Summer Internships Offered @ IIITD
The summer internship program 2021. Each year our faculty members invest their time, effort & innovation in some great real-time projects.The projects are listed below. A student can apply for a max of 03 projects. The last date to apply is 31st March 2021. The results will be up on the website by 15th April & the internship will commence from 6th May 2021.
Process to apply for internship
- The details of the project openings are listed below.
- Click on Apply Here (From 24th Feb 2021)
- Login with your google account
- Fill the registration form. If you want hostel select the checkbox accordingly.
- For hostel booking details please click here
- To apply for any project just click the apply button
- Students who get selected for the summer internship at IIIT-Delhi will be receiving a stipend of 5k per month
Summer Internship Projects 2021-2022 at IIIT-D
|Medical imagining application to create photorealistic inside views of human body using radiological imaging.
|XR shape modelling
|Free form 3D Shape modelling in extended reality (AR/VR).
|Camera optimization for capturing dynamic scenes
|Real-world geometry can be captured and reconstructed using multi-view stereo algorithms. This project will explore camera pose optimization for dynamic scenes (i.e, those with moving objects in addition to static objects).
|Social network analysis + NLP
|Various aspects of social media analytics
|Building Novel Augmented Reality and Virtual Reality applications
Selected students will have the chance to work on a cutting-edge research project to be published in world's best conferences or release their work on App store/Google Play store or for a fundraising campaign.
We offer the best HCI research environment in India and world-class infrastructure with latest gadgets such as Oculus Quest 2 and iMac workstations.
|Rajiv Ratn Shah
|Multimedia and AI for Social Good
|Multimedia and AI for Social Good
|Engagement analysis using EEG Signals and Machine Learning
|The aim of the project is to evaluate attention level among using EEG signals and Machine Learning.
|Scalable Vehicle Detection on Edge Devices
|The safety of smart cities can be improved by identifying vehicles that are violating traffic rules. While a lot of work has been done to design computer vision based algorithms for such detection, they do not currently scale well if done on a city-scale. The goal of this project is to make such algorithms scalable, by identifying ways of reducing the compute or using more intelligent scheduling of jobs on edge devices. This requires interest in systems work as well as some basic understanding of algorithms and computer networks.
|Reinforcement Learning for Algorithmic Trading
|The MAB problem is a sequential decision-making framework where an agent chooses one or multiple actions, from a set of actions, based on the feedback of rewards from the previous choices. The MAB framework has found applications in the field of randomized clinical-trials, online recommendation systems, computational advertisement, and wireless communications. Advances in computational, storage and communication capacity have also led to the rapid advancement of deep-learning including deep-reinforcement learning (deep-RL) solutions.
In this project, the student will explore bandit frameworks for algorithmic trading and propose novel bandit algorithms tuned for algorithmic trading. In particular, algorithmic trading consists of mechanisms enabling automated execution of pre-programmed trading instructions by taking into account variables such as time, price, and volume. Thus, the aim is to leverage the speed and computational resources of computers relative to human traders.
|Intelligent reflective surface implementation
|The student would be require to assist in implementation of intelligent reflective surface (also known as RIS (reconfigurable intelligent surfaces)). A good background on antenna design or RF circuits is mandatory. Knowledge on wireless communication systems and Matlab coding will be an added advantage.
|Indoor localisation using Visible light communication
|The student would be require to assist in implementation of indoor localization system using visible light communication (VLC). A good background on wireless communication systems and Matlab coding will be needed. Knowledge of LabVIEW and GNU Radio will be an added advantage.
|Machine Learning for Wireless Networks
|Read these papers:
To get shortlisted, email a 1-pager explaining your understanding of these papers.
Email id: email@example.com
|High Performance Computing
|Parallel programming in C/C++
|AI for Social Good
|AI for Social Good with Rajiv Ratn Shah
|Md Shad Akhtar
|Emotion Dynamic in Code-mixed Conversation
|To understand the dynamics of user's emotion in a multiparty code-mixed conversation.
|Discretizations of Exterior Calculus for Analysis, Geometry and Topology
|The library https://github.com/dialkforkaushik/decagt has been developed at IIIT-Delhi and essentially implements in C++ computations for discrete exterior calculus and higher-order finite element exterior calculus. The current implementation has elements of multicore computations in it. The goal of the project for this summer would be two fold and independent of each other. One is to extend this package by porting some of the parallelizable computations to accelerators ("GPUs"). Such an implementation would necessarily involve, for instance, rethinking some data structures in the library (and supporting computational kernels) to make them more amenable to this parallelization. A second aspect of the project would be to improve some key aspects of the library's computational performance. While this is a more software engineering goal, it will be informed by the fundamentals of the computations rather than simply evaluating hot spots (or like) via black box performance analyzers.
|Analysis of Plant Pollinator Network
|Using network approaches to analyse the plant pollinator network and predict the stability and resilience of the ecosystem
|The project involves biology experiments and observational data collection involving plant-insect and predator-prey interactions.
|Insect pollinator Conservation
|This project involves specimen preparation, pinning, and microscope photography for the digitisation of insect specimen and pollen grains
|Developing tools and technologies for a sustainable planet
|We are developing novel technological approaches based on UAVs, harmonic radar, network analysis and modelling, computer vision, and machine learning to improve the ecological data collection, processing, and analysis
|Koteswar Rao Jerripothula
|Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. In this project, we will explore and implement novel ideas for performing TL.
|Koteswar Rao Jerripothula
|Self-supervised learning is autonomous supervised learning. It is a representation learning approach that eliminates the pre-requisite requiring humans to label data. Self-supervised learning systems extract and use the naturally available relevant context and embedded metadata as supervisory signals. In this project we will explore and implement novel methods for Self-supervised Learning
|Koteswar Rao Jerripothula
|Medical Image Computing
|Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care. In this project, we will try to solve a novel MIC problem.
|Ethics of AI, Digital Ethics, Civil Disobedience
|The internship requires the student to work on themes of Ethics of AI, Digital Ethics, or Civil Disobedience
|Locally interpretable ML models in Survival Analysis
|In this project, we design locally interpretable models in the context of survival analysis. In particular, we look at Bayesian methods for measuring uncertainty in interpretations.
|Explainable AI methods using Bayesian techniques for tabular data
|In the context of tabular data, interpretable AI methods fail due to inconsistency issues. Here, we use Bayesian techniques to extend the statistics learnt from the training data for explainability.
|Computational Gastronomy Framework
|To build an integrative computational gastronomy framework constituting of flavor (FlavorDB), recipes (RecipeDB), and health (DietRx).
|To build APIs for the Ayurveda Informatics framework