Program

Tentative program

  Tutorials
Location: Sunwah 1st Floor
Invited talks
Location: Sunwah 2nd Floor
8:30 – 9:15 Dr. Pham Viet Thang (VU University Medical Center, Netherland)
Pham Viet Thang received a BSc degree in computer science from RMIT University, Australia in 1998. After a brief period working as teaching assistant at Vietnam National University in 1999, he joined the Intelligent Systems Lab Amsterdam, the Netherland where he earned a doctoral degree on the topic of machine learning and computer vision. During this period, he devised new methods for Bayesian network classification, support vector classification, boosting algorithms, and sparse representation of images. Since 2006, he has been working first as research scientist and now as assistant professor at the Cancer Center Amsterdam, VU University Medical Center, the Netherlands. His current interest is to advance computer algorithms to explore the vast amount of data in cancer research. A highlight of his work is the development of statistical methods for significance analysis of mass spectrometry-based proteomics data.

AI and data in cancer research (Part 1)
Artificial intelligence is playing an increasingly important role in cancer research. Advancement in acquisition technologies has led to largescale data production of various molecular readouts. Coupled with smart learning algorithms, this brings exciting challenges and opportunities for early detection, diagnostic, treatment selection and monitoring of cancers. Is it possible to identify cancers early in a non-invasive manner? Can cross-cancer analysis offer guidance to treatment of patients whose tumors reside in a different organ and yet sharing driver gene mutations with other tumors that have previously been successfully managed?
In this presentation, I will survey current methods and data in cancer genomics and proteomics with a view towards real-life applications. I will also discuss sustainable software tools and computing infrastructures to maintain the momentum of AI in the field of cancer research.
Assoc. Prof. Sylvain Lagrue (University of Artois, France)
LAGRUE Sylvain is an Associate Professor of Computer Science at Université d’Artois/CRIL CNRS UMR 8188 (France). His research includes Artificial Intelligence, Knowledge Representation, Uncertainty in AI and Games. He is currently invited at the VNU in the framework of the EU Project Aniage.

General Game Playing: a Challenge for AIGames represent an exciting challenge for Artificial Intelligence. The ability of computers to confront human beings in a convincing manner, or even to defeat them, fascinate most people. Besides, games are a good framework to test algorithms developed for more general problems. Thus games are a good area to test out AI techniques and to develop new approaches. Recently, stronger results were provided and, for many games, computer skills are far away from human abilities.
We focus in this talk on the challenge of General Game Playing. The topic of General Game Playing (GGP) is to develop artificial agents able to play any game, without human intervention. The rules of each game are described in a declarative representation language, called Game Description Language (GDL). These rules are given to the agent only a few minutes before playing, which makes it difficult to apply current techniques. We discuss in this talk the difficulty of this challenge, some recent results and some possible applications in real life.
9:15 – 10:00 Dr. Nguyen Thanh Tung (Thuyloi University, Vietnam)
Tung Nguyen received his BS and MSc. degree in Information Technology from Vietnam National University (Hanoi) and a PhD. in Computer Applied Technology from Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China. He has worked as a lecturer, researcher, consultant positions related to data management and mining for more than 20 years. He has been a visiting researcher at Vietnam Institute for Advanced Study in Mathematics (VIASM), big data institute-Shenzhen University in China (2015), Machine learning and Robot Lab, Department of Computer Science, SUSTech and Center for Automata Processing, Department of Computer Science, University of Virginia.
His current research interests include Artificial intelligence, Machine learning and Data mining, especially advanced machine learning methods for the problems of classification; statistical learning, regression ensemble methods for high-dimensional data. He has published around 20 papers in international journals and conference proceedings in these areas.

Applied AI for predicting the quality of irrigation services in the Red river delta
To predict the satisfaction of users who use the water services is very important for the fee exemption policy to water and agriculture services. This policy has positive impacts on the water exploited and management enterprises, the national budget and social security. In this talk, some machine learning models are presented to predict the satisfaction of users related to the quality of irrigation service in the red river delta. Experimental results showed that the non-linear machine learning models achieve lower regression errors than linear models, these linear models are commonly used by irrigation experts. The diversity and feasibility of these machine learning models can be applied for dealing with economic problems in the domain of water resource management.
10:00 – 10:15 Coffee Break
10:15 – 11:00 Dr. Pham Viet Thang (VU University Medical Center, Netherland)
Pham Viet Thang received a BSc degree in computer science from RMIT University, Australia in 1998. After a brief period working as teaching assistant at Vietnam National University in 1999, he joined the Intelligent Systems Lab Amsterdam, the Netherland where he earned a doctoral degree on the topic of machine learning and computer vision. During this period, he devised new methods for Bayesian network classification, support vector classification, boosting algorithms, and sparse representation of images. Since 2006, he has been working first as research scientist and now as assistant professor at the Cancer Center Amsterdam, VU University Medical Center, the Netherlands. His current interest is to advance computer algorithms to explore the vast amount of data in cancer research. A highlight of his work is the development of statistical methods for significance analysis of mass spectrometry-based proteomics data.

