• Ph.D. in Applied Artificial Intelligence (A.I.)
• Master of Data Science

• Programming languages: Python, Scala, Java, R, Hive, SQL
• Deep learning frameworks: PyTorch, HuggingFace Transformers, Tensorflow
• Machine learning libraries: NumPy, Pandas, Scikit-Learn
• Big data analytics: Apache Spark
• Cloud computing services: Amazon Web Services (AWS), Google Colab
• Containerisation technologies: Docker, Kubernetes (MiniKube)
• Data visualisation: Dash (Plotly), Seaborn, Matplotlib
• Version control systems: GitHub, GitLab
• Deep learning Domains: Computer Vision, Natural Language Processing, Speech/Audio Processing
• Miscellaneous: Exploratory Data Analysis, Data Engineering, API Development, Graphical User Interface, Continuous Integration, Model Deployment, Web Application Development, LLM Finetuning, RAG

Worked as Sessional Lecturer at La Trobe University for the following post-graduate subjects:

• Deep Learning
• Big Data Management on Cloud
• Data Mining

In the past, I worked as an A.I. Researcher, Lab Demonstrator and Research Officer. As a lab demonstrator, I have taught concepts like fundamental programming language concepts to complex machine & deep learning algorithm concepts. In addition, I also taught data engineering concepts that make use parallel processing or distributed computing frameworks like Apache Spark to process large scale data.

I received very good and memorable feedback from students and my subject coordinators for my teaching and demonstration skills.

Currently, I am working on developing a multimodal algorithm (from scratch) that can combine two different modalities: Images and Text.