LLM / VLMs: LLM and VLM inference, fine-tuning such as LLama and MLlama, distributed computing with Ray, scalable ML inference, Ray-based ML serving and fine-tuning.
Programming Language & DB: Python (Paper1), C++ (Paper2 & Paper3), C#.Net, ASP.Net, MongoDB, SQL, Oracle DB Designer, JavaScript, NodeJS, AngularJS, MS SQL, My SQL (DBMS implementation-Paper6), MapReduce programming model, OpenMP, MPI and CUDA (Paper2), SDN (Paper4).
Machine Learning (Showcased in Demo1): Stanford Core NLP, Tensorflow, SciKit-Learn, spaCy, OpenCV, Keras, Hydra.
NoSQL databases: Azure Table (Paper5), MongoDB (Paper1).
Python Libraries/Tools: Pytorch, Fast API, NLTK, Scrapy, Selenium, Numpy, Panadas, Flask, matplotlib, SciPy, Sphinx, PySpark, .
Front-end: AngularJS, NodeJS, Javascript, HTML, CSS, XML;
Tools: Selenium, git, GitHub, Adobe Photoshop, Adobe Premiere, Maya, 3D-Studio Max, Azure Cloud Services, AWS, Google Engine, Visio, API Design;
Other: Docker, Lex and YACC (Compiler Design), Rational Rose, XP Methodology, SSADM Methodology, ERP, UML and RUP, Raspberry Pi, Node-red, Linux OS, Windows development, Web development, Apache Web Server, Web automation system development, Network security coding.
