Kaushik Roy
Engineering · Purdue University West Lafayette
Publications
1,431
Citations
48,633
Est. group size
~26
Recurring co-author estimate
Active years
36
Publishing since 1991
Kaushik Roy works on low-power computer chip design, focusing on how to build hardware that runs artificial intelligence and machine learning more efficiently. His work spans memory-based computing (where calculations happen directly in memory chips to save energy), specialized accelerators for AI models like transformers and spiking neural networks, and applications such as drone navigation and malware detection. This research is aimed at making AI systems faster and less power-hungry, particularly for use in devices with limited energy resources.
Publication output has gradually declined over the last decade, from roughly 50-70 papers per year in 2017-2020 to under 40 per year by 2025.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- COMET: A Framework for Modeling Compound Operation Dataflows with Explicit Collectives
2026
- ROSETTA: ROM-Overlaid STT-MRAM for Efficient MVM and Softmax Operations Toward Accelerating Transformer Inference
IEEE Journal on Emerging and Selected Topics in Circuits and Systems · 2026
- WAGONN: Weight Bit Agglomeration in Crossbar Arrays for Reduced Impact of Interconnect Resistance on DNN Inference Accuracy
IEEE Transactions on Circuits and Systems I Regular Papers · 2025
- What, When, Where to Compute-in-Memory for Efficient Matrix Multiplication During Machine Learning Inference
IEEE Transactions on Emerging Topics in Computing · 2025
- HASTILY: Hardware-Software Co-Design for Accelerating Transformer Inference Leveraging Compute-in-Memory
IEEE transactions on circuits and systems for artificial intelligence. · 2025
- Comparative Analysis of Federated Learning, Deep Learning, and Traditional Machine Learning Techniques for IoT Malware Detection
2025
- HCiM: ADC-Less Hybrid Analog-Digital Compute in Memory Accelerator for Deep Learning Workloads
2025
- Large Language Models for Mental Health Diagnostic Assessments: Exploring The Potential of Large Language Models for Assisting with Mental Health Diagnostic Assessments -- The Depression and Anxiety Case
arXiv (Cornell University) · 2025
- SAP: Corrective Machine Unlearning with Scaled Activation Projection for Label Noise Robustness
Proceedings of the AAAI Conference on Artificial Intelligence · 2025
- Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques*
2025
- TAXI: Traveling Salesman Problem Accelerator with X-bar-based Ising Macros Powered by SOT-MRAMs and Hierarchical Clustering
2025
- Neuro-LIFT: A Neuromorphic, LLM-based Interactive Framework for Autonomous Drone FlighT at the Edge
2025
- Neuro-LIFT: A Neuromorphic, LLM-based Interactive Framework for Autonomous Drone FlighT at the Edge
arXiv (Cornell University) · 2025
- Evaluating Compute in Memory Architectures for Matrix Multiplication: A Dataflow-Centric Perspective
2025
- PIXELS: Progressive Image Xemplar-based Editing with Latent Surgery
arXiv (Cornell University) · 2025
- arXiv (Cornell University)×164
- IEEE Transactions on Electron Devices×18
- Frontiers in Neuroscience×15
- IEEE Transactions on Very Large Scale Integration (VLSI) Systems×15
- Scientific Reports×10
- Kaushik Roy
Engineering · Purdue University West Lafayette
- Radu Teodorescu
Engineering · The Ohio State University
- Lakshmi Narasimhan Chakrapani
Engineering · The Ohio State University
- Risi Jaiswal
Engineering · Purdue University West Lafayette
- Deepika Sharma
Engineering · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile