IITBTCAAI

Technocraft Center for Applied Artificial Intelligence

TCA2I_145x100-01

Projects

Round 1

Crowdsourcing

The crowdsourcing platform is assumed to be divided into multiple classes, based on workers skill, experience, etc. We consider the problem of cost optimal utilization of a crowdsourcing platform for binary, unsupervised classification of a collection of items, given a prescribed error threshold.

Faculty associated

Prof. N. Hemachandra (IEOR) and Prof. Jayakrishnan Nair (EE)

Learning for Beam alignment in 5G

In this work, we propose to develop learning algorithms for beam alignment between base stations and various users to maintain high rates. One thus needs regular updates of sufficiently good estimates of the positions of all the mobiles.

                                                                                                                                              Faculty associated

Prof. Veeraruna Kavita(IEOR) and Prof. Manjesh K. Hanawal(IEOR)

Automating Threat Detection and Response in Linux Endpoints

 

Endpoint Detection and Response EDR is an advanced technology for the detection and prevention of attacks on cyberinfrastructure. It is an integrated security solution that combines realtime continuous monitoring and collection of endpoint data with rules-based automated response and analysis capabilities.                                               

Faculty associated
Prof. Manjesh K. Hanawal (IEOR)

3D Medical Image data synthesis for classification and segmentation using deep generative techniques abeling 3D medical images

Typical prediction tasks using 3D medical images like classification and segmentation using deep neural networks require large amounts of labeled training data. In this project, we will develop novel variants of deep networks e.g. generative adversarial networks GANs, variational encoders VAEs to synthesize 3D medical image data for classification and segmentation tasks.

Faculty associated 
Prof. P. Balamurugan (IEOR)

Automating Threat Detection and Response in Linux Endpoints

Endpoint Detection and Response EDR is an advanced technology for the detection and prevention of attacks on cyberinfrastructure. It is an integrated security solution that combines realtime continuous monitoring and collection of endpoint data with rules-based automated response and analysis capabilities.                                               

Faculty associated
Prof. Manjesh K. Hanawal (IEOR)

3D Medical Image data synthesis for classification and segmentation using deep generative techniques abeling 3D medical images

Typical prediction tasks using 3D medical images like classification and segmentation using deep neural networks require large amounts of labeled training data. In this project, we will develop novel variants of deep networks e.g. generative adversarial networks GANs, variational encoders VAEs to synthesize 3D medical image data for classification and segmentation tasks.

Faculty associated 
Prof. P. Balamurugan (IEOR)

Round 2

Assistive AI Technology Development for Tactical and Operational Planning of Supply Chains

Modern supply-chain networks are highly competitive among the entities who operate at the retailer level interfacing directly with the stochastic demands of the consumers. At the same time, the retailers/distributors are well connected to each other via a transportation network. Our project aims to develop and analyze an explainable framework for these two objectives using game theoretic modeling and by providing mechanism design solutions.                                               

Faculty associated
Dr. Swaprava Nath, Assistant Professor, Dept of CSE, IIT Bombay

AI/ML Applications for Enhanced Smart Metering for Residential Electricity Consumption

Residential electricity consumption accounts for 24% of the total electricity demand in India and is the largest connected on the Indian power grid. Smart metering allows utilities to continuously monitor and potentially control this load. In this project, we are working with SustLabs to develop AI/ML-based algorithms for load disaggregation at the smart meter level.

 

Faculty associated 
Dr. Anupama Kowli, Associate Professor, Dept of EE, IIT Bombay