Homanga Bharadhwaj

homangab at cse dot iitk dot ac dot in

Hi! I am a senior undergraduate in the Department of Computer Science and Engineering at IIT Kanpur. I am engaged in the quest for Artificial General Intelligence. Although this quest has kept me fully occupied for the past three years, I also paint, and have a Bachelor of Arts degree in Fine Arts. Some of my paintings can be found here.

I am fortunate to have been advised by fantastic researchers over the course of my undergraduate studies. Most recently, I have worked under the guidance of Prof Yoshua Bengio and Prof Liam Paull at Mila, Montreal during the summer of 2018. My work at Mila focused on the problem of sim-to-real transfer of deep learning based planning algorithms in robotic navigation. Prior to this, I spent a wonderful winter at NUS, Singapore in Prof Brian Lim's lab. I continued collaborating remotely and successfully completed two projects on Recommendation Systems and Multi-Task Learning respectively. In IIT Kanpur, I work with Prof Nisheeth Srivastava on Human-Computer Interaction and with Prof Piyush Rai on Zero-Shot Learning and Program Correction.

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I'm interested in deep reinforcement learning, robotics, machine learning, computer vision and explainable AI. Much of my current research is about developing efficient planning algorithms for robotic navigation. My previous research was focused on designing deep learning (and meta-learning) based algorithms, primarily for recommender systems and trying to make those algorithms explainable. I have explored a wide breadth of research topics around AI during my undergraduate studies, which have led to multiple primary author publications. Some of my papers are mentioned below.

Most significant bits
A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies
Homanga Bharadhwaj, Zihan Wang, Yoshua Bengio, Liam Paull
ICRA, 2019  

Adversarial domain adaptation can be used for training a gradient descent based planner in simulation and transferrring the learned model to a real navigation environment.

RecGAN: Recurrent Generative Adversarial Networks for Recommendation Systems
Homanga Bharadhwaj, Homin Park, Brian Y. Lim
RecSys, 2018

Recurrent Neural Network based Generative Adversarial Networks can learn to effectively model the latent preference trends of users in time-series recommendation.

Meta-Learning for User Cold-Start Recommendation
Homanga Bharadhwaj
IJCNN, 2019  

A Meta-Learning strategy can be used to develop a recommendation model that performs resonably good enough for a wide range of users and that can be cost-effectively updated during test time for a specific user

A Hierarchical Multi-Task Learning Framework for Healthy Drink Recognition
Homin Park, Homanga Bharadhwaj, Brian Y. Lim
IJCNN, 2019  

A Hierarchical Multi-Task Learning model can leverage several auxiliary tasks like detection of container properties to overcome the limitation of insufficient visual cues for drinks, while predicting the healthiness of drinks from their images

My tryst with HCI research
New tab page recommendations cause a strong suppression of exploratory web browsing behaviors
Homanga Bharadhwaj, Nisheeth Srivastava
WebSci, 2019

Passive website recommendations embedded in the new tab displays of browsers (that recommend based on frecency) inhibit peoples' propensity to visit diverse information sources on the internet

Less significant bits
A Synchrophasor Assisted Optimal Features based Scheme for Fault Detection and Classification
Homanga Bharadhwaj Avinash Kumar Abheejeet Mohapatra
IJCNN, 2019

An optimal features' classifier developed using evolutinary heuristics can be used for real time fault detection and identification

Layer-wise Relevance Propagation for Explainable Recommendations
Homanga Bharadhwaj
EARS Workshop, SIGIR, 2018

Layer-wise relevance propagation can be used for explaaining the predictions of a convolutional neural network based recommendation model

Explanations for Temporal Recommendations
Homanga Bharadhwaj, Shruti Joshi
XAI Workshop, IJCAI, 2018  

A neighborhood style explanation scheme can be used as an auxiliary mechanism for interpreting the predictions of a Recurrent Neural Network based temporal recommendation model

Layer-wise relevance propagation for explainable deep learning based speech recognition
Homanga Bharadhwaj,
ISSPIT, 2018

Layer-wise Relevance Propagation can used for explaining the predicitons of a Bi-directional Gated Recurrent Unit based speech recognition model

My freshman year dabble with Quantum Entanglement

Phase matching in Spontaneous Parametric Down Conversion
Suman Karan, Shaurya Aarav, Homanga Bharadhwaj, Lavanya Taneja, Girish Kulkarni, Anand K Jha

Spontaneous Parametric Down Conversion is used to generate entangled photon pairs. SPDC can be studies through the lens of Wave Optics by making some simplifying theoretical assumptions without compromising on empirical results. Also, a simulation for SPDC can be conveniently designed, given the assumptions.



Teaching Assitant (TA),
Computational Cognitive Science (CS786), Winter 2019

Course Project Mentor,
Topics in Probabilistic Modeling and Inference (CS698), Winter 2019

Course Project Mentor,
Introduction to Machine Learning (CS771), Autumn 2018


Student Volunteer at ACM RecSys 2018, Vancouver, BC

Student Volunteer at ACM SIGIR 2018, Ann Arbor, Michigan

Reviewed manuscripts for the journal Computers in Human Behavior and the conference ICRA 2019

I love his website design.

Miscellaneous stuffs - Co-authors  /  Fav books  /  My paintings  /  Travel  /  Love  /  ResearchIntern  / 

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