Jayanth Koushik

[email] [github] [google scholar] [twitter]

I am a PhD student at Carnegie Mellon University studying computational neuroscience and machine learning. I am advised by Mike Tarr and Aarti Singh. I am interested in developing machine learning algorithms, and using them to design neuroscience experiments. My current research is on real-time optimization in EEG.

Publications and Preprints

A Brain Phenotype for Stressor‐Evoked Blood Pressure Reactivity [paper]
Peter J. Gianaros, Lei K. Sheu, Fatma Uyar, Jayanth Koushik, J. Richard Jennings, Tor D. Wager, Aarti Singh, Timothy D. Verstynen
Journal of the American Heart Association, 2017

Hypothesis Transfer Learning via Transformation Functions [arxiv]
Simon S. Du, Jayanth Koushik, Aarti Singh, Barnabás Póczos
31st Conference on Neural Information Processing Systems (NIPS 2017)

Eve: A Gradient Based Optimization Method with Locally and Globally Adaptive Learning Rates [web] [arxiv] [code]
Hiroaki Hayashi'*, Jayanth Koushik*, Graham Neubig

Deep Multimodal Fusion for Persuasiveness Prediction [pdf]
Behnaz Nojavanasghari*, Deepak Gopinath*, Jayanth Koushik*, Tadas Baltrušaitis, Louis-Philippe Morency
18th International Conference on Multimodal Interaction (ICMI 2016)

* Equal contribution

Conference Presentations

Influence Functions for Adaptive Stimulus Selection [poster]
Jayanth Koushik, Austin Marcus, Aarti Singh, Michael J. Tarr
18th Annual Meeting of the Vision Sciences Society (VSS 2018)

* Equal contribution


  • torch-gel: PyTorch implementation of group elastic-net [code]
  • neural-style: Theano/Keras implementation of style transfer algorithms [project] [code]
  • Understanding Convolutional Neural Networks [arxiv]