Amlaan Bhoi

I am a graduate student in Computer Science at University of Illinois at Chicago where I'm working on action recognition. I expect to graduate in Spring 2019. My advisor is Xinhua Zhang. I am currently interning as a R&D Intern at CCC Information Services where I work in the Photo Analytics (Computer Vision) team. I am also an Intel AI Student Ambassador through which I share upcoming research on Artificial Intelligence, Machine Learning, & Deep Learning. I graduated from Amity University with a B.Tech in Computer Science & Engineering in May 2017 with First Class honors where I was advised by Sushil Kumar.

I love reading machine learning papers on Arxiv Sanity and am always keen on learning something new! I also work on iOS development and Apple's ARKit to develop augmented reality experiences.

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Research Interests

I'm interested in computer vision, machine learning, statistics, optimization, image processing, augmented reality, and computational photography. Specifically, I'm interested in spatio-temporal action recognition, object detection, and image segmentation.

News
  • [Aug 2018] Continuing my internship at CCC Information Services for the Fall 2018 semester.
  • [Aug 2018] Featured on Intel's website discussing my work, computer vision problems, and how Intel can help. Check it out here!
  • [Jun 2018] Presented poster on Tiramisu DenseNet Architecture for Precise Segmentation at Intel AI Booth at CVPR 2018.
  • [May 2018] Joined as an Intern, R&D in the Photo Analytics and Machine Learning group at CCC Information Services.
  • [Apr 2018] Joined Intel AI Ambassador Program.
  • [Jan 2018] Mentioned in UCSC newsletter for developing a low-poly VR application (Google Pixel 2 + DayDream) at CruzHacks 2017.
  • [Oct 2017] Awarded Best Microsoft Hack at HackHarvard 2017.
  • [May 2017] Awarded Best Technical Innovation at Amity University Convocation 2017.
Papers
A Comprehensive Comparison between Neural Style Transfer and Universal Style Transfer
Somshubra Majumdar, Amlaan Bhoi, Ganesh Jagadeesan
arXiv Preprint, 2018
arxiv | code

Style transfer aims to transfer arbitrary visual styles to content images. We explore algorithms adapted from two papers that try to solve the problem of style transfer while generalizing on unseen styles or compromised visual quality. Majority of the improvements made focus on optimizing the algorithm for real-time style transfer while adapting to new styles with considerably less resources and constraints. We compare these strategies and compare how they measure up to produce visually appealing images. We explore two approaches to style transfer: neural style transfer with improvements and universal style transfer. We also make a comparison between the different images produced and how they can be qualitatively measured.

3DSP Various Approaches to Aspect-based Sentiment Analysis
Amlaan Bhoi, Sandeep Joshi
arXiv Preprint, 2018
arxiv | code

The problem of aspect-based sentiment analysis deals with classifying sentiments (negative, neutral, positive) for a given aspect in a sentence. A traditional sentiment classification task involves treating the entire sentence as a text document and classifying sentiments based on all the words. Let us assume, we have a sentence such as "the acceleration of this car is fast, but the reliability is horrible". This can be a difficult sentence because it has two aspects with conflicting sentiments about the same entity. Considering machine learning techniques (or deep learning), how do we encode the information that we are interested in one aspect and its sentiment but not the other? Let us explore various pre-processing steps, features, and methods used to facilitate in solving this task.

Projects
Conditional Random Fields for Structured Output Prediction
Amlaan Bhoi, Somshubra Majumdar, Ganesh Jagadeesan
Advanced Machine Learning, Spring 2018
code | report

An optical character recognition system to detect letters and words using conditional random fields.

  • Implemented linear-chain Conditional Random Fields from scratch to detect characters on UPenn OCR dataset.
  • Implemented the Viterbi algorithm for forward-backward message passing between nodes, calculated the log probabilities and gradients, and used LBFGS solver to reach convergence.
  • Achieved 84% letter-wise accuracy with dynamic programming implementation.
  • Wrote a PETSc/Tao version to run on ACER cluster in parallel using MPI code.
  • Implemented SGD with Nestorov Momentum, AMSGrad, and Adam with MCMC for CRFs to compare with LBFGS implementation and plot comparison charts on different λ values.

MS Apriori: Rule Mining with Multiple Minimum Supports
Amlaan Bhoi, Sandeep Joshi
Data Mining & Text Mining, Spring 2018
code

An association rule mining (unsupervised learning) algorithm with multiple minimum support. This algorithm can be used for product recommendations based on historical data.

Alethea: Data science, visualization, and analysis
Somshubra Majumdar, Amlaan Bhoi, Debojit Kaushik, Christopher Alphones
Introduction to Data Science, Spring 2018
code | demo

An ETL pipeline, visualization, classical ML prediction, and ML&DL sentiment analysis application on publicly available Chicago and Yelp data.

  • Performed data discovery, integration, and visualization on Chicago datasets using Pandas, Numpy, and React Recharts.
  • Achieved 81.9% sentiment analysis accuracy using Multiplicative LSTMs on Yelp Reviews dataset.
  • Achieved 91.3% accuracy predicting types of robberies occuring in Chicago for the Summer of 2018 based on previous crime and weather datasets.
Lifeguard: Action Recognition of Drowning while Swimming
Sudipta Swarnakar, Amlaan Bhoi, Chetan Velivela
HackHarvard, 2017
devpost

We trained a 3D Convolutional Neural Network model on Microsoft Azure to detect drowning people in swimming pools. We also created the bounding boxes for our train, test, and validation set.

ARYouThereYet
Sandeep Joshi, Amlaan Bhoi, Debojit Kaushik
Virtual and Augmented Reality, Fall 2017
project page | code | video

An ARKit iOS application utilizing Google Maps and Mapbox APIs to show nearby attractions in Augmented Reality with support for visualizing the distance, detailed description of places, an AR walking guide to destinations, support for saving favorite places, and more.

Autocolor: Color Segmentation using Clustering
Amlaan Bhoi Summer, 2017
code

A K-means clustering algorithm using OpenCV and Scikit-Learn that detects K dominant colors in an image. Autopicks K using Silhouette Coefficient metric and MiniBatchKMeans for testing.



"If we want machines to think, we need to teach them to see" ~ Fei-Fei Li
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