I, My, Me!
I research, develop tools and techniques to standardize (to make it machine readable), federate and derive meaningful information from the heterogeneous “Big Biomedical Data”. I particularly focus on making Big Data FAIR (Findable, Accessible, Interoperable and Reusable) by incorporating the semantic web, machine learning and related data science technologies. With the advancement of high-throughput technologies, data in life sciences and healthcare is growing exponentially and in near future it will be beyond the capability of the traditional methods of data management and data analytics. Therefore, there is now, more than ever, a need to research and develop new techniques, and infrastructures to leverage vast amount of data effectively. My specific research efforts are concentrated on several core problems from the area of semantic knowledge engineering. On the application side, my research aims at providing non-technical users with scalable self-service access to data, typically distributed and heterogeneous. Semantic technologies, based on semantic data standards and automated reasoning, alleviate many data access-related challenges faced by biologists and clinicians, such as data fragmentation, necessity to combine data with computation and declarative knowledge in querying, and the difficulty of accessing data for non-technical users. At Yale, I am working on MiAIRR, CAIRR, LinkedImm, AIRRPort and HIPC Signature standardization projects. MiAIRR (Minimum information about an Adaptive Immune Receptor Repertoire Sequencing Experiment) is a community agreed standardization effort for the adaptive immune receptor repertoire data, we have recently published a paper in Nature Immunology about MiAIRR. As part of CEDAR team (CEDAR is an NIH BD2K Center of Excellence whose goal is to create a unified framework that researchers in all scientific disciplines can use to create consistent, easily searchable metadata), I am working on development of data submission pipelines (CAIRR and cedar to NCBI) to the NCBI. I received two master’s degrees in Engineering and Information Technology from Gyeongsang National University Korea and University of the Punjab. While my stay at Korea, I mainly worked on the fuzzy ontology applications, developed autonomous vessels collision assessment algorithms and worked on development of new path planning approaches for the AUVs (Autonomous Underwater Vehicles) by applying semantic knowledge management and Artificial Intelligence techniques (Fuzzy systems and Neural Networks). During my PhD at the UNB Canada, I worked on several projects in semantic web domain. Notably, I devised a framework to solve the long-awaited problem of context-based searching in biological sequence images by introducing the semantic image enrichment methodology. My research work has been picked by the CBC Canada, PakWired, and UNB News. Details of other research projects can be found here.
Data Science, Machine Learning, Semantic Web, Knowledge Management, Biomedical Informatics, IoT for health, Big Image Data, Text/Image Mining, Marine Robotics, Software Development
Our poster titled “An Interoperable Framework for Biomedical Image Retrieval and Knowledge Discovery” has won best poster award at Conference on Semantics in Healthcare and Life Sciences 2014 Boston
First Prize Winner I won the first prize in NBHRF conference held at Trade and Convention Centre Saint John on my research work titled as “A contemporary methodology for rapid health care policy-making though semantic aware open data (Une méthode moderne pour définir rapidement des politiques en soin de santé grâce à l’utilisation de données sémantiques ouvertes)”
- DAAD Grant winner: ICCL summer school 2013 on semantic web, ontology language and their use held at Dresden, Germany
- UNB President Award: I received grant for my PhD studies at University of New Brunswick, Canada
- Master Studentship Award: I received full scholarship for my Master studies in Korea
- Outstanding Talent Scholarship: Received from Punjab Information Technology Board