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School of Computer and IT has been expanding its horizons to explore, accommodate and accelerate the prospects of Experimental as well as Theoretical Research in COMPUTER studies. The research initiatives are expression of effective, efficient and positive attribute towards the development and sustenance of sustainable and progressive society.


SCIT has envisaged “Research Groups” to create a platform for interdisciplinary research in Cloud Computing, Data Analytics, Image Processing, OS, Parallel Computing, Information Security,  GIS and Spatial Technology, Algorithmics and Software Engg, AI, Soft Computing & Robotics, Computer Vision, Computer Networks and Distributed Computing, Information Security and other areas of computer and IT. 


 The group aims to facilitate Ph.D. Studies, as well as to develop State of the Art Laboratories, while synergizing with other disciplines.



1. AI & Machine Learning

How is it possible for a slow, tiny brain, whether biological or electronic, to perceive, understand, predict, and manipulate a world far larger and more complicated than itself? How do we learn from experience, so that we can be more efficient at what we do, and can avoid making the same mistakes twice? These are the most fascinating problem on which AI and Robotics research is focused.

AI and Robotics have produced many significant and impressive products even at this early stage in its development. Although no one can predict the future in detail, it is clear that computers with human-level intelligence would have a huge impact on our everyday lives and on future course of civilization. Even robots are increasingly becoming a part of our lives. From cars that park and brake themselves, to airliners that (mostly) fly themselves, we are benefiting from robots often without even realizing it but the huge challenges still to be overcome before robots could have anything like the capability of humans in terms of judging situations and making decisions.  Some key areas include machine learning, natural language processing, probabilistic reasoning, automated planning, machine reading, and intelligent user interfaces.



Dr Sandeep Chaursia (Coordinator)

Dr Rajveer S Shekhawat

Dr VS Dhaka

Dr.Pratistha Mathur

Dr. Alka Choudhary

Ashish Jain

Vivek Kumar Verma

Mahesh Jangid

Rishi Gupta

Shikha Kabra

Pradeep Kumar

Ankit Shrivastava

Kuntal Gaur

Tarun Jain

Arvind Kumar

Anubha Parashar

Neha V Sharma

Manu Srivastava

Harish Sharma


2. Computer Networks and Security

The main objective of CNS Research Group is to focus on the following broad areas: Computer Networks and Network Security.

 Our research group is working on broad area of designing and analysis of network architectures and protocols related to different topics of wireless networks, Sensor networks, mobile networks and security. Our objective is to model the new and emerging concepts of mix topics on the basis of real world requirements. The communication using wireless media is done using mobile communication, satellite communication, and cellular networks, spread spectrum technologies, IPv6, sensor networks. The group focuses on implementing wireless networked architectures and performance analysis of networks through experiments, simulation models and mathematical models. Our group focuses on designing and implementing various cryptographic algorithms applied to web security, Intrusion detections, Risk Management, Vulnerability testing, network threats.

Wired and wireless Networks are the basic technologies use for sharing information in communications and social world. Our group is excited to develop and design protocols which supports scalable, secure and fast communication. We are also concentrating on mobility management protocols in wireless networks. Presently, CNS group engage in doing research in the following areas: Wireless Communication and Networking,       Signal Processing, Information Theory, Sensor Networks, Mobile Ad hoc Network(MANET), Network Security, P2P Network.



Dr Sandeep Joshi (Coordinator)

Dr Rajveer S Shekhawat

Dr US Rawat

Dr Arjun Singh

Dr Jyoti Grover

Dr Devershi Pallavi Bhatt

Dr Anju Yadav

Dr Anshuman Kalla

Dr Alka Chaudhary

Ashish Jain

Satyabrata Roy

Krishna Kumar

Lokesh Sharma

Monika Jain

Rohit Kumar Gupta

Virendra Dehru

Gaurav Prasad

Anita Shrotiya

Saket Acharya

Ankit Mundra

Prashant Manuja

Venkatesh G S

3. Data Science (Data Mining, Big Data & Analytics)

Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories. Data analytics is distinguished from data mining by the scope, purpose and focus of the analysis. Data miners sort through huge data sets using sophisticated software to identify undiscovered patterns and establish hidden relationships. Data analytics focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher. The science is generally divided into exploratory data analysis (EDA), where new features in the data are discovered, and confirmatory data analysis (CDA), where existing hypotheses are proven true or false. Qualitative data analysis (QDA) is used in the social sciences to draw conclusions from non-numerical data like words, photographs or video. In information technology, the term has a special meaning in the context of IT audits, when the controls for an organization's information systems, operations and processes are examined. Data analysis is used to determine whether the systems in place effectively protect data, operate efficiently and succeed in accomplishing an organization's overall goals. The term "analytics" has been used by many business intelligence (BI) software vendors as a buzzword to describe quite different functions. Data analytics is used to describe everything from online analytical processing (OLAP) to CRM analytics in call centers. Banks and credit cards companies, for instance, analyze withdrawal and spending patterns to prevent fraud or identity theft. Ecommerce companies examine Web site traffic or navigation patterns to determine which customers are more or less likely to buy a product or service based upon prior purchases or viewing trends. Modern data analytics often use information dashboards supported by real-time data streams. So-called real-time analytics involves dynamic analysis and reporting, based on data entered into a system less than one minute before the actual time of use.



