Shinhyeok Hwang
Shinhyeok Hwang
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Shinhyeok Hwang
Shinhyeok Hwang

Shinhyeok Hwang

 
Research Interests : Natural Language Processing, Federated Learning, Compressing LLMs.
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EDUCATION

  • Korea Advanced Institute of Science and Technology (KAIST) Mar. 2017 - Present
    • Major in Electrical Engineering
    • Minor in Computer Science
    • Clubs: Street Dance, Cooking, GDSC KAIST
  • Sejong Science High School Mar. 2015 - Feb. 2017
 

WORK EXPERIENCE

  • SK hynix Internship Jun. 2019 - Aug. 2019
    • Worked at SoC Analog IP
    • Designed a Verilog Model reflecting the analog characteristics of PLL (Phase-Locked Loop)
 

RESEARCH EXPERIENCE

  • KAIST Optimization and Statistical Inference Lab. Mar. 2023 - Present
    • Research Intern (Advisor: Prof. Seyoung Yun)
    • Improved personal Re-ID performance in Federated Learning
  • KAIST Statistical Speech & Sound Computing Lab. Dec. 2019 - Feb. 2020
    • Research Intern (Advisor: Prof. Hoirin Kim)
    • Learned basic concepts of Automatic Speech Recognition
    • Trained a DNN-HMM model using Kaidl and LibriSpeech dataset
 

PROJECTS

Sweet Spot Sep.2023 - Current
- Python, Pytorch, SQL
  • Developed an AI recommendation system that recommends brands that fit the building
  • Collaborated with startup and led the ML team.
FeelLike Mar.2023 - Jun.2023
- Javascript, HTML, CSS, Python
  • An AI-Driven Music Playlist Generator.
  • Developed a Chrome extension using ChatGPT and Stable Diffusion
GLO Sep.2022 - Dec.2022
- Javascript, HTML, CSS, React, Next.js, MongoDB, AWS, PWA
  • GLO allows users to record their activities easily and also analyze them effectively.
  • Developed a full-stack web application and deployed it with PWA
RL Soccer Agent Nov.2020
- Python3, Pytorch, Reinforcement learning with PPO
  • Trained RL soccer agent in Unity ML-Agents Environment using self-play strategy
  • Algorithm: PPO(Proximal Policy Optimization)
Autonomous Driving Car simulation Oct.2020
- Python3, Pytorch, Reinforcement learning with DDPG
  • Trained Autonomous Driving Car in Unity ML-Agents Environment
  • Algorithm : DDPG(Deep Deterministic Policy Gradient)
THABNOS Nov.2019
- Javascript, HTML, CSS, Chrome extension
  • Randomly manage number of tabs with mini game inspired by google thanos easter egg
  • Registered in the Chrome Web Store
 

SKILLS

  • C/C++, Python, Pytorch, Tensorflow, Tensorflow Lite
 

ADDITIONAL INFORMATION

  • Military Service, KATUSA Mar. 2021 - Sep. 2022
    • Location : USAG-Humphreys
    • Unit : 142nd MP CO, 94th MP BN, 19th ESC
  • E*5 KAIST, Startup Competition Finalist (Sep. 2020 - Dec. 2020)
  • KAIST Overseas Entrepreneurship training (Aug. 2019)
    • Suzhou, China
 

Volunteer Activities

  • Samsung Dreamclass mentor Mar. 2019 - Jun. 2019
    • Math tutor
  • 2019 KAIST Indonesia ICT Volunteers Dec. 2018 - Jan. 2019
    • KAIST Global Student Volunteers
  • Rural Science Mentoring Camp Aug. 2018
    • KAIST Global Student Volunteers

RELEVANT COURSES

  • CS231n Deep Learning for Computer Vision (Stanford)
  • CS376 Machine Learning
  • EE495 Individual Study
  • CoE202 Basics of Artificial Intelligence<Physical AI>
  • EE210 Probability and Introductory Random Processes
  • EE209 Programming Structure for Electrical Engineering
  • MAS109 Introduction to Linear Algebra

HONORS & AWARDS

  • KAIST's Alumni Academic Scholarship Foundation Mar. 2018 - Dec. 2020
    • by 임형규 이사장님

TEACHING

Publications

 
 
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Paper Reading
Network Pruning
Learning both Weights and Connections for Efficient Neural Network
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Rethinking the Value of Network Pruning
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
A Simple and Effective Pruning Approach for Large Language Models
Federated Learning
Communication-Efficient Learning of Deep Networks from Decentralized Data
How To Backdoor Federated Learning
Analyzing Federated Learning through an Adversarial Lens
Deep Leakage from Gradients
Advances and Open Problems in Federated Learning
FEDERATED OPTIMIZATION IN HETEROGENEOUS NETWORKS
Performance Optimization for Federated Person Re-identification via Benchmark Analysis
Inverting Gradients - How easy is it to break privacy in federated learning?
Model-Contrastive Federated Learning
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning
FEDBABU: TOWARD ENHANCED REPRESENTATION FOR FEDERATED IMAGE CLASSIFICATION
Gradient Inversion with Generative Image Prior
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling
Knowledge Distillation
Distilling the Knowledge in a Neural Network
LLM
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
LLaMA: Open and Efficient Foundation Language Models
Network Quantization
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
AWQ: Activation-aware Weight Quantization for LLM
SqueezeLLM: Dense-and-Sparse Quantization
PEFT
Parameter-Efficient Transfer Learning for NLP
LoRA: Low-Rank Adaptation of Large Language Models
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
QLoRA: Efficient Finetuning of Quantized LLMs
No description
Large Language Models Are Reasoning Teachers
Revisiting Intermediate Layer Distillation for Compressing Language Models: An Overfitting Perspective
Big Little Transformer Decoder
SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference
Gradient Inversion
Inverting Gradients - How easy is it to break privacy in federated learning?
Gradient Inversion with Generative Image Prior
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning