Faisal Nadeem Khan

·         Ph.D., Electronic and Information Engineering, The Hong KongPolytechnicUniversity, Hong Kong

·         M.Sc., Communications Technology, University of Ulm,Germany                                                     

·         B.Sc. (with Honours), Electrical Engineering, University of Engineering and Technology Taxila, Pakistan               

2021-Present, Associate Professor, Tsinghua-Berkeley Shenzhen Institute, TsinghuaUniversity

2015-2020, Research Fellow, Photonic Research Centre, The Hong KongPolytechnicUniversity

2012-2015, Senior Lecturer, School of Electrical and Electronic Engineering, University of ScienceMalaysia

2011-2012, Postdoctoral Research Associate, Photonic Research Centre, The Hong KongPolytechnicUniversity

2006–2006, Research Assistant, Institute of Optoelectronics, University of Ulm, Germany

2005–2006, LogicaCMG (currently CGI Inc.), Ulm, Germany

·         Data Communication Networks (86001103) 

·         Queuing Theory and Its Application (86000593) 

·         ML-aided intelligent fiber-optic communication networks

·         ML-aided fiber-optic sensing systems and their applications

1.       2022-2025: “Machine learning-enabled intelligent fiber-optic communication networks,” funded by Shenzhen Finance Bureau and TsinghuaShenzhenInternationalGraduateSchool

2.       2024-2025: “Machine learning-enabled faults management, transmission quality prediction, and performance monitoring in fiber-optic networks,” funded by National Natural Science Foundation of China (NSFC)

3.       2024-2027: “The fibers of nature: Ecohydrological flows assessment via distributed fiber-optic sensing networks,” funded by Cross-disciplinary Research and Innovation Fund of Tsinghua Shenzhen International Graduate School

[1] F.N. Khan, “Non-technological barriers: The last frontier towards AI-powered intelligent optical networks,” Nature Communications, vol. 15, Article no. 5995, Jul. 2024. 

[2] Z. Cai, Q. Wang, Y. Deng, P. Zhang, G. Zhou, Y. Li, and F.N. Khan^*, “Domain adversarial adaptation framework for few-shot QoT estimation in optical networks,” IEEE/OSA Journal of Optical Communications and Networking, vol. 16, no. 11, Nov. 2024. (Editors’ Pick)

[3] Q. Wang, Z. Cai, and F.N. Khan^*, “Lifelong QoT prediction: An adaptation to real-world optical networks,” IEEE/OSA Journal of Optical Communications and Networking, vol. 16, no. 11, Nov. 2024.

[4] D. Wang, Y. Song, Y. Zhang, X. Jiang, J. Dong, F.N. Khan, T. Sasai, S. Huang, A.P.T. Lau, M. Tornatore, and M. Zhang, “Digital twin of optical networks: A review of recent advances and future trends” IEEE/OSA Journal of Lightwave Technology, vol. 42, no. 12, Jun. 2024. (Invited Paper)

[5] F.N. Khan, “Data perspectives in AI-assisted fiber-optic communication networks,” IEEE Network, vol. 37, no. 5, Sept. 2023.

[6] F.N. Khan, “Machine learning-enabled intelligent fiber-optic communications: Major obstacles and the way forward,” IEEE Communications Magazine, vol. 61, no. 4, Apr. 2023.

[7] Q. Wang, Z. Cai, A.P.T. Lau, Y. Li, and F.N. Khan^*, “Invariant convolutional neural network for robust and generalizable QoT estimation in fiber-optic networks, IEEE/OSA Journal of Optical Communications and Networking, vol. 15, no. 7, Jul. 2023.

