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@@ -181,6 +181,7 @@ <h2>News</h2>
 
                     <b style="color:rgb(68, 68, 68)">[Aug. 2024]</b>
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+                            <li> We are grateful to receive the Best Paper Finalist Award from VLDB 2024 <a href="https://llm-pbe.github.io/LLM-PBE.pdf">[Paper]</a></li> 
                             <li> Dr. Wang is grateful to receive the <a href="https://h2o.ai/ai-100/winners/">AI 100: The Top AI Thought Leaders</a> award, presented by H2O.ai</a> </li>
                             <li> 1 JMLR (pruning provably improves generalization) accepted</li> 
                             <li> 1 JMLR (tighter theoretical analysis of sparse activation) accepted</li> 

publication.html

@@ -207,7 +207,7 @@ <h2>Conference Paper</h2>
                             <li>Z. Zhu*, Z. Fan*, Y. Jiang*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting”</b><br>European Conference on Computer Vision (ECCV), 2024. <a href="https://arxiv.org/abs/2312.00451">[Paper]</a> <a href="https://github.com/VITA-Group/FSGS">[Code] </a> </li>
                             <li>S. Zhou, Z. Fan*, D. Xu*, H. Chang, P. Chari, T. Bharadwaj, S. You, Z. Wang, and A. Kadambi<br> <b style="color:rgb(71, 71, 71)">“DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic Gaussian Splatting”</b><br>European Conference on Computer Vision (ECCV), 2024. <a href="https://arxiv.org/abs/2404.06903">[Paper]</a> <a href="https://dreamscene360.github.io/">[Code] </a> </li>
                             <li>R. Li, Z. Fan*, B. Wang, P. Wang*, Z. Wang, and X. Wu<br> <b style="color:rgb(71, 71, 71)">“VersatileGaussian: Real-time Neural Rendering for Versatile Tasks using Gaussian Splatting”</b><br>European Conference on Computer Vision (ECCV), 2024. <a href="">[Paper]</a> <a href="">[Code] </a> </li>
-                            <li>Q. Li, J. Hong*, C. Xie, J. Tan, R. Xin, J. Hou, X. Yin, Z. Wang, D. Hendrycks, Z. Wang, B. Li, B. He, and D. Song<br> <b style="color:rgb(71, 71, 71)">“LLM-PBE: Assessing Data Privacy in Large Language Models”</b><br>International Conference on Very Large Data Bases (VLDB), 2024. <a href="https://llm-pbe.github.io/LLM-PBE-May29.pdf">[Paper]</a> <a href="https://llm-pbe.github.io/home">[Code] </a> </li>
+                            <li>Q. Li, J. Hong*, C. Xie, J. Tan, R. Xin, J. Hou, X. Yin, Z. Wang, D. Hendrycks, Z. Wang, B. Li, B. He, and D. Song<br> <b style="color:rgb(71, 71, 71)">“LLM-PBE: Assessing Data Privacy in Large Language Models”</b><br>International Conference on Very Large Data Bases (VLDB), 2024. (Best Paper Finalist)  <a href="https://llm-pbe.github.io/LLM-PBE.pdf">[Paper]</a> <a href="https://llm-pbe.github.io/home">[Code] </a> </li>
                             <li>L. Sun*, N. Bhatt*, J. Liu*, Z. Fan*, Z. Wang, T. Humphreys, and U. Topcu<br> <b style="color:rgb(71, 71, 71)">“MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements”</b><br>IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024. <a href="https://arxiv.org/abs/2404.00923">[Paper]</a> <a href="https://github.com/VITA-Group/MM3DGS-SLAM">[Code] </a> </li>
                             <li>R. Cai*, S. Muralidharan, G. Heinrich, H. Yin, Z. Wang, J. Kautz, and P. Molchanov<br> <b style="color:rgb(71, 71, 71)">“Flextron: Many-in-One Flexible Large Language Model”</b><br>International Conference on Machine Learning (ICML), 2024. (Oral) <a href="https://openreview.net/pdf?id=9vKRhnflAs">[Paper]</a> <a href="">[Code] </a> </li>
                             <li>R. Cai*, Y. Tian, Z. Wang, and B. Chen<br> <b style="color:rgb(71, 71, 71)">“LoCoCo: Dropping In Convolutions for Long Context Compression”</b><br>International Conference on Machine Learning (ICML), 2024. <a href="https://arxiv.org/abs/2406.05317">[Paper]</a> <a href="https://github.com/VITA-Group/LoCoCo">[Code] </a> </li>

research.html

@@ -171,7 +171,7 @@ <h2>About PI</h2>
 
                         <p>Prof. Wang has broad research interests spanning from the theory to the application aspects of machine learning (ML). At present, his core research mission is to leverage, understand and expand the role of low dimensionality  in ML and optimization, whose impacts span over many important topics such as the efficiency and trust issues in large language models (LLMs) as well as generative vision. His research is gratefully supported by NSF, DARPA, ARL, ARO, IARPA, DOE, as well as dozens of industry and university grants. Prof. Wang co-founded the new <a href="https://cpal.cc/">Conference on Parsimony and Learning (CPAL)</a> and serves as its inaugural Program Chair. He is an elected technical committee member of IEEE MLSP and IEEE CI; and regularly serves as (senior) area chairs, invited speakers, tutorial/workshop organizers, various panelist positions and reviewers. He is an ACM Distinguished Speaker and an IEEE senior member.</p>
 
-                        <p>Prof. Wang has received many research awards, including an NSF CAREER Award, an ARO Young Investigator Award, an IEEE AI's 10 To Watch Award, an AI 100 Top Thought Leader Award, an INNS Aharon Katzir Young Investigator Award, a Google Research Scholar award, an IBM Faculty Research Award, a J. P. Morgan Faculty Research Award, an Amazon Research Award, an Adobe Data Science Research Award, a Meta Reality Labs Research Award, and two Google TensorFlow Model Garden Awards. His team has won the Best Paper Award from the inaugural Learning on Graphs (LoG) Conference 2022; and has also won five research competition prizes from CVPR/ICCV/ECCV since 2018. He feels most proud of being surrounded by some of the world's most brilliant students: his Ph.D. students include winners of seven prestigious fellowships (NSF GRFP, IBM, Apple, Adobe, Amazon, Qualcomm, and Snap), among many other honors.</p>
+                        <p>Prof. Wang has received many research awards, including an NSF CAREER Award, an ARO Young Investigator Award, an IEEE AI's 10 To Watch Award, an AI 100 Top Thought Leader Award, an INNS Aharon Katzir Young Investigator Award, a Google Research Scholar award, an IBM Faculty Research Award, a J. P. Morgan Faculty Research Award, an Amazon Research Award, an Adobe Data Science Research Award, a Meta Reality Labs Research Award, and two Google TensorFlow Model Garden Awards. His team has won the Best Paper Award at the inaugural Learning on Graphs (LoG) Conference 2022, the Best Paper Finalist Award at the International Conference on Very Large Databases (VLDB) 2024, and five research competition prizes at CVPR/ICCV/ECCV since 2018. He feels most proud of being surrounded by some of the world's most brilliant students: his Ph.D. students include winners of seven prestigious fellowships (NSF GRFP, IBM, Apple, Adobe, Amazon, Qualcomm, and Snap), among many other honors.</p>
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