ANKIT AGGARWAL

aggarwal9@wisc.edu https://www.linkedin.com/in/ankit-aggarwal-b6b35417 608-622-0225

EDUCATION University of Wisconsin-Madison, WI Dec 2016 (Expected)

Master’s of Science in Computer Science CGPA: 3.73/4

Birla Institute of Technology and Science, (BITS-Pilani), India May 2012

B.E. (Honors) in Computer Science CGPA: 8.39/10

TECHNICAL SKILLS

Programming Languages: C, C++, C#, Java, Python, lua, SQL/PLSQL, MATLAB

Frameworks: Torch, Caffe, Scikit-learn, Hadoop, Hive, Spark, Visual Studio, Eclipse, Amazon AWS, SQL Server, GIT, Docker

Related Courses: Machine Learning, Pattern Recognition, Advanced Machine Learning & Optimization, Computer Vision, Data Models & Languages, BigData Systems and Introduction to Operating Systems

PROFESSIONAL EXPERIENCE Data Scientist/Machine Learning Intern, AmFam Labs, Chicago, IL May'16-Sep’16

Developed deep learning models for image classification and segmentation using Torch & Caffe

Used Convolutional Neural Networks, Transfer Learning, Image segmentation, Data Cleaning and Augmentation techniques, & machine learning ensembles using image metadata to boost the accuracy

Senior Software Engineer, Texas Instruments, Bangalore, India July'12-July'15

Developed robust software applications using COM and Microsoft .NET framework in C++ and C# using object oriented programming methodologies and design patterns

Design and deployment of large scale databases which included implementing archiving techniques, performance optimization and scalability based requirements

Software Engineer Intern, NVIDIA, Bangalore, India Jan’12-June’12

Developed an algorithm to predict timing delays in memory interface signals for NVIDIA TEGRA memory controller design

Developed a bug detection tool that detects error patterns from the log files generated by MVRC (Synopsis tool) and displays the error reports to facilitate debugging

RESEARCH AND PUBLICATIONS

Implemented deep reinforcement learning algorithms for unsupervised training of an intelligent agent under the guidance of Prof. Jude W. Shavlik, UW-Madison Jan’16-Present

“Minimizing fuel consumption of vehicles with respect to path parameters using standard routing algorithm (Djikstra’s Algorithm)” in IEEE INDICON 2011 (Team: Sagnik Choudhury, Ankit Aggarwal, Y. V. Mahesh Kumar) team members

PROJECTS Sentiment Analysis using Recursive Neural Networks (Python, Keras)

Implemented Recurrent Neural Networks using Long Short-Term Memory architecture to perform sentiment analysis

Pre-processed datasets using beautifulsoup and word2vec to convert movie reviews into word vector representation

Performance comparison with Least Squares (L1 and L2 regularization), SVM, Neural Network and Random Forest classifiers

Performance Analysis of various Machine Learning and Pattern Recognition Models (Python, WEKA, MATLAB) Implemented ID3 decision-tree, neural network, Naïve Bayes and Tree Augmented Bayesian network classifiers in Python

Implemented PCA, stochastic gradient descent, power methods using iterative techniques to compute approximate solution for large datasets within reasonable time

Performance benchmarking of MapReduce and Tez frameworks over Apace Hive for various workloads Deployed Hadoop-style big data analytics stack to estimate performance (execution time, disk and network bandwidth) for

varying workloads under specific operating parameters and environment

Developed a MapReduce application to group all anagrams together and sort the groups in descending order of their size

Modern Operating System Features implementation into primitive xv6 kernel (C, Linux) Implemented Multi-Level Feedback Queue scheduler using round-robin scheduling for individual priority queues

Implemented Thread library which includes methods for kernel thread creation, thread join and locking mechanism

Data Science Pipeline for Information Extraction, Data Cleaning & Entity Matching (Python, Scikit-Learn) Analyzed and extracted attributes values from a massive set of pair of Walmart and competitor products to predict a match

using an ensemble of machine learning and string matching techniques for competitive pricing.