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<!DOCTYPE HTML>
<html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Lanqing Li (李蓝青)</title>
<meta name="author" content="Lanqing Li">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" type="text/css" href="stylesheet.css">
<link rel="icon" href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 100 100%22><text y=%22.9em%22 font-size=%2290%22>🌐</text></svg>">
</head>
<body>
<table style="width:100%;max-width:800px;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr style="padding:0px">
<td style="padding:0px">
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr style="padding:0px">
<td style="padding:2.5%;width:63%;vertical-align:middle">
<p style="text-align:center">
<name>
Lanqing Li (李蓝青)
<name>
</p>
<p>I am a principal investigator (PI) at <a href="https://en.zhejianglab.com/">Zhejiang Lab</a>, leading the molecular design & synthesis group at the
Research Center for Computational Drug Discovery starting from Jan 2023. Prior to that, I was a senior research scientist at <a href="https://ai.tencent.com">
Tencent AI Lab</a>, where I work on machine learning and its applications in drug discovery and autonomous control. I also have experience in developing
computer-aided detection (CAD) algorithms and softwares as a Tech Lead at a pre-IPO startup, <a href="https://us.infervision.com/">InferVision</a>.
</p>
<p>
Starting from August 2022, I'm fortunate to study as a part-time PhD supervised by <a href="https://scholar.google.com/citations?user=OFdytjoAAAAJ&hl=en&oi=ao">Prof. Pheng Ann Heng</a>,
at The Chinese University of Hong Kong. Previously, I hold a Master's Degree in Physics (PhD program) from The University of Chicago and received my Bechelor's
Degree in Physics from MIT in 2015, where I was advised by <a href="https://physics.mit.edu/faculty/alan-guth/">Prof. Alan Guth</a>,
<a href="https://web.mit.edu/dikaiser/www/">Prof. David Kaiser</a> and <a href="https://blog.uta.edu/weinbergnn/">Prof. Nevin Weinberg</a> to conduct research in theoretical physics.
</p>
<p>
At Tencent I've worked on <a href="https://drug.ai.tencent.com/">iDrug</a> and <a href="https://www.tencent.com/en-us/articles/2201057.html">iGrow</a> solutions. I'm enthusiastic about artificial intelligence and its prospect of making a better world.
</p>
<p style="text-align:center">
<a href="mailto:lanqingli1993@gmail.com">Email</a>  / 
<a href="data/CV_LanqingLi-2024-12.pdf">CV</a>  / 
<a href="data/">Bio</a>  / 
<a href="https://scholar.google.com.hk/citations?user=n8IjgKkAAAAJ&hl=zh-CN&authuser=1">Google Scholar</a>  / 
<a href="https://www.linkedin.com/in/lanqing-li-%EF%BC%88%E6%9D%8E%E8%93%9D%E9%9D%92%EF%BC%89-49209a83/">LinkedIn</a>  / 
<a href="https://github.com/LanqingLi1993">Github</a>
</p>
</td>
<td style="padding:2.5%;width:40%;max-width:40%">
<a href="images/profile.jpeg"><img style="width:100%;max-width:100%" alt="profile photo" src="images/profile.jpeg" class="hoverZoomLink"></a>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<heading>Research</heading>
<p>
I'm interested in robust machine learning, AI-aided drug discovery (AIDD), reinforcement learning (RL), and AI for science.
In particular, I work on the understanding and development of machine learning algorithms that are robust to distribution shift, imbalanced/offline data, etc.,
to better facilitate the computational design and synthesis of drugs. For those keen visitors, please find my related publications below.
