The system can't perform the operation now. CIKM, 2020. He also specializes in education of technology entrepreneurship. Tsinghua Asia-Pacific President Federation Scholarship, 2011. I also visited Prof. Lawrence Carin's group at Duke University from Oct. 2017 to Oct. 2018. [GitHub] He won the NSF Career Award in 1997, and was elected Fellow of IEEE in 2010. The ones marked, IEEE Symposium on Security and Privacy (S & P), M Jagielski, A Oprea, B Biggio, C Liu, C Nita-Rotaru, B Li, 2018 IEEE Symposium on Security and Privacy (SP), 19-35, X Xu, C Liu, Q Feng, H Yin, L Song, D Song, Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications …, N Carlini, C Liu, J Kos, Ú Erlingsson, D Song, C Liu, A Harris, M Maas, M Hicks, M Tiwari, E Shi, XS Wang, K Nayak, C Liu, THH Chan, E Shi, E Stefanov, Y Huang, Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications …, 2014 IEEE Symposium on Security and Privacy, 623-638, N Carlini, C Liu, Ú Erlingsson, J Kos, D Song, 28th {USENIX} Security Symposium ({USENIX} Security 19), 267-284, Advances in neural information processing systems, 2547-2557, 2013 IEEE 26th Computer Security Foundations Symposium, 51-65. Security Programming Language Deep Learning. [Paper & Appendix] Understanding and Accelerating Particle-Based Variational Inference. Proudly created with Wix.com, or contributions to bioinspired and polymer micro electro-mechanical systems, Investigation of micro machined sensors and actuators for fluid mechanics applications. Graduate work in the Electrical Engineering division. Thesis title "Investigation of micro machined sensors and actuators for fluid mechanics applications". Microsoft Research Asia View Chang Liu’s profile on LinkedIn, the world's largest professional community. My research interests are primarily on statistical machine learning, especially general Bayesian inference methods (e.g., variational inference and MCMC), their collaboration with manifold structures, and their applications in Bayesian deep learning and large scale learning tasks. His IEEE Fellow was based on the citation "for contributions to bioinspired and polymer micro electro-mechanical systems". Learning to Simulate on Sparse Trajectory Data (Best ADS Paper Award) Hua Wei, Chacha Chen, Chang Liu, Guanjie Zheng, Zhenhui Li. Bioinspired Sensors and Neuromorphic Sensing. degree in 2014 from the Department of Physics of Tsinghua University. He also specializes in education of technology entrepreneurship. ECML-PKDD, 2020. Presented as part of the 10th {USENIX} Symposium on Operating Systems Design …, Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security …, Advances in Neural Information Processing Systems, 4574-4582, International Semantic Web Conference, 405-420, Tsinghua science and technology 15 (6), 613-622, X Xu, X Chen, C Liu, A Rohrbach, T Darell, D Song, New articles related to this author's research, Professor of Computer Science, UC Berkeley, Professor of Computer Science, University of Maryland, Lloyd T. Smith Creativity in Engineering Chair, Kansas State University, Manhattan, Kansas, Assistant Professor, Computer Science and Engineering, IIIT-Delhi, India, Department of Computer Science, The University of Hong Kong, Delving into transferable adversarial examples and black-box attacks, ObliVM: A Programming Framework for Secure Computation, Targeted backdoor attacks on deep learning systems using data poisoning, Manipulating machine learning: Poisoning attacks and countermeasures for regression learning, Neural network-based graph embedding for cross-platform binary code similarity detection, Sqlnet: Generating structured queries from natural language without reinforcement learning, The secret sharer: Measuring unintended neural network memorization & extracting secrets, Ghostrider: A hardware-software system for memory trace oblivious computation, Automating efficient RAM-model secure computation, The secret sharer: Evaluating and testing unintended memorization in neural networks, Tree-to-tree neural networks for program translation, Spotting code optimizations in data-parallel pipelines through PeriSCOPE, Robust linear regression against training data poisoning, Latent attention for if-then program synthesis, Towards efficient SPARQL query processing on RDF data, Lightweight integration of IR and DB for scalable hybrid search with integrated