CV

B.Tech

• Relevant Courses:
  • Gained a comprehensive understanding of key areas through courses like Data Structures, Algorithms, Operating Systems, Big Data, and Software Engineering.
• Skills Acquired:
  • Acquired hands-on experience with multiple programming languages, such as Python, and C++, along with proficiency in Data Analysis and Machine Learning.
• Projects and Research:
  • Actively involved in projects focused on Machine Learning with Natural Language Processing and Computer Vision.
  • Developing a comprehensive benchmark for performance-improving code-generation analysis of LLMs.
  • Building a system to help users try different clothes virtually by just uploading the image.

Research Fellow

• Developing LLM with a focus on code generation, instruction-following, and reasoning, working on post-pretraining, online and offline RL methods.
• Led large-scale model training on multi-GPU clusters, optimizing distributed training workflows and fine-tuning to achieve high-performance code synthesis and execution.

NumPy Maintainer (NumPy-QuadDType)

• Created and maintaining the cross-platform 128-bit precision floating point data-type (50K+ downloads) and NumPy C API.

Open Source Engineer (Numpy)

• Implementing an extended precision floating point Data Type
• Extending current level of hardware support for extended precision data types. It no longer looks like x86 is the only game in town.
• Packaging and distribution of a multi-platform python package that includes C extensions.

Data Science Intern

• Assisted in the development and deployment of predictive models to minimize infrastructural discrepancies and mitigate potential revenue losses. This proactive approach resulted in a 25% uptick in user engagement.
• Developed automation pipelines to auto-pre-process user engagement data and curate monthly reports and insights on personalized recommendations.
• Utilized statistical analysis and machine learning techniques to identify infrastructure issues and developed mitigation methodologies.
• Made core contributions to Scaler modules in Data Science and Machine Learning.

Open Source Research Engineer

• Contributed significantly to research projects enhancing code generation and large language models for code, and evaluated mathematical reasoning strategies.
• Collaborated on StarCoder, a 15.5B parameter multi-lingual code-gen model trained on 1 trillion tokens, surpassing OpenAI code-Cushman-001 model.
• Collaboratively developed OctoPack, leveraging Git commits for instruction tuning and leading to the creation of CommitPack data, Extended HumanEval benchmarks and Octo-duo models.

Machine Learning Engineer Intern

• Developed Deep Learning models for business market growth from domains of Computer Vision and Language modelling.
• Engineered a comprehensive API to automate the conversion of a pretrained model into ONNX format and its seamless deployment via Nvidia Triton Server, culminating in a 12% reduction in VM load.
• Developed custom CUDA kernels to accelerate 3D image processing for medical scans. Achieved a 2x speedup in segmentation tasks compared to existing CPU-based solutions, thereby improving the overall throughput of the system.
• Worked on state-of-the-art techniques like DreamBooth, Dichotomous Image Segmentation.
• Supporting model deployment team in model code analysis and optimizations for DeployX

Applied Machine Learning Instructor

• Mentored final year undergraduate students in their course of Machine Learning.
• Evaluated and improved the projects developed by students.

Virtual Clothing Assistant(480+ ⭐️)

• Allow user to try on different clothes virtually, without going to any trial room.
• Used ResNet101 and UNet to segment the cloth and model respectively and pose estimation using openpose then final predictions are done using the PyTorch implementation of VITON.
• SSIM (Structural Similarity Index Metric) of 0.895

Numpy-QuadDType(50K Downloads)

• A cross-platform extended precision (128-bit) floating point data type for NumPy
• longdouble and other extended precision dtypes are platform dependent leading to inconsistencies in reliability for cross platform applications

QBLAS

• A set of efficient linear algebra routines fir criss-platform 128 bit precision floating points, built on top of SLEEF.
• Provides fast APIs for ~21× faster dot, ~77× faster GEMV and ~3× faster GEMM operations on x86-64 and AARCH machines.

MIRA - Multimodal Image Reconstruction with Attention

• MIRA is a multimodal transformer for Text/Image to 3D reconstruction just using single 2D image of object within seconds
• It uses pre-trained DINO-V2 as encoder and custom triplane decoder that learns to project features on triplane via cross-attention and model the relations among the spatially-structured triplane tokens via self-attention, camera features are modulated within the decoder.
• Trained by minimizing the difference between the rendered images and ground truth images at novel views, without the need for excessive 3D-aware regularization or delicate hyper-parameter tuning.

3D Cervical Spine Segmentation and Multi-Vertebrae Fracture Detection

• Developed "3D Cervical Spine Segmentation and Multi-Vertebrae Fracture Detection" to automate cervical spine fracture detection from CT scans, aiming to match radiologist accuracy and ensure timely medical interventions.
• Leveraged the RSNA 2022's Kaggle Competition dataset; implemented a two-stage model, with the first stage focusing on 3D segmentation using EfficientNet + UNet to create binary masks for the cervical vertebrae (C1-C7).
• The second stage combined Convnext + LSTM for classification. Extracted 15 even slices per vertebrae sample by the z-dimension and added the predicted segmentation mask as an additional channel to distinguish between multiple vertebrae.
• Created a 3D reconstruction of spine to visualize the bone and corresponding fracture vertebrae.
• Achieved a Multilabel Dice Score of 0.92 for segmentation and used a Modified BCE loss with a score of 0.365 for classification.

• Became Kaggle Competition Expert
• Research work "OctoPack Instruction Tuning Code Large Language Models" is accepted as the SPOTLIGHT at ICLR 2024 (top 5%)
• Top 7% (Bronze medal) in Kaggle’s UBC Ovarian Cancer Subtype Classification and Outlier Detection (UBC-OCEAN) Competition

• My project Clothes Virtual Try On hit 275+ stars on GitHub! 🌟
• My collaborative research work, "StarCoder may the source be with you!" has been accepted at the TMLR (Transactions on Machine Learning Research).
• 1.8k+ citations of my research work on Google Scholar
• My collaborative research work, "OctoPack Instruction Tuning Code Large Language Models" has been accepted at the Instruction Workshop @ NeurIPS 2023.
• Selected in Amazon ML Summer School 2023, Engaged in advanced ML modules and collaborated with leading Amazon ML Scientists.

• Secured Top 3 % global rank in Kaggle's 30 days ML challenge.