</p>
<strong style="color:red">Hiring!</strong> We are looking for <strong>full-time researchers, engineers, post docs and highly-motivated Ph.D. students and interns</strong> to join our team. The openings cover the following exciting areas:
</p>
<ul>
<li><strong>Computational Drug Design & Synthesis</strong>, e.g., De Novo Drug/Protein Design, Generative Models, Retrosynthesis</li>
<li><strong>Robust Machine Learning</Strong>, e.g., OOD/Imbalanced/Continual Learning</li>
<li><strong>Reinforcement Learning</Strong>, e.g., Offline/Meta RL, RL for Molecular Design, Physics-informed RL</li>
</ul>
<p>
Feel free to shot an <a href="mailto:lanqingli1993@gmail.com">email</a> if you are interested.
</p>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<heading>News</heading>
<ul>
<li>
2024/09/25: Our work on Offline Meta-RL (<a href="#UNICORN">UNICORN</a>) accepted to NeurIPS 2024 as a spotlight paper.
</li>
<li>
2024/08/23: Application approved for Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 62406295).
</li>
<li>
2023/09/27: Our work on protein inverse folding and protein large language model at Zhejiang Lab are reported by CCTV News,
check out 01:41:00-01:46:00 in this <a href="https://live.baidu.com/m/media/pclive/pchome/live.html?room_id=8637147730&source=h5pre">recording</a>!
</li>
<li>
2023/09/26: Our CVPR paper <a href="https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Class-Conditional_Sharpness-Aware_Minimization_for_Deep_Long-Tailed_Recognition_CVPR_2023_paper.html">CC-SAM</a>
is selected as a <a href="https://mp.weixin.qq.com/s/FxfRshuTShQUM86ieVhVvw">highlight</a> by CCF多媒体专委会.
</li>
<li>
2023/06/18: 2 CVPR papers, <a href="https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Class-Conditional_Sharpness-Aware_Minimization_for_Deep_Long-Tailed_Recognition_CVPR_2023_paper.html">CC-SAM</a>
on deep long-tailed recognition, and <a href="https://openaccess.thecvf.com/content/CVPR2023/html/Wang_On_the_Pitfall_of_Mixup_for_Uncertainty_Calibration_CVPR_2023_paper.html">MIT</a> on uncertainty
calibration are published.
</li>
<li>
2023/02/28: 2/2 papers accepted to CVPR 2023.
</li>
<li>
2022/11/20: 3/3 papers, <a href="#ImGCL">ImGCL</a>, <a href="https://arxiv.org/abs/2201.09637">DrugOOD</a> and
<a href="https://arxiv.org/pdf/2211.16771.pdf">MEGAE</a>, are accepted to AAAI 2023.
</li>
<li>
2022/09/19: Our AIDD benchmark for imbalanced learning algorithms, ImDrug, now appears on <a href="https://arxiv.org/abs/2209.07921">arXiv</a>.
</li>
<li>
2022/09/15: 1 paper accepted to NeurIPS 2022.
</li>
<li>2022/07/11: 1 paper <a href="#VPQ">VPQ</a> appears at ACM SIGIR 2022.
</li>
<li>2022/05/15: 1 paper <a href="#LA-GNN">LA-GNN</a> accepted to ICML 2022.
</li>
<li>2022/04/30: I will co-mentors this year‘s <a href="https://www.withzz.com/project/detail/158">Tencent AI Lab Rhino-Bird Focused Research Program</a>,
with a focus on 3D de novo drug design and drug-target interaction.
</li>
<li>2022/03/02: I will co-mentor this year's <a href="https://m.withzz.com/project?id=155">Tencent AI Lab Rhino-Bird Elite Training Program</a>,
with a focus on deep graph learning and its applications in OOD and long-tailed settings.
</li>
<li>2022/01/24: Our AIDD benchmark for out-of-distribution algorithms, DrugOOD, now appears on
<a href="https://arxiv.org/abs/2201.09637">arXiv</a>.
