Room X1.52, Lautrupvang 15, 

2750 Ballerup, Denmark

Lei You (Ph.D.)

Assistant Professor in Applied Mathematics

Energy Technology and Computer Science

Department of Engineering Technology

Technical University of Denmark

I received my Ph.D. in Computer Science (specialized in Mathematical Optimization) from the Department of Information Technology at Uppsala University in 2019. My previous research was focused on the application of information theory to optimize resource allocation for data throughput and network reliability in advanced communication systems. During the PhD, I interned in The Boston Consulting Group (BCG) Gamma as a visiting data scientist. After the PhD, I had been working as a data scientist in Bolt and Wolt (Doordash) in the domain of on-demand logistics optimization. 

Optimal Model Refinement is my current research interest, centered around leveraging mathematical optimization to enhance the interpretability and efficiency of machine learning models. I explore strategies to streamline complex models without performance loss, as well as to unravel the intricate mechanisms of decision-making models. Central to this pursuit is understanding the synergy between model simplification and explainability: Reducing a model's complexity aids in elucidating its functions, and concurrently, and explainability drives the efficient compression of the model for learning.

Recent work

This is some research under double-blind review.

(If you are interested, feel free to reach out and let's see if there can be better solution)

L. You, L. Cao, M. Nilsson, B. Zhao, and L. Lei, DIStributional COUNTerfactual Explanation With Optimal Transport", preprint. [arXiv] [code]

Counterfactual Explanations (CE) is the de facto method for providing insight and interpretability in black-box decision-making models by identifying alternative input instances that lead to different outcomes. This paper extends the concept of CEs to a distributional context, broadening the scope from individual data points to entire input and output distributions, named Distributional Counterfactual Explanation (DCE). In DCE, our focus shifts to analyzing the distributional properties of the factual and counterfactual, drawing parallels to the classical approach of assessing individual instances and their resulting decisions. We leverage Optimal Transport (OT) to frame a chance-constrained optimization problem, aiming to derive a counterfactual distribution that closely aligns with its factual counterpart, substantiated by statistical confidence. Our proposed optimization method, DISCOUNT, strategically balances this confidence across both input and output distributions. This algorithm is accompanied by an analysis of its convergence rate. The efficacy of our proposed method is substantiated through a series of illustrative case studies, highlighting its potential in providing deep insights into decision-making models.

Z. Senane, L. Cao, V. L. Buchner, Y. Tashiro, L. You, P. Herman, M. Nordahl, R. Tu, and V. Ehrenheim, "Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask", accepted in Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) 2024. [arXiv] [code]

Time Series Representation Learning (TSRL) focuses on generating informative representations for various Time Series (TS) modeling tasks. Traditional Self-Supervised Learning (SSL) methods in TSRL fall into four main categories: reconstructive, adversarial, contrastive, and predictive, each with a common challenge of sensitivity to noise and intricate data nuances. Recently, diffusion-based methods have shown advanced generative capabilities. However, they primarily target specific application scenarios like imputation and forecasting, leaving a gap in leveraging diffusion models for generic TSRL. Our work, Time Series Diffusion Embedding (TSDE), bridges this gap as the first diffusion-based SSL TSRL approach. TSDE segments TS data into observed and masked parts using an Imputation-Interpolation-Forecasting (IIF) mask. It applies a trainable embedding function, featuring dual-orthogonal Transformer encoders with a crossover mechanism, to the observed part. We train a reverse diffusion process conditioned on the embeddings, designed to predict noise added to the masked part. Extensive experiments demonstrate TSDE’s superiority in imputation, interpolation, forecasting, anomaly detection, classification, and clustering. We also conduct an ablation study, present embedding visualizations, and compare inference speed, further substantiating TSDE’s efficiency and validity in learning representations of TS data.

L. You and H. V. Cheng, "SWAP: Sparse Entropic WAsserstein Regression for Robust Network Pruning", International Conference on Learning Representations (ICLR) 2024. [arXiv] [code]

This study tackles the issue of neural network pruning that inaccurate gradients exist when computing the empirical Fisher Information Matrix (FIM). We introduce SWAP, an Entropic Wasserstein regression (EWR) network pruning formulation, capitalizing on the geometric attributes of the optimal transport (OT) problem. The “swap” of a commonly used standard linear regression (LR) with the EWR in optimization is analytically showcased to excel in noise mitigation by adopting neighborhood interpolation across data points, yet incurs marginal extra computational cost. The unique strength of SWAP is its intrinsic ability to strike a balance between noise reduction and covariance information preservation. Extensive experiments performed on various networks show comparable performance of SWAP with state-of-the-art (SoTA) network pruning algorithms. Our proposed method outperforms the SoTA when the network size or the target sparsity is large, the gain is even larger with the existence of noisy gradients, possibly from noisy data, analog memory, or adversarial attacks. Notably, our proposed method achieves a gain of 6% improvement in accuracy and 8% improvement in testing loss for MobileNetV1 with less than one-fourth of the network parameters remaining. 

