Nilaksh Das
| CV

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 ML and interpretability of vulnerabilities in AI models. I am currently investigating strategies to secure AI models from causative and exploratory attacks.

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

Publications

Zero-resource Speech Translation and Recognition with LLMs
K. Mundnich, X. Niu, P. Mathur, S. Ronanki, B. Houston, V. R. Elluru, N. Das, Z. Hou, G. Huybrechts, A. Bhatia,
D. Garcia-Romero, K. J. Han, K. Kirchhoff
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025.
DOI PDF
SpeechVerse: A Large-scale Generalizable Audio Language Model
N. Das*, S. Dingliwal*, S. Ronanki, R. Paturi, Z. Huang, P. Mathur, J. Yuan, D. Bekal, X. Niu, S. M. Jayanthi,
X. Li, K. Mundnich, M. Sunkara, S. Bodapati, S. Srinivasan, K. J. Han, K. Kirchhoff
arXiv preprint arXiv:2405.08295, 2024.
PDF
Towards Effective GenAI Multi-agent Collaboration: Design and Evaluation for Enterprise Applications
R. Shu*, N. Das*, M. Yuan*, M. Sunkara, Y. Zhang
arXiv preprint arXiv:2412.05449, 2024.
PDF
RoundTable: Investigating Group Decision-Making Mechanism in Multi-Agent Collaboration
Y. M. Cho, R. Shu, N. Das, T. Alkhouli, Y. A. Lai, J. Cai, M. Sunkara, Y. Zhang
arXiv preprint arXiv:2411.07161, 2024.
PDF
SpeechGuard: Exploring the Adversarial Robustness of Multi-modal Large Language Models
R. Peri, S. M. Jayanthi, S. Ronanki, A. Bhatia, K. Mundnich, S. Dingliwal, N. Das, Z. Hou, G. Huybrechts, S. Vishnubhotla,
D. Garcia-Romero, S. Srinivasan, K. Han, K. Kirchhoff
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024.
DOI PDF
Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries
H. Park, S. Lee, B. Hoover, A. P. Wright, O. Shaikh, R. Duggal, N. Das, K. Li, J. Hoffman, D. H. Chau
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM), 2023.
DOI PDF
Mask The Bias: Improving Domain-Adaptive Generalization of CTC-based ASR with Internal Language Model Estimation
N. Das, M. Sunkara, S. Bodapati, J. Cai, D. Kulshreshtha, J. Farris, K. Kirchhoff
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.
DOI PDF
SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning
N. Das, S. Peng, D. H. Chau
ECCV 2022 Workshop on Adversarial Robustness in the Real World (ECCV-AROW), 2022.
DOI PDF Code
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning
N. Das, D. H. Chau
Proceedings of the Annual Conference of the International Speech Communication Association (Interspeech), 2022.
DOI PDF
Listen, Know and Spell: Knowledge-Infused Subword Modeling for Improving ASR Performance of OOV Named Entities
N. Das, M. Sunkara, D. Bekal, D. H. Chau, S. Bodapati, K. Kirchhoff
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022.
  Top 50 ICASSP22 posters
DOI PDF Project
A Cluster-then-label Approach for Few-shot Learning with Application to Automatic Image Data Labeling
R. Wu, N. Das, S. Chaba, S. Gandhi, D. H. Chau, X. Chu
ACM Journal of Data and Information Quality (JDIQ), 2022.
DOI
NeuroMapper: In-browser Visualizer for Neural Network Training
Z. Zhou, K. Li, H. Park, M. Dass, A. P. Wright, N. Das, D. H. Chau
IEEE Visualization Conference (IEEE VIS), 2022.
PDF Demo Code
DetectorDetective: Investigating the Effects of Adversarial Examples on Object Detectors
S. Vellaichamy, M. Hull, Z. J. Wang, N. Das, S. Peng, H. Park, D. H. Chau
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
DOI PDF Demo Video
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks
H. Park, N. Das, R. Duggal, A. P. Wright, O. Shaikh, F. Hohman, D. H. Chau
IEEE Transactions on Visualization and Computer Graphics (IEEE VIS), 2021.
  Top 4 IEEE VIS21 papers
  Invited to ACM SIGGRAPH 22
DOI PDF Demo
Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning
N. Das, S. Bodapati, M. Sunkara, S. Srinivasan, D. H. Chau
Proceedings of the Annual Conference of the International Speech Communication Association (Interspeech), 2021.
DOI PDF Project Video
SkeletonVis: Interactive Visualization for Understanding Adversarial Attacks on Human Action Recognition Models
H. Park, Z. J. Wang, N. Das, A. S. Paul, P. Perumalla, Z. Zhou, D. H. Chau
Proceedings of the AAAI Conference on Artificial Intelligence, Demonstration Track (AAAI Demo), 2021.
DOI PDF Demo Video
EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models
O. Shaikh, J. Saad-Falcon, A. P. Wright, N. Das, S. Freitas, O. Asensio, D. H. Chau
Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (CHI), 2021.
DOI PDF Code Video
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 Visualization Conference (IEEE VIS), 2020.
DOI 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 the 2020 CHI 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 (IEEE VIS), 2020.
  Top of GitHub Trending list
  Top 4 TVCG Papers
  Invited to ACM SIGGRAPH 21
DOI 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