Dooyong Koh

About Me

I am a Ph.D. candidate in Electrical Engineering and Computer Science (EECS) at Massachusetts Institute of Technology (MIT), where I work with Professors Marc A. Baldo and Suraj S. Cheema. My research focuses on (i) negative-permittivity ferroelectrics for energy-efficient logic and memory and (ii) hardware accelerators for machine learning and probabilistic energy-based models. More broadly, my interests span emerging materials and devices through computer systems architecture for energy-efficient computing.

I previously earned my master’s degree in EECS at MIT under the guidance of Professors Marc A. Baldo and Luqiao Liu. I received a bachelor's degree in Electrical and Computer Engineering (ECE) and Materials Science and Engineering (MSE) from Seoul National University (SNU), where I worked with Professors Sangbum Kim and Jong-Ho Lee. Upon graduation, I received the SNU Alumni Association Award, ranking second in the College of Engineering.

Publications

Stochastic Nanomagnets as Current Digitizers for Efficient Probabilistic Machine Learning

Dooyong Koh^†, Ethan G. Rogers^†, Kshemal K. Gupte, Qiuyuan Wang, Brooke C. McGoldrick, Jungsoo Lee, Marc A. Baldo, Cheng Wang, Luqiao Liu

Physical Review Applied 24, 044012 (2025)

Closed Loop Superparamagnetic Tunnel Junctions for Reliable True Randomness and Generative Artificial Intelligence

Dooyong Koh^†, Qiuyuan Wang^†, Brooke C. McGoldrick, Chung-Tao Chou, Luqiao Liu, Marc A. Baldo

Nano Letters 25, 10, pp. 3799-3806 (2025)

Triplet Exciton Sensitization of Silicon Mediated by Defect States in Hafnium Oxynitride

Narumi Nagaya, Alexandra Alexiu, Collin F. Perkinson, Oliver M. Nix, Dooyong Koh, Moungi G. Bawendi, William A. Tisdale, Troy Van Voorhis, Marc A. Baldo

Advanced Materials 2415110 (2024)

A New Back‐End‐Of‐Line Ferroelectric Field‐Effect Transistor Platform via Laser Processing

Sang Woo Kim, Wonjun Shin, Ryun-Han Koo, Jangsaeng Kim, Jiseong Im, Dooyong Koh, Jong-Ho Lee, Suraj S. Cheema, Daewoong Kwon

Small 2406376 (2024)

Pattern Training, Inference, and Regeneration Demonstration Using On‐Chip Trainable Neuromorphic Chips for Spiking Restricted Boltzmann Machine

Uicheol Shin, Masatoshi Ishii, Atsuya Okazaki, Megumi Ito, Malte J. Rasch, Wanki Kim, Akiyo Nomura, Wonseok Choi, Dooyong Koh, Kohji Hosokawa, Matthew BrightSky, Seiji Munetoh, SangBum Kim

Advanced Intelligent Systems 4, 8, 2200034 (2022)

Selected Courseworks

At MIT EECS:

Applied Quantum and Statistical Physics
Analysis and Design of Digital Integrated Circuits
Inference and Information
Discrete Stochastic Processes
Introduction to Modeling and Simulation
Introduction to Applied Convex Optimization

At SNU ECE:

Semiconductor Devices
Semiconductor Processes
Introduction to Circuit Theory and Laboratory
Introduction to Electronic Circuits and Laboratory
Digital Logic Design and Lab
Computer Organization
Programming Methodology

At SNU MSE:

Electric, Magnetic and Optical Properties of Materials
Two-dimensional Materials and Applications
Introduction to Crystallography
Mechanical Behavior of Materials
Thermodynamics of Materials
Phase Transformation in Materials
Chemistry of Organic Materials