The Department focuses its research and academic endeavours on several key thrust areas that align with contemporary advancements in computing and technology. These areas reflect the department's commitment to fostering innovation and addressing complex challenges in the digital landscape. The areas guide our curriculum development, faculty recruitment, and research initiatives, ensuring our students and faculty to engage with the most impactful and relevant aspects of the field. Our primary thrust areas include:
Artificial Intelligence: Artificial Intelligence (AI) is a broad field focused on creating intelligent agents, which are systems that can reason, learn, and act autonomously. Our research in AI covers various aspects, including problem-solving, knowledge representation, planning, natural language processing, perception, and the development of intelligent systems that can mimic human cognitive functions or perform tasks that typically require human intelligence.
Machine Learning: Machine Learning (ML) is a subset of AI that focuses on the development of algorithms allowing computers to learn from and make predictions or decisions based on data without being explicitly programmed. Our work in ML explores various techniques such as supervised learning, unsupervised learning, reinforcement learning, and their applications in areas like data analysis, pattern recognition, predictive modeling, and decision making across diverse domains.
Deep Learning: Deep Learning (DL) is a subfield of Machine Learning that utilizes artificial neural networks with multiple layers (hence "deep") to model complex patterns in data. Our research in DL delves into the architecture, training, and application of deep neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, for tasks involving images, audio, text, and other complex data types, achieving state-of-the-art results in various pattern recognition and generation problems.
Neural Networks including CNN and ANN: This area specifically focuses on the study and application of artificial neural networks (ANNs), which are computational models inspired by the structure and function of biological neural networks. Our research includes foundational work on ANN architectures, learning algorithms, and their practical implementation. We have a particular focus on Convolutional Neural Networks (CNNs), which are highly effective for processing grid-like data such as images, and other advanced ANN models for various tasks like classification, regression, and feature extraction.
Internet of Things (IoT): IoT research is geared toward connecting the digital and physical worlds. The department is engaged in designing and deploying sensor-based smart systems for agriculture, healthcare, urban planning, and environmental monitoring. Emphasis is placed on secure data transmission, real-time analytics, and energy-efficient computing.
Large Language Models (LLMs): With the rise of transformative AI, the department has begun focusing on large-scale language models such as GPT and BERT. Research involves natural language understanding, machine translation, question answering systems, and building domain-specific chatbots for education, governance, and customer support.
Cyber Security: Cyber security is a critical research area focused on safeguarding digital infrastructure and data from malicious threats. The department addresses issues such as network security, cryptography, ethical hacking, secure software development, and privacy-preserving technologies. Current projects involve intrusion detection systems, blockchain-based security models, and cyber threat intelligence frameworks.
These thrust areas not only drive the department’s academic and research mission but also provide students with opportunities to work on real-world problems, collaborate with industry partners, and pursue interdisciplinary innovation.
Our faculty's research contributes significantly to the body of knowledge in computer science and provides students with opportunities to participate in cutting-edge projects. We encourage students to explore these areas and engage with faculty on research initiatives.