About

Liming Feng is an Associate Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois Urbana-Champaign. He holds a Ph.D. in Industrial Engineering and Management Sciences from Northwestern University, obtained in 2006, and has a strong academic background in mathematics with a B.S. from Beijing Normal University and an M.S. from Northwestern University. Feng serves as the Director of the Master of Science in Financial Engineering program at Illinois and has been a faculty member since 2006. His research interests encompass computational methods, operations research, stochastic modeling, and financial engineering. Feng has contributed to the field through various publications in reputable journals, focusing on topics such as derivatives pricing, portfolio optimization, market impact, and jump processes in finance. He has also been involved in editorial activities for several academic journals and serves on multiple departmental and college committees. Recognized for his teaching excellence, Feng has received numerous awards and honors, including being named an ISE Faculty Fellow and receiving the Department Head Teaching Award. His work integrates advanced mathematical and computational techniques to address complex problems in financial engineering and operations research.

Research topics

• Computer Science
• Economics
• Machine Learning
• Econometrics
• Political Science
• Biology
• Artificial Intelligence
• Mathematics
• Environmental science
• Fishery
• Mathematical optimization
• Law
• Social psychology
• Evolutionary biology
• Financial economics
• Psychology

Selected publications

• New intent discovery with syntactic structure masking pretraining and density-aware contrastive learning

  Neurocomputing · 2025-10-15 · 1 citations

  article
  PublisherDOI

• Volatility Calibration via Automatic Local Regression

  ArXiv.org · 2025-09-19

  preprintOpen accessSenior author

  Managing exotic derivatives requires accurate mark-to-market pricing and stable Greeks for reliable hedging. The Local Volatility (LV) model distinguishes itself from other pricing models by its ability to match observable market prices across all strikes and maturities with high accuracy. However, LV calibration is fundamentally ill-posed: finite market observables must determine a continuously-defined surface with infinite local volatility parameters. This ill-posed nature often causes spiky LV surfaces that are particularly problematic for finite-difference-based valuation, and induces high-frequency oscillations in solutions, thus leading to unstable Greeks. To address this challenge, we propose a pre-calibration smoothing method that can be integrated seamlessly into any LV calibration workflow. Our method pre-processes market observables using local regression that automatically minimizes asymptotic conditional mean squared error to generate denoised inputs for subsequent LV calibration. Numerical experiments demonstrate that the proposed pre-calibration smoothing yields significantly smoother LV surfaces and greatly improves Greek stability for exotic options with negligible additional computational cost, while preserving the LV model's ability to fit market observables with high fidelity.

  PublisherOA PDFDOI

• Robust and Fast Bass Local Volatility

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Multiple feature-anisotropic regularization for out-of-domain intent detection

  Knowledge and Information Systems · 2025-05-25

  articleSenior author
  PublisherDOI

• Semi-Supervised New Intention Discovery for Syntactic Elimination and Fusion in Elastic Neighborhoods

  Computers, materials & continua/Computers, materials & continua (Print) · 2025-01-01

  articleOpen access

  Semi-supervised new intent discovery is a significant research focus in natural language understanding. To address the limitations of current semi-supervised training data and the underutilization of implicit information, a S... | Find, read and cite all the research you need on Tech Science Press

  PublisherOA PDFDOI

• Few-shot Intent Recognition with an Adaptive Masking Strategy

  2024-10-25

  article1st authorCorresponding
  PublisherDOI

• Robust and Fast Bass local volatility

  arXiv (Cornell University) · 2024

  Senior authorCorresponding
  • Fishery
  • Environmental science
  • Econometrics

  The Bass Local Volatility Model (Bass-LV), as studied in [Conze and Henry-Labordere, 2021], stands out for its ability to eliminate the need for interpolation between maturities. This offers a significant advantage over traditional LV models. However, its performance highly depends on accurate construction of state price densities and the corresponding marginal distributions and efficient numerical convolutions which are necessary when solving the associated fixed point problems. In this paper, we propose a new approach combining local quadratic estimation and lognormal mixture tails for the construction of state price densities. We investigate computational efficiency of trapezoidal rule based schemes for numerical convolutions and show that they outperform commonly used Gauss-Hermite quadrature. We demonstrate the performance of the proposed method, both in standard option pricing models, as well as through a detailed market case study.

