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Kornrapat Pongmala

dblp:329/5996 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 2 · 2 since 2021Software engineering, systems software and programming languages · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Trustworthy machine learning · 50% Image recognition and object detection · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning › robustness
adversarial robustness
0.712023
Part-Based Models Improve Adversarial Robustness · ICLR 2023
Computer vision › Image recognition and object detection
part-based model
0.712023
Part-Based Models Improve Adversarial Robustness · ICLR 2023

Methods — techniques the papers use, named apart from their topics

part-based model · 0.7
YearPublicationVenuePosition
2024 What Drives the (In)stability of a Stablecoin?
abstract
In May 2022, an apparent speculative attack, followed by market panic, led to the precipitous downfall of UST, one of the most popular stablecoins at that time. However, UST is not the only stablecoin to have been depegged in the past. Designing resilient and long-term stable coins, therefore, appears to present a hard challenge. To further scrutinize existing stablecoin designs and ultimately lead to more robust systems, we need to understand where volatility emerges. Our work provides a game-theoretical model aiming to help identify why stablecoins suffer from a depeg. This game-theoretical model reveals that stablecoins have different price equilibria depending on the coin’s architecture and mechanism to minimize volatility. Moreover, our theory is supported by extensive empirical data, spanning 1 year. To that end, we collect daily prices for 22 stablecoins and on-chain data from five blockchains including the Ethereum and the Terra blockchain.
Yujin Potter, Kornrapat Pongmala, Kaihua Qin, Ariah Klages-Mundt, Philipp Jovanovic, Christine A. Parlour, Arthur Gervais, Dawn Song
ICBC2
2024 Unpacking How Decentralized Autonomous Organizations (DAOs) Work in Practice
abstract
Decentralized Autonomous Organizations (DAOs) have emerged as a novel way to coordinate a group of (pseudonymous) entities toward a shared vision (e.g., promoting sustainability). In just a few years, over 4,000 DAOs have been launched in various domains, such as investment, education, health, and research. Despite such rapid growth and diversity, it is unclear how these DAOs actually work in practice. Given this, we aim to unpack how (well) DAOs work in practice. We conducted an in-depth analysis of a diverse set of 10 DAOs of various categories and smart contracts, leveraging on-chain data and interviewing DAO members. Specifically, we define metrics to characterize key aspects of DAOs, such as the degrees of decentralization and autonomy. We observed some DAOs having poor decentralization in voting, while decentralization has improved over time for one-person-one-vote DAOs. Lastly, we offer a set of design implications for future DAOs based on our findings.
Tanusree Sharma, Yujin Potter, Kornrapat Pongmala, Henry Wang, Andrew Miller 0001, Dawn Song, Yang Wang 0005
ICBC3
2023 Part-Based Models Improve Adversarial Robustness
Chawin Sitawarin, Kornrapat Pongmala, Yizheng Chen 0001, Nicholas Carlini, David A. Wagner 0001
ICLR2