Faculty Publications:
The Computer Science and Engineering (CSE) department is equipped with a comprehensive set of advanced laboratories aligned with the UR20 curriculum, supporting both academic learning and research activities. These laboratories provide hands-on exposure to core and emerging technologies such as Programming, Data Structures, Machine Learning, Computer Networks, Web Technologies, and Software Engineering.
The infrastructure includes high-performance computing systems, GPU-enabled machines, networking devices, and industry-standard software tools. These facilities enable students to work on mini-projects, research prototypes, and real-world problem-solving scenarios.
Key Research Laboratories:
Programming & Data Structures Laboratory: Supports programming and algorithm development using C, C++, Python, and Java. Tools include GCC, PyCharm, Jupyter Notebook, and Git.
Machine Learning & Data Science Laboratory: Focused on model building, deep learning, and data processing using TensorFlow, PyTorch, Scikit-learn, Pandas, and GPU systems.
Computer Networks Laboratory: Provides exposure to networking, protocol analysis, and security using Cisco Packet Tracer, Wireshark, and routers/switches
Database Management Systems Laboratory: Focuses on OS concepts and Java programming using Linux, Docker, Java JDK, and system tools.
Operating Systems & OOP Laboratory: Covers SQL, NoSQL, and data visualization using Oracle, MySQL, PostgreSQL, MongoDB, RStudio, Tableau, and Power BI.
Full Stack & Web Technologies Laboratory: Enables web development and DevOps using HTML, CSS, JavaScript, React, Node.js, Django, Docker, and cloud platforms.
Common Equipment and Tools:
Software:
Python (Anaconda, JupyterLab, PyCharm)
Java (JDK, Eclipse, IntelliJ IDEA)
C/C++ (GCC, Code::Blocks)
ML Libraries (TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy)
Development Tools (VS Code, GitHub, Docker, Jenkins)
Networking Tools (Cisco Packet Tracer, Wireshark, Nmap)
Data Visualization (Tableau, Power BI)
Hardware:
High-performance desktop systems (8â??16 GB RAM, SSD)
GPU-enabled workstations for deep learning
Networking devices (routers, switches, cabling)
Central servers and NAS storage
UPS and backup power systems
