Tech Stack, Research Roadmap and Tool Chains


Current Tech Stack

  • Programming
    • Functional Programming: Scheme, Common Lisp, Racket (Mastered)
    • Constraint-Based Reasoning and Programming: Java, Scheme (Mastered)
    • Declarative Programming: Haskell, Prolog (Familiar)
    • Objective-Oriented Programming: Python, Java, Object Pascal (Familiar)
    • Others: Lua, Shell, RGSS (Beginner)
  • Front-end Web Development
    • Java EE, Spring, Hibernate, Struts (Familiar)
    • Jekyll, Booktstrap, JQuery (Familiar)
  • Computing Methodologies for Artificial Intelligence
    • Statistical Machine Learning (Beginner)
    • Foundations for Data Science (Mastered)
    • Automated Theorem Proving: Coq & Gallina, Agda (Familiar)
  • Computing Methodologies for Problem Solving
    • Boolean Function Manipulation (Mastered)
    • Scheduling with Contraint-based Programming (Familiar)
    • Decision Problems, Decision Procedure (Beginner)
  • Theoretical Computer Science and Mathematical Foundations
    • Mathematical Logic: Proof Theory, Recursion Theory, Category Theory, Type Theory (Familiar)
    • Computing Models: Lambda-Calculus, Process Algebra (Beginner)

Research Roadmap

Mathematical Foundations for Programming Languages

  1. Concepts of Programming Language: (SF(2018) |TOPL | PFPL) > EOPL(2014) & CSAPP(2016) & CTMCP(2018)
  2. Mathematical Foundation: COPL(2019)
  3. Type Systems & Type Theory: TAPL(2019) & Advanced TAPL(2020) & HoTT(2019)
  4. Formal Semantics: LCISS(2019) & FSPL(2020) & FFPL(2019)
  5. Abstract Algebra: Abstract Algebra, The Basic Graduate Year(2019)
  6. Category Theory: Category Theory A Gentle Introduction(2019)
  7. Lambda Calculus: LCISS(2019) > PLLC(2022) | LCAC
  8. Process Algebra: ITPA(2020)
    • Pi-Calculus: An Introduction to the pi-Calculus
    • CPS:Communicating Sequential Processes
  9. Recursive Theory and Recursive Functions: Theory of Formal Systems & COMPUTABILITY, An introduction to recursive function theory(2019)

Program Analysis & Verification, Model Checking

  1. Compiler Design: LIP(2018) TigerBook(2020)
  2. Data Flow Analysis: DFA(2020) > POPA(2020)
  3. Abstract Interpretation: DFA(2020)
  4. Symbolic Execution
  5. Model Checking: POMC & LCSMRS(2019)

Computing Methodoloies for Artificial Intelligence

  1. Introduction to Artificial Intelligence: AIMA(2017)
  2. Data Science: FODS(2019)
  3. Statistical Machine Learningc: SML(2018)
  4. Deep Learning: Neural Networks and Deep Learning(2022)
  5. Optimal Learning: OL(2021)
  6. Reinforcement Learning: RLAI(2021)
  7. Knowledge-Based Systems: AIES(2019)
  8. Information Compilation (2019)
  9. Automated Thereom Proof: CPDT(2019)

Computing Methodologies for Problem Solving

  1. Decision Procedures, An Algorithmic Point of View(2019)
  2. Parallel Constraint Reasoning
  3. Constraint-Based Reasoning and Programming