in developing efficient Real-time Computer Vision/Image processing/ Natural language processing algorithms using Deep learning/Traditional Machine learning
Robust acumen
Contributed to L4-Autonomous driving under percept…
in developing efficient Real-time Computer Vision/Image processing/ Natural language processing algorithms using Deep learning/Traditional Machine learning
Robust acumen
Contributed to L4-Autonomous driving under perception module covering camera/LiDAR based Semantic-Segmentation and 2D/3D Object-Detection, LiDAR based NDT Mapping and Localization
Contributed to Advanced Driver Assistance systems-ADAS (Driver Drowsy Detection, Day and Night Time Pedestrian Detection, Traffic Sign Detection, Object Tracking, Stereo Vision for Depth, Disparity & Object Detection) along with Machine Vision for mining industry related projects
Experience working with Structure-From-Motion, Bundle Adjustment, Multi-View-Stereo, Camera Relocalization, LiDAR-SLAM, RADAR-SLAM, Loop-closure, Camera-LiDAR calibration, LiDAR-IMU calibration, SLAM-backend optimization using GTSAM, Ceres
Experience of Text/Label Detection, Natural-language inference, Topic-modeling, Text Summarization, Named Entity Recognition, Question-Answering, Build & Fine-Tune State of the art LLM models on custom PDF/receipt/ticket forms, OCR Development
Experience working with Supervised-finetuning (SFT), Reward modeling (RM) and RLHF using open-source LLMs (LLama/ Mistral) on open-source datasets
Experience working with RAG technologies and LLM frameworks (Langchain and LLamaIndex), LLM model registries (Hugging Face), LLM APIs, LLM evaluation (perplexity, ROGUE, BLEU, BERTSCORE) embedding models, and vector databases (FAISS)
Skilled in applying methods to structured/unstructured problems using CNN, RNN, Geometric deep-learning, Transformers, Dimensionality reduction (PCA/t-SNE/UMAP) and clustering techniques
Knowledge of Few-shot/Zero-shot/Meta-learning/Lifelong-learning/Continual-learning/Out-of-distribution/camera-ISP pipeline for business use-case
Demonstrated advanced foundation in Linear Algebra, Calculus and Statistics
Knowledge of DNN performance optimization using ONNXRunTime, TensorRT (Fp32/Fixed-point/INT8 precision) covering: Quantization Aware Tuning; Post-Training Quantization (PTQ); Network Pruning; Machine learning compilers (TVM/GLOW);
Knowledge of Vectorization, Tiling, Parallelizing, Loop unrolling, CUDA-programming, Multi-threading/ Multi-processing/ Asynchronous programming
Worked on complex and rich data sets to build and deploy the best data solutions to complex business problems using cutting-edge machine learning, deep learning, and computer vision techniques to invent solutions
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<p>Worked on 2D object detection for warehouse customer<br>
Gathered external datasets<br>
Built synthetic datasets as per the business needs<br>
Worked on Explainable AI</p>
<p>Worked on video reconstruction from event cameras using supervised/self-supervised/GAN and spiky-networks<br>
Worked on Transformers<br>
Worked on Knowledge distillation<br>
Worked on XAI<br>
Worked on HDR imaging<br>
Worked on Graph networks<br>
Worked on Structure-from-motion<br>
Worked on Multi-view-stereo<br>
Worked on Tracking and Mapping<br>
Worked on DNN optimization using post-training quantization<br>
Worked on Quantization aware fine-tuning (QAT) using TensorRT<br>
Worked on PyTorch<br>
Worked on TVM Relay<br>
Worked on TVM AutoTVM/Autoscheduler<br>
Worked on ONNXRunTime<br>
Worked on NLP text classification<br>
Worked on text summarization<br>
Worked on Question and Answering<br>
Worked on Topic Modeling<br>
Worked on Natural language inference<br>
Worked on Time-series</p>
<p>Conducted out-of-calibration detection<br>
Conducted dynamic calibration between camera-LiDAR sensors<br>
Worked on video surveillance application on Intel Open-vino framework</p>
<p>Worked on L4 Autonomous Driving<br>
Detected 3D objects using LiDAR point cloud on single-frame/multi-frame<br>
Tracked 3D objects using LiDAR point cloud<br>
Detected traffic light using tensorflow object detection API<br>
Integrated Apollo/Autoware algorithm for 3D object detection and traffic light<br>
Worked on simulated autonomous driving using Reinforcement Learning Framework with RayLib and tensorflow<br>
Partook in competitions SemanticKITTI/Nuscenes/Argoverse/KITTI<br>
Worked on LiDAR based mapping and localization for autonomous driving<br>
Conducted DNN optimization using post-training quantization<br>
Conducted Quantization aware fine-tuning (QAT) using TensorRT C++<br>
Used PyTorch<br>
Used ONNXRunTime</p>
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