</li>
</ul>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<heading>Selected Publications (<sup>*</sup>: co-first author, <sup>†</sup>: corresponding author, <sup>‡</sup>: my group members or interns)</heading>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<heading>AI for Drug Discovery</heading>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;" id="ImGCL"><tbody>
<tr onmouseout="hnerf_stop()" onmouseover="hnerf_start()">
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification</papertitle>
<br>
<a href="https://scholar.google.com/citations?user=yG8fNjIAAAAJ&hl=en&oi=sra">Liang Zeng<sup>‡</sup></a>,
<strong>Lanqing Li<sup>†</sup></strong>,
<a href="https://scholar.google.com/citations?user=UHwNFy8AAAAJ&hl=en&oi=sra">Ziqi Gao<sup>‡</sup></a>,
<a href="https://scholar.google.com/citations?user=HPeX_YcAAAAJ&hl=en&oi=sra">Peilin Zhao </a>,
<a>Jian Li</a><sup>†</sup>
<br>
<em>AAAI</em>, 2023
<br>
<a>paper</a>
/
<a href="https://arxiv.org/abs/2205.11332">arXiv</a>
/
<a>poster</a>
/
<a>code</a>
<p></p>
<p>We introduce an important yet under-explored problem, namely graph contrastive learning on imbalanced node classification. We propose a novel ImGCL self-training framework as an effective solution, which utilizes the node centrality based progressively balanced sampling methods to obtain balanced labels, with theoretical insight for its convergence.</p>
</td>
</tr>
</tbody></table>
<table id="MEGAE"><tbody>
<tr onmouseout="hnerf_stop()" onmouseover="hnerf_start()">
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>Handling Missing Data via Max-Entropy Regularized Graph Autoencoder</papertitle>
<br>
<a href="https://scholar.google.com/citations?user=UHwNFy8AAAAJ&hl=en&oi=sra">Ziqi Gao<sup>‡</sup></a>,
<a>Yifan Niu</a>,
<a href="https://scholar.google.com/citations?user=HVKMb10AAAAJ&hl=en&oi=sra">Jiashun Cheng</a>,
<a href="https://scholar.google.com/citations?user=w4kWvXEAAAAJ&hl=en&oi=sra">Jianheng Tang</a>,
<a href="https://scholar.google.com/citations?user=6gIs5YMAAAAJ&hl=en&oi=sra">Tingyang Xu </a>,
<a href="https://scholar.google.com/citations?user=HPeX_YcAAAAJ&hl=en&oi=sra">Peilin Zhao </a>,
<strong>Lanqing Li<sup>†</sup></strong>,
<a href="https://scholar.google.com/citations?user=yQVoXS0AAAAJ&hl=en&oi=ao">Fugee Tsung</a>,
<a href="https://scholar.google.com/citations?user=1gSbcYoAAAAJ&hl=en">Jia Li<sup>†</sup></a>,
<br>
<em>AAAI</em>, 2023
<br>
<a href="https://arxiv.org/pdf/2211.16771.pdf">paper</a>
/
<a>poster</a>
/
<a>code</a>
<p></p>
<p>We present a regularized graph autoencoder for graph attribute imputation, named MEGAE, which aims at mitigating
spectral concentration problem by maximizing the graph spectral entropy. Notably, we first present the method for
estimating graph spectral entropy without the eigen-decomposition of Laplacian matrix and provide the theoretical
upper error bound. </p>
</td>
</tr>
</tbody></table>
<table id="PPI"><tbody>
<tr onmouseout="hnerf_stop()" onmouseover="hnerf_start()">
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>Hierarchical Graph Learning for Protein–Protein Interaction</papertitle>
<br>
<a href="https://scholar.google.com/citations?user=UHwNFy8AAAAJ&hl=en&oi=sra">Ziqi Gao<sup>‡</sup></a>,
<a>Yifan Niu</a>,
<a>Chenran Jiang</a>,
<a>Jiawen Zhang</a>,
<a>Xiaosen Jiang</a>,
<strong>Lanqing Li</strong>,
<a href="https://scholar.google.com/citations?user=HPeX_YcAAAAJ&hl=en&oi=sra">Peilin Zhao </a>,
<a>Huanming Yang</a>,
<a href="https://scholar.google.com/citations?user=VUWKxDIAAAAJ&hl=en&oi=ao">Yong Huang</a>,
<a href="https://scholar.google.com/citations?user=1gSbcYoAAAAJ&hl=en">Jia Li</a>,
<br>
<em>Nature Communications 14.1 (2023): 1093.</em><strong> (selected to Editors’ Highlight collection)</strong>
<br>
<a href="https://www.nature.com/articles/s41467-023-36736-1">paper</a>
/
<a>poster</a>
/
<a>code</a>
<p></p>
<p>We present a double-viewed hierarchical graph learning model, HIGH-PPI, to predict PPIs and extrapolate the molecular details involved.
