"Nowhere is it written on a stone tablet what kind of model should be used to solve problems involving data."

- Breiman, L., 2001. Statistical modeling: The two cultures. Statistical science, 16(3), pp.199-231.

Labs Advisory

John Whaley Founder, CEO
Vinay Prabhu Principal Machine Learning Engineer
Dan Boneh Advisor


At UnifyID labs, we strive to establish and maintain an innovation-first approach. We imbibe a pragmatic, egalitarian and anti-dogmatic approach when it comes to data modeling.

On this page, you will find resources pertaining to our research including peer-reviewed publications (yes, we are a startup that publishes papers!), technical essays and datasets.

Our research spans several areas:

  1. Tensor compression based pre-processing of sensor inputs that includes studying the compression invariant behavior of CNNs to CPD compressed tensor inputs

  2. Decision fusion frameworks

  3. Time series analysis, especially in the irregularly sampled regime

  4. Adversarial attacks and defenses for deep neural networks

  5. Transfer learning

  6. Initialization strategies for training compressed neural networks

  7. CNN deploying on off-cloud computation-constrained environs such as smart-phones and embedded systems

  8. RNN compression strategies

  9. Analyzing generalization behavior of CNNs

  10. Implicit deep generative models

  11. Sensor fusion

  12. Supervised dataset collation

  13. Physics-inspired data modeling

In case you are intrigued by our story and would like to join our ranks, please apply. Frequentists, Bayesians, deep learners, shallow learners, Geometers, Topologists, Non-parametricians, Heavy parametricians, Tensor-flowers, PyTorchers are all welcome!

Abbreviations used:
CPD: Canonical Polyadic Decomposition
CNN: Convolutional Neural Network
RNN: Recurrent Neural Network
ML: Machine Learning


UnifyID has presented at workshops and talks at the following conferences: ICML 2017 in Sydney, CVPR 2017 in Honolulu, and NIPS 2016 in Barcelona.

Smile in the face of adversity much? A print based spoofing attack

In this paper, we demonstrate a simple face spoof attack targeting the face recognition system of a widely available commercial smart-phone.

Vulnerability of deep learning-based gait biometric recognition to adversarial perturbations

In this paper, we would like to draw attention towards the vulnerability of the motion sensor-based gait biometric in deep learning-based implicit authentication solutions.

Announcing the UnifyID Spring AI Fellowship

Today, we would like to announce the UnifyID AI Fellowship program for Spring 2017.

Apply for Fellowship

Follow us on Twitter @UnifyID_Labs