AI and data in cancer research (Part 2)
Artificial intelligence is playing an increasingly important role in cancer research. Advancement in acquisition technologies has led to largescale data production of various molecular readouts. Coupled with smart learning algorithms, this brings exciting challenges and opportunities for early detection, diagnostic, treatment selection and monitoring of cancers. Is it possible to identify cancers early in a non-invasive manner? Can cross-cancer analysis offer guidance to treatment of patients whose tumors reside in a different organ and yet sharing driver gene mutations with other tumors that have previously been successfully managed?
In this presentation, I will survey current methods and data in cancer genomics and proteomics with a view towards real-life applications. I will also discuss sustainable software tools and computing infrastructures to maintain the momentum of AI in the field of cancer research.
Assoc. Prof. Do Van Thanh (Nguyen Tat Thanh University, Vietnam)
Assoc. Prof. PhD. Do Van Thanh was a senior researcher, deputy director of the National Center for Socio-Economic Information and Forecasts, Ministry of Planning and Investment, and a part – time lecturer of the Information Technology Department, University of Technology, Hanoi National University. Now he is a full time lecturer, the Information Technology Department, Nguyen Tat Thanh University. He received PhD’s degree in Information Technology in 1996 from the Vietnamese Academy of Science and Technology. His research interests include Data Analysis and Data Mining, Knowledge representation and automatic reasoning, Economic – Financial Analysis and Forecast. He is the author and co-authors of more than 70 peer – reviewed publications concerning the all fields above. He has strong experience in knowledge discovery in databases and building economic-financial forecast models.

Application of artificial intelligence techniques in building economic-financial forecast models on high dimensional data sets.
Forecasting in the economic-financial field plays a very important role for the direction and regulation of the government, the development and implementation of business-production strategies of enterprises, investment and consumption of people. So far, the econometric approach based on a combination of mathematics, economic theory and statistical prediction with regression techniques, remains the most popular and important one to make forecasts in the field of economics and finance.
With the increasing economic globalization and the rapid development of science and technology, each event in the field of economics – finance is affected by many other economic-financial and social factors in the country as well as abroad. The current econometric approach to build economic-financial forecasting models is not applicable to input data sets with high dimensions and it is a very big challenge for the implementation of economic-financial forecasts.
The purpose of this report is to present some approachs of application of artificial intelligence techniques in building economic-financial forecasting models on huge input data sets. Artificial intelligence techniques are very useful for dimensionality reduction of input data sets and to improve the quality of forecasting models built under the econometric approach. Some forecasting models in the economic – financial field built arcoding to the mentioned approachs on the real data sets of the economy show that the forecast accuracy by the models is not only increased but also can use these models for evaluation of the impact of economic-financial shocks as well as of many other external factors. Some new researchs by author and some open issues will also be presented and discussed in this report.
11:00 – 11:45 Dr. Tara Thiagarajan (Sapien labs, India)
Dr. Tara Thiagarajan is Founder and Chief Scientist of Sapien Labs, a non-profit research institute dedicated to the nature of the human brain. Her research focuses on developing new analytical approaches to brain signals to drive understanding of how environment influences human brain diversity and, in turn, cognitive outcome. In addition to her scientific work she has built and run successful companies. She holds a Ph.D. in Neuroscience from Stanford University as well as a BA in Mathematics from Brandeis University and an MBA from the Kellogg School of Management. She did her postdoctoral work at the National Institutes of Health (NIH) in the Section on Critical Brain Dynamics and was a Visiting Scientist at the National Center for Biological Sciences in India.

Understanding the Human BrainHuman intelligence has conceived of and created artificial intelligence, but what do we know about the human brain itself and how it adapts to its changing environment?
11:45 – 13:30 Lunch
  Tutorials
Location: Sunwah 1st Floor
Invited talks
Location: Sunwah 2nd Floor
13:30 – 14:15 Assoc. Prof. Nguyen Le Minh (Japan Advanced Institute for Science and Technology, Japan)
Minh Le Nguyen is currently an Associate Professor of School of Information Science, JAIST. He leads the lab on Machine Learning and Natural language Understanding at JAIST. He received his B.Sc. degree in information technology from Hanoi University of Science, and M.Sc. degree in information technology from Vietnam National University, Hanoi in 1998 and 2001, respectively. He received his Ph.D. degree in Information Science from School of Information Science, Japan Advanced Institute of Science and Technology (JAIST) in 2004. He was an assistant professor at School of information science, JAIST from 2008-2013. His research interests include machine learning, natural language understanding, question answering, text summarization, machine translation, big data mining, and Deep Learning.