ranking support, Can you fool ai with adversarial examples on a visual turing test. Articles Cited by Co-authors. I want to study them. No. View Chang Liu’s profile on LinkedIn, the world's largest professional community. Verified email at eecs.berkeley.edu - Homepage. Their, This "Cited by" count includes citations to the following articles in Scholar. Liu has 6 jobs listed on their profile. Outstanding Ph.D. Graduate, Department of Computer Science and Technology, 2019. Sort. I received my B.Sc. Sort by citations Sort by year Sort by title. He has published a textbook with Pearson "Foundations of MEMS" and two texts with Tsinghua University Press on entrepreneurship and lab-market technology transfer. I am currently a researcher at Microsoft Research Asia, Machine Learning Group, headed by Tao Qin starting from 2019. Chang has 6 jobs listed on their profile. Assistant and then Associate Professor (with tenure) in the department of Electrical and Computer Engineering (ECE) and Mechanical and Industrial Engineering (MIE). The following articles are merged in Scholar. Chang Liu*, Huichu Zhang*, Weinan Zhang, Guanjie Zheng, Yong Yu. Email: liuchangsmail at gmail dot com Outstanding Doctoral Dissertation, Tsinghua University, 2019. Artist Liu Chang uses Processing (an open source programming tool) as her tool to create the 24 different dynamic patterns. View Liu Chang’s profile on LinkedIn, the world's largest professional community. Try again later. Advisor: Dr. YC Tai. Before that, I received my Ph.D. degree in 2019 from the TSAIL Group at the Department of Computer Science and Technology of Tsinghua University, supervised by Prof. Jun Zhu. Nature offers the best sensors in the world. CityFlow A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario; One Approach is to build biomimetic systems. Full professor with tenure in Mechanical Engineering and Electrical Engineering. Chang has 5 jobs listed on their profile. © 2020 by Chang Liu. Title. [Google Scholar] Undergraduate studies in the Department of Precision Engineering and Mechatronics, Technology Commercialization, Entrepreneurship. University of Illinois at Urbana-Champaign. I am interested in teaching engineers business, and entrepreneurship. Prof. Chang Liu works in the Sensors and MEMS research area with 30 years of experiences in both research and commercialization. chang-li14 at mails dot tsinghua dot edu dot cn. He won the NSF Career Award in 1997, and was elected Fellow of IEEE in 2010. Beijing, China. Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu, and Lawrence Carin. International Conference on Machine Learning (ICML), 2019. For that purpose, I wrote two books, left academia (perhaps temporarily) and formed a company to explore. All engineers should learn some business. Prof. Chang Liu works in the Sensors and MEMS research area with 30 years of experiences in both research and commercialization. Chang Liu, Jingwei Zhuo, and Jun Zhu. 5 Danling Street SENSIC Corporation is dedicated to building sensor systems and for enhancing smartness and safety of home. Artists use one year long create a series of visualization of solar terms based on each term’s climate features, agricultural transitions and cultural contexts. 100080. [Semantic Scholar], Department of Computer Science and Technology, Variance Reduction and Quasi-Newton for Particle-Based Variational Inference, Understanding MCMC Dynamics as Flows on the Wasserstein Space, Understanding and Accelerating Particle-Based Variational Inference, Variational Annealing of GANs: A Langevin Perspective, Straight-Through Estimator as Projected Wasserstein Gradient Flow, Message Passing Stein Variational Gradient Descent, Riemannian Stein Variational Gradient Descent for Bayesian Inference, Stochastic Gradient Geodesic MCMC Methods, A Study on Efficient Bayesian Inference Methods Using Manifold Structures, Sampling Methods on Manifolds and Their View from Probability Manifolds, Maximum Entropy Discrimination Latent Dirichlet Allocation with Determinantal Point Process Prior, Statistical-Mechanics-Related Concepts in Machine Learning, Hamiltonian Monte Carlo on Riemannian Manifolds, Introduction to MCMC and Hamiltonian Monte Carlo, Duke-Tsinghua Machine Learning Summer School, Tsinghua University Symphonic Band (THUMB). Chang Liu. Quant Researcher, Citadel Securities.