I am currently a quantitative researcher at Y-intercept (Hong Kong) Limited. Until 2021, I was working as a quant researcher at Qube Research & Technologies (Hong Kong) Limited.
In 2019, I graduated from HKUST with a PhD degree in Electronic and Computer Engineering. I was under the supervision of Prof. Matthew McKay (now at the University of Melbourne, Australia) and Prof. David Jimenez-Morales (now at the University of Granada, Spain). From Sep. 2016 until Feb. 2017, I was a visiting student at the Large Networks and Systems Group (LANEAS) at CentraleSupélec (France), where I was advised by Prof. Romain Couillet (now at the University of Grenoble-Alpes, France). My PhD thesis focused on random matrix theory, statistical signal processing, and machine learning.
I also obtained a Master of Philosophy from the Hong Kong University of Science and Technology (Hong Kong) in 2013, and a Diploma of Engineering from Télécom SudParis (Evry, France) in 2014.
- 2014 – 2019: PhD in Electronic and Computer Engineering, The Hong Kong University of Science & Technology, Hong Kong
Thesis: Random Matrix Theory Applied to the Robust Estimation of Large Covariance Matrices
- 2012 – 2013: MPhil in Electronic and Computer Engineering, The Hong Kong University of Science & Technology, Hong Kong
Thesis: Identification of Live or Studio Versions of a Song via Supervised and Semi-Supervised Learning
- 2010 – 2013: Diploma of Engineering in Information Technology, Télécom SudParis, Evry, France
- 2007 – 2010: Preparation for national competitive entrance exams to leading French “grandes écoles”, specializing in maths and physics, Lycée Henri Poincaré, Nancy, France
- Oct. 2018 – Apr. 2019: Quantitative Research Intern, Special Situation team, Qube Research & Technologies, Hong Kong
- Sep. 2016 – Mar. 2017: Research Intern, Romain Couillet’s team, Large Networks and Systems Group, CentraleSupélec, Gif-sur-Yvette, France
- Feb. 2014 – Aug. 2014: Research Assistant, Department of Electronic and Computer Engineering, The Hong Kong University of Science & Technology, Hong Kong
Since 2014, I have been Teaching Assistant for the following classes:
- ELEC 5450, Random Matrix Theory and Applications, Spring 2016, Spring 2017
- ELEC 2600H, Honors Probability and Random Processes in Engineering, Fall 2017
- ELEC 2600, Probability and Random Processes in Engineering, Fall 2014, Spring 2015, Fall 2015
- SISP 1303, Intelligent Sensing Systems, Summer 2015
- N. Auguin, D. Morales-Jimenez, and M. R. McKay, “Large-Dimensional Characterization of Robust Linear Discriminant Analysis“, IEEE Transactions on Signal Processing, vol. 69, pp. 2625-2638, 2021.
- N. Auguin, D. Morales-Jimenez, M. R. McKay, and R. Couillet, “Large-Dimensional Behavior of Regularized Maronna’s M-Estimators of Covariance Matrices”, IEEE Transactions on Signal Processing, vol. 66, no. 13, pp. 3529-3542, 2018.
- N. Auguin, D. Morales-Jimenez, and M. R. McKay, “Exact Statistical Characterization of 2×2 Gram Matrices with Arbitrary Variance Profile“, IEEE Transactions on Vehicular Technology, vol. 66, no. 9, pp. 8575 – 8579, 2017.
- N. Auguin, D. Morales-Jimenez, and M. R. McKay, “Robust Linear Discriminant Analysis Using Tyler’s Estimator: Asymptotic Performance Characterization”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton (UK), May 2019.
- N. Auguin, D. Morales-Jimenez, M. R. McKay, and R. Couillet, “Robust Shrinkage M-Estimators of Large Covariance Matrices”, IEEE Statistical Signal Processing Workshop, Palma de Mallorca (Spain), June 2016.
- N. Auguin, and P. Fung, “Semi-Supervised Learning for Classification of Live or Studio Music Recordings”, International Conference on Language Resources and Evaluation, Reykjavik (Iceland), May 2014.
- N. Auguin, S. Huang, and P. Fung, “Identification of Live or Studio Versions of a Song via Supervised Learning”, Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Kaohsiung (Taiwan), October 2013.
- D. Su, P. Fung, and N. Auguin, “Multimodal Music Emotion Classification Using AdaBoost with Decision Stumps” , IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver (Canada), May 2013.