Are you interested in defining your own topic and conducting research with me? 

Support for MSCA Postdoctoral Fellowship. 

Find open call here.

Support for DDSA Postdoctoral Fellowship.

Open call for Danish Data Science Academy (DDSA) two-year postdoctoral fellowships. 

Support for DDSA PhD Fellowship.

Open call for Danish Data Science Academy (DDSA) 3-year doctoral program

Please feel free to reach out by email for support of proposal writing.

Other Research work (Selected)

(See a full list of publications here)

L. You, "Weighted Sum-Rate Maximization With Causal Inference for Latent Interference Estimation", IEEE International Conference on Communications (ICC) 2023. [link] [code]

The paper investigates the weighted sum-rate maximization (WSRM) problem with latent interfering sources outside the known network, whose power allocation policy is hidden from and uncontrollable to optimization. The paper extends the famous alternate optimization algorithm weighted minimum mean square error (WMMSE) under a causal inference framework to tackle with WSRM. Specifically, with the possibility of power policy shifting in the hidden network, computing an iterating direction based only on the observed interference inherently implies that counterfactual is ignored in decision making. A method called synthetic control (SC) is used to estimate the counterfactual. For any link in the known network, SC constructs a convex combination of the interference on other links and uses it as an estimate for the counterfactual. Power iteration in the proposed SC-WMMSE is performed taking into account both the observed interference and its counterfactual. SC-WMMSE requires no more information than the original WMMSE in the optimization stage. To our best knowledge, this is the first paper explores the potential of SC in assisting mathematical optimization in addressing classic wireless optimization problems. Numerical results suggest the superiority of the SC-WMMSE over the original in both convergence and objective. 

L. You, D. Yuan, L. Lei, S. Sun, S. Chatzinotas, and B. Ottersten. “Resource optimization with load coupling in multi-cell NOMA”. IEEE Transactions on Wireless Communications, vol.17, no.7, 2018. [arXiv] [code] 

Optimizing non-orthogonal multiple access (NOMA) in multi-cell scenarios is much more challenging than the single-cell case because inter-cell interference must be considered. Most papers addressing NOMA consider a single cell. We take a significant step of analyzing NOMA in multi-cell scenarios. We explore the potential of NOMA networks in achieving optimal resource utilization with arbitrary topologies. Towards this goal, we investigate a broad class of problems consisting in optimizing power allocation and user pairing for any cost function that is monotonically increasing in time-frequency resource consumption. We propose an algorithm that achieves global optimality for this problem class. The basic idea is to prove that solving the joint optimization problem of power allocation, user pair selection, and time-frequency resource allocation amounts to solving a so-called iterated function without a closed form. We prove that the algorithm approaches optimality with fast convergence. Numerically, we evaluate and demonstrate the performance of NOMA for multi-cell scenarios in terms of resource efficiency and load balancing. 

A note that strengthens the result of this paper is as follows.

L. You and D. Yuan. “A note on decoding order in user grouping and power optimization for multi-cell NOMA with load coupling”. IEEE Transactions on Wireless Communications, vol.20 no.1, 2021. [arXiv]

L. You, Q. Liao, N. Pappas, and D. Yuan. “Resource Optimization with Flexible Numerology and Frame Structure for Heterogeneous Services”. IEEE Communications Letters, vol.22, no.12, 2018. [arXiv] [code]

We explore the potential of optimizing resource allocation with flexible numerology in frequency domain and variable frame structure in time domain, in presence of services with different types of requirements. We analyze the computational complexity and propose a scalable optimization algorithm based on searching in both the primal space and dual space that are complementary to each other. Numerical results show significant advantages of adopting flexibility in both time and frequency domains for capacity enhancement and meeting the requirements of mission critical services.

L. You and D. Yuan. “User-centric performance optimization with remote radio head cooperation in C-RAN”, IEEE Transactions on Wireless Communications, vol.19, no.1, 2019. [arXiv]

In a cloud radio access network (C-RAN), distributed remote radio heads (RRHs) are coordinated by baseband units (BBUs) in the cloud. The centralization of signal processing provides flexibility for coordinated multi-point transmission (CoMP) of RRHs to cooperatively serve user equipments (UEs). We target enhancing UEs' capacity performance, by jointly optimizing the selection of RRHs for serving UEs, i.e., resource allocation (and CoMP selection). We analyze the computational complexity of the problem. Next, we prove that under fixed CoMP selection, the optimal resource allocation amounts to solving a so-called iterated function. Towards user-centric network optimization, we propose an algorithm for the joint optimization problem, aiming at maximumly scaling up the capacity for any target UE group of interest. The proposed algorithm enables network-level performance evaluation for quality of experience.