Graduate Coursework

3.81 GPA, University of Massachusetts Amherst, CICS, 2018


COMPSCI 689 Advanced Machine Learning

COMPSCI 682 Neural Networks: A Modern Introduction

COMPSCI 690N Advanced Natural Language Processing

COMPSCI 688 Probabilistic Graphical Models

  • Assignments
    • Heart disease prediction model
    • OCR using CRFs
    • Image Denoising and Gibbs Sampling

COMPSCI 585 Intro to Natural Language Processing

COMPSCI 590D Algorithms for Data Science

  • Clustering
  • Estimating Statistical Properties of Data
  • Near Neighbor Search
  • Algorithms over Massive Graphs and Social Networks
  • Learning Algorithms
  • Randomized Algorithms
  • Assignments

COMPSCI 590S Systems for Data Science

  • Parallelism: Threads, message-passing , Fault-tolerance
  • Large-Scale Analytics: MapReduce / Hadoop
  • Distributed computing, Cloud Computing, “Big Data”
  • Databases: SQL, database architectures, optimization, consistency, NoSQL, key-value stores, consistent hashing (Redis, MongoDB)
  • Optimizing MapReduce: FlumeJava
  • Storage: Google File System / HDFS
  • Parallel Processing: Spark SQL, TensorFlow,Dynamo
  • Distributed Shared Memory, Data Warehousing, BigTable

COMPSCI 645 Database Design and Implementation

  • Relational Algebra, SQL, Datalog
  • Storage and Indexing
  • Query processing and optimization
  • FDs and normalization
  • Transactions and concurrency
  • OLAP
  • Data understanding, fairness,diversity, and transparency
  • Security & Database Theory


  • Reinforcement Learning (David Silver, John Schulmann)
  • Machine Learning (Andrew Ng)
  • Recommender Systems (Yong Feng)