In this model, we create a hierarchical graph, in which a node in the PPI network (top outside-of-protein view) is a protein graph (bottom
inside-of-protein view). In the bottom view, a group of chemically relevant descriptors, instead of the protein sequences, are used to better
capture the structure-function relationship of the protein.</p>
</td>
</tr>
</tbody></table>
<table id="LA-GNN"><tbody>
<tr onmouseout="hnerf_stop()" onmouseover="hnerf_start()">
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>Local Augmentation for Graph Neural Networks</papertitle>
<br>
<a href="https://scholar.google.com/citations?user=OPZ_9L4AAAAJ&hl=en&oi=sra">Songtao Liu<sup>‡</sup></a>,
<a href="https://scholar.google.com/citations?user=6fqNXooAAAAJ&hl=en&oi=ao">Rex Ying<sup>†</sup></a>,
<a href="https://scholar.google.com/citations?user=g9WLzWoAAAAJ&hl=en&oi=sra">Hanze Dong</a>,
<strong>Lanqing Li<sup>†</sup></strong>,
<a href="https://scholar.google.com/citations?user=6gIs5YMAAAAJ&hl=en&oi=sra">Tingyang Xu </a>,
<a href="https://scholar.google.com/citations?user=itezhEMAAAAJ&hl=en&oi=sra">Yu Rong </a>,
<a href="https://scholar.google.com/citations?user=HPeX_YcAAAAJ&hl=en&oi=sra">Peilin Zhao </a>,
<a href="https://scholar.google.com/citations?user=X7KrguAAAAAJ&hl=en&oi=ao">Junzhou Huang </a>,
<a href="https://scholar.google.com/citations?user=Vxaxv10AAAAJ&hl=en&oi=ao">Dinghao Wu<sup>†</sup></a>
<br>
<em>ICML</em>, 2022
<br>
<a href="https://arxiv.org/pdf/2109.03856.pdf">paper</a>
/
<a>poster</a>
/
<a>code</a>
<p></p>
<p>We propose a general graph augmentation strategy by generate conditioned node features in their local neighborhood to enhance the expressive
power of existing GNN. </p>
</td>
</tr>
</tbody></table>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<heading>Robust Machine Learning</heading>
<table id="CC-SAM"><tbody>
<tr onmouseout="hnerf_stop()" onmouseover="hnerf_start()">
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>Class-Conditional Sharpness-Aware Minimization for Deep Long-Tailed Recognition</papertitle>
<br>
<a href="https://scholar.google.com/citations?user=tHNpZuoAAAAJ&hl=en&oi=sra">Zhipeng Zhou<sup>‡</sup>*</a>,
<strong>Lanqing Li</strong>*,
<a href="https://scholar.google.com/citations?user=HPeX_YcAAAAJ&hl=en&oi=sra">Peilin Zhao</a>,
<a href="https://scholar.google.com/citations?user=OFdytjoAAAAJ&hl=en&oi=sra">Pheng-Ann Heng</a>,
<a href="https://scholar.google.com/citations?user=CtbzNl8AAAAJ&hl=en&oi=sra">Wei Gong</a>,
<br>
<em>CVPR</em>, 2023
<br>
<a href="https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Class-Conditional_Sharpness-Aware_Minimization_for_Deep_Long-Tailed_Recognition_CVPR_2023_paper.html">paper</a>