[8] J. Lu, G. Zhou, Q. Fan, D. Zeng, C. Guo, L. Lu, J. Li, C. Xie, C. Lu, F.N. Khan, and A.P.T. Lau, “Performance comparisons between machine learning and analytical models for quality of transmission estimation in wavelength-division-multiplexed systems,” IEEE/OSA Journal of Optical Communications and Networking, Special Issue on Machine Learning Applied to QoT Estimation in Optical Networks, vol. 13, no. 4, Apr. 2021. (Invited Paper)

[9] F.N. Khan, Q. Fan, C. Lu, and A.P.T. Lau, “An optical communication’s perspective on machine learning and its applications,” IEEE/OSA Journal of Lightwave Technology, vol. 37, no. 2, Jan. 2019. (Invited Paper)                                                                    

[10] F.N. Khan, K. Zhong, X. Zhou, W.H. Al-Arashi, C. Yu, C. Lu, and A.P.T. Lau, “Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks,” Optical Society of America (OSA) Optics Express, vol. 25, no. 15, Jul. 2017.                                           

[11] A.K. Azad, F.N. Khan^*, W.H. Al-Arashi, N. Guo, A.P.T. Lau, and C. Lu, “Temperature extraction in Brillouin optical time-domain analysis sensors using principal component analysis based pattern recognition,” Optical Society of America (OSA) Optics Express, vol. 25, no. 14, Jul. 2017.

[12] Z. Dong, F.N. Khan, Q. Sui, K. Zhong, C. Lu, and A.P.T. Lau, “Optical performance monitoring: A review of current and future technologies,” IEEE/OSA Journal of Lightwave Technology, vol. 34, no. 2, Jan. 2016. (Invited Paper)                                                                                                

[13] M.C. Tan, F.N. Khan^*, W.H. Al-Arashi, Y. Zhou, and A.P.T. Lau, “Simultaneous optical performance monitoring and modulation format/bit-rate identification using principal component analysis,” IEEE/OSA Journal of Optical Communications and Networking, vol. 6, no. 5, May 2014.

[14] F.N. Khan, Y. Zhou, A.P.T. Lau, and C. Lu, “Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks,” Optical Society of America (OSA) Optics Express, vol. 20, no. 11, May 2012.

Invited Talks

[15] F.N. Khan, “Comparison of ML and analytical models for lightpaths QoT estimation,” presented at workshop “How machine learning can revolutionize optical fiber communications?”, European Conference on Optical Communication, Bordeaux, France, Sep. 2021.

[16] F.N. Khan, Q. Fan, C. Lu, and A.P.T. Lau, “Machine learning-assisted optical performance monitoring in fiber-optic networks,” 2018 IEEE Photonics Society Summer Topical Meeting Series, Waikoloa, Hawaii,  USA, July 2018, Paper MB4.1.

[17] F.N. Khan, C. Lu and, A.P.T. Lau, “Optical performance monitoring in fiber-optic networks enabled by machine learning techniques,” Invited Talk, Optical Fiber Communication Conference, San Diego, California, USA, Mar. 2018, Paper M2F.3.

[18] F.N. Khan, C. Lu, and A.P.T. Lau, “Machine learning methods for optical communication systems,” Optical Society of America (OSA) Advanced Photonics Congress, New Orleans, Louisiana, USA, Jul. 2017, Paper SpW2F.3.

Book

[19] A.P.T. Lau and F.N. Khan,Machine Learning for Future Fiber-Optic Communication Systems, Academic Press, USA, 2022. (ISBN: 9780323852272)

Book Chapters

[20] F.N. Khan, Q. Fan, C. Lu, and A.P.T. Lau, “Introduction to machine learning techniques: An optical communication’s perspective,” in Machine Learning for Future Fiber-Optic Communication Systems, A.P.T. Lau and F.N. Khan, Editors, Chapter 1, pp. 1-42, Academic Press, USA, 2022.

[21] F.N. Khan, Q. Fan, C. Lu, and A.P.T. Lau, “Machine learning methods for optical communication systems and networks,” in Optical Fiber Telecommunications, 7th ed., Alan E. Willner, Editor, Chapter 21, pp. 921-978, Academic Press, USA, 2019.

[22] F.N. Khan, Z. Dong, C. Lu, and A.P.T. Lau, “Optical performance monitoring for fiber-optic communication networks,” in Enabling Technologies for High Spectral-Efficiency Coherent Optical Communication Networks, Xiang Zhou and Chongjin Xie, Editors, Chapter 14, pp. 473-505, John Wiley & Sons, New Jersey, USA, 2016.