Key Skills & Areas
Open Source Technologies | Research Tools
Use of open-source technologies within teaching, research and experimentation environments. This includes working extensively with open source ecosystems and other open platforms to support automation, prototyping and reproducible research workflows.
Artificial Intelligence (AI) | Machine Learning (ML) | Large Language Models (LLMs)
The training on AI spans both foundational concepts and applied perspectives. This includes work across artificial intelligence, machine learning, deep learning and natural language processing (NLP), primarily using Python-based development environments.
Practical exposure to large language model (LLM) platforms, including hands-on work with prompt engineering, retrieval-augmented generation (RAG) architectures, vector databases and OLLAMA-based local deployments.
AI for Academics and Research | AI Hallucination Detection and Mitigation
Responsible and academically sound use of generative AI. This includes work on AI hallucination detection and mitigation, human-in-the-loop validation, and frameworks for responsible generative AI adoption.
Role of AI in academic research, maintaining research integrity in the AI era, and guiding learners on how to make AI-assisted outputs academically defensible and methodologically sound.
Web & Application Development
Web technologies, PHP-based development, Content Management System (CMS) along with exposure to semantic web principles and NoSQL databases.
The emphasis has always been on clarity of architecture, maintainability, and understanding system design choices rather than reliance on specific frameworks alone.
Internet of Things (IoT) | Radio Frequency (RF) Communications | Software Defined Radio (SDR)
Hands-on development and conceptual system design with microcontroller-based platforms such as ESP32 and Arduino, along with MicroPython-based development for introducing device-level programming in an accessible and practical manner.
Hands-on work with MQTT and explored communication patterns using CoAP, BLE and AMQP for backend messaging scenarios.
Beyond device-level implementation, LoRa, NB-IoT and LTE-M / Cat-M at conceptual and simulation levels are integrated. This is complemented by experience with edge and fog computing concepts, cloud integration, and network and IoT architecture simulation.
Radio Frequency (RF), Software Defined Radio (SDR) using SDR++ and GNU Radio
Digital Pedagogy | Learning Management System (LMS) | Instructional Design | Content Authoring
MOODLE LMS, AI-assisted tools for teaching and research, online assessments, e-learning delivery and SCORM-compliant content.
The emphasis remains on pedagogy-first design, with technology serving as an enabler rather than the driver of learning outcomes.
The training in instructional design focuses on aligning learning objectives, instructional flow and assessment strategies. This includes instructional design methodologies, storyboarding, and digital content development using tools such as Articulate Storyline.
Blockchain & FinTech
Blockchain technologies, smart contract programming concepts, Decentralized Applications, cryptocurrency mechanisms and broader FinTech ecosystems.
Cyber Security & Digital Forensics
Building foundational understanding rather than tool-driven specialization. This includes exposure to penetration testing, server fingerprinting techniques, and introductory digital forensics workflows relevant to academic and enterprise environments.
Data Science | Statistical Computing
The training in data science emphasizes interpretation and reasoning over automated outcomes. Experience includes working with Python, R, Julia and Mojo along with statistical tools such as SPSS and PSPP.
Work in this area focuses on data mining, statistical data interpretation, and communicating insights with methodological clarity.
Technical & Scientific Computing
Tools used in technical documentation and scientific computation including LaTeX, MATLAB, SciLab and Octave. These tools are applied primarily for research communication, numerical experimentation, and structured problem-solving.
Systems & Software Engineering Foundations
The training include systems-level understanding such as Linux administration, shell programming, software testing practices and audit and quality assurance concepts.
Mobile, AR/VR & Immersive Technologies
Exposure to cross-platform, hybrid, and native mobile application development has been complemented by exploratory and educational work in augmented reality (AR) and virtual reality (VR).
Quantum Computing & Quantum Circuits Simulations
The training in quantum computing is focused on foundational understanding, including quantum computing principles, quantum information processing, qubit implementation concepts and quantum machine learning (QML) using Google Cirq, IBM Qiskit and other platforms for simulation.
The emphasis remains on conceptual clarity and realistic expectations.