/
<a href="https://github.com/zzpustc/CC-SAM">code</a>
<p>Based on PAC-Bayesian framework, we develop a novel sharpness-aware robust optimization scheme to improve the generalization of deep long-tailed learning methods.</p>
</td>
</tr>
</tbody></table>
<table id="MIT"><tbody>
<tr onmouseout="hnerf_stop()" onmouseover="hnerf_start()">
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>On the Pitfall of Mixup for Uncertainty Calibration</papertitle>
<br>
<a href="https://scholar.google.com/citations?user=QCA7j2cAAAAJ&hl=en&oi=sra">Deng-Bao Wang<sup>‡</sup></a>,
<strong>Lanqing Li<sup>†</sup></strong>,
<a href="https://scholar.google.com/citations?user=HPeX_YcAAAAJ&hl=en&oi=sra">Peilin Zhao</a>,
<a href="https://scholar.google.com/citations?user=OFdytjoAAAAJ&hl=en&oi=sra">Pheng-Ann Heng</a>,
<a href="https://scholar.google.com/citations?user=uFHCIM0AAAAJ&hl=en&oi=sra">Min-Ling Zhang</a><sup>†</sup>,
<br>
<em>CVPR</em>, 2023
<br>
<a href="https://openaccess.thecvf.com/content/CVPR2023/html/Wang_On_the_Pitfall_of_Mixup_for_Uncertainty_Calibration_CVPR_2023_paper.html">paper</a>
/
<a href="https://github.com/dengbaowang/Mixup-Inference-in-Training">code</a>
<p> We interrogate the general perception that mixup training improves uncertainty calibration. Through systematic empirical studies,
we conclude that the answer is quite the opposite when post-hoc calibration is considered. To circumvent this "pitfall", we propose a
general strategy named mixup inference in training (MIT), which adopts a simple decoupling principle for recovering the outputs of raw samples
at the end of forward network pass.
</p>
</td>
</tr>
</tbody></table>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<heading>Reinforcement Learning</heading>
<table id="UNICORN"></tbody>
<tr onmouseout="hnerf_stop()" onmouseover="hnerf_start()">
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning</papertitle>
<br>
<strong>Lanqing Li</strong>*,
<a>Hai Zhang <sup>‡</sup>*</a>,
<a>Xinyu Zhang </a>,
<a>Shatong Zhu </a>,
<a>Yang Yu </a>,
<a>Junqiao Zhao </a>,
<a>Pheng-Ann Heng </a>
<br>
<em>NeurIPS <strong style="color:red">Spotlight</strong> </em>, 2024
<br>
<a href="https://openreview.net/forum?id=QFUsZvw9mx">paper</a>
/
<a href="https://neurips.cc/media/PosterPDFs/NeurIPS%202024/95247.png">poster</a>
/
<a href="https://github.com/betray12138/UNICORN">code</a>
<p></p>
<p> We propose a novel information theoretic framework of the context-based offline meta-RL paradigm,
which unifies several mainstream methods and leads to two robust algorithm implementations.
