Skills and Tools Details
Programming
Short Description: Python, Java, Swift, Objective-C, C, C++, C#, SQL, PHP
Description: Python: Used extensively in research and development during Masters and PhD at Deakin and Coventry University.
Java: Utilized in B.Sc. thesis work and mobile application development at HSTU.
Swift, Objective-C: Industrial-level development experience.
C, C++, C#: Academic and professional (teaching) experience.
SQL: Applied in both academic settings and teaching roles.
PHP: Gained experience through academic and collaborative personal/team-based projects.
Level: 100%
Datascience
Short Description: Data Collection, Cleaning, Preprocessing, EDA, Analysis, Feature Engineering, Numpy, Pandas, Matplotlib
Description: Data Cleaning: Identifying, removing, and imputing missing values.
EDA: Performing univariate, bivariate, and multivariate analysis with visualizations.
Data Preprocessing: Data type conversion, encoding, scaling, and data splitting.
Feature Engineering: Feature removal and derivation for optimal model input.
Data Analytics: Conducting statistical, distribution, and significance analysis using p-values, AUC, chi-square tests, correlation, and mutual information.
Numpy: Manipulating 1D and 2D arrays using various array operations.
Pandas: Data manipulation and visualization using dataframes and built-in functions.
Matplotlib: Creating a variety of visualizations including scatter plots, line plots, histograms, bar charts, box plots, violin plots, spider plots, area plots, and grouped plots.
Level: 100%
Machine Learning
Short Description: Concept and Application, Scikit-learn, Auto-ML
Description: Concept and Application: Covers fundamentals of ML including supervised, unsupervised, semi-supervised, and reinforcement learning. Also includes understanding of algorithms, ensemble methods, data leakage prevention, performance metrics, cross-validation, model pipelining, and post-model analysis.
Scikit-learn: Used for implementing various ML concepts.
Auto-ML: Tools such as PyCaret, Auto-sklearn, AutoGluon, lazypredict, and TPOT.
Darts: Applied in time series data analysis.
Level: 95%
Deep Learning
Short Description: Concept and Application, Pytorch, Tensorflow, Keras
Description: Concept and Application: In-depth understanding of neural network architecture and optimization including ANN, CNN (1D/2D), RNN, LSTM, transfer learning, transformer models, and architectures like VGG, ResNet, and AlexNet.
Pytorch: Used for image classification projects.
Tensorflow and Keras: Used for both image classification and time series analysis.
Level: 85%
Generative & Agentic AI
Short Description: Gemini & Huggingface: Multi-Agent, Tools & MCP, Session & Memory, Observability, A2A
Description: Concept and Application: Experience building single and multi-agent systems with parallel, loop, and human-in-the-loop (HITL) capabilities.
Context Management: Session-based, database, and persistent vector store context handling.
Tools and MCP: Hands-on with built-in tools, function tools, sub-agents, and MCP structure.
Observability: Debugging and tracing agent behavior.
A2A Communication: Agent-to-agent communication and agent deployment workflows.
Level: 60%
Health Informatics and Signal Processing
Short Description: Disorder detection, Epilepsy, Sleep disorders, EEG
Description: Disorder Detection: Focus on conditions related to epilepsy, sleep, and aging.
Epilepsy: Detection of epileptic seizures using time series and EEG data analysis.
Sleep Disorders: Analysis of various sleep-related conditions such as bruxism, insomnia, breathing and movement disorders, REM behavior disorders, hypersomnia, and nervous system impairments.
EEG Signals: Preprocessing and segmenting EEG data, extracting features, and optimizing channels for specific applications.
Level: 95%
Could Platforms
Short Description: AWS, AWS Sagemaker, MS Azure, Azure ML Designer
Description: AWS: Experience with EC2, S3, ETL pipelines, task scheduling, and Sagemaker including AutoGluon integration.
AWS Sagemaker: Proficient in Sagemaker Studio, notebooks, and Lambda functions.
MS Azure: Hands-on with virtual machines and virtual networks.
Azure ML Designer: Familiar with designing and deploying ML models using Azure ML tools.
Level: 80%
Teaching and Training
Short Description: Demonstration, Communication, Supervision
Description: Demonstration: Delivering concepts with real-life examples and intuitive explanations.
Communication: Effective knowledge transfer with continuous feedback for student growth.
Supervision: Overseeing projects and theses, providing career guidance.
Evaluation: Assessing performance, giving constructive feedback, and suggesting personalized improvement strategies.
Level: 100%
Reporting and Presentation
Short Description: LaTeX, MS Word, MS Excel, MS PowerPoint, Illustrator
Description: LaTeX: Proficient in LaTeX document and beamer presentation preparation.
MS Word: Creating academic and technical reports.
MS Excel: Organizing data and generating visualizations.
MS PowerPoint: Designing presentations and visual storytelling.
Illustrator: Editing and preparing professional illustrations.
Level: 95%
Management and Leadership
Short Description: Project Management, Team Lead, Client Management
Description: Project Management: Managing software projects, scheduling tasks, milestone tracking, and reporting.
Team Lead: Coordinating team tasks, managing testing, and integrating feedback.
Client Management: Gathering requirements, communicating progress, and ensuring milestone deliveries.
Level: 90%
Soft Skills
Short Description: Critical Thinking, Problem Solving, Communication, Collaboration
Description: Critical Thinking: Identifying, interpreting, evaluating, and analyzing problems.
Problem Solving: Planning and designing structured solutions, prioritizing steps, and integrating components.
Communication: Clearly presenting ideas and insights.
Collaboration: Working effectively in team environments.
Level: 90%
