PhD (AI & ML), MSc(Big Data), MSc(Computer Networks), BSc(Computer Engineering) , | AMIESL | A.Eng (ECSL) | ACMA | CGMA
- Top 1% of all TestDome candidates in data science and machine learning
- AI ML PhD researcher (University of Queensland) with awards from Microsoft and CSIRO
- Solid mathematics and computer engineering background
- 15+ years hands-on technical experience + exposure
Innovative AI, ML engineer with over 15+ years of software development experience in performance analysis, troubleshooting and high-performance world-class systems design including the London Stock Exchange. High proficiency in ML frameworks, python, graph networks, python and C variants. Strong skills in computer architecture, software engineering, computer vision and ability to solve challenging problems using out of the box approaches.
Very strong and an expert in:
- python, pytorch, TensorFlow, dataframes, numpy, scipy, matplotlib and visualizations
- machine learning models using NLP (Natural Language Processing), Langchain models and workflows, LLM (Large Language Model), Hugging Face, model switching (Clipper, Tensorflow Serving)
- Vector Embedding, Vector Database, Whisper AI, Vertex AI, OpenAI, Generative AI
- GPT 1/2/3/4 chatGPT API integration, fine tuning, prompt engineering
- Data engineering (PowerBi, Tableau, DAX, python-based web scraping for insights
- High performance computing (School of Math and Physics) – AVX, Open MP, Open MPI, CUDA
- Computer vision & Pattern recognition – topics: YOLO, Siamese networks, VQVAQ, stable diffusion, transformers, StyleGAN, ControlNet, live video classification, segmentation
- Graph Neural networks - clustering, classification, contrastive learning, efficient training and inference, heterophilic datasets, dynamic attention models
- Finance related models - transaction classification and auto reconciliation, fraud detection
- Prompt Engineering and Retrieval Augmented Generation (RAG)
- AI Generated Art (PIKA, Stable Diffusion + Midjourney), private stable diffusion models
- Research work, Mathematica, Latex, Overleaf and discovery
* I also do programming and development using asm, c, c++, c#, flutter, dart and most shell scripting for AI and other tasks
Example major machine learning accomplishments
⦿ Real time video classification for object detection on low end mobile devices using transfer learning
Bult a fruit classifier that works on cheap mobile devices. Used a pretrained Resnet and a fined tuned classifier, built on tensorflow.js (so that it runs on a browser) and deployed as a PWA, able to run on any device (Windows, Mac, iOS, Android). On a Samsung A20, it can detect good/bad apples from a live video stream, taken from the device camera.
