AI ENGINEER @BPH200 | RESEARCH ASSISTANT @UAVR LABS(UCSC) | AR/VR/MR, VLM, CV, LLM, GraphML, RS
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👨🎓 B.Tech: Graduate in the Department of Electrical & Electronics Engineering (EEE), National Institute of Technology Tiruchirappalli
💼 AI Engineer specialized in VLMs, LLM, SSL, Multitask-learning,CL,Deepmetric Learning, GenAI @ BPH200
Former Research Assistant specialized in RS,SSL @ University of Colombo School of Computing:UAVR Lab
Former Undergraduate Research Fellow @ Indian Space Research Organisation:ISRO
Former Research Intern specialized in Remote Sensing, CV @LIRNEasia
💻 Have hands-on experience with Modeling, Training, and Deploying Deep Learning and Machine Learning algorithms related to Computer Vision, NLP on Cloud Platforms and Embedded Systems (TinyML)
- Vision Language Models (VLMs): Highly Experienced in working with state-of-the-art VLMs such as CLIP, BLIP, CLIPSeg, ViLT and X-CLIP along with creating pretext task training for an efficient way of adaptation.
- Large Language Models (LLMs): Highly Experienced in working with state-of-the-art LLMs such as GPT-3, LLaMA, Falcon, and Mistral along with Fine-tuning.
- Self-supervised Learning (SSL): Proficient in generating and tweaking pretext training strategies for various sectors of data, including vision problems, text problems, audio problems, vision and text problems, multi-spectral problems, and hyperspectral problems.
- Generative AI: Proficient in generative AI techniques and their applications.
- Deep Learning: Proficient in building and fine-tuning neural network models.
- Machine Learning: Skilled in developing machine learning algorithms.
- Computer Vision: Knowledgeable in image processing and computer vision techniques.
- Time Series:Skilled in developing deep learning algorithms for analyzing temporal content in hyperspectral/multispectral cube data. Also experienced in state-of-the-art models in time series transformer models like PatchTST, TimeGPT, Lag-Llama, TimesFM, and Chronos..
- Embedded Systems (TinyML): Expertise in deploying machine learning on resource-constrained systems.