Dept. of Comp. Sci. & Engg.
410, SIT Building,
Delhi - 110016, India.
Call : +91-8826859252
email : firstname.lastname@example.org
I am a PhD scholar in computer science department at Indian Institute of Technology, Delhi, working with Dr. Parag Singla. I joined here in July 2013. I completed M.tech in Computer Science at computer science department at IIT Delhi in June 2013. Previously, I completed B.tech in Information Technology at YMCA University of Science and Technology, Faridabad in the year 2011.
I work in the area of Statistical relational learning. My thesis topic is "Exploiting symmetries in Probabilistic Graphical Models".
I am also a part of Data Analytics & Intelligence Research (DAIR), which is a fledgling research group at IITD-CSE focused on combining and integrating various fields of data sciences such as machine learning, data management, and data mining towards the goal of building intelligent software systems.
Currently, I am doing internship with Kristian Kersting at Technical University of Dortmund. There, I am working on learning tractable deep neural networks, and learning more interpretable object embeddings.
Please find my Curriculum Vitae here.
My primary research area is "Exploiting symmetries in Probabilistic Graphical Models". Markov Logic is a statistical relational language to express both "relationship between entities" and "uncertainity in the relationships" in a unified way. It expresses relations between entities (such as Friends) by first order logic formulas and uncertainity is specified by associating weight with each formula. First order formulas naturally come with symmetry between the objects (by quantifiers), and we exploit those symmetries to make inference tractable. This technique, called Lifted Inference, has recently gained much popularity among SRL (Statistical Relational Learning) community. Currently we are exploiting symmetry in learning algorithms also.