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I'm an ML Systems Research Intern at Embedded LLM (Singapore) and a final-year Computer Engineering student at Thapar Institute of Engineering and Technology. My research is on GPU kernel engineering and cross-vendor performance portability across NVIDIA and AMD, applied to biological and clinical AI workloads. I write portable fused Triton kernels for protein, genomic, molecular and pathology models, and study why the same code can perform very differently across hardware. |
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364 Slides a Shift, Not 222, on the GPU You Already Own: A Deployment Card for Cancer Pathology Foundation Models at Hospital Scale |
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Change the Metric, Change the Winner: Auditing How We Decide Which Clinical Foundation Model to Deploy in Cancer Pathology |
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From 805 ms to 23 ms: Accelerating State-Space Models for Real-Time ICU Monitoring with Fused Triton Kernels |
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From 13 Hours to 4.6: Profiling-Driven Kernel Fusion for TensorNet in Molecular Simulation |
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When the LLM-Tuned Stack Misses: An Infrastructure View of Biological Foundation Model Inference Across NVIDIA and AMD |
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Deploying Clinical Language and Vision-Language Models Where the Data Lives: An Energy-Aware, Cross-Vendor Protocol for On-Premises Healthcare AI |
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Error-Bounded Fused Attention Compression for Long-Context Genomic Foundation Models Across Heterogeneous GPUs |
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Accelerating Molecular Simulations with Triton: Fused GPU Kernels for TensorNet Neural Potentials |
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From 805 ms to 23 ms: Accelerating State-Space Models for Real-Time ICU Monitoring |
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BioTriton: Portable Cross-Vendor GPU Kernels for High-Throughput Bioinformatics via OpenAI Triton |
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Hardware-Portable Fused GPU Kernels for High-Throughput Biological Foundation Models |
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Portable GPU Kernel Acceleration for Biological Foundation Models and Algorithms using OpenAI Triton |
