• Since 03/2014, Lecturer, Hanoi National University of Education, Department of Mathematics and Informatics.
• 2012-2014, Postdoc, ENSTA Bretagne
• 2004-2008, Assistant Lecturer, Hanoi National University of Education, Department of Mathematics and Informatics.

• 2008-2012, Tối ưu và Vận trù học (Optimization and Operations Research), INSA de Rouen, France, Advisor: Prof. Pham Dinh Tao and Prof. Le Thi Hoai An, Level: Bachelor/Engineer, Type: Regular
• 2005-2007, Tối ưu và Tính toán khoa học, Viện Toán học , Advisor: , Level: Master, Type: Regular
• 2000-2004, Cử nhân toán học, Đại học Sư phạm Hà Nội, Advisor: , Level: Bachelor/Engineer, Type: Regular

• 2018-, Member, (Nafosted Project) Network Coding for Spectrum-Efficient and Reliable Optical Core Networks, HNUE
• 2015-2016, (Postdoc), Join the project: "Numbbo/coco: Comparing Continuous Optimizers", INRIA Saclay & Paris Sud
• 2012-2014, (Postdoc), Projet PFMC: Planification et fusion multi-capteurs , DGA and ENSTA Bretagne, France

[17] Duc Manh Nguyen. 2022. Benchmarking some variants of the CMAES-APOP using Keeping Search Points and Mirrored Sampling combined with Active CMA on the BBOB Noiseless Testbed. In Genetic and Evolutionary Computation Conference Companion (GECCO ’22 Companion), July 9-13, 2022, Boston, MA, USA. ACM, New York, NY, USA, 9 pages. (Accepted)

[16] Duc Manh Nguyen. 2022. The Effect of Mirrored Sampling with active CMA and Sample Reuse in the CMAES-APOP Algorithm. In Genetic and Evolutionary Computation Conference Companion (GECCO ’22 Companion), July 9-13, 2022, Boston, MA, USA. ACM, New York, NY, USA, 4 pages. (Accepted)

[15] Konstantinos Varelas, Ouassim Ait El Hara, Dimo Brockhoff, Nikolaus Hansen, Duc Manh Nguyen, Tea Tušar, Anne Auger, Benchmarking large-scale continuous optimizers: The bbob-largescale testbed, a COCO software guide and beyond, Applied Soft Computing, 2020, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2020.106737.

[14] Duc Manh Nguyen, Le Anh Ngoc, Pham Thi Viet Huong, Ngo Hong Son, and  Dao Thanh Hai. An Efficient Column Generation Approach for Solving the Routing and Spectrum Assignment Problem in Elastic Optical Networks. The 6th NAFOSTED Conference on Information and Computer Science (NICS 2019), Hanoi, Vietnam, 2019, pp. 130-135. doi: 10.1109/NICS48868.2019.9023831

[13] Duc Manh Nguyen. A Combination of CMAES-APOP Algorithm and Quasi-Newton Method. In Proceedings of the International Conference on Computer Science, Applied Mathematics and Applications (ICCSAMA 2019), pp 64-74. https://link.springer.com/chapter/10.1007/978-3-030-38364-0_6

[12] Duc Manh Nguyen, Frédéric Dambreville, Abdelmalek Toumi, Jean-Christophe Cexus and Ali Khenchaf (2019). Solving the Problem of Coordination and Control of Multiple UAVs by using the Column Generation Method. In Proceedings of 6th World Congress on Global Optimization, pp 1097-1108, 2019. 

[11] Ouassim Ait ElHara, Konstantinos Varelas, Duc Manh Nguyen, Tea Tušar, Dimo Brockhoff, Nikolaus Hansen, and Anne Auger (2019), COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite. CoRR abs/1903.06396 (2019)  (https://arxiv.org/pdf/1903.06396.pdf)

[10] Duc Manh Nguyen (2018), Benchmarking a variant of the CMAES-APOP on the BBOB noiseless testbed. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '18), Hernan Aguirre (Ed.). ACM, New York, NY, USA, 1521-1528. DOI: https://doi.org/10.1145/3205651.3208299

[9] Duc Manh Nguyen (2018), An adapting population size approach in the CMA-ES for multimodal functions. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '18), Hernan Aguirre (Ed.). ACM, New York, NY, USA, 219-220. DOI: https://doi.org/10.1145/3205651.3205801

[8] Duc Manh Nguyen and Nikolaus Hansen (2017), Benchmarking CMAES-APOP on the BBOB Noiseless Testbed. In Proceedings of GECCO’17 Companion, Berlin, Germany, July 15-19, 2017, Pages 1756-1763.

