Aakanksha Mathuria
Member Of Technical Staff at Oracle
Member Of Technical Staff at Oracle
Oracle
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Member Of Technical Staff
Portland State University
https://www.linkedin.com/company/portland-state-university
Portland, Oregon
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Graduate Research Assistant
Portland State University
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Portland, Oregon, USA
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Research Intern
Indian Institute Of Information Technology
https://www.linkedin.com/company/indian-institute-of-information-technology-allahabad-india
Allahabad, India
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Teaching Assistant, Information Retrieval
Portland State University
https://www.linkedin.com/company/portland-state-university
Portland, Oregon, USA
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Research Intern
Master of Technology - MTech
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Indian Institute Of Information Technology
https://www.linkedin.com/school/indian-institute-of-information-technology-allahabad-india/
Master of Science - MS
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Portland State University
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Bachelor of Technology - BTech
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Indian Institute of Information Technology
https://www.linkedin.com/school/indian-institute-of-information-technology-allahabad-india/
With recent developments in deep networks, there have been significant advances in visual object detection and recognition. However, some of these networks are still easily fooled/hacked and have shown "bag of features" failures. Some of this is due to the fact that even deep networks make only marginal use of the complex structure that exists in real-world images, even after training on huge numbers of images. Biology appears to take advantage of such a structure, but how?

In our research, we are studying approaches for robust pattern matching using still, 2D Blocks World images based on graphical representations of the various components of an image. Such higher order information represents the "structure" of the visual object. Here we discuss how the structural information of an image can be captured in a Sparse Distributed Representation (SDR) loosely based on cortical circuits.

We apply probabilistic graph isomorphism and subgraph isomorphism to our 2D Blocks World images and achieve O (1) and O (n^k) complexity for an approximate match. The optimal match is an NP-Hard problem. The image labeled graph is created using OpenCV to find the object contours and objects' labels and a fixed radius nearest neighbor algorithm to build the edges between the objects. Pattern matching is done using the properties of SDRs. Our research shows the promise of applying graph-based neuromorphic techniques for pattern matching of images based on such structure.
Approximate Pattern Matching using Hierarchical Graph Construction and Sparse Distributed Representation
ICONS '19 Proceedings of the International Conference on Neuromorphic Systems / ACM New York, NY, USA ©2019
https://dl.acm.org/citation.cfm?doid=3354265.3354286
Course project: Deep Learning Theory and Practices - Implemented a recommendation system using the Wide-Deep model. The system achieves both memorization and generalization together. The neural network is a combination of a feed-forward model with embeddings and a linear model with feature transformations for generic recommendation with sparse inputs.

Languages and Tools: Python, Google Colab, PyTorch
Built a Recommendation System using Wide-Deep model
Course Project: Mathematical Foundation of Machine Learning - Implemented a Low-rank matrix completion system using GROUSE (Grassmannian Rank-One Update Subspace Estimation) algorithm, an online identification, and a tracking algorithm for subspaces using highly incomplete data.

Languages and Tools: Matlab
Low rank matrix completion using GROUSE algorithm
https://github.com/aakanksha14/Low-Matrix-Completion-GROUSE-Algorithm
Course Project: System-on-chip using FPGAs - Implemented the classic NES game Duck Hunt, utilizing the Nexys4 DDR FPGA to run the game and an Android phone as a shooting device. The Android phone interfaces with the FPGA board using Bluetooth. Gyroscope information is used for moving the cursor on the screen. The game mechanics and logic are implemented using the MIPS core on the FPGA. The game is displayed on a monitor using a colorizer to a VGA output.

Languages and Tools: Verilog, MIPS Assembly, Java, ModelSim, Xilinx Vivado, Android Studio, Nexys4 DDR FPGA
Built Duck Hunt Game using Nexys4 DDR FPGA and Android device
Course Project: Computer Vision - Implemented an embedded vision system with BeagleBone Blackboard and Logitech Internet Video camera. Did time analysis for different resolutions for edge detection and feature tracking methods.

Language/s and Tool/s: Python, OpenCV, BeagleBone Blackboard
Time analysis for Embedded Vision System on Beagleboard
Course Project: Microprocessor System Design - Designed and simulated a cache coherence system between a four-way set-associative instruction cache, an eight-way set associative data cache, and a shared cache using MESI protocol and LRU replacement policy.

Language/s and Tool/s: Verilog, ModelSim
Built a Cache Coherence Simulator using MESI and LRU
Course Project: Embedded Systems Programming - The aim of this project is to convert the mobile device orientation into color on the 360-degree color wheel. The color is being displayed on the app’s screen and also on the LED connected to an Android Things device. The orientation is determined using the accelerometer data sensor readings from the android sensor manager.

Language/s and Tool/s: Java, Android Studio, Google Firebase, Bluetooth, Android Things
Built a Color display system using Accelerometer sensor (Wall of fame – runner up)
Developed a software system which generates captions from the videos to describe the visual content and relationship among the visual features. This is done by summarizing the keyframes, creating classes for captions and train the system for the caption classes. For extracting features CNN model is used.

Advisor: Dr. Uma Shanker Tiwary
Built a system for Description of videos in the textual form
Summer Project - The goal of the project was to develop algorithms that can convert sign language into textual form. A Kinect camera is used for recording the input depth data. The software works for numbers identification from 0 to 10 and also for a set of alphabets signs which are used all over the world that can be identified using the depth data. To detect and recognize signs, we pass the information regarding hands through three types of classifiers.

Advisor: Dr. Anupam Agarwal
Sign Language Alphabets and Digits Recognition using Kinect in Real Time
This project is an extension of the project ‘Hybrid Intelligence Approach for 3D Bin Packing Problem’ with more than one container. The project is based on AI and ML techniques. The aim of the project is to place different kinds of boxes into different kinds of containers while maintaining high volume utility with minimum leftovers.

Advisor: Dr. Anupam Agarwal
Container Loading Optimization Problem
The main purpose of this project is to place the boxes optimally and efficiently in a single container. We applied a discrete optimization method GH5E (greedy heuristic with five evaluations). This project is under the framework of 'Distributing Industrial Optimization Tasks to Rural Workers' under the collaboration between the University of Strathclyde and IIIT Allahabad. Final deployment would be at several rural BPO offices to show that industrial tasks can be outsourced to rural workers providing a sustainable source of skilled employment.

Advisor: Dr. Anupam Agarwal
Hybrid Intelligence Approach for 3D Bin Packing Problem
Had done a research project on machine learning of high dimensional data using random projections. This is an applied computational theory project based on a generalization of Johnson-Lindenstrauss algorithm, proposed by Prof. Sharath Raghavendra from Virginia Tech. As a part of this project, I implemented k-mean clustering using reduced dimensions, k-means streaming, and coreset implementation to understand the algorithm proposed.
Study on Generalized Johnson Lindenstrauss Theorem
Arduino is a fast prototyping platform that can be expanded for Internet of things. Intel Edison kit comprises of a powerful 22 nm SoC (system on chip) with an atom core and multiple sub systems. We implemented several functions.
Arduino and Intel Edison Hardware platform (Various implementations)
Sasi Manipatruni
      
          



            
              
        
              
  

    
        Aakankha is bright and smart and has the mathematical acumen to do great work in computer sciences. I guided her during JEE preparation and found her to be an excellent student who works hard without breaking a sweat. I expect to see great things from her and recommend her for any opportunity she apples to.