we condition on common classes (which are relatively easy to classify) to improve performance on rare ones. The first technique helps to remove extraneous descriptors that result from the imperfect distance metrics/representations of the data. The second contribution re-balances the class frequencies, away from the highly-skewed distribution found in real-world scenes.

Sender IP Reputation from Spam Trap Rates
at IronPort/Cisco, 2010

Created a system to classify IP addresses as likely spam or ham senders for email based on recent trap rates, using as input live streams of spam trap hits and overall mail volume estimates.

Automated Web Content Classifier
at IronPort/Cisco, 2009

Created a system to automatically classify web page content into 30 categories, based on Naive Bayes classification methods. The system categorized over 10 million sites with an estimated misclassification rate of under 10% at 50% recall.

Web Reputation
at IronPort/Cisco, 2007 - 2009

Developed a system that rates HTTP requests with a score indicating the chance the request might fetch malicious content. The system combines multiple sources of data consisting of URL portions or IP ranges; each source may contribute positively or negatively. Reputation is used on web proxy devices to block potentially malicious requests, as well as divert from further scanning traffic that is highly likely to be clean.

Web Sensor Telemetry and Corpus
at IronPort/Cisco, 2007 - 2009

Developed systems to process traffic samples sent back from web proxy appliances. IronPort has several thousand appliances deployed at customer sites throughout the world, forming a sensor network of web-related data. This data is automatically fed back into Web Reputation and other systems. We also use it to evaluate efficacy and measure new techniques.

Updateable Web Reputation Client
at IronPort/Cisco, 2008

Extracted static client code and dependencies into a dynamically updateable package, taking into account potential differences in system libraries and hardware architecture between client platform releases. Distinct from system upgrades, the engine is automatically updated live on the appliance to the current provisioned version, without any input or interaction by the administrator.

NVLog Parallelization
at NetApp, 2007

The NVLog is an intent journal used in WAFL (Write-Anywhere File Layout) to ensure data integrity in the event of a system crash or abrupt shutdown. Since all writes are logged to the journal, this part of the system was a serialization point and bottleneck. I rewrote the way journal writes are done to nearly eliminate lock contention, leading to an overall system performance gain of over 10% by throughput.

Dynamic Microcores
at NetApp, 2007

Outlined a design for dynamic microcores, a reporting and debugging feature. Microcores are partial coredump files, only a few megabytes. The project aimed to let engineers write descriptive recipes to identify memory regions, and trigger microcore generation upon hitting system events. For example, if a system message warns about possible corruption in a block, one could identify interesting memory regions relative to the in-core structures for the block and inode in the message.

Effects of Interaction on Human Memory
Master's Thesis, 2004
at Brown Univ., 2004

Investigated the question of whether the use of different interaction techniques might impact the memory of a user. I designed a set of two experiments to address this: The first, a preliminary study confirming a well-known difference in performance between positional- and velocity-based controls, helped to verify my experimental methodology. The second, a comparison between three interaction modes in an immersive environment, was statistically inconclusive. Anecdotal evidence, however, suggested that for many subjects, memory performance improved with a full-body walking interaction.

Visualizing Deep Brain Stimulation Settings in Obsessive Compulsive Disorder
with Daniel Grollman, David Laidlaw, Benjamin Greenberg, Erin Einbinder
SIGGRAPH Poster 2004
at Brown Univ., 2003-2004
(www) (abstract pdf)

Wrote and reviewed proposals for a 6-week project in a mock grant proposal process during a class on scientific visualization. Carried out research on my project on electrode parameter settings in deep brain stimulation for obsessive compulsive disorder, collaborating with Benjamin Greenberg, a psychiatrist at Butler Hospital. Presented a poster of this project in SIGGRAPH 2004.

Visualization for Differential Geometry
Senior Thesis, 2003
work done as Research Assistant with Prof. Banchoff, Brown Univ., 2000-2003
(www) (pdf)

Created a software package for creating interactive differential geometry visualizations, and produced class labs and demonstrations using this software. Staff and students continue to use this package in new applications and to explore mathematical concepts in several courses at Brown, including differential geometry, combinatorial topology, calculus, geometry, and linear algebra. It has also been used by Prof. Banchoff in classes at UCLA, Notre Dame, University of Georgia, and Stanford.