Research and Teaching

Research and Teaching Interests

• Applied Mathematics: fundamental mathematics and applications.

• Statistics:  (nonlinear, non-parametric)  state-space models,  particle filters/smoothers, particle Markov chain Monte Carlo, Bayesian inference, EM and EM-based algorithms, stochastic processes,  inverse problems, missing-data imputation, extreme phenomena, optimization.

• Machine Learning (Artificial Intelligence): big data, data processing and analysis, regressions, neural networks.

• Environmental Sciences: human carbon dioxide emissions, marine carbonate system, ocean carbon uptake, and ocean acidification.

Teaching experience

Visiting Professor:

2023 - 2024: AGH University of Krakow (Department of Applied Mathematics), Poland.

Courses (postgraduate students and researchers): Applied Statistics (state-space models and relevant approaches) and Machine Learning  & Data Science (data-driven estimation).

• Lecture notes: 

• Codes:

Research: "Sensitivity of data distribution to estimation of the global ocean CO2 uptake".

Guest Lecturer:

2022 - present:  RMIT University (Saigon South campus), Ho Chi Minh City (Vietnam).

Courses (undergraduate and Master students) on Machine Learning approaches for data processing (regression, classification, gap-filling) with applications in environmental science.

• Lecture example: slides

• Codes:  k-NN algorithm

Teaching assistant:

2011 - 2015: Teaching and tutoring in fundamental mathematics for Vietnamese secondary and high school students.

Communications

Organization:

1. Section co-convener, OS3.5 - Recent advances in constraining the marine carbon cycle, EGU General Assembly, Vienne (Austria), April 23-28, 2023.

Referee:

Nonlinear Processes in Geophysics, Global Biogeochemical Cycles

Talks: 

• 1. Ensemble machine learning for estimation of global ocean carbon uptake and acidification (slides). University of Science, Vietnam National University Ho Chi Minh city, June 2023.

  2. Recent developments at MOB TAC on the evolution of pCO2- DIC - ALK -pH CMEMS-LSCE-FFNN products (slides). Biogeochemical data assimilation in Copernicus Marine Services (BioDA-WG) virtual meeting, March 2023.

  3. Global ocean carbon sink estimates based on an ensemble of neural network models (slides).  Vietnam Applied Statistics Network (VASN) Annual Meeting, Ha Noi (Vietnam), October 28th, 2022. 

  4. CMEMS General Assembly (virtual), Feb 20-22, 2022.

  5. Statistical inference in nonlinear state-space models.  Conference National Colloquium on data assimilation, Rennes (France),  September  26-28, 2018.

  6. Reconstruction and estimation for state-space models. Workshop Stochastic Dynamical Systems, Quang Ninh (Vietnam), July 10-15, 2017.

  7. Estimation in non-parametric state-space models.  Seminar TOMS, Brest (France), March 23, 2017.

  8. Local linear regression in extended Kalman filter. Meeting of researchers in Télecom Bretagne, Brest (France), May 9, 2016.

Posters:

• 1. EGU General Assembly, Vienne (Austria), April 23-28, 2023 (file).

  2. CMEMS General Assembly, Brussels (Belgium), May 20-22, 2019.

  3. Conference OceanPredict '19, Halifax (Canada), May 6-10, 2019.

  4. Sumer school and Workshop SMC2017, Uppsala (Sweden), August 24- September 1, 2017.

  5.  Conference TIES-GRASPA 2017: Climate and Environment,  Bergamo (Italy),  July 24-26, 2017.

  6.  Conference DAS2017: Data science & Environment, Brest (France), July 3-7, 2017.