Jiawei "Tyler" Gu

B.S. Computer Sciences, Math, University of Wisconsin-Madison

E-mail: jgu57 at wisc dot edu

Bio

I am an undergraduate student studying Computer Science in University of Wisconsin-Madison. I am also pursuing a degree in Mathematics.

My research interest relies in system, system security, binary code

Currently I am working as a peer mentor for undergraduate system course: UW-Madison-CS 537 Intro to OS: Spring 2020

Courses

Year Taken Course Name Description
Spring 2020 Introduction to Information Security Senior level undergraduate course covering various topics on information security. Covers a wide range of topics, such as cryptographic primitives, security protocols, system security, and emerging topics.
Spring 2020 Database Management Systems: Design And Implementation What a database management system is; different data models currently used to structure the logical view of the database: relational, hierarchical, and network. Hands-on experience with relational and network-based database systems. Implementation techniques for database systems. File organization, query processing, concurrency control, rollback and recovery, integrity and consistency, and view implementation.
Spring 2020 Analysis I The real numbers, elements of set theory, metric spaces and basic topology, sequences and series, limits, continuity, differentiation, integration, sequences and series of functions, uniform convergence.
Fall 2019 Intro to Operating Systems Input-output hardware, interrupt handling, properties of magnetic tapes, discs and drums, associative memories and virtual address translation techniques. Batch processing, time sharing and real-time systems, scheduling resource allocation, modular software systems, performance measurement and system evaluation.
Fall 2019 Introduction To Artificial Intelligence Principles of knowledge-based search techniques, automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning. Applications in tasks such as problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.
Fall 2019 Introduction To Algorithms Basic paradigms for the design and analysis of efficient algorithms: greed, divide-and-conquer, dynamic programming, reductions, and the use of randomness. Computational intractability including typical NP-complete problems and ways to deal with them.