Our framework provides a principled roadmap to novel COMRL algorithms. </p>
</td>
</tr>
</tbody></table>
<table id="VPQ"></tbody>
<tr onmouseout="hnerf_stop()" onmouseover="hnerf_start()">
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>Value Penalized Q-Learning for Recommender Systems</papertitle>
<br>
<a >Chengqian Gao<sup>‡</sup></a>,
<a >Ke Xu</a>,
<a href="https://scholar.google.com/citations?user=VMfWnsUAAAAJ&hl=en&oi=sra">Kuangqi Zhou</a>,
<strong>Lanqing Li</strong>,
<a>Xueqian Wang </a>,
<a>Bo Yuan </a>,
<a href="https://scholar.google.com/citations?user=HPeX_YcAAAAJ&hl=en&oi=sra">Peilin Zhao </a>
<br>
<em>SIGIR</em>, 2022
<br>
<a href="https://dl.acm.org/doi/pdf/10.1145/3477495.3531796">paper</a>
/
<a>poster</a>
/
<a>code</a>
<p></p>
<p>We propose Value Penalized Q-learning (VPQ), a novel uncertainty-based offline RL algorithm that penalizes
the unstable Q-values in the regression target using uncertainty-aware weights, achieving the conservative Q-function without the
need of estimating the behavior policy. </p>
</td>
</tr>
</tbody></table>
<table id="iGrow"><tbody>
<tr onmouseout="hnerf_stop()" onmouseover="hnerf_start()">
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>iGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control</papertitle>
<br>
<a href="https://scholar.google.com.hk/citations?user=ZYkHM6wAAAAJ&hl=en&oi=sra">Xiaoyan Cao<sup>‡</sup></a>,
<a href="https://scholar.google.com.hk/citations?user=hNO0NdEAAAAJ&hl=en&oi=sra">Yao Yao</a>,
<strong>Lanqing Li</strong>,
<a href="https://scholar.google.com.hk/citations?user=_IKNf9EAAAAJ&hl=en&oi=sra">Wanpeng Zhang </a>,
<a href="https://scholar.google.com.hk/citations?user=LoCbOEoAAAAJ&hl=en&oi=sra">Zhicheng An<sup>‡</sup> </a>,
<a>Zhong Zhang</a>,
<a href="https://scholar.google.com.hk/citations?user=RPAVxiAAAAAJ&hl=en&oi=ao">Shihui Guo </a>,
<a>Li Xiao</a>,
<a href="https://scholar.google.com.hk/citations?user=yxyC-o0AAAAJ&hl=en&oi=sra">Xiaoyu Cao </a>,
<a href="https://www.semanticscholar.org/author/Dijun-Luo/1780029">Dijun Luo </a>
<br>
<em>AAAI</em>, 2022
<br>
<a href="https://www.aaai.org/AAAI22Papers/AISI-10095.CaoX.pdf">paper</a>
/
<a href="https://arxiv.org/abs/2107.05464">arXiv</a>
/
<a href="https://aaai-2022.virtualchair.net/poster_aisi10095">poster</a>
/
<a>code</a>
<p></p>
<p>A pioneering smart agriculture solution to autonomous greenhouse control based on reinforcement learning, genetic algorithms and black-box simulator. </p>
</td>
</tr>
</tbody></table>
<table id="FOCAL"><tbody>
<tr onmouseout="blocknerf_stop()" onmouseover="blocknerf_start()">
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization</papertitle>
<br>
<strong>Lanqing Li<sup>†</sup></strong>,
<a href="https://scholar.google.com.hk/citations?user=QHSUy3MAAAAJ&hl=en&oi=sra">Rui Yang<sup>‡</sup></a>,
<a href="https://www.semanticscholar.org/author/Dijun-Luo/1780029">Dijun Luo<sup>†</sup></a>
<br>
<em>ICLR</em>, 2021
<br>
<a href="https://openreview.net/forum?id=8cpHIfgY4Dj">paper</a>
/ <a href="https://iclr.cc/virtual/2021/poster/3343">poster</a>
/ <a href="https://slideslive.com/38953507/focal-efficient-fullyoffline-metareinforcement-learning-via-distance-metric-learning-and-behavior-regularization?ref=search">presentation</a>
/ <a href="https://arxiv.org/abs/2010.01112">arXiv</a>