Ashish Kumar (Coordinator)

Dr.Roheet Bhatnagar

Dr. Sumit Shrivastava

Dr. Sandeep Chaurasia

Dr. Prakash Ramani

Dr. Akhilesh Kumar Sharma

Ravinder Kumar

Shashank Sharma

Venkatesh G S

Harish Sharma

Gaurav Aggarwal

Rishi Gupta

Bhavna Saini


4. Graph Theory & Algorithmics

Accelerated and Optimized parallel Graph algorithms using GPGPU

For many disciplines in Engineering and computer science graph Algorithms are fundamentally used technique to process data. Now a days with the advent of Hardware technology we have processors with many cores and we can use Graphical processing Units (GPU) for general purpose computing. The need for high computation power lead to new programming technique called parallel programming through which we can achieve high processing speed. GPUs largely consists of Single Instruction Multiple Data (SIMD) architecture so we can make use of GP-GPUs efficiently for graph algorithms.

Problems on Graph Analysis:

1. Finding Betweenness Centrality: Measuring the relative importance of each vertex in a network is one of the most fundamental building blocks in network analysis. Betweenness centrality is the measure through which influence of each vertex flows through a node is calculated. It is primarily used in social networks and computation biology. On dynamically Changing graphs it become more difficult to find the betweenness centrality so we can try to approximate it.

2. Finding K-shortest path: K-shortest path is used in various fields like sequence alignment problem in molecular bioinformatics, robot motion planning, path finding in gene network where speed to calculate paths plays a vital role. Parallel implementation is one of the best ways to fulfill the requirement of these applications. A GPU based parallel algorithm can be developed to find k number of shortest path in a positive edge-weighted directed large graph. Shortest path can be calculated between two pair of vertices of a graph with n nodes and m vertices.




Jaya Krishna (Coordinator)

Dr Rajveer S Shekhawat

Sumit Srivastava

5. IOT & Embedded Systems

More and more computers can be found controlling common everyday utilities, such as microwave ovens, washing machines, video recorders, all kinds of industrial processes and,

Increasingly, car subsystems. They are often called embedded computer control systems.   This trend is expected to continue in the future. Several research projects on ambient intelligence, pervasive systems, home automation, and ubiquitous computing, aim at integrating computers in our environment even more in a way that they are hidden. Most of today’s embedded systems are required to work in dynamic environments, where the characteristics of the computational load cannot always be predicted in advance.

Still timely responses to events have to be provided within precise timing constraints in order to guarantee a desired level of performance. Hence, embedded systems are, by nature, inherently real-time. These must be permanently ready to respond to requests from their environments within pre-determined time frames; hence their other names, responsive or reactive systems.




Dr Rajveer Shekhawat (Coordinator)

Dr. Manoj Bohra

Dr. Gulrej Ahmad

Mr. Vidyadhar Aski

Ms. Anubha Parashar

6. GIS and Spatial Technology

A majority of Information systems require geographical data for making useful decisions. Thus geographical Information Systems (GIS) which have capability to store locational information of the entities stored as attributes are essential for computerized information management. This has been made possible by increased processing and visualization power of the computing platforms of modern times. The availability of spatial information has been made easy and cost effective due to large scale deployment of geo-stationary remote sensing satellites. This has opened up a vast area of research in data science, data bases and many other related fields. At the school, expertise on spatial analysis, network analysis and spatial queries is vested with many senior faculty and research specifically on urban growth prediction, accessibility, infrastructural planning for smart grids etc is going on.



Dr Rajveer S Shekhawat (Coordinator)

Dr Roheet Bhatnagar

Dr Pratishta Mathure

7. Virtualization and Cloud Computing

The most important job of an operating system is to provide a context within which programs can be created and run. The objective of our group is to conducts fundamental and applied research into the base abstractions to create this context. To make our work relevant, we will try to build

prototype systems to evaluate our ideas and to prove their implemention into practical systems. We are also interested in how compiler and architecture technology can lead to better performance in parallel, high-performance, and application-specific computer systems. We are interested in developing theory and optimisation techniques for both compilers and architecture. There is ongoing work in the development of auto-parallelising compilers and program transformation theoryfor high performance and embedded systems. Iterative, feedback directed compilation is another current research area where we can investigate different optimisation spaces and search strategies.

We can also currently investigate hardware and software issues in speculative parallelisation for both small and large scale multiprocessors. The cloud computing is also part of this research group. We can engage the researchers in the investigation, design and development of scalable and utility-oriented Internet computing systems, programming environments, and algorithms that harness resources from enterprise networks to the

Internet and deliver application services in seamless manner to end users. The design challenges arise from the distribution of resources across various administrative and usage domains coupled with their availability, access price, and demand varying with time. We can investigate these issues in the context of emerging computing paradigms such as cloud computing, peer-to-peer computing, data-intensive computing, and sensor networks; and explore their utilisation in supporting application domains in life sciences, engineering, business analytics, gaming, digital media, and social networking.



Dr. US Rawat(Coordinator)

Dr. Satish Chandra Kulhari

Dr. Sandeep Joshi

Dr Prakash Ramani

Dr. Sulabh Bansal

Mahesh Jangid

Rohit Kumar Gupta

Manu Srivastava

Piryank Singh Hada

8. Software Engineering & DevOps


As ‘Software’ is an engineering solution to the problem, the ‘Software Engineering’ aims to address the problems in the development of software. Almost all engineering projects, needless to say, are software engineering projects. The team, therefore, focuses on Systems Analysis and Modeling, Model-driven Software Development, Software Architectures, Software Quality Assurance, Project Planning, and Project Progress Control. The team, in general, is working on two ends of software development namely requirement engineering and software testing. The research in the requirement engineering is to bring novel stakeholder recommender models to address the problems in requirements engineering. Software testing area intends to explore aspects of test automation, and the technical and management metrics of pervasive Object-Oriented (OO) systems.



Dr Devesh Srivastava(Coordinator)

Dr. Rajveer S Shekhawat

Dr Roheet Bhatnagar

Dr Neha Chaudhary

Anurag Bhatnager

Pragnesh Thaker

Ginika Mahajan

Kavita Chaudhary