刘思廷教授本科和硕士均毕业于哈尔滨工业大学；受国家留学基金委资助在加拿大阿尔伯塔大学完成博士学习，主要研究方向为数字集成电路与系统、新型计算机体系结构等；博士毕业后在加拿大麦吉尔大学完成博士后研究；并于2021年加入上海科技大学信息学院后摩尔中心。具体研究内容包括一元计算、AI加速芯片、新型计算机体系架构设计等。

发表高水平国际会议与期刊二十余篇，并曾担任知名国际会议nanoarch、APCCAS、DFT、ICCAD等TPC委员、2022/2023 nanoarch publicity chair、GLSVLSI session chair等。

本科生课程 CS110 计算机体系结构I

研究生课程 EE219 智能计算系统

以往成果：

期刊：

1. S. Liu, W. J. Gross and J. Han, Introduction to Dynamic Stochastic Computing, in IEEE Circuits and Systems Magazine, vol. 20, no. 3, pp. 19-33, thirdquarter 2020, doi: 10.1109/MCAS.2020.3005483.

2. Y. Liu, S. Liu, Y. Wang, F. Lombardi and J. Han, A Survey of Stochastic Computing Neural Networks for Machine Learning Applications, in IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 2809-2824, July 2021, doi: 10.1109/TNNLS.2020.3009047.

3. S. Liu, H. Jiang, L. Liu and J. Han, Gradient Descent Using Stochastic Circuits for Efficient Training of Learning Machines, in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 37, no. 11, pp. 2530-2541, Nov. 2018, doi: 10.1109/TCAD.2018.2858363.

4. S. Liu and J. Han, Toward Energy-Efficient Stochastic Circuits Using Parallel Sobol Sequences, in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 26, no. 7, pp. 1326-1339, July 2018, doi: 10.1109/TVLSI.2018.2812214.

5. Y. Liu, S. Liu, Y. Wang, F. Lombardi and J. Han, A Stochastic Computational Multi-Layer Perceptron with Backward Propagation, in IEEE Transactions on Computers, vol. 67, no. 9, pp. 1273-1286, 1 Sept. 2018, doi: 10.1109/TC.2018.2817237.

会议：

1. S. Liu and J. Han, Dynamic Stochastic Computing for Digital Signal Processing Applications, 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), Grenoble, France, 2020, pp. 604-609, doi: 10.23919/DATE48585.2020.9116562.

2. S. Liu and J. Han, Energy efficient stochastic computing with Sobol sequences, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017, Lausanne, Switzerland, 2017, pp. 650-653, doi: 10.23919/DATE.2017.7927069.

3. S. Liu and J. Han, Hardware ODE solvers using stochastic circuits, 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC), Austin, TX, USA, 2017, pp. 1-6, doi: 10.1145/3061639.3062258.