[7] Le Thi Hoai An, Nguyen Duc Manh and Pham Dinh Tao, A based-DC programming approach for planning a multisensor multizone search for a moving target, Advances in Intelligent Systems and Computing Volume 359, pp. 107-118 (2015).

[6] Duc Manh Nguyen, Frédéric Dambreville, Abdelmalek Toumi, Jean-Christophe Cexus and Ali Khenchaf, A column generation based label correcting approach for the sensor management in an information collection process, Advanced Computational Methods for Knowledge Engineering Studies in Computational Intelligence, Vol. 479, pp. 77-89 (2013).

[5] Le Thi Hoai An, Nguyen Duc Manh and Pham Dinh Tao, A DC programming approach for planning a multisensor multizones search for a target, Computers & Operations Research Volume 41, January 2014, Pages 231–239.

[4] Nguyen Duc Manh, Le Thi Hoai An and Pham Dinh Tao, Solving the Multidimensional Assignment Problem by a Cross-Entropy method, Journal of Combinatorial Optimization, May 2014, Volume 27(4) , pp. 808-823.

[3] Le Thi Hoai An, Nguyen Duc Manh and Pham Dinh Tao (2012), Globally solving a Nonlinear UAV Task Assignment Problem by stochastic and deterministic optimization approaches, Optimization Letters 6(2): 315-329.

[2] Nguyen Duc Manh, Le Thi Hoai An and Pham Dinh Tao, A Cross-Entropy Method for Value-at-Risk Constrained Optimization, Intelligent Information and Database Systems. N. Nguyen, C.-G. Kim and A. Janiak (Eds.), Springer Berlin / Heidelberg. 6592: 442-451 (2011).

[1] Nguyen Duc Manh, Le Thi Hoai An and Pham Dinh Tao, A Cross-Entropy method for Nonlinear UAV Task Assignment Problem, 2010 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), Hanoi, 2010, pp. 1-5.

Talks: 

24. Benchmarking some variants of the CMAES-APOP using Keeping Search Points and Mirrored Sampling combined with Active CMA on the BBOB Noiseless Testbed. In Genetic and Evolutionary Computation Conference Companion (GECCO ’22 Companion), July 9-13, 2022, Boston, MA, USA. ACM, New York, NY, USA.

23. The Effect of Mirrored Sampling with active CMA and Sample Reuse in the CMAES-APOP Algorithm. In Genetic and Evolutionary Computation Conference Companion (GECCO ’22 Companion), July 9-13, 2022, Boston, MA, USA. ACM, New York, NY, USA.

​22. A Combination of CMAES-APOP algorithm and quasi-Newton method. The 6th International Conference on Computer Science, Applied Mathematics and Applications (ICCSAMA 2019), Hanoi, Vietnam, 2019.

21. An Efficient Column Generation Approach for Solving the Routing and Spectrum Assignment Problem in Elastic Optical Networks. The 6th NAFOSTED Conference on Information and Computer Science (NICS), Hanoi, Vietnam, 2019.

20. Benchmarking a Variant of the CMAES-APOP on the BBOB noiseless testbed. The Genetic and Evolutionary Computation Conference Companion (GECCO '18), Kyoto, Japan, 2018.

19. An Adapting Population Size Approach in the CMA-ES for Multimodal Functions. The Genetic and Evolutionary Computation Conference Companion (GECCO '18), Kyoto, Japan, 2018. (poster)

18. Benchmarking CMAES-APOP on the BBOB noiseless testbed, Department of Mathematics and Informatics, Hanoi National University of Education, May 2018, Hanoi, Vietnam.

17. An Adapting Population Size Approach in the CMA-ES for Multimodal Functions. International Workshop Optimization Algorithms and Some Related Problems. Institute of Mathematics Vietnam Academy of Science and Technology, Hanoi, Vietnam 2017.

