Semin Kwak

Research Scientist at Sony AI · Zurich, Switzerland

About

I am currently a Research Scientist at Sony AI, focusing on developing cutting-edge machine learning models for optimizing chip design. Previously, I was a postdoctoral researcher at the University of Southern California, collaborating with Prof. Antonio Ortega in electrical engineering and supported by the Swiss National Science Foundation (SNSF) through the Postdoc.Mobility fund. I completed my Bachelor’s at Yonsei University under the guidance of Prof. Dong-Hyun Kim and my Master’s at the Korea Advanced Institute of Science and Technology (KAIST) with Prof. Joohwan Chun. I earned my Ph.D. at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, studying under Prof. Nikolas Geroliminis from 2017 to 2022. My research interests span graph signal processing, traffic flow prediction, radar signal processing, deep neural networks, and AI-driven semiconductor design.

Background

Education and professional experience

Education

2017 – 2022
Ph.D. in Electrical Engineering
École Polytechnique Fédérale de Lausanne (EPFL) · Switzerland
Multivariate Time Series Forecasting for Freeway Networks
2013 – 2015
M.S. in Electrical Engineering
Korea Advanced Institute of Science and Technology (KAIST) · South Korea
2007 – 2013
B.S. in Electrical Engineering
Yonsei University · South Korea

Experience

11/2024 – Present
Research Scientist · Sony AI
Zurich, Switzerland
05/2023 – 11/2024
Postdoctoral Researcher · University of Southern California
Los Angeles, USA
11/2022 – 04/2023
Postdoctoral Researcher · EPFL
Lausanne, Switzerland
04/2017 – 10/2022
PhD Research · EPFL
Lausanne, Switzerland
05/2016 – 03/2017
Research Associate · KIST
Seoul, South Korea
03/2013 – 02/2015
Research Assistant · KAIST
Daejeon, South Korea

Research Interests

Areas of focus and expertise

Graph Signal Processing

Signal processing on non-Euclidean domains with applications to network analysis

Traffic Flow Prediction

Time series forecasting for transportation systems using deep learning

Neural Networks on Graphs

Graph convolutional networks and spectral methods for structured data

AI-driven Semiconductor Design

Machine learning for chip design optimization and performance prediction

Deep Learning

Advanced architectures including CNNs, RNNs, attention mechanisms

Machine Learning & Optimization

Classical and modern optimization algorithms for complex problems

Technical Skills

Programming

Python, MATLAB, C/C++, JavaScript, SQL

ML/DL

PyTorch, TensorFlow, scikit-learn, NumPy, Pandas

Methods

Graph Neural Networks, CNNs, RNNs, LSTMs, Transformers

Tools

Git, Docker, Ray Tune, Plotly, D3.js

Get in Touch

I’m always happy to discuss research, collaborate on interesting problems, or chat about machine learning and signal processing.

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