Bikash Santra School of AIDE
The objective is to identify/segment and assess the abnormalities seen on medical images like histopathology, CTs, PET/CTs and MRIs. We plan to develop AI models introducing unique computer vision and deep learning approaches for characterizing such abnormalities in medical images.
Contact: bikash@iitj.ac.in
Bikash Santra School of AIDE
The objective is to determine the genetic mutations of carcinoma/tumor by automatically identifying them from medical images like histopathology, CTs, PET/CTs and MRIs. We plan to develop AI models introducing unique computer vision and deep learning approaches for characterizing such genetic characterization of carcinoma/tumor from medical images.
Contact: bikash@iitj.ac.in
Bikash Santra School of AIDE
The objective is to address machine vision problems (such as object recognition/detection/segmentation and image generation/synthesis) for various applications (e.g. retail stores, facial expression recognition, pose estimation and precision agriculture) by introducing deep learning algorithms. We intend to emphasize on building annotation-efficient machine learning frameworks introducing few-shot, zero-shot, self/weakly/semi-supervised approaches.
Contact: bikash@iitj.ac.in
Manish Aggarwal School of AIDE
Uncertainty modelling and data-driven construction of preference models
Contact: ma@iitj.ac.in
Bhivraj Suthar School of AIDE/CBSA
This study focuses on the development of artificial muscles and an EEG/EMG-driven sensitive prosthetic hand aimed at enhancing manipulation capability for individuals with missing limbs. Traditional prosthetic hands often lack the dexterity and sensitivity required for intricate tasks. Through the integration of advanced TSA artificial muscle technology and a sophisticated EEG/EMG interface, our prosthetic hand offers improved functionality and intuitive control. The artificial muscles replicate the natural movements of human muscles, enhancing the hand’s agility and responsiveness. Moreover, the EEG/EMG-driven interface allows for seamless interaction between the user’s neural signals and the prosthetic hand, enabling precise and intuitive manipulation. In addition, touch sensors will be integrated with prosthetic hand for enhancing control for in-hand object manipulation. This innovative approach holds great promise in significantly improving the quality of life for individuals with limb loss by restoring natural-like hand functionality and enhancing their ability to perform daily tasks with ease and confidence.
Contact: bhivraj@iitj.ac.in
Bhivraj Suthar School of AIDE/CBSA
The research aims to revolutionize prosthetic technology through the development of a Brain-Muscle-Controlled Prosthetic Leg. By integrating cutting-edge advancements in neuroscience, biomechanics, and robotics, this innovative prosthetic limb will be capable of translating both brain signals and muscle activity into precise and natural movements. Leveraging sophisticated signal processing algorithms, the prosthetic leg will respond intuitively to the user’s intentions, providing unprecedented levels of mobility, functionality, and comfort. This research not only addresses the immediate needs of amputees by offering a highly adaptive and user-centric prosthetic solution but also contributes to the broader field of assistive technologies, paving the way for enhanced quality of life and autonomy for individuals with limb loss.
Contact: bhivraj@iitj.ac.in
Bhivraj Suthar School of AIDE/CBSA
The research focuses on advancing the capabilities of in-hand manipulation through the integration of vision-based techniques and artificial intelligence (AI). By leveraging computer vision algorithms and machine learning models, the system will be able to perceive and analyze the objects held in-hand, enabling precise manipulation and interaction. This technology holds significant potential for various applications, including robotics, manufacturing, and healthcare. Through the seamless integration of AI-driven vision systems, the research aims to revolutionize the way objects are handled and manipulated, paving the way for more intuitive and efficient human-machine interactions.
Contact: bhivraj@iitj.ac.in
Saurabh Gandhi School of AIDE/CBSA
In this project, the student will first build a computational model of the whole brain to simulate some experiments. These experiments can potentially help in detecting the onset of neurodegenerative diseases like dementia at very early stages. In the second part of the study, they will validate the models with actual experiments, and then generate a large experimental dataset.
Strong quantitative background with undergraduate training in physics, computer science, electrical engineering or other quantitative fields is required. Keen interest in applying physics to biological systems: MRI, electromagnetism, dynamical systems (thorough knowledge of one or more of these will be a plus point). Students looking for a career in academia are preferred (the PI will be able to provide best guidance and advice to such a student).
Contact: sgandhi@iitj.ac.in
Saurabh Gandhi School of AIDE/CBSA
The student will design, perform experiments and analyze data to understand the neural impact of smartphone overuse, and its relationship to gambling. The experiments will involve EEG based paradigms. Generous top-up funding is available starting Feb. 2025.
Students looking for a career in academia are preferred. We are looking for applicants with a background in quantitative fields such as CS, EE, Physics, and interested in experimental neuroscience OR backgrounds in biology / psychology, interested in learning and applying computational techniques and experimental neuroscience.
Contact: sgandhi@iitj.ac.in
Shilpa Dang School of AIDE/CBSA
Brain connectivity analysis using fMRI data
Contact: sdang@iitj.ac.in
Vignesh Muralidharan School of AIDE/CBSA
In everyday life, we are faced with situations where we have to control what to do, what to say, and what we think. How are we able to stop unwanted/inappropriate actions, intrusive thoughts that bother us, and negative emotions which hinder our ability to perform daily tasks? Impairments in inhibitory control manifests as disorders such as Tourette’s Syndrome (uncontrollable actions or tics), obsessive compulsive disorder and anxiety disorders. Using neurofeedback, behavioral, and non-invasive brain stimulation approaches we will explore ways in which the underlying brain networks can be modulated to have functional effects on controlling actions and thoughts.
Contact: vigneshmdharan@iitj.ac.in
Abhilasha Maheshwari Chemical Engineering
Modelling, AI-IOT based architecture for Digital Twin framework and two-way communication from physical system to virtual system for developing a fully synchronized Digital twin.
Contact: abhilasham@iitj.ac.in
Angan Sengupta Chemical Engineering
The goal of our project is to enable and advance the emerging field of data-driven research
(broadly covering the use of
artificial intelligence (AI), machine learning (ML), data science,
informatics, as well as modelling and computational research) for the development of novel
carbon-capture materials and value-added products from CO2 in conjunction with the use of molecular simulations. The present work, therefore, aims to reduce the computational cost in the search and design of new materials and valorisation products by bypassing the computational intensive DFT calculations. The other objective of the problem is to design the complete pilot system using 1st principle-induced AI algorithms, which can be implemented in existing industries (viz. Methane reforming plants, Steel Plants, Biogas production units, etc.) with high carbon footprints thus making the process more environment friendly.
Contact: angan@iitj.ac.in
Ranju Mohan Civil and Infrastrcuture Engineering
An integrated framework of traffic flow and driver behavior models, at network level, using a hybrid (micro +macro) modelling approach will be developed. Traffic congestion will be modelled through macroscopic approach (continuum modelling of traffic flow) and driving behavior will be modelled through microscopic approach. Preliminary model development use data feed from driving simulator experiments, and later will be validated and will be refined using data feed from an instrumented vehicle. The instrumented vehicle i.e. OBU (On Board Unit) will be used to collect field data on driving behavior and driving patterns in different traffic environments that will be used to develop realistic models of driver behavior in heterogeneous and unorderly traffic conditions. AI techniques such as deep learning will be used to process data from these sensors in real-time. A fusion of microscopic traffic flow theories and machine learning algorithms will be used to learn the driving behavior parameters.
Contact: ranju@iitj.ac.in
Saran Aadhar Civil and Infrastrcuture Engineering
Despite the large efforts from various national or International organizations for drought and water stress monitoring in India, the existing drought or water stress monitoring systems are unreliable in providing city-level water stress information and show significant differences in the actual drought conditions. The existing drought monitoring systems do not consider the human influence on the water system, which causes differences in the actual drought conditions. Therefore, there is an urgent need to include regional human activities in drought monitoring using the hybrid approach (hydrological modeling and machine learning).
Contact: saran.aadhar@iitj.ac.in
Deepak Mishra Computer Science and Engineering
This project aims to develop deep learning approach for analysis of whole slide images for diagnostic pathology. The approach would be helpful in identifying tissue histotypes and predicting status of different biomarkers.
Contact: dmishra@iitj.ac.in
Amit Bhardwaj Electrical Engineering
Here, in this project, we would like to relate the psychophysical findings for texture perception with the neurological data (EEG). This project will be in joint collaboration with Dr. Vignesh.
Contact: amitb@iitj.ac.in
Nishant Kumar Electrical Engineering
This project focuses on developing AI tools for monitoring the health and detecting faults in renewable energy systems, aiming to improve their reliability and efficiency. By leveraging advanced algorithms, it seeks to enhance the performance and longevity of these systems, ultimately contributing to a more sustainable energy future.
Contact: nishantkumar@iitj.ac.in
Harshal Akolekar Mechanical Engineering
Using machine learning methods such as gene expression programming and neural networks,
models are developed to improve the turbulence and transition modelling for gas turbine applications. This project would be in collaboration with the aerospace industry. Should be familiar with machine learning and python. Experience in fluid mechanics is an additional bonus.
Contact: harshal.akolekar@iitj.ac.in
Anuj Pal Kapoor School of Management and Entrepreneurship
Developing a mobile app for digital wellbeing, by harnessing the power of multi-sensory experiences
Contact: anujkapoor@iitj.ac.in
Chhanda Chakraborti SoLA
Braod Area: AI and Ethics.
In automated decisions, often the concern is that the decision may be efficient and informed and yet not ‘good’ in an ethical sense, e.g. it may violate the right of some stakeholders. This project tries to define what ‘goodness’ of a decision may mean in case of AI systems, and strives to find whether decision-making algorithms can possibly include ‘goodness’ as one of the parameters.
Contact: chhanda@iitj.ac.in