Research Areas
CEDM Research group actively works in following research themes:
Computational Biology and Bioinformatics
This theme focuses on developing algorithms for Biological data analysis including genomic, proteimic and transcriptomic data. The long term goal of this research is to develop computational ways to understand the biology of human life and improve the quality of life. The key research involves gene sequence analysis, non-coding RNAs and disease association and explainablity of ML models for biological data analysis.
Computational Linguistics and NLP
This theme focuses on how language computing can be improved by better natural language understanding. Our major interest is in text data, covering key areas such as language modeling, question answering, semantic analysis, knowledge graphs and recommendation systems. We are also interested in other aspects of NLP related to Indian Languages and culture.
Computational Science for Social Good
Computing for the Social Good is a canopy term for any activity which strives to consider the following fundamental question; how can one use computing to do good in the world? Computational Science for social good research focus on complex societal challenges that deliver real change to people and society. The key domains of the research are Computational Science for agriculture, waste management, smart city,and health.
Computer Vision and Pattern recognition
Computer Vision and Pattern Recognition associated with artificial intelligence and machine learning. Computer vision is used to extract meaningful information from images. It involves capturing, processing and analyzing images for identification and classification. Pattern recognition is used to identify patterns and regularities in data, and then classify the data based on the information gained from patterns. Applications include driverless car testing, computer-aided diagnosis, medical diagnostics, defect detection, image analysis etc.
Theoretical Perspectives of Machine Learning
This research theme focuses on theoretical computer science, specifically the theoretical perspectives of Machine Learning algorithms. Researches include theory of algorithms and data structures, which deals with the design and analysis of data structures and algorithms, and the complexity theory, which examines whether there exists an efficient algorithm for a given problem.