Publications

Venue:
Theme:

Conference

Distributed Network Parallel ML
Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers
H. Ramesh and J. Dass
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2026
Acceptance rate: 24%
Paper Code Slide
Distributed Network Parallel ML
FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models
H. Ramesh and J. Dass
Annual Conference on Machine Learning and Systems (MLSys), 2026
Acceptance rate: 26.7%
Paper Code Slide
Distributed Network Parallel ML Computer Systems
SpikeFed: Federated Training of Spiking Neural Networks for Event-Based Vision
H. Zanganeh, L. Obeng, H. Ramesh, J. Seekings, J. Dass and Ramtin Zand
Proceedings of the 16th ACM International Green and Sustainable Computing Conference (IGSC), 2026
Paper Slide
Distributed Network Parallel ML Computer Systems
FedLLM4HW: Carbon-Aware Federated Triage of Hardware Weaknesses in LLM-Generated Verilog Code
P. Mirfasihi, A. Kim, H. Ramesh, J. Dean, M. Akbarsharifi, R. Akbarsharifi, J. Encinas, P. Satam, J. Dass , R. Yasaei and S. Salehi
Proceedings of the 16th ACM International Green and Sustainable Computing Conference (IGSC), 2026
Paper
Parallel ML
LLM-MC-Affect: LLM-Based Monte Carlo Modeling of Affective Trajectories and Latent Ambiguity for Interpersonal Dynamic Insight
Y. Lin, B. Shih, J. Encinas, E. Achom, K. Patel, J. Pacheco, S. Shao, J. Dass, S. Salehi and P. Satam
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL Main), 2026
Acceptance rate: 18.9%
Paper Video
Computer Systems Parallel ML
ViTALiTy: Unifying Low-rank and Sparse Approximation for Vision Transformer Acceleration with a Linear Taylor Attention
J. Dass, S. Wu, H. Shi, C. Li, Z. Ye and Y. Lin
IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2023
Acceptance rate: 25%
Paper Code Slide
Parallel ML
Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers
J. Dass and R. N. Mahapatra
International Conference on Machine Learning (ICML), 2021
Acceptance rate: 21.47%
Paper+Supp Code Slide Poster Video
Distributed Network Computer Systems
FPGA-based Distributed Edge Training of SVM
J. Dass, Y. Narawane, R. N. Mahapatra and V. Sarin
ACM/SIGDA International Symposium on Field Programmable Gate Arrays (FPGA), 2019
Abstract Slide Poster
Computer Systems
ConvLight: A Convolutional Accelerator with Memristor Integrated Photonic Computing
D. Dang, J. Dass and R. Mahapatra
IEEE International Conference on High Performance Computing (HiPC), 2017
Acceptance rate: 23%
Paper Slide
Distributed Network Parallel ML
Distributed QR Decomposition Framework for Training Support Vector Machines
J. Dass, V. N. S. P. Sakuru, V. Sarin and R. N. Mahapatra
IEEE International Conference on Distributed Computing Systems (ICDCS), 2017
Acceptance rate: 16.9%
Paper Slide
Parallel ML
A Relaxed Synchronization Approach for Solving Parallel Quadratic Programming Problems with Guaranteed Convergence
K. Lee, R. Bhattacharya, J. Dass, V. N. S. P. Sakuru and R. N. Mahapatra
IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2016
Acceptance rate: 23%
Paper Slide Poster
Parallel ML
A density based method for automatic hairstyle discovery and recognition
J. Dass, M. Sharma, E. Hassan and H. Ghosh
National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2013
Paper

Journal

Computer Systems
NetDistiller: Empowering Tiny Deep Learning via In-Situ Distillation
S. Zhang, Y. Fu, S. Wu, J. Dass, H. You and Y. Lin
IEEE Micro, 2023 — Special Issue on tinyML
Impact factor: 3.6
Paper
Distributed Network Computer Systems
Distributed Training of Support Vector Machine on a Multiple-FPGA system
J. Dass, Y. Narawane, R. N. Mahapatra and V. Sarin
IEEE Transactions on Computers (TC), 2020 — Special Issue on Machine-Learning Architectures and Accelerators
Acceptance rate: 21% Impact factor: 3.131
Paper
Distributed Network Parallel ML
Fast and Communication-Efficient Algorithm for Distributed Support Vector Machine Training
J. Dass, V. Sarin and R. N. Mahapatra
IEEE Transactions on Parallel and Distributed Systems (TPDS), 2018
Impact factor: 3.402
Paper

PhD Dissertation

Distributed Network Computer Systems Parallel ML
Efficient and Scalable Machine Learning for Distributed Edge Intelligence
J. Dass
PhD Committee: Prof. Rabi Mahapatra (Chair), Dr. Xia Hu, Dr. Eun Jung Kim, Dr. Raktim Bhattacharya
Abstract Slide Video