Data science and Optimization of complex systems

About Us

Who we are ?

We are a group of data scientists and optimizers with core competencies in optimization, statistics, machine learning and signal processing. The leaders of group are Le Thi Hoai An, Professor of exceptional class, Head of Informatics and Applications Lab., university of Lorraine and Pham Dinh Tao, Emeritus professor at National Institute for Applied Sciences (INSA) – Rouen, France. They are co-founders of DC (Difference of Convex functions) programming and DCA (DC Algorithm), reconized by the world as a state-of-the-art and powerful mathematic programming approach which has been successfully applied on various problems in several application domains.

Most of the other members of our group are doctors in applied mathematics and/or in computer science trained in French (under the supervision of those leaders) and are teaching-researchers in universities at Ha noi or its neighbor.

Our activities concern research and development in machine learning and optimization techniques for solving relevant problems arising in different application domains. We use data to build models and extract knowledge, we exploit knowledge to automate the discovery of improving solutions, and then we connect insight to decisions and actions. From the data to action in passing by knowledge, our passion is to build powerful tools for the new prescriptive analytics wave.

Our motivations

Those leaders have a “dream”, since twenty years ago, to develop in Viet nam an excellent centre of research on applied mathematics and computing technology. This is motivated by the fact that applied mathematics has not yet developed in Viet Nam, in universities as well as research centers. Meanwhile the needs of society in front of the high-speed development of technology are constantly growing. After so many years of fruitful scientific collaborations with the Vietnam, in particular the greatly successful training of PhD  (18 vietnamese doctors and currently 6 PhD students),  we can now form in Viet nam a strong team in applied mathematics and computing technology, with a particular emphasis on Data science and Optimization of complex systems.

Our objective

– Scientifically, our main objective is double

  • To tackle the challenges of Data science by developing new techniques and tools for storing, transferring, accessing, visualizing, and analyzing large datasets, leading to new discoveries in the use of “complex data” in “complex systems”.
  • The ultimate goal is to analyze and manage complex systems in optimal ways.

– As for the research development in Vietnam, the objective of our center is multiple

  • PhD training with high level;
  • Gathering and supporting talented young scientists, in order to form a strong team in applied mathematics and computing, with a particular emphasis on Data science in complex systems;
  • Attracting international scientists, especially, reputable scientists in the world, to become a sustainable world-class research center;
  • Technology transfer.


To tackle “Big data” we focus on pursuing theoretical developments and innovations to accommodate high dimensional data and very large-scale sets. These efforts are dedicated to discovering, evolving, and maturing analytic algorithms that efficiently scale to facilitate rapid analyses of massive data sets. The first research interest is to design high accuracy, predictive models based on massive data sets, which take advantage of emerging computing architectures. The second research interest is maturation of methodologies to reduce data set dimensionality prior to modeling; thereby, greatly shortening computational time.

For “Big data in complex systems”, several areas should be developed:

  • Discovering algorithms for real-time processing and analysis of raw data from high throughput scientific instruments.
  • Quantifying, analyzing sensitivity and assessing uncertainty in data in complex systems: models, and methods.
  • Developing new techniques for scientific data storage and management that actively enable analytics.

For optimizing complex systems the research directions should be

  • Understanding the structure, mechanics, and dynamics of complex real-world networks.
  • Modeling and analysis of large-scale networks with complex interactions by sophisticate mathematical models via deterministic and statistical approaches.
  • Developing scalable mathematical techniques for manipulating, transforming and optimizing large complex systems
  • Risk management: analysis of how risk should be incorporated into complex systems models and how to manage the risks.


We are focusing on four main specific interests of data science – optimization, statistics, machine learning and signal processing, they are the core of our scientific competencies. The development of machine learning, a branch of artificial intelligence used to uncover patterns in data from which predictive models can be developed, has enhanced the growth and importance of data science. Optimization and statistic are indispensable tools and techniques for machine learning and other areas of data science on one hand, and for decision making in complex systems on another hand.

The main challenge is developing efficient, fast and scalable algorithms for mining large data set and optimizing complex systems, which often leads to nonsmooth, nonconvex optimization problems. The methodological basis of the research, which embodies its original and innovative characters, is the non-convex programming whose backbone is the DC (Difference of Convex functions) programming and DCA (DC Algorithms) which are internationally recognized as “the State-of-Art Technology nonconvex programming and global optimization” and have been successfully applied, by researchers and practitioners on the world, for modelling and solving their large-scale nonconvex programs in different fields of Applied Sciences. DC programming and DCA are introduced in 1985 by Pham Dinh Tao in their preliminary form and intensively developed by Le Thi Hoai An & Pham Dinh Tao since 1993 to become now classics and increasingly popularized by researchers and practitioners all the world over, in different areas of applied sciences. Their popularity is due to their robustness and efficiency compared to existing methods, their adaptation to the structures of treated problems and their ability to solve large-scale real world nonconvex programs.


  1. Data mining & Machine learning
  2. Data security and Security of information systems
  3. Data visualization
  4. Nonconvex programming, DC programming
  5. Large-scale optimization
  6. Combinatorial optimization


As Data science and Optimization techniques affect research in many domains, including the biological sciences, medical informatics, health care, social sciences and the humanities and it heavily influences economics, business and finance …, we can apply these technologies in many scientific and engineering domains. We focus on the following areas

  1. Communication systems
  2. Health care
  3. Economy, Financial and banking
  4. Biology and bioinformatics
  5. Transport-logistic and production management
  6. Energy and environment
  7. Mechanical engineering, robotics
  8. Social network
Conference ICCSAMA 2019
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