Semester: Spring 2020

Involvement: My team implemented an API-driven resumeable NUMA-aware task scheduler and thread pool to update the execution framework implemented in the self-driving DBMS "Terrier". Our implementation allowed for asynchronous execution of tasks in a memory region-aware manner, reducing contention on system latches and wait times from long-running operations. We improved system performance by approximately 60-70x on general scan workloads and up to 600x on worst-case contentious workloads. We also implemented a B+Tree acting as a secondary index for the system. Our implementation supported optimistic inserts and deletes, relying on latches only for splitting inserts. We utilized thread-safe memory allocators and garbage collection queues for each thread and implemented epoch-based garbage collection.

Description: This course is a comprehensive study of the internals of modern database management systems. It will cover the core concepts and fundamentals of the components that are used in both high-performance transaction processing systems (OLTP) and large-scale analytical systems (OLAP). The class will stress both efficiency and correctness of the implementation of these ideas. All class projects will be in the context of a real in-memory, multi-core database system. The course is appropriate for graduate students in software systems and for advanced undergraduates with dirty systems programming skills.

Semester: Spring 2020

Involvement: My team implemented a set of image classification models for the Caltech-101 Dataset. We developed a series of neural network models (VGG, ResNet, and Inception-v3) and applied the data augmentation techniques of weighted random sampling, data transformation, data duplication, and GAN-based augmentation to improve model accuracy from a 47% baseline to 76% on a representative testing set.

Description: Machine learning studies the question "How can we build computer programs that automatically improve their performance through experience?" This includes learning to perform many types of tasks based on many types of experience. For example, it includes robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on data mining of historical health records, and speech recognition systems that learn to better understand your speech based on experience listening to you. This course is designed to give PhD students a thorough grounding in the methods, mathematics and algorithms needed to do research and applications in machine learning.

Semester: Fall 2020

Description: This course covers the design and implementation of compiler and run-time systems for high-level languages, and examines the interaction between language design, compiler design, and run-time organization. Topics covered include syntactic and lexical analysis, handling of user-defined types and type-checking, context analysis, code generation and optimization, and memory management and run-time organization.

Semester: Spring 2020

Involvement: In this course, my team developed a fully-verified backtracking SAT solver with unit propagation and an additional partially-verified version that added the pure literal optimization and additional performance increases from the use of an adjacency list data structure. I also implemented a fully-verified hash-table data structure that supported resizing inserts and deletes.

Description: High-profile bugs continue to plague the software industry, leading to major problems in the reliability, safety, and security of systems. This course teaches students how to write bug-free code through the process of software verification, which aims to prove the correctness of a program with respect to a mathematical specification. Along the way, students will learn how to specify correct program behavior, prove the correctness of their code, use formal semantics to reason about the soundness of proof rules, write practical and efficient verified code, and use decision procedures and model checkers to reduce verification effort. Students will learn the principles and algorithms behind automated verification tools, and understand their practical limitations while gaining experience writing verified, machine-checked code that solves real problems.

Semester: Fall 2020

Description: Computational photography is the convergence of computer graphics, computer vision and imaging. Its role is to overcome the limitations of the traditional camera, by combining imaging and computation to enable new and enhanced ways of capturing, representing, and interacting with the physical world. This advanced undergraduate course provides a comprehensive overview of the state of the art in computational photography. At the start of the course, we will study modern image processing pipelines, including those encountered on mobile phone and DSLR cameras, and advanced image and video editing algorithms. Then we will proceed to learn about the physical and computational aspects of tasks such as 3D scanning, coded photography, lightfield imaging, time-of-flight imaging, VR/AR displays, and computational light transport. Near the end of the course, we will discuss active research topics, such as creating cameras that capture video at the speed of light, cameras that look around walls, or cameras that can see through tissue. The course has a strong hands-on component, in the form of seven homework assignments and a final project. In the homework assignments, students will have the opportunity to implement many of the techniques covered in the class, by both acquiring their own images of indoor and outdoor scenes and developing the computational tools needed to extract information from them. For their final projects, students will have the choice to use modern sensors provided by the instructors (lightfield cameras, time-of-flight cameras, depth sensors, structured light systems, etc.). This course requires familarity with linear algebra, calculus, programming, and doing computations with images. The course does not require prior experience with photography or imaging.

Semester: Fall 2020

Description: This course is about the design and analysis of algorithms. We study specific algorithms for a variety of problems, as well as general design and analysis techniques. Specific topics include searching, sorting, algorithms for graph problems, efficient data structures, lower bounds and NP-completeness. A variety of other topics may be covered at the discretion of the instructor. These include parallel algorithms, randomized algorithms, geometric algorithms, low level techniques for efficient programming, cryptography, and cryptographic protocols.

Semester: Fall 2019

Involvement: I developed a series of CV-related projects, including Lucas-Kanade and Matthews-Baker video trackers, 3D image reconstructions, Augmented Reality image rectifications, and randomized feature detectors.

Description: This course provides a comprehensive introduction to computer vision. Major topics include image processing, detection and recognition, geometry-based and physics-based vision and video analysis. Students will learn basic concepts of computer vision as well as hands on experience to solve real-life vision problems.