Natasha Mittal

I’m in first year of my Ph.D. in Computer Science at UCSC. Currently, passionate about designing, building and deploying consistent and fault tolerant middleware for enhancing efficiency and reliability of distributed systems.

General Info

  • HobbiesCooking, Watching Movies, Travelling
  • LocationSanta Cruz, California, USA

Work Experience

  • 2018-Present

    Languages, Systems and Data Lab, UCSC

    Research Assistant

    Exploring consistency models and fault-tolerant algorithms required for distributed database. systems. Researching on middleware that enables current benign systems to adopt byzantine-fault-tolerant protocols.

  • 2017-2018

    Autonomous Agents and Intelligent Robotics Lab, ASU

    Research Assistant

    Worked on Planning Networks project with UC-Berkley which includes use of Deep Learning networks to solve Sokoban and TSP.Explored the field of Active Perception using partially-observable Markov Decision Processes (POMDPs).

  • 2014-2017

    Nextag Inc., India

    Senior Software Engineer

    End-to-End development and deployment of platform-independent APIs followed by integration testing with frontend. Discussion, design and implementation of team-specific requirements for improvising current backend framework. Performance Testing of Cache Services in real-time environment on servers using Visual VM and JMX. Load Balancing of servers and monitoring client-server request threads for Cache Service optimizations.


    • Platform Independent Cache Services

      Designed and deployed platform-independent cache services for efficient processing and fast retrieval of data from database in the real-time client-server environment which helped in the smooth transition to Amazon Cloud.

    • Database Replaced - Cassandra/MySQL to Aerospike

      Developed batch & real-time scrubbers to modify data in NoSQL database like Aerospike by modeling complex data structures which improved websites response time by 20%

    • Database Failed Events Reprocessing Framework

      Created and implemented the framework from scratch by pushing real-time database failed events to HBase via Apache Kafka for further reprocessing of events using Apache Storm topologies which reduced stale data issues on the website


  • 2018-Present

    Ph.D. Computer Science

    University of California, Santa Cruz

  • 2017-2018

    Ph.D. Computer Science

    Arizona State University

    GPA 3.9
    Transferred to UCSC
  • 2012-2014

    Masters of Computer Science

    University of Delhi, India

    Rank 1
  • 2009-2012

    Bachelors of Computer Science

    University of Delhi, India

    Rank 1

Academic Projects

  • Smart Video Surveillance System

    Spring 2018

    Implemented a smart video surveillance system for determining the optimal course of actions taken by the camera for monitoring intruder’s movement in real-time settings. Used YOLO for capturing intruder’s location and direction. POMDP is then integrated to solve the decision-making problem of tracking the intruder by moving the camera in intruder’s direction.

  • 3D Object Reconstruction and Grasp Pose Detection

    Spring 2018

    Implemented an end-to-end framework for 3D object reconstruction and grasp pose detection. Used 3D-R2N2 neural network which takes object images from arbitrary viewpoints and outputs a reconstruction, which is visualized in RVIZ using Point Cloud Library (PCL). Grasp Pose Detection (GPD) framework is then integrated to predict grasp candidates and classify them as good or bad grasp.

  • 2-Dimensional PCA for Face Recognition

    Fall 2017

    Implemented 2-Dimensional Principle Component Analysis(2DPCA) for Face Recognition, tested the algorithm on ORL face dataset and compared the results with normal PCA technique


  • FS-SDS: Feature Selection for JPEG Steganalysis using Stochastic Diffusion Search

    Published in: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

    Feature extraction and classification based on feature sets are two major components of steganalysis process. The high dimension of feature sets used for steganalysis makes classification a complex and time-consuming process. This paper proposes a novel feature selection algorithm (FS-SDS) for steganalysis. FS-SDS is a wrapper-type feature selection algorithm which selects reduced feature set using Stochastic Diffusion Search. The Stochastic Diffusion Search is a generic population-based search method, which has been adopted successfully in this work for steganalytic feature selection. The experiments are conducted with steganograms of the common JPEG steganography techniques. To show the usefulness and effectiveness of FS-SDS, experiments were conducted on two different feature sets used for steganalysis. The experimental results show that the proposed feature selection not only effectively reduces the dimensionality of the features, but also improves the detection accuracy of the steganalysis process.

    Link to Paper

Recent Readings