Nilaksh Das

Hi! I am Nilaksh, a PhD student at Georgia Institute of Technology in the School of Computational Science and Engineering, where I am advised by Dr. Duen Horng Chau.

My research focuses on adversarial machine learning, and interpretability and explainability of deep convolutional neural networks. I am currently investigating strategies to secure deep architectures from causative and exploratory attacks.

I am also an active member of the Polo Club of Data Science where we conduct research that spans across the domains of data mining, machine learning, human-computer interaction and information visualization.


Publications

GOGGLES: Automatic Image Labeling with Affinity Coding
N. Das, S. Chaba, R. Wu, S. Gandhi, D. H. Chau, X. Chu
ACM International Conference on Management of Data (SIGMOD), 2020.
DOI PDF Code Tweet
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks
N. Das*, H. Park*, Z. J. Wang, F. Hohman, R. Firstman, E. Rogers, D. H. Chau
IEEE Transactions on Visualization and Computer Graphics (VIS), 2020.
PDF Demo Code
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning
N. Das*, H. Park*, Z. J. Wang, F. Hohman, R. Firstman, E. Rogers, D. H. Chau
Extended Abstracts of ACM Conference on Human Factors in Computing Systems (CHI), 2020.
DOI PDF Code
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
Z. J. Wang, R. Turko, O. Shaikh, H. Park, N. Das, F. Hohman, M. Kahng, D. H. Chau
IEEE Transactions on Visualization and Computer Graphics (VIS), 2020.
  Top of GitHub Trending list
PDF Demo Code Video
CNN 101: Interactive Visual Learning for Convolutional Neural Networks
Z. J. Wang, R. Turko, O. Shaikh, H. Park, N. Das, F. Hohman, M. Kahng, D. H. Chau
Extended Abstracts of ACM Conference on Human Factors in Computing Systems (CHI), 2020.
DOI PDF Code Video
MLsploit: A Framework for Interactive Experimentation with Adversarial Machine Learning Research
N. Das, S. Li, C. Jeon, J. Jung*, S. T. Chen*, C. Yagemann*, E. Downing*, H. Park, E. Yang, L. Chen,
M. E. Kounavis, R. Sahita, D. Durham, S. Buck, D. H. Chau, T. Kim, W. Lee

KDD Project Showcase, 2019.
Proceedings PDF
The Efficacy of SHIELD under Different Threat Models
C. Cornelius, N. Das, S. T. Chen, L. Chen, M. E. Kounavis, D. H. Chau
KDD Workshop - Learning and Mining for Cybersecurity (LEMINCS), 2019.
Proceedings PDF
Visual Analytics for Interpretability on Deep Neural Networks
H. Park, F. Hohman, N. Das, C. Robinson, D. H. Chau
NeurIPS Workshop - Women in Machine Learning (WiML), 2019.
MLsploit: A Cloud-Based Framework for Adversarial Machine Learning Research
N. Das, S. Li, C. Jeon, J. Jung*, S. T. Chen*, C. Yagemann*, E. Downing*, H. Park, E. Yang, L. Chen,
M. E. Kounavis, R. Sahita, D. Durham, S. Buck, D. H. Chau, T. Kim, W. Lee

Black Hat Asia - Arsenal, 2019.
Abstract Code Project Video
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio
N. Das, M. Shanbhogue, S. T. Chen, L. Chen, M. E. Kounavis, D. H. Chau
European Conference on Machine Learning & Principles & Practice of Knowledge Discovery in Databases (ECML-PKDD), 2018.
DOI PDF Code Video
SHIELD: Fast, Practical Defense and Vaccination for Deep Learning Using JPEG Compression
N. Das, M. Shanbhogue, S. T. Chen, F. Hohman, S. Li, L. Chen, M. E. Kounavis, D. H. Chau
ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2018.
  Audience Appreciation Award (runner-up)
DOI PDF Code Project Article Video Tweet
Compression to the Rescue: Defending from Adversarial Attacks Across Modalities
N. Das, M. Shanbhogue, S. T. Chen, F. Hohman, S. Li, L. Chen, M. E. Kounavis, D. H. Chau
KDD Project Showcase, 2018.
Proceedings PDF
Defense against Adversarial Attacks using JPEG Compression
N. Das, M. Shanbhogue, S. T. Chen, F. Hohman, L. Chen, M. E. Kounavis, D. H. Chau
NIPS Workshop - Women in Machine Learning (WiML), 2017.
Training a Generative Agent Grounded in Cooperative Visual Dialog with Deep Reinforcement Learning
A. Kalia, N. Das, M. Shanbhogue, V. Parthasarathy
NIPS Workshop - Women in Machine Learning (WiML), 2017.
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression
N. Das, M. Shanbhogue, S. T. Chen, F. Hohman, L. Chen, M. E. Kounavis, D. H. Chau
arXiv preprint arXiv:1705.02900, 2017.
PDF Article
PASSAGE: A Travel Safety Assistant with Safe Path Recommendations for Pedestrians
M. Garvey, N. Das, J. Su, M. Natraj, B. Verma
ACM International Conference on Intelligent User Interfaces (IUI), 2016.
DOI