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Data Science Lab (DSL)



Data Science Lab was established in 2017 to cater the challenges of current age which may be termed as the era of "data big bang". Driven by the internet economy, mobile phone, cheaper hardware and the Internet of Things (IoT), the user and other sensory devices are continuously generating a lot of data. As data size increases, the demand for multi scale approaches in transforming data to knowledge also becomes very important.
Research in DSL involves the design of intelligent algorithms and development of decision-making models to form risk management systems and process modeling systems. Interpreting data and visualizing it to define patterns and extract knowledge can help businesses to compete with other competitors. The lab focuses on both the structured and unstructured data analytics for clustering, classification, and association rule mining to identify trends and make useful predictions.

The lab is currently focused on developing a knowledge extraction framework which can be applied to multiple types of textual data including news articles, scientific literature, social media, and police investigation reports. The lab aims to bring together researchers, industry experts, and students to provide a platform for flourishing the field of data science in Pakistan.

Lab Objective's

  1. Finding new approaches for data collection, integration, and data/information sharing technologies
  2. Develop new statistical and mathematical algorithms, prediction method, modeling methods, compaction schemes
  3. Seeking a new way to derive useful, reliable, and verifiable information from big and complex data sets, by using advances in information processing, integration, machine learning, data mining, compression, and visualization of data
  4. Focus on data science research and education that address the challenges of large data sets, high consumption rates, short analysis time windows, different content and media types, and contradicting, incorrect, and missing information
  5. Develop scalable data processing systems, and showcase their solutions to challenging real-world use cases of relevance to science, industry, and society