  PublisherOA PDFDOI

• Diversity Evolutionary Policy Deep Reinforcement Learning

  Computational Intelligence and Neuroscience · 2021 · 12 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  The reinforcement learning algorithms based on policy gradient may fall into local optimal due to gradient disappearance during the update process, which in turn affects the exploration ability of the reinforcement learning agent. In order to solve the above problem, in this paper, the cross-entropy method (CEM) in evolution policy, maximum mean difference (MMD), and twin delayed deep deterministic policy gradient algorithm (TD3) are combined to propose a diversity evolutionary policy deep reinforcement learning (DEPRL) algorithm. By using the maximum mean discrepancy as a measure of the distance between different policies, some of the policies in the population maximize the distance between them and the previous generation of policies while maximizing the cumulative return during the gradient update. Furthermore, combining the cumulative returns and the distance between policies as the fitness of the population encourages more diversity in the offspring policies, which in turn can reduce the risk of falling into local optimal due to the disappearance of the gradient. The results in the MuJoCo test environment show that DEPRL has achieved excellent performance on continuous control tasks; especially in the Ant-v2 environment, the return of DEPRL ultimately achieved a nearly 20% improvement compared to TD3.

  PublisherOA PDFDOI

• Optimal portfolio execution with a Markov chain approximation approach

  IMA Journal of Management Mathematics · 2021 · 5 citations

  • Computer Science
  • Computer Science
  • Mathematical optimization

  Abstract We study the problem of executing a large multi-asset portfolio in a short time period where the objective is to find an optimal trading strategy that minimizes both the trading cost and the trading risk measured by quadratic variation. We contribute to the existing literature by considering a multi-dimensional geometric Brownian motion model for asset prices and proposing an efficient Markov chain approximation (MCA) approach to obtain the optimal trading trajectory. The MCA approach allows us not only to numerically compute the optimal strategy but also to theoretically analyse the influence of factors such as price impact, risk aversion and initial asset price on the optimal strategy, providing both quantitative and qualitative guidance on the trading behaviour. Numerical results verify the theoretical conclusions in the paper. They further illustrate the effects of cross impact and correlations on the optimal execution strategy in a multi-asset liquidation problem.

  PublisherDOI

• Constant elasticity of variance models with target zones

  Physica A Statistical Mechanics and its Applications · 2019-09-18 · 2 citations

  article1st author
  PublisherDOI

Recent grants

• Collaborative Research: Market-Based Calibration of Pricing Models for Financial and Energy Option Contracts

  NSF · $340k · 2010–2015

• Hilbert Transform Methods in Financial Engineering

  NSF · $289k · 2009–2013

Frequent coauthors

• Vadim Linetsky

  Northwestern University

  9 shared

• Lin Xiong

  China Spallation Neutron Source

  7 shared

• Jiming Peng

  University of Houston

  5 shared

• Jingnan Chen

  5 shared

• Zisheng Chen

  4 shared

• Jorge Nocedal

  4 shared

• José Luis Morales

  Instituto Politécnico Nacional

  2 shared

• Yinyu Ye

  2 shared

Labs

• Industrial & Enterprise Systems EngineeringPI

Awards & honors

• ISE Faculty Fellow, Department of Industrial and Enterprise…
• On List of Teachers Ranked as Excellent by Their Students, U…
• Department Head Teaching Award, Department of Industrial and…
• Sharp Outstanding Teaching Award in Industrial Engineering,…
• First runner-up of the 2012 Morgan Stanley Prize for Excelle…