/ <a href="https://github.com/LanqingLi1993/FOCAL-ICLR">code</a>
<p></p>
<p>The first end-to-end model-free offline meta-RL algorithm, in pursuit of more practical RL.</p>
</td>
</tr>
</tbody></table>
<table id="iGrowPlanning"><tbody>
<tr onmouseout="hnerf_stop()" onmouseover="hnerf_start()">
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>A Simulator-based Planning Framework for Optimizing Autonomous Greenhouse Control Strategy</papertitle>
<br>
<a href="https://scholar.google.com.hk/citations?user=LoCbOEoAAAAJ&hl=en&oi=sra">Zhicheng An<sup>‡</sup> </a>,
<a href="https://scholar.google.com.hk/citations?user=ZYkHM6wAAAAJ&hl=en&oi=sra">Xiaoyan Cao<sup>‡</sup></a>,
<a href="https://scholar.google.com.hk/citations?user=hNO0NdEAAAAJ&hl=en&oi=sra">Yao Yao</a>,
<a href="https://scholar.google.com.hk/citations?user=_IKNf9EAAAAJ&hl=en&oi=sra">Wanpeng Zhang </a>,
<strong>Lanqing Li</strong>,
<a>Yue Wang</a>,
<a href="https://scholar.google.com.hk/citations?user=RPAVxiAAAAAJ&hl=en&oi=ao">Shihui Guo </a>,
<a href="https://www.semanticscholar.org/author/Dijun-Luo/1780029">Dijun Luo </a>
<br>
<em>Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling (ICAPS)</em>, 2021
<br>
<a href="https://ojs.aaai.org/index.php/ICAPS/article/view/15989">paper</a>
/
<a>arXiv</a>
/
<a href="https://icaps21.icaps-conference.org/link/posters/assets/posters/ICAPS-2021_Poster_190.pdf">poster</a>
/
<a>code</a>
<p></p>
<p>We explore and examine the feasibility of black-box optimization based on a data-driven simulator for optimal control of autonomous greenhouses. </p>
</td>
</tr>
</tbody></table>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<heading>Physics</heading>
<table id="InflationI"><tbody>
<tr onmouseout="hnerf_stop()" onmouseover="hnerf_start()">
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>Theory of self-resonance after inflation. I. Adiabatic and isocurvature Goldstone modes</papertitle>
<br>
<a href="https://scholar.google.com.hk/citations?user=gzI5gAwAAAAJ&hl=en&oi=sra">Mark P Hertzberg </a>,
<a href="https://scholar.google.com.hk/citations?user=aEnn5IcAAAAJ&hl=en&oi=sra">Johanna Karouby</a>,
<a href="https://www.linkedin.com/in/william-spitzer-195773a1/">William Spitzer</a>,
<a href="https://scholar.google.com.hk/citations?user=l-1bE4cAAAAJ&hl=en&oi=sra">Juana C. Becerra </a>,
<strong>Lanqing Li</strong>
<br>
<em>Physical Review D 90 (12), 123528</em>, 2014
<br>
<a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.90.123528">paper</a>
/
<a href="https://arxiv.org/abs/1408.1396">arXiv</a>
<p>We develop a theory of self-resonance after inflation. </p>
</td>
</tr>
</tbody></table>
<table id="InflationII"><tbody>
<tr onmouseout="hnerf_stop()" onmouseover="hnerf_start()">
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>Theory of self-resonance after inflation. II. Quantum mechanics and particle-antiparticle asymmetry</papertitle>
<br>
<a href="https://scholar.google.com.hk/citations?user=gzI5gAwAAAAJ&hl=en&oi=sra">Mark P Hertzberg </a>,
<a href="https://scholar.google.com.hk/citations?user=aEnn5IcAAAAJ&hl=en&oi=sra">Johanna Karouby</a>,
<a href="https://www.linkedin.com/in/william-spitzer-195773a1/">William Spitzer</a>,
<a href="https://scholar.google.com.hk/citations?user=l-1bE4cAAAAJ&hl=en&oi=sra">Juana C. Becerra </a>,
