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What I learnt:

• principled schema design
• data structures for large data
• relational database query languages

Sample Works:

• computing minimal equivalent set of functional dependencies
• quantitate the effect of clustered and secondary index on mysql
• database using in-memory implementation of B-trees and hash structures
• a miniature relational database with order

What I learnt:

• data parallelism
• kernel functions and threading
• synchronization and transparent scalability
• CUDA memory types
• tiling
• warps and SIMD hardware

Sample Works:

• computing heat distribution using synchronous iteration on GPU
• CUDA prime numbers generator
• interior point method implemented on GPU

What I learnt:

• Hadoop MapReduce
• HDFS
• Spark architecture
• Spark SQL
• Spark Streaming
• MLlib

Sample Work

• Exploring Loan Discrimination with HMDA Data Set

What I learnt:

• vectors and matrices
• vector spaces
• geometry
• decompositions
• probabilities
• statistical methods
• markov models

Sample Works:

• SVD for image compression
• simulating the birthday paradox
• simulating Bertrand’s box problem
• central limit theorem simulation using Python
• estimate population mean by drawing random samples and computing sample means in Python

What I learnt:

• singular value decomposition
• principal component analysis
• measure similarity in datasets
• selecting and extracting features
• MapReduce, Hadoop and HDFS
• data clustering
• make predictions using data classification algorithms
• mining hidden associations from large datasets

Sample Works

• data preprocessing
• convert unstructured text documents to structured data (document-term matrix)
• implementing sliding window, LDA and k-means

What I learnt:

• lexical analysis
• top-down syntax analysis
• syntax-directed translation
• syntax-directed definitions and type analysis
• intermediate code generation
• runtime environment
• code generation
• register allocation
• bottom-up analysis and optimization

Sample Works

• parser in HACS
• type checker in HACS
• MinARM32 code generator in HACS

What I learnt:

• supervised learning
• regularization
• generalized linear models
• trees and forests
• neural networks
• latent variable models
• sampling and variatinal inference
• deep generative models
• causal inference
• reinforcement learning

What I Learned

• network stack
• application layer
• transport layer
• network player
• link layer
• physical layer
• wireless networks

What I learnt

• computer architecture, history and basics
• processes and threads
• scheduling
• concurrency and synchronization
• memory management
• input/output
• file systems
• networking

Sample Works

• two-pass linker in C
• scheduler implemented with discrete event simulation in C
• virtual memory manager in C

What I Learned

• syntax
• names, scopes, and bindings
• control flow
• subprograms and control abstraction
• functional programming
• types
• object-oriented programming
• generic programming
• exceptions
• concurrency

Sample Works

• higher-order functions in Scheme
• miniML: an interpreter for a dynamically typed subset of OCaml
• implement Big Natural Numbers module in OCaml
• BigInt module that provides arbitrary precision integer arithmetic operations in OCaml
• generic functional maps in Scala

What I Learned

• growth of functions
• heap, queue, stack
• sorting
• hashing
• BST
• greedy algorithm
• dynamic programming
• graphs
• number theory