Deep Learning for Natural Language Processing and Beyond
The Tutorial begins with the basic of feed-forward neural network and relevant fundamental knowledge for deep learning. We then introduce more specialized neural network models, including Convolutional Neural Network, Recurrent Neural Network, and attention-based models. In the second part, we will present how these models and techniques can be applied to some interesting problems of natural language processing including sentiment classification, textual entailment recognition, natural language generation, and question answering. The last part of the tutorial will show how we can adapt deep learning and natural language processing techniques for program analysis.
Dr. Le Hong Phuong (Vietnam National University, Vietnam)
Le Hong Phuong received his PhD in computer science at Université de Lorraine, France in 2010; master of information technology at Institut de la Francophonie pour l’Informatique (IFI) in 2005; bachelor of science in applied mathematics-informatics at Hanoi University of Science in 2002. He was a researcher and assistant professor at Ecole des Mines de Nancy and INRIA Lorraine, France from 2010 to 2011. He is currently the head of the Data Science Laboratory at Hanoi University of Science, Vietnam National University. He has been also an associate researcher at FPT Research & Development, FPT Corporation since 2013; a scientific counselor for several AI companies. He has been working in the fields of natural language processing and applied mathematics for nearly 20 years. He has published over 40 scientific papers and has been in programme committees of many national and international scientific conferences. He is the author of some softwares toolkits which are widely used in the Vietnamese text processing community. His website is at http://mim.hus.vnu.edu.vn/phuonglh/

How to Make Chatbots Smarter: Computational Semantics Beyond Events and Roles
Current chatbot systems have mostly used a swallow semantic representation of text, which have focused on extracting propositional meaning, capturing “who does what to whom, how, when and where”. These chatbots tend to disregard significant meaning encoded in human language. For this reason, it is difficult for them to understand utterances such as “What is the tallest mountain in Vietnam?” or “What is the largest prime less than 2018?”. In this talk, I show that a deep understanding and reasoning of natural language is required in order for a chatbot to better understand an input utterance and produce an appropriate action. This goal can be achieved by semantic parsing, an area within the field of natural language processing. Chatbots can be made more intelligent only by computer scientists with a deep knowledge of computational semantics.
14:15 – 15:00 Assoc. Prof. Tran Minh Triet (Vietnam National University Ho Chi Minh City, Vietnam)
Minh-Triet Tran obtained his B.Sc., M.Sc., and Ph.D. degrees in computer science from University of Science, VNU-HCM, in 2001, 2005, and 2009. He joined the University of Science, VNU-HCM, in 2001. His research interests include cryptography and security, computer vision and human-computer interaction, and software engineering. He was a visiting scholar at National Institutes of Informatics (NII, Japan) in 2008, 2009, and 2010, and at University of Illinois at Urbana-Champaign (UIUC) in 2015-2016.
He is currently Head of Software Engineering Laboratory and Deputy Head of Artificial Intelligence Laboratory, University of Science, VNU-HCM. He is also the Deputy Head of Software Engineering Department, Faculty of Information Technology, University of Science, VNU-HCM. He was a member of the Executive Committee of the Information Security Program of Ho Chi Minh city. He is a member of the Management Board of Vietnam Information Security Association (South Branch) and also a member of the Executive Committee of ICT Program for Smart Cities (2018-2020) of Ho Chi Minh city.

