Trisha Mittal

I am a Computer Science graduate student at University of Maryland, College Park. I joined the graduate program in August 2018 (Fall 2018).

I graduated with a Bachelor of Technology (B.Tech) and a Master of Technology (M.Tech) in Information Technology from International Institute of Information Technology, Bangalore (IIIT-B), India. This was a five-year dual-degree undergraduate program (August 2013 - July 2018).

My research is broadly in Affective Computing. I am currently focusing on the perceived human emotion recognition by fusing multiple modalities and also using contextual information.


Assisted Inverse Reinforcement Learning

We study the problem of inverse reinforcement learning (IRL) with the added twist that the learner is assisted by a helpful teacher. More formally, we tackle the following question: How could a teacher provide an informative sequence of demonstrations to an IRL agent to speed up the learning process? We prove rigorous convergence guarantees of a new iterative teaching algorithm that adaptively chooses demonstrations based on the learner’s current performance. Extensive experiments with a car driving simulator environment show that the learning progress can be speeded up drastically as compared to an uninformative teacher.

MPI, Saarbrucken, Germany

P. Kamalaruban, R. Devidze, T. Yeo, T. Mittal*, V. Cevher, A. Singla

Pictionary-style Word Guessing on Hand-drawn Object Sketches: Dataset, Analysis and Deep Network Models

The ability of intelligent agents to play games in human-like fashion is popularly considered a benchmark of progress in Artificial Intelligence. In our work, we introduce the first computational model aimed at Pictionary, the popular word-guessing social game. We first introduce Sketch-QA, a guessing task. We analyze the resulting dataset and present many interesting findings therein. To mimic Pictionary-style guessing, we propose a deep neural model which generates guess-words in response to temporally evolving human-drawn object sketches. Our model even makes human-like mistakes while guessing, thus amplifying the human mimicry factor. We evaluate our model on the large-scale guess-word dataset generated via Sketch-QA task and compare with various baselines.

IISc Bangalore, India

Ravi Kiran Sarvadevbatla, Shiv Surya, Trisha Mittal*, Venkatesh Babu Radhakrishnan

In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018

A Logo-Based Approach for Recognising Multiple Products on a Shelf

This paper addresses detecting, localizing and recognizing various grocery products in retail store images. Our object recognition algorithm achieves this goal using just one image per product for training, assuming that the category of the products (like cereals, rice, etc.) is known. This algorithm uses logo detection as a precursor to product recognition. So, the first step involves detecting and classifying products, at a broader level, based on their brands. The second step is the finer classification step for recognizing and localizing the exact product label, which involves using colour information. This hierarchical approach limits the confusion in classifying similar looking products and outperforms product recognition that was implemented without logo detection.

IIIT Bangalore, India

Trisha Mittal*, B. Laasya, and J. Dinesh Babu

SAI Intellisys 2016

Course Projects

Viz-Debugger for PostgreSQL

Existing DBMS's are equipped with syntactical checkers. But, often the queries do not produce the expected results because of some logical errors. These errors can often go unnoticed. We developing a prototype for a visual debugger to ease the debugging of recursive and long SQL queries. Our debugger uses informative visualisations like Query Node Graphs. We also incorporate provenance, another important aspect which can aid the degugging process.

University of Maryland, College Park

Course: Interactive Data Analytics (CMSC 828D) offered by Prof. Leilani Battle

Modeling User Engagement and Disengagement metrics in Educational Videos

VideoKen is an online platform for educational videos. It enables the learning process by adding features that will help learners while watching educational videos. Our work involved understanding what led to disengagement of users while watching the educational videos. Also, what were possible interventions the system could provide to engage the users back.

IIIT Bangalore, India

Semester Project guided by Prof. Manish Gupta (founder of VideoKen)

Smart Shoe

Developed a prototype on Intel Edison platform for a smart shoe which establishes the relationship between the physical inactivity and exposure to polluted environment. The project involved both software and hardware experise. Incorporated accelerometers, pressure sensors and gas sensors and used naive machine learning algorithms like k-Nearest Neighbours and Decision Trees.

IIIT Bangalore, India

Summer project guided by Prof. Madhav Rao


CMSC 828I: Advanced Techniques in Visual Learning and Recognition

Prof. Abhinav Shrivastav

CMSC 726: Machine Learning

Prof. Soheil Feizi

CMSC 798: Independent Research Project

Prof. Dinesh Manocha

University of Maryland, College Park

CMSC 818N: Robotics

Prof. Dinesh Manocha

CMSC 764: Numerical Optimisation

Prof. Tom Goldstein

CMSC 798: Independent Research Project

Prof. Dinesh Manocha

University of Maryland, College Park

CMSC 828D: Interactive Data Analytics

Prof. Leilani Battle

CMSC 723: Computational Linguistics I

Prof. Jordan Boyd-Graber

University of Maryland, College Park

CS/DS 704 Multi-Agent Systems

Prof. Srinath Srinivasa

CS 551 Introduction to Automata Theory & Computability

Prof. Shrisha Rao

CS/DS 812 Foundations of Big Data Algorithms

Prof. G Srinivasaraghavan

CS/DS 864 Machine Learning

Prof. G Srinivasaraghavan

DS/NC 866 Advanced Machine Perception

Prof. Dinesh babu Jayagopi

CS 606 Computer Graphics

Prof. Jaya Nair and Prof. T K Srikanth

DS/NC 821 Automatic Speech Recognition

Prof. V Ramasubramanian

IIIT Bangalore, India


CMSC 250

University of Maryland, College Park

Course Instructor: Prof. Clyde Kruskal

CMSC 250

University of Maryland, College Park

Course Instructor: Jason Filippou


Visiting Research Fellow at Max-Planck Institute for Software Systems, Saarbrucken, Germany

Interned under Prof. Adish Singla and worked on problems at the intersection of Inverse Reinforcement Learning and Machine Teaching.

Intern at Video Analytics Lab, Indian Institute of Science, Bangalore, India.

Guided by Prof. Venkatesh Babu in an attempt to train a RL agent to be able to draw sketches.

Intern at Video Analytics Lab, Indian Institute of Science, Bangalore, India.

Guided by Prof. Venkatesh Babu and worked with Ravikiran Sarvadevbatla and Shiv Surya to develop computational models for Pictionary-style word guessing.