Research
Research Objectives
SAFE AI
SAFE AI aims at designing truly safe AI systems that are ethically fair, privacy & security assured, algorithmically stable, and interpretable to humans.
Fintech (Financial Technology)
Fintech encompasses the development of innovative financial instruments and processes and the creative solutions to challenges to compete with traditional financial methods in the delivery of financial services. Artificial intelligence, Blockchain, and Cybersecurity are regarded as the "ABC" (three key areas) of Fintech.
Original Technology
Privacy & Safety
Adversarial Machine Learning
the study of the attacks on AI algorithms, and of the defenses against such attacks.
Source: I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples
Attacks: evasion attacks (attack on the learned model), data poisoning attacks(attack on the learning process), Byzantine attacks and model extraction…
Adversarial Example: FGSM, BIM, PGD, CW, MIM, EAD, ZOO …
Defenses: Detecting and mitigating potential adversarial threats → Make model robust!
Defensive distillation, Adversarial training, and …
Verification: Provable robustness assessment and quantification
Privacy-preserving AI techniques
Homomorphic Encryption (HE)
A form of encryption with an additional evaluation capability for computing over encrypted data without access to the secret key ****
We aim to develop HE-friendly AI algorithms that are both efficient and free from specific parameter requirements, suitable for implementation with any commonly used HE scheme.
[S Park, J Lee, JH Cheon, J Lee, J Kim, J Byun, 2019], [S Park, J Byun, J Lee, JH Cheon, J Lee, 2020], [J Byun, J Lee, S Park, 2021], [J Byun, S Park, Y Choi, J Lee, 2023], [J Byun, H Ko, J Lee, 2023]
Differential Privacy
Designed in such a way that the outputs of a data analysis do not reveal whether a specific individual's information has been utilized in the analysis and formation of those results.
Stability analysis and statistical error bound analysis can help safeguarding the identity of individuals with private data and privacy concerns, particularly in large databases.
[J Park, Y Choi, J Byun, J Lee, S Park*, 2023], [J Park, H Kim, Y Choi, J Lee, 2023], [J Byun, J Lee, 2023]
Fair Machine Learning
Stability analysis of multi-basin systems (MBS) may help correct and eliminate algorithmic biases (class, disability, gender, race and ethnicity, sexual orientation, etc.) in machine learning models.
[T Yoon, J Lee, W Lee, 2020], [W Lee, H Ko, J Byun, T Yoon, J Lee, 2021], [J Byun, J Lee, 2023]
AI Forecasting
AI Forecasting in Finance
Many traders use non-economic numeric data such as new, blogs, sentiments more to forecast their portfolio and predict various economic and financial variables.
Artificial Intelligence (AI) models construct a financial predictor from the large structured financial data and non-structured data and can be applied to multi-factor asset pricing, time series forecasting and global stock market volatility forecasting.
[B Son, J Lee, 2022], [B Son, Y Lee, S Park, J Lee, 2023], [J Park, H Kim, Y Choi, W Lee, J Lee, 2023]
AI models for predicting European/ American options, volatilities, and credit derivatives.
[GS Han, J Lee, 2008], [GS Han, BH Kim, J Lee, 2009], [SH Yang, J Lee, 2011], [H Park, N Kim, J Lee, 2014], [Y Son, H Byun, J Lee, 2016], [H Jang, J Lee, 2019], [H Jang, J Lee, 2019]
Predictive Models for Imbalanced Data
Imbalanced data describes datasets in which the target class exhibits a disproportionate distribution, characterized by one class having significantly more observations than another. Some of the key use cases includes fraud detection, anomaly detection, churn prediction, credit scoring, medical diagnosis, and etc.
[H Heo, H Park, N Kim, J Lee, 2009], [N Kim, KH Jung, YS Kim, J Lee, 2012], [K Kim, J Lee, 2012], [K Kim, CH Jun, J Lee, 2014]
Collaborative filtering
Collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste from many users (collaborating).
Suggest new items or to predict the utility of a certain item for a particular user based on the user’s previous likings and the opinions of other like-minded users
[JS Lee, CH Jun*, J Lee, S Kim, 2005], [B Jeong, D Lee, H Cho, J Lee, 2008], [B Jeong, D Lee, J Lee, H Cho, 2009], [B Jeong, J Lee, H Cho, 2009], [B Jeong, J Lee, H Cho, 2009], [B Jeong, J Lee, H Cho, 2010]
Computational Finance
Blockchain and DeFi Markets
Blockchain is an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way.
Decentralized finance (DeFi) aims to create an open, permissionless, and highly interoperable financial ecosystem using smart contracts on blockchains, primarily Ethereum, to provide financial instruments without central intermediaries.
[H Ko, B Son, Y Lee, H Jang, J Lee, 2022], [S Lee, J Lee, Y Lee, 2023], [S Park, S Lee, Y Lee, H Ko, B Son, J Lee, H Jang, 2023]
By Fabian Schär - https://research.stlouisfed.org/publications/review/2021/02/05/decentralized-finance-on-blockchain-and-smart-contract-based-financial-markets, CC BY-SA 4.0,
Cryptocurrencey
[H Jang, J Lee, 2018], [S Pyo, J Lee, 2020]CBDC: Atomic cross-chain settlement model for central banks digital currency
[Y Lee, B Son, H Jang, J Byun, T Yoon, J Lee, 2021]
Risk and portfolio management: the P world
Modeling the statistically derived 'real' probability measure P of market prices for assets at a future investment horizon enables the buy-side community to make informed decisions on security purchases to enhance their portfolio's profit-and-loss profile. With the advent of AI, more and more of this process is being automated.
Derivatives Pricing: the Q world
Derivatives pricing, a complex extrapolation exercise used by the sell-side community, aims to establish a fair, arbitrage-free price for a claim based on the prices of more liquid underlying securities determined by supply and demand, using a risk-neutral measure Q equivalent to the real probability measure, P.
Econometric models (with jumps) widely used in derivative pricing include Black-Sholes Models, Affine Jump Diffusion Models, Infinite Activity Levy Process Models, Local Volatility Models, GARCH models, and etc.
Stability analysis for nonlinear optimization can be applied to developing efficient and stable calibration of the financial models with jumps to market data under risk-neutral measure Q.
[S Yang, Y Lee, G Oh, J Lee, 2011], [S Yang, J Lee, 2012], [J Lee, Y Lee, 2013], [N Kim, J Lee, 2013], [S Yang, J Lee, 2014], [J Lee, Y Lee, 2015]
