Insu Han
Korea Advanced Institute of Science and Technology · Engineering
About the Lab
Professor Insu Han's research lab specializes in computational modeling and optimization for industrial processes, with a strong focus on polymer processing, rubber curing, and large-scale matrix computations. The lab develops advanced numerical algorithms—such as randomized trace estimation, Chebyshev approximation, and stochastic methods—for efficient solution of large-scale problems in machine learning, materials science, and chemical engineering. Key research directions include black-box modeling of polymerization processes, dynamic optimization of curing cycles, and scalable computation of matrix functions like traces and log-determinants. The lab also contributes to analog circuit design, particularly in tunable transconductance amplifiers for low-power signal processing applications.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Abstract This article presents the application of three black‐box modeling methods to two industrial polymerization processes to predict the melt index, which is considered an important quality variable determining product specifications. The modeling methods covered in this study are support vector machines (SVMs; known as state‐of‐the‐art modeling methods), partial least squares (PLS), and artificial neural networks (ANNs); the processes are styrene–acrylonitrile (SAN) and polypropylene (PP) p
Computation of the trace of a matrix function plays an important role in many scientific computing applications, including applications in machine learning, computational physics (e.g., lattice quantum chromodynamics), network analysis, and computational biology (e.g., protein folding), just to name a few application areas. We propose a linear-time randomized algorithm for approximating the trace of matrix functions of large symmetric matrices. Our algorithm is based on coupling function approxi
Abstract A systematic procedure is presented for the optimal curing of rubber compounds showing reversion type cure behavior. First, a cure kinetic model is proposed that can explain the reversion and the induction period commonly found in the vulcanization of rubber compounds. The state of cure behavior is analyzed as a function of cure temperature and time on the basis of the derived kinetic model. Then, the problem of determining optimal cure temperature profile for a rubber slab in a simple
Logarithms of determinants of large positive definite matrices appear\nubiquitously in machine learning applications including Gaussian graphical and\nGaussian process models, partition functions of discrete graphical models,\nminimum-volume ellipsoids, metric learning and kernel learning. Log-determinant\ncomputation involves the Cholesky decomposition at the cost cubic in the number\nof variables, i.e., the matrix dimension, which makes it prohibitive for\nlarge-scale applications. We propose
A new tunable transconductance amplifier is proposed for the programmable analog signal processing or low power filter applications. The transconductor linearization is based on the compensation of nonlinear behaviour by two MOS transistors. The transconductance amplifier in this brief exhibits the good common-mode dynamic range and the voltage-controlled transconductance. HSPICE circuit simulation using 0.18-mum standard CMOS technology shows the plusmn50% tunable transconductance range with th
Research Areas
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