张景昭

张景昭现任清华交叉信息研究院助理教授，博士毕业于麻省理工学院计算机科学专业，曾获伯克利研究生奖学金，MIT Lim奖学金，IIIS青年学者奖学金, MIT最佳AI&Decision Making 硕士论文， MIT 最佳 AI & Decision Making 博士论文 等奖项。 研究主要包含大规模优化算法，神经网络训练，算法复杂性分析，机器学习理论，以及人工智能应用。

个人荣誉

IIIS青年学者奖学金 

麻省理工学院最佳人工智能和决策硕士论文 

麻省理工学院最佳人工智能和决策博士论文 

伯克利研究生奖学金 

麻省理工学院Lim研究生奖学金 

•优化算法及理论 

•大规模神经网络训练 

•机器学习理论 

•机器学习在动力系统中应用

一、

研究方向：

大模型训练

岗位职责：

服务器集群GPU并行训练开发

任职资格：

1.计算机、电子、自动化、软件和物理等相关专业背景，学术能力强； 

2.具备优秀的领域内理论基础知识和编程功底（Python、Linux、C++等）；

二、

研究方向：

电池健康算法研发

岗位职责：

基于现有数据和模型进行模型研发，算法比较，结果整理。

任职资格：

1.计算机、电子、自动化、软件和物理等相关专业背景，学术能力强；

2.具备优秀的领域内理论基础知识和编程功底（Python、Linux、C++等）；

3. 英文写作扎实，有发表经验。

简历投递：

jingzhaoz@mail.Tsinghua.edu.cn

Wen, Kaiyue, Jiaye Teng, and Jingzhao Zhang. “ Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models.” International Conference on Learning Representations(2023).

Cheng, Xiang, Jingzhao Zhang, and Suvrit Sra. “ Efficient Sampling on Riemannian Manifolds via Langevin MCMC..” Advances in Neural Information Processing Systems 35 (2022): 5995-6006.

Ahn, Kwangjun, Jingzhao Zhang, and Suvrit Sra. “Understanding the unstable convergence of gradient descent.” In International Conference on Machine Learning, pp. 247-257. PMLR, 2022.

Zhang, Jingzhao, Haochuan Li, Suvrit Sra, and Ali Jadbabaie. “Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective.” In International

Conference on Machine Learning, pp. 26330-26346. PMLR, 2022.

Gu, Xinran, Kaixuan Huang, Jingzhao Zhang, and Longbo Huang. “Fast federated learning in the presence of arbitrary device unavailability.” Advances in Neural Information Processing Systems 34 (2021): 12052-12064.

Li, Haochuan, Yi Tian, Jingzhao Zhang, and Ali Jadbabaie. “Complexity lower bounds for nonconvex-strongly-concave min-max optimization.” Advances in Neural Information Processing Systems 34 (2021): 1792-1804.

Zhang, Jingzhao, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, and Ali Jadbabaie. “Complexity of finding stationary points of nonconvex nonsmooth functions.” In International Conference on Machine Learning, pp. 11173-11182. PMLR, 2020.

Yu, Tiancheng, Yi Tian, Jingzhao Zhang, and Suvrit Sra. “Provably efficient algorithms for multi-objective competitively.” In International Conference on Machine Learning, pp. 12167-12176. PMLR, 2021.

Zhang, Jingzhao, Aditya Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, and Suvrit Sra. “Coping with label shift via distributionally robust optimization.” ICLR(2020).

Zhang, Jingzhao, Hongzhou Lin, Subhro Das, Suvrit Sra, and Ali Jadbabaie. “Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity.” In International Conference on Machine Learning, pp. 26347-26361. PMLR, 2022.

Jingzhao Zhang, Suvrit Sra, and Ali Jadbabaie.“Acceleration in First Order Quasi-strongly Con-vex Optimization by ODE Discretization.” Conference on Decision and Control(2019)

Zhang, Jingzhao, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank Reddi, Sanjiv Kumar, and Suvrit Sra. “Why are adaptive methods good for attention models?.” Advances in Neural Information Processing Systems 33 (2020): 15383-15393.

Jingzhao Zhang, C ́esar A. Uribe, Aryan Mokhtari, and Ali Jadbabaie. “Achieving Acceleration in Distributed Optimization via Direct Discretization of the Heavy-Ball ODE.” American Control Conference (2019).

Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, and Ali Jadbabaie.“Direct Runge-Kutta Discretization Achieves Acceleration.” NeurIPS Spotlight (2018)

Jingzhao Zhang, Hongyi Zhang, Suvrit Sra. “R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate.” arXiv (2018)

Jingzhao Zhang, Nicolas C. P ́egard, Jingshan Zhong, Hillel Adesnik, and Laura Waller. “3D computer-generated holography by non-convex optimization.” Optica 4, no. 10 (2017)

Nicolas C. P ́egard, Alan R. Mardinly, Jingzhao Zhang, Savitha Sridharan, Laura Waller, and Hillel

Adesnik. “Holographic temporal focusing for 3d photo-activation with single neuron resolution.” In Optics and the Brain, Optical Society of America, 2017.

Jingzhao Zhang, Jingshan Zhong, and Laura Waller. “Nonlinear optimization for partially coherent phase recovery with Abbe’s method.” In Digital Holography and Three-Dimensional Imaging,Optical Society of America, 2016.