Bayesian Reasoning And INference
Faculty
Srijith P K
Associate Professor
Computer Science and Engineering (affiliated to Department of Artificial Intelligence)
My research interest lies in developing probabilistic machine learning and Bayesian data analysis techniques to solve real world learning problems. I have developed techniques based on probabilistic methods such as Gaussian processes, Differential equations, Dirichlet processes, point processes, and kernel methods to solve problems in natural language processing, information retrieval and social networks.
Homepage : https://sites.google.com/site/pksrijith/home
Ph.D.
Sakshi Varshney
Research scholar , CSE department ( Completed, Currently Postdoc at Alto University, Finland)
Thesis : Advancing Generative Adversarial Networks for Modeling Data
Research Interests:
Deep Learning.
Computer Vision.
Generative Modelling.
Continual learning.
Srinivas Anumasa
Research Scholar, CSE Department (Completed, Postdoc at Mohammad Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi)
Thesis : Neural Differential Equations : Continuous Deep learning from Differential Equations.
RESEARCH AREAS
Inference Techniques
Deep Gaussian Process
Neural Ordinary Differential Equations
Manisha Dubey
Research Scholar, CSE Department ( Completed, Currently Postdoc at Unversity of Manchester, United Kingdom)
Thesis : Spatial and temporal modeling of online social networks.
RESEARCH AREAS
Probabilistic Social Network Analysis
Machine Learning / Deep Learning
Information Retrieval
Natural Language Processing
Jayashree P
My research primarily focuses on uncertainty modelling for NLP tasks.
Homepage: https://sites.google.com/a/iith.ac.in/jayashree-pougajendy/home
M.Tech.
DINESH JAIN
MTECH in MACHINE LEARNING
EE Department
RESEARCH AREAS :
Machine Learning/Deep Learning
Deep Convolutional Gaussian Processes
Bayesian Inference
Active Learning
SHOUNAK KUNDU
MTECH (RA)
CSE Department
RESEARCH AREAS :
Time Series Analysis
Traffic Data Analytics
Atul Gupta
M. Tech in Data Science
RESEARCH AREA
Point Process
Poisson Process
COX Process
Survival Model
Multi Tasking in Point Process
Supriya E N
M.Tech(TA)
EE Department
RESEARCH AREAS :
Machine Learning/Deep Learning
Bayesian Deep Learning
Bayesian non-parametrics
Generative Adversarial networks
Past Students
Vaibhav Singh (M.Tech, currently at Goldman Sachs, Bangalore)
Worked on convolutional Deep Gaussian Processes
Vinayak Kumar, Vaibhav Singh, P. K. Srijith, Andreas Damianou, Deep Gaussian Processes with Convolutional Kernels, Uncertainty in Deep Learning workshop at Uncertainty in Artificial Intelligence (UAI), 2018
Sherin Thomas (M.Tech, currently at Schlumberger, Pune)
Worked on Bayesian point processes for recommendation systems
Sherin Thomas, P. K. Srijith, and Michal Lukasik. 2018. A Bayesian Point Process Model for User Return Time Prediction in Recommendation Systems. In User Modeling, Adaptation and Personalization (UMAP), 2018
Shamik Kundu (M. Tech, currently at SMS data tech, Japan)
Worked on deep learnign for social media analysis
Shamik Kundu, P.K. Srijith, M. S. Desarkar. Classification of Short-Texts Generated During Disasters: A Deep Neural Network Based Approach. In FOSINT-SI at Advances in Social Networks Analysis and Mining (ASONAM) 2018.
