Our research group aims to develop programming language technologies for addressing challenges in various computer science domains. Especially, we are interested in PL4SE (Developing programming language technologies for software engineering) and PL4ML (Developing programming language technologies for machine learning). We aim to design domain-specific programming languages (DSLs) that can describe solutions to problems and develop program synthesis algorithms to automatically find these solutions in the DSLs.

저희는 다양한 컴퓨터 과학 분야의 문제를 해결하기 위한 프로그래밍 언어 기술 개발에 관심 있는 모든 수준의 의욕적인 연구자들을 적극적으로 모집하고 있습니다.

Data-driven context-sensitivity for points-to analysis

Precise and scalable points-to analysis via data-driven context tunneling

Multilayer all‐polymer metasurface stacked on optical fiber via sequential micro‐punching process

Learning graph-based heuristics for pointer analysis without handcrafting application-specific features

A Machine-Learning Algorithm with Disjunctive Model for Data-Driven Program Analysis

Return of CFA: call-site sensitivity can be superior to object sensitivity even for object-oriented programs

High‐Purity Single Photon Extraction from Diamond Nitrogen‐Vacancy Centers in Low‐Numerical‐Aperture Optical Systems via Defect‐Selective Metalens Printing

PL4XGL: A Programming Language Approach to Explainable Graph Learning

A practical algorithm for learning disjunctive abstraction heuristics in static program analysis

PAFL: Enhancing Fault Localizers by Leveraging Project-Specific Fault Patterns

Data-Driven Context-Sensitivity for Points-to Analysis

Versatile multilayered all-polymer metasurfaces on optical fiber apex

Corrigendum: PAFL: Enhancing Fault Localizers by Leveraging Project-Specific Fault Patterns

Improving Photon Gathering Efficiency in Nitrogen-Vacancy Centers using Transferred Metalenses

Automating Endurance Test for Flash-based Storage Devices in Samsung Electronics

A machine-learning algorithm with disjunctive model for data-driven program analysis

Pl4xgl: A programming language approach to explainable graph learning

GDLNN: Marriage of Programming Language and Neural Networks for Accurate and Easy-to-Explain Graph Classification

Program analysis device and method

Return of CFA: Call-Site Sensitivity Can Be Superior to Object Sensitivity Even for Object-Oriented Programs (Supplementary Material)

Return of CFA: Call-Site Sensitivity Can Be Superior to Object Sensitivity Even for Object-Oriented Programs

A Practical Algorithm for Learning Disjunctive Abstraction Heuristics in Static Program Analysis

Learning Graph-based Heuristics for Pointer Analysis without Handcrafting Application-Specific Features

PLX Lab

교수님 소개

전민석

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

High‐Purity Single Photon Extraction from Diamond Nitrogen‐Vacancy Centers in Low‐Numerical‐Aperture Optical Systems via Defect‐Selective Metalens Printing

PAFL: Enhancing Fault Localizers by Leveraging Project-Specific Fault Patterns

Corrigendum: PAFL: Enhancing Fault Localizers by Leveraging Project-Specific Fault Patterns

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

전민석

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

High‐Purity Single Photon Extraction from Diamond Nitrogen‐Vacancy Centers in Low‐Numerical‐Aperture Optical Systems via Defect‐Selective Metalens Printing

PAFL: Enhancing Fault Localizers by Leveraging Project-Specific Fault Patterns

Corrigendum: PAFL: Enhancing Fault Localizers by Leveraging Project-Specific Fault Patterns

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집