Analyzing Daily Activity Logs for Smart Interactions
Collecting and analyzing daily activity logs can provide potential insights for better understanding and possible optimization for individual and organizational activities and operations. There are multiple sources to gather information in various formats during daily activities. People usually post photos, video clips, or messages to their social channels everyday. People may record their daily activities with wearable cameras or other types of sensors. Millions of surveillance cameras capture various events in traffic systems, offices, or supermarkets. It is an increasing demand to process and analyze such information, mostly in visual format, to develop useful services and utilities for smart environments.
In this talk, we present several modalities to analyze and interact with daily activity logs to develop potential applications for smart environments. Our proposed systems are based on practical social needs and aim to provide people natural experience with smart services and utilities.
- People can access to augmented data and services for tourism or shopping by recognizing the current context and retrieving similar known cases.
- Lost items can be found or memories can be retrieved or verified by searching daily logs.
- Reminiscence can help people to positively revive past memories and connections with their relatives.
- Regular events and anomalies can be detected from surveillance systems for appropriate actions.
- Event simulation in virtual or mixed reality environments can be generated from real life data for education and training.
We also discuss about privacy and security issues in collecting and analyzing daily activity logs.
15:00 – 15:15 Coffee Break
15:15 – 16:00 Assoc. Prof. Nguyen Duc Dung (Institute of Information Technology)
Duc-Dung NGUYEN received the Bachelors degree in mathematics in 1994. He received the Masters and Ph.D. degrees in knowledge science from the Japan Advanced Institute of Science and Technology, Japan, in 2003 and 2006, respectively.
He was a Research Engineer at KDDI R&D Laboratories Inc., Japan, from 2007 to 2009. He is now with the Institute of Information Technology, Vietnam Academy of Science and Technology, Ha Noi, Vietnam. His research interests include machine learning, pattern recognition, and data mining. Dr. Nguyen was awarded the Innovative Medal from the Youth Union of Vietnam in 1998 for developing the first Vietnamese optical character recognition software, and the Technical Support Achievement Award in 2008 for his contributions at KDDI R&D Laboratories.

Pattern recognition: feature engineering and (deep) feature learning
Feature extraction is one of the most important steps in any pattern recognition tasks. The traditional approach is to design different types of local and global function to build-up the feature map. In contrast, the new deep architecture of convolutional neural networks automatically forms the feature maps by learning convolution operators. How the two approaches are similar and different is one main topic of this tutorial. The second topic of the talk is how context information is used in recognition tasks. Example in optical character recognition will be used to characterize the difference between the traditional dictionary/language model and recently emerging long sort-term memory networks.
Assoc. Prof. Le Hoang Son (Vietnam National University, Vietnam)
Le Hoang Son obtained the PhD degree on Mathematics – Informatics at VNU University of Science, Vietnam National University (VNU). He has been promoted to Associate Professor in Information Technology since 2017. Currently, Dr. Son works as a researcher and Vice Director at the Center for High Performance Computing, VNU University of Science, Vietnam National University. His major field includes Artificial Intelligence, Data Mining, Soft Computing, Fuzzy Computing, Fuzzy Recommender Systems, and Geographic Information System. He is a member of International Association of Computer Science and Information Technology (IACSIT), Center for Applied Research in e-Health (eCARE), Vietnam Society for Applications of Mathematics (Vietsam). Dr. Son serves as Editorial Board of International Journal of Ambient Computing and Intelligence (IJACI, SCOPUS), Editorial Board of Vietnam Journal of Computer Science and Cybernetics (JCC), Associate Editor of International Journal of Engineering and Technology (IJET), Associate Editor of Neutrosophic Sets and Systems (NSS), and Associate Editor of Vietnam Research and Development on Information and Communication Technology (RD-ICT).

Developing Intelligent Systems based on Internet of Things: Some preliminary resultsRecently, there has been a great interest to develop intelligent systems based on Internet of Things (IoT), which connects physical objects like sensors nodes to collect real time data accessible through the Internet. Nowadays, in simple terminology, IoT includes almost things such as cell phones, building maintenance services, jet engine of an airplane. It also aids clinicians in diagnosis of heart monitor implant or farmers in a biochip transponder in farm animals. The IoT-connected devices transfer data over a network and are the component members of IoT. In this talk, we would like to summarize our preliminary recent achievements in developing intelligent systems based on IoT typically smart city, electricity generation system, and air pollution minimization: A smart city utilizes the information and communication technology to make efficient consumption of limited resources like space, mobility, energy, etc. This research focuses on developing an effective system for impairments monitoring, traffic monitoring, and smart city innovation with digitalized software for fast and effective implementations; Electrical energy generation from multiple sensors for household appliances and industrial areas is conducted. Electricity from the renewable energy sources such as stress generated by the body weight, heat generated by human body, and movements of the body can be measured by different sensors and transferred to the control system for storing; Air pollution minimization is performed using IoT. Various sensors have been used such as temperature sensor, humidity sensor, smoke sensor and many others to collect data from dust and environment. This model allows finding vehicles which releases more carbon dioxide to reduce the pollution.
16:00 – 16:45 Dr. Nguyen Do Van (Military Institute of Science and Technology, Vietnam)
Dr. Nguyen Do Van received B.Eng. degree in Information Technology from Le Quy Don University in 2004 with honor, M.Eng and PhD degree in 2011 and 2014 respectively, in Information Science and Control Engineering from Nagaoka University of Technology, Japan.
He currently is a researcher at Institute of Information Technology, MIST. He also is an adjunct lecturer at Faculty of Information Technology, University of Engineering and Technology, Vietnam National University, Hanoi.
His research interest includes Machine Learning (Deep Learning, Reinforcement Learning), Data Mining (Statistical Model, Big Data), Intelligent Systems and Robotics. He is now advising an industrial project on Graph representation learning for social network mining and leading a research project on Deep reinforcement learning for intelligent mobile robots.
Dr. Van is member of the Institute of Electrical and Electronics Engineers (IEEE), International Rough Set Society (IRSS) and The Vietnamese Association for Pattern Recognition (VAPR). He was the recipient of Vietnam Intelligence Award in 2003 and AIAI Best Paper Adward in 2015.