<strong>Lanqing Li</strong>
<br>
<em>Physical Review D 90 (12), 123529</em>, 2014
<br>
<a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.90.123529">paper</a>
/
<a href="https://arxiv.org/abs/1408.1398">arXiv</a>
<p>We develop a theory of self-resonance after inflation. </p>
</td>
</tr>
</tbody></table>
</td>
</tr>
</tbody></table>
<table width="100%" align="center" border="0" cellspacing="0" cellpadding="20"><tbody>
<tr>
<td>
<heading>Scientific Community Activities</heading>
<p>
<strong>Invited Talks and Seminars</strong>
</p>
<ul>
<li><strong>Intelligent Drug Discovery Platform and Its Applications</strong>, presented at the "Computation + Biology" Youth Academic Research Symposium, Zhejiang lab. (09/2023)</li>
<li><strong>Guest lecture on reinforcement learning applications.</strong> The Chinese University of Hong Kong, Shenzhen. (02/2023) <a href="data/CUHKSZ-2023-02-23.pdf">Slides</a> </li>
</ul>
<p>
<strong>Academic Services</strong>
</p>
<ul>
<li>Reviewer, ICLR 2024</li>
<li>Reviewer, CVPR 2023</li>
<li>Reviewer, ICML 2022, 2023</li>
<li>Reviewer, NeurIPS 2022, 2023</li>
<li>Reviewer, IJCAI 2021, 2022</li>
<li>Reviewer, TPAMI</li>
</ul>
</td>
</tr>
</tbody></table>
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<td width="75%" valign="center">
<a href="https://cvpr2022.thecvf.com/area-chairs">Area Chair, CVPR 2022</a>
<br>
<a href="http://cvpr2021.thecvf.com/area-chairs">Area Chair & Longuet-Higgins Award Committee Member, CVPR 2021</a>
<br>
<a href="http://cvpr2019.thecvf.com/area_chairs">Area Chair, CVPR 2019</a>
<br>
<a href="http://cvpr2018.thecvf.com/organizers/area_chairs">Area Chair, CVPR 2018</a>
</td>
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<td style="padding:20px;width:25%;vertical-align:middle">
<img src="images/cs188.jpg" alt="cs188">
</td>
<td width="75%" valign="center">
<a href="http://inst.eecs.berkeley.edu/~cs188/sp11/announcements.html">Graduate Student Instructor, CS188 Spring 2011</a>
<br>
<a href="http://inst.eecs.berkeley.edu/~cs188/fa10/announcements.html">Graduate Student Instructor, CS188 Fall 2010</a>
<br>
<a href="http://aima.cs.berkeley.edu/">Figures, "Artificial Intelligence: A Modern Approach", 3rd Edition</a>
</td>
</tr>
<tr>
<td align="center" style="padding:20px;width:25%;vertical-align:middle">
<heading>Basically <br> Blog Posts</heading>
</td>
<td width="75%" valign="middle">
<a href="https://arxiv.org/abs/2112.11687">Squareplus: A Softplus-Like Algebraic Rectifier</a>
<br>
<a href="https://arxiv.org/abs/2010.09714">A Convenient Generalization of Schlick's Bias and Gain Functions</a>
<br>
<a href="https://arxiv.org/abs/1704.07483">Continuously Differentiable Exponential Linear Units</a>
</td>
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<td style="padding:20px;width:40%;max-width:40%;vertical-align:middle">
<script type="text/javascript" id="clustrmaps" src="//cdn.clustrmaps.com/map_v2.js?cl=ffffff&w=400&t=m&d=4MNJI1YSzk4QCX3vdcwHVUpwmUbcFwzBeiwhUH8At-0&co=2d78ad&cmo=3acc3a&cmn=ff5353&ct=ffffff"></script>
</td>
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<p style="text-align:center">
Source code credit to <a href="https://jonbarron.info/">Dr. Jon Barron</a>
</p>
</td>
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