16. A column generation approach for coordination and control of multiple UAVs, International Conference on High Performance Scientific Computing, March 16-20, 2015, Hanoi, Vietnam.

15. A DC programming approach for planning a multisensor multizone search for a target, Department of Mathematics and Informatics, Hanoi National University of Education, April 2015, Hanoi, Vietnam.

14. A column generation approach for the sensor management in an information collection process, Department of Mathematics and Informatics, Hanoi National University of Education, April 2014, Hanoi, Vietnam.

13. A column generation based label correcting approach for the sensor management in an information collection process, ICCSAMA 2013, Warsaw, May 9-10, 2013.

12. Une méthode de génération de colonnes pour la planification des capteurs dans un processus de collecte d’informations, 14e conférence ROADEF de la société Française de Recherche Opérationnelle et Aide à la Décision, Université de Technologie de Troyes, 13-15 février 2013.

11. Combining multi-objective constraint satisfaction and sequential control for solving the sensors management of the intelligence process, 25th EURO Conference on Operational Research, Vilnius, July 8-11, 2012.

10. Solving the Multidimensional Assignment Problem via the Cross-Entropy method, 25th European Conference on Operational Research, Vilnius, Lithuania, July 8 - 11, 2012.

9. Solving the Multidimensional Assignment Problem via the Cross-Entropy method., Workshop: Optimization and Learning: Theory, Algorithms and Applications, Metz, France, May 2011.

8.  A  Cross-Entropy method  for  Value-at-Risk constrained Optimization,  The  3rd  Asian  Conference on Intelligent Information and Database Systems, Daegu, Korea, April 2011.

7.  A Cross-Entropy method for Nonlinear UAV Task Assignment Problem, Journée de Doctorat SPMII 2011, Université de Rouen, France, April 2011.

6. A Cross-Entropy method for Value-at-Risk constrained Optimization, 12e congrès annuel de la Société française de Recherche Opérationnelle et d’Aide à la Décision (ROADEF), Saint Etienne, France, March 2011.

5. A Cross-Entropy method for Nonlinear UAV Task Assignment Problem, IEEE-RIVF International Conference on Computing and Communication Technologies Research, Innovation and Vision for the Future, Hanoi, Vietnam, November 2010.

4. A new approach for solving Value-at-Risk constrained Optimization using the DC programming and DCA, 24rd European Conference on Operational Research, Lisbon, July 11 - 14, 2010.

3. A deterministic optimization approach for planning a multi-sensor multi-zone search for a moving target, Workshop: Optimization and Learning: Theory, Algorithms and Applications, Metz, France, 2010.

2. A deterministic optimization approach for planning a multi-sensor multi-zone search for a moving target, The 3rd Asian Conference on Intelligent Information and Database Systems, Hue, Vietnam, March 2010.

1. A deterministic optimization approach for planning a multi-sensor multi-zone search for a target, 23rd European Conference on Operational Research, Bonn, July 2009.

PC Member:

• GECCO (The Genetic and Evolutionary Computation Conference): 2018, 2019, 2020, 2021, 2022, 2023
• IEEE CEC (IEEE Congress on Evolutionary Computation, in the IEEE WCCI (IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE​)): 2022, 2023
• Computer and Operations Research: 2016
• IEEE Transactions on Evolutionary Computation: 2020, 2022
• Soft Computing: 2020, 2021
• WCGO (World Congress on Global Optimization): 2019
• ACIIDS (Asian Conference on Intelligent Information and Database Systems): 2014, 2016
• ICCSAMA (International Conference on Computer Science, Applied Mathematics and Applications): 2015, 2019
• ICCCI (International Conference on Computational Collective Intelligence Technologies and Applications): 2013.
• NICS (The NAFOSTED Conference on Information and Computer Science): 2019

• “I believe in intuition and inspiration. Imagination is more important than knowledge. For knowledge is limited, whereas imagination embraces the entire world, stimulating progress, giving birth to evolution. It is, strictly speaking, a real factor in scientific research.” -- Albert Einstein --

• Combinatorial Optimization (Master's degree)
• Convex Analysis and Nonlinear Optimization (Master's degree) - updated 11/2018
• Linear Programming (Updated 02/12/2016)
• Numerical Analysis
• Probability & Statistics
• Softwares for Maths - updated 23/04/2021