Deep Reinforcement Learning
Deep Reinforcement Learning have proven robustness in many areas such as robotics, games. In Deep Reinforcement Learning, neural networks have strong ability to deal with high dimensional data, a good mean to learn features and functional approximation while reinforcement learning can make a system learn itself for a new goal and in a new environment.
From success of deep reinforcement learning, we are motivated to investigate methods that enable unmanned vehicle remember environment, identify location of target objects and navigate themselves to the target.
In the presentation, we briefly review notion of reinforcement learning and how deep learning enhance the success of reinforcement learning recent years. Some related research and toolboxes also will be introduced. Lastly, we will discuss challenges and some achievement in our research project.
  Opening
Location: Nguyen Van Dao auditorium
8:00 - 8:30 Welcoming delegates and visiting the techshows
8:30 - 9:00 Opening and Speechs
  Planery talks
Location: Nguyen Van Dao auditorium
9:00 - 9:45 Dr. Hung Tran (Got it, USA)
Dr. Hung Tran is the founder of Got It, Inc., a tech startup developing the world's first Knowledge as a Service (KaaS) platform to instantly connect a knowledge seeker with a vetted expert for an interactive and personalized explanation. Got It is led by an experienced executive team including former executives from tech giants like Google, Lyft, Rakuten, etc., and is headquartered in Silicon Valley with an engineering office in Hanoi, Vietnam. The company has raised $15M in funding from well-known investors in Silicon Valley like Capricorn Investment Group who is also an early investor of SpaceX, Tesla, and PlanetLabs.
Dr. Hung Tran received a VEF Fellowship in 2007 and obtained his Ph.D. in Computer Science focusing on data mining and big data analytics from the University of Iowa. Prior to the Fellowship, he served as the leader of Vietnam OpenCourseWare Project working with MIT and Rice University to build a national scale open courseware program from inception to launch serving millions of college students in Vietnam. Dr. Tran received his Bachelor of Engineering in Information Technology from Hanoi University of Science and Technology. He is also a recipient of numerous national and international technology and entrepreneurship awards.
AI Strategy and Implementation on Got It’s Knowledge as a Service Platform
Got It's mission is to connect and economically empower people everywhere. We’re enabling knowledge to be traded directly between one human being and another. Got It's Knowledge as a Service (KaaS) is delivered on-demand via a “knowledge-time” unit: a 10- or 20-minute chat session in which a user with a knowledge problem is connected immediately with a domain expert at a set price. And like many other services, KaaS guarantees a solution to your knowledge-based problem. While there are some specific expert-based services for specific topics, Got It's KaaS is the first platform for multiple topics. In this talk we will present how Got It leverages AI to understand a user’s knowledge-based problem, match a problem with a suitable expert in seconds, audit chat sessions, and update expert's ranking. We had amazing results, our AI powered KaaS platform already served over three million sessions from over twenty five thousand experts from seventy nine countries with great user satisfaction.
9:45 - 10:15 Coffe Break and Techshow
10:15 - 11:00 Prof. Ho Tu Bao (John von Neumann Institute, Vietnam)
Ho Tu Bao graduated (1978) from the Faculty of Mathematics -Physics, Hanoi University of Technology, MA (1984) and Doctor (1987) in Artificial Intelligence at the Universite Paris 6. He has been doing research, application and teaching since then in the fields of Artificial Intelligence (AI), Machine Learning (ML) and Data Mining (DM), and more recently in Data Science (DS). He has been professors and emeritus professor of Japan Advanced Institute of Science and Technology (JAIST) since 7.1993. From 4.2018, he is Professor of John von Neumann Institute of VNUHCM and Head of Data Science Lab of the Vietnam Institute for Advanced Study in Mathematics (VIASM). He is members of the Steering Committee of PRICAI (Pacific Rim International Conference on Artificial Intelligence), PAKDD (Pacific Asia Knowledge Discovery and Data Mining, Chair 2014-2017), ACML (Asia Conference on Machine Learning, Co-chair 2013) -2016). http://www.jaist.ac.jp/~bao
Data science: A key in the digital transformation time
This talk consists of two parts. First to discuss the decision making in a digital economy and a digital society, as well as the relationship between AI and recent ICT breakthrough including data science. Second to illustrate the power of data science through problems and solutions in medicine, transportation, customer relationship, and more.
11:00 - 11:45 Dr. Ha Vu (Allen Institute for AI, USA)
Vu Ha (Hà Anh Vũ) is a technologist with 20 years of experience building products at some of the largest companies in the world. Most notably, Vu led applied research and engineering teams at Microsoft's AdCenter Labs and Bing. He co-founded SemanticScholar.org and led its development through its first public launch. Vu is currently a technical director at the Allen Institute for AI's startup incubator, advising startups on real-world AI and mentoring technical folks who wish to start an AI company, as part of the incubator's CTO residency program.
AI2’s startup incubator: progress and directions
The Allen Institute for AI (AI2) is a Seattle-based research institute founded by Microsoft co-founder and philanthropist Paul Allen. Led by renown AI researcher Oren Etzioni, AI2 has about 100 research scientists, software engineers, and other staffs that are dedicated to long-term research to advance AI, particularly in natural language processing, commonsense knowledge representation and reasoning, computer vision, and machine learning. Within AI2, the startup incubator seeks to commercialize AI by investing in and advising early-stage AI-focused startups, as well as incubating and spinning out technologies and ideas from within the institute. In this talk I will give an overview of the incubator’s progress and the road ahead.
11:45 - 13:00 lunch
  Planery talks
Location: Nguyen Van Dao auditorium
13:00 - 13:45 Mr. Le Hong Viet (FPT Corporation, Vietnam)
Mr. Le Hong Viet was appointed Chief Technology Officer (CTO) and President of FPT Technology Council in November, 2015. With a strong technical background and hands-on experience working abroad in designing and developing complex IT systems with leading enterprises, his vision is to enhance FPT technology capability; bring good surprise to align with customer values and create FPT’s technology eco-system.
Implementation of AI Platforms to create competitive advantages for enterprises
Artificial Intelligence has been considering as a changing factor in multiple industries which will lead to disruptions and will permanently change the competitive landscape. A winning company will be the one who apply AI in the most innovative and efficient ways. This talk would give a picture on how companies are investing in AI in order to build up competitive advantages, where are the capitals from, and the prediction of future indistries. FPT, as the technology leader in Vietnam, would like to share our strategy on this area.
13:45 - 14:30 Assoc. Prof. Pham Hong Quang (Vietnam Academy of Science and Technology, Vietnam)
Pham Hong Quang graduated from the Faculty of Applied Mathematics in Russian (1983), got Ph.D degree (1987) in Game Theory. From 2010 he is Director of Center for Informatics and Computing – Vietnam Academy of Science and Technology. His research interests are optimization control, high performance computing, signal processing and embedded computing… He has deep experiences in designing and realization of ICT infrastructure for Intelligent Transportation System, Smart Traffic Management for urban and highway network.
An application of Deep learning in smart city traffic management
The report is on an effort to build smart traffic network management based on video surveillance and traffic light centralized in Vietnamese cities. A presentation of smart transportation management infrastructure with integration of network IP cameras, video analytic application running on high performance computing systems, automatic traffic data recognition, automatic calculation of traffic light control strategy for maximal flows in main city corridors. For extracting traffic flow data to input in optimization modelling system, an attempt to deploy deep learning/ tensorflow on specific hardware and software infrastructure: results and challenges.
14:30 - 15:15 Mr. Nguyen Manh Khang (IBM, Vietnam)
Khang is Big Data Architect in IBM’s Analytics Group. He has been trusted advisor for customers across industries such as banking, telecommunication, public sector and retails for digital transformation journey. He has been visible on the market as the leader and strategic advisor with the role of IBM Software Architect before promoted to ASEAN.
With more than 10 years in IT industry. Khang experienced a lot of positions at MNC (Multinational Corporation) and local companies such as Software Engineering, Project Manager, Solution Architect, Presale. Khang has strong background and experience in solution architecture and industry knowledge.
Cognitive Banking
Ngày này, ngành ngân hàng đang phải đối mặt với sự tái sinh khi thời đại kỹ thuật số biến đổi thành thời đại nhận thức. Thành công phụ thuộc vào sự chuyển đổi triệu để cho phép tích hợp của phân tích nâng cao, trí tuệ nhân tạo, máy học, người máy, blockchains và hơn nữa. 64% số người được khảo sát trong năm 2016, chiẹu quả của tổ chức họ vẫn đang không thay đổi hoặc sụt giảm trong vòng 3 năm qua. Khai thác số lượng dữ liệu khổng lồ đang ngủ, dữ liệu do ngân hàng sở hữu, phần lớn là không có cấu trúc là nền tảng để tiếp cận đến từng cá nhân khách hàng, chuyển đổi các hoạt động vận hành, hưởng lợi từ đổi mới của fintech. Vậy làm thế nào bạn có thể tận dụng những công nghệ này để xây dựng một ngân hàng nhận thức?
15:15 - 15:30 Coffee Break
Panel Discussion
Location: Nguyen Van Dao auditorium
15:30 - 17:30
  • Prof. Nguyen Thanh Thuy (Vietnam National University, Hanoi) - Panel chair
  • Prof. Hoang Van Kiem (Baria-Vungtau University, Vietnam)
  • Assoc. Prof. Bui The Duy (Ministry of Science and Technology)
  • Prof. Ho Tu Bao (John von Neumann Institute)
  • Assoc. Prof. Vu Hai Quan (VNUHCM University of Science, Vietnam)
  • Assoc. Prof. Nguyen Xuan Hoai (Hanoi University, Vietnam)
  • Mr. Nguyen Long (Vietnam Informatics Association)
  • Dr. Hung Tran (Got it, USA)
18:00 - 21:00 Gala Dinner
Location: Crowne Plaza West Hanoi, 36 Le Duc Tho, My Dinh, Nam Tu Liem, Hanoi
  Invited talks
Location: Sunwah 1st Floor
Invited talks
Location: Sunwah 2nd Floor
8:30 – 9:15 Dr. Tran Dang Minh Tri (Harrison-AI, Australia)
Dimitry Tran (Trần Đặng Minh Trí), a health care innovator with bases in Australia and Vietnam, is a co-founder of Harrison-AI with his brother Aengus Tran (Trần Đặng Đình Áng).
Harrison-AI is a Sydney-based healthcare artificial intelligence lab. Taking the implementation path, Harrison-AI seeks to bring the latest developments in AI to solve the biggest challenges in healthcare – making it better and cheaper for all. In 2017, Harrison-AI made a breakthrough in the field of reproductive health. The technology is now patent-pending and undergoing multi-national and multi- centre clinical trial with a global healthcare provider.
Dimitry is the Head of Innovation at Ramsay Health Care – a USD 10 billion enterprise, and one of the largest hospital operators in the world with over 200 facilities in Australia, France, UK, Indonesia and Malaysia. In this role, Dimitry works on growing innovative care models and technologies to expand healthcare services and improve patient outcomes.
He is also a founding investor and on the board of directors of MediRecords.com – the first cloud-based doctor practice management software in Australia.
Dimitry is a Honorary Associate at the Hoc Mai Foundation, a 20-year-old charity operated by University of Sydney Medical School in partnership with Hanoi Medical University. He is also the Co-Founder and Chairman of the Centre for Healthcare Improvement (CHIRvn.org), a social enterprise with the goal of accelerating the pace of change and ‘make healthcare better for patients, professionals and population’ in Vietnam.
Dimitry holds the Chartered Financial Analysts (CFA) designation, Executive MBA degree from the University of New South Wales, and Executive Education Certificate from Harvard Business School. He is studying toward the Master of Applied Science in Patient Safety and Healthcare Quality degree at Johns Hopkins University.
Dimitry graduated first in his class at Bond University, where he completed bachelor degrees in Accounting and Finance and was the recipient of the Dean Scholarship and University Medal.

AI in Healthcare – Opportunities for Vietnam
“Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”
(Clive Humby, UK Mathemetician and architect of Tesco’s Clubcard, 2006)
Healthcare has been long understood as the most promising application area for AI. In the past five years, 300 deals in the field of healthcare AI have been closed, with total investments of over USD 2 billion. This trend is accelerating quickly.
The two most active areas of research and deployment are Medical Imaging & Diagnostics (e.g. automated detection of medical conditions on X-Ray) and Patient Data & Risk Analytics (e.g. identifying patients with high risk of stroke from their past medical record), both of which require vast amount of labeled data.
In a world where algorithms are open-sourced and processing power is easily accessible over the cloud, the future superpowers in healthcare AI will be the countries that best capture and apply their population’s health data to develop machine learning solutions.
Every single day, each Vietnamese hospital create tens of thousands of new data points – X-Ray images, CT scans, blood tests, disease diagnoses. All these data are the crude oil that currently being wasted. How do we refine this oil to run the healthcare AI machine?
How do we – government, hospitals, tech companies, AI researchers – work together to capture this opportunity and make Vietnam into a healthcare AI superpower in the next 3 years?
Dr. Hoang Anh Tuan (VCCorporation, Vietnam)
Tuan Hoang is the Chief Technology Officer of Admicro - VCCORP - a company pioneer in technology and software development in Vietnam. Tuan has over 10 years of experience in the field of intelligence and large-scale processing system. His numerous contributions mainly focus on the AI applications, big data processing services which are able to server millions of customers and internet users in Vietnam.

ML&AI Approach to User Understanding Ecosystem at VCCorp: Applications to News, Ads, and E-commerce
Nowadays, computer vision algorithms - automated translation, image recognition - have surpassed others in the industry, even humans. AI technology improves human life, facilitating their working performance, thanks to the breakthroughs in computational technology with the rapid development of hardware (CPUs/GPUs). In this presentation, we will be discussing AI platforms in VCCORP, the challenges and possibilities.
9:15 – 10:00 Assoc. Prof. Vu Hai Quan (VNUHCM University of Science, Vietnam)
Vu Hai Quan received the Ph.D. degree from University of Trento (Italy) in Information and Telecommunications. He did postdoctoral training at University of Leuven (Belgium) from 2005 until 2006. He served as Vice President Vietnam National University – HoChiMinh city. His research interrest is about AI, particularly including: Acoustic Modeling, Language Modeling for LVCSR; Corpus Developments (audio & text) Audio, Music Retrieval; Speech Translation. He has published various articles on international journals and participated in many academic research projects. He won two-time Vietnamese Talent Awards for the TTS and the Automatic Speech Recognition systems.

20 years of Vietnamese Spoken Language Processing: Research & Achievements
In this talk, I will represent some of various research carried out over the last 20 years in the area of spoken language processing and discusses the major themes and advance made in the last 20 years of research at the Artificial Intelligence Laboratory (AILab), the University of Science (VNUHCM), in order to show the outlook of technology, progress and applications that have been achieved in this field.
Assoc. Prof. Perry Stuart (University of Technology Sydney, Australia)
Stuart Perry received the B.S. degree (first-class honors) in electrical engineering and the Ph.D. degree from the University of Sydney, Sydney, Australia, in 1995 and 1999, respectively. He has previously worked for the Commonwealth Science and Industrial Research Organisation, Australia (CSIRO), Australia, Defence Science and Technology Organization (DSTO), Australia and Canon Information Systems Research Australia (CiSRA). He is currently an Associate Professor at University of Technology Sydney in FEIT’s Perceptual Imaging Laboratory (PILab) conducting research into colour and perceptual quality in 3D environments. His research interests include virtual and augmented reality, image processing, and perceptual quality.

Virtual and Augmented Reality: Applications and Issues in a Smart City context
Virtual and Augmented Reality has the potential revolutionise how we play, work and learn. In this talk we will consider the fundamentals of Virtual and Augmented Reality technologies including how these technologies can be applied to education, entertainment and data visualisation applications associated with the Smart Cities paradigm. There is a great potential to make use of Virtual and Augmented Reality technologies to enable Smart City applications, however we must solve a number of problems first. Solving these problems will require both an understanding of the data to be visualised and the physiology and psychology of the human user. We cannot afford to ignore either and so in this talk I will touch on the human aspects of Virtual and Augmented applications to Smart Cities and show how an understanding of human perception is crucial to building effective solutions to these problems.
10:00 – 10:15 Coffee Break
10:15 – 11:00 Mr. Le Minh (Five9, Vietnam)
To be updated.

Social graph analysis for verification and fill out users’ information
Dữ liệu mạng xã hội bên cạnh dữ liệu hồ sơ người dùng còn có thêm thông tin về quan hệ của họ với những người khác. Hai nhóm thông tin này có mối quan hệ chặt chẽ với nhau. Do vậy, nếu được phân tích đúng chúng có thể được dùng để bổ sung hoặc kiểm tra chéo lẫn nhau. Một trong những khó khăn lớn nhất của việc ứng dụng dữ liệu mạng xã hội trong kinh doanh là dữ liệu hồ sơ người dùng rất thưa và kém chính xác. Bằng việc sử dụng một số thuật toán embedding thông tin về mối quan hệ xã hội và các thuật toán dự đoán, phân lớp khác trên đó, ta có thể kiểm chứng hoặc/và bổ sung thông tin hồ sơ của người dùng để chúng trở nên hoàn thiện và có ý nghĩa kinh doanh hơn.
Prof. Tran Cao Son (New Mexico State University, USA)
Tran Cao Son received his doctoral degree from the University of Texas at El Paso in 2000. He is currently a Computer Science Professor at the New