Sungwoo Park on Muck Rack

Sungwoo Park

  • PHD Student and Freelance Writer
As seen in: UOL, Bloomberg Businessweek, Bloomberg Green Daily, Bloomberg News, Nature, The Washington Post, Hindustan Times, Yahoo Finance, MDPI, ACS Publications and
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Articles

Regime-aware static and dynamic graph structure learning for stock price forecasting

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By Amna Sarwar, Asma Sattar, Zahoor Ur Rehman, Sungwoo Park, Seungmin Rho, Fizza Bukhari
| Nature
Abstract Stock price forecasting is a key task in the financial technology (FinTech) sector, as it supports informed investment decision-making and enhances risk management strategies. However, accurate forecasting of stock prices remains highly challenging due to the unpredictable, noisy, and ever-changing nature of financial markets, which are influenced by complex macroeconomic factors and rapid regime changes.
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A chromatin-associated pool of Aurora A controls kinetochore-microtubule attachments to ensure chromosome biorientation

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By Valdemar Priebe, Peter Sheridan Dodds, Mohammad A Saad, Johnathan L. Meaders, Alyssa Rodriguez, Smriti Variyar, Sungwoo Park, Alessandro E. Cirulli, Karen Oegema, Kevin Corbett, Arshad Desai
| Science Magazine
Abstract Accurate chromosome segregation requires dynamic kinetochore-microtubule attachments that, under the regulation of Aurora family kinases, biorient and align replicated chromosomes. In Caenorhabditis elegans, Aurora A acts with the TPX2-related activator TPXL-1 to regulate these attachments and control spindle length. We show that, in addition to prominent spindle pole localization, TPXL-1–AurA has a chromatin-associated pool positioned between the sister kinetochores.
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Advanced stacked modeling techniques for material porosity estimation via high-resolution computed tomography imaging - Scientific Reports

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By Bubryur Kim, Yuvaraj Natarajan, Sungwoo Park, Sri Preethaa K.R, Sukhoon Pyo
| Nature
Abstract This study presents a deep convolutional neural network (DeepCNN) based framework for the automatic estimation of porosity in concrete materials from two-dimensional computed tomography (2D CT) scan images. Addressing the limitations of manual and time-consuming traditional porosity measurement methods, the proposed approach integrates advanced image processing techniques to improve robustness under low resolution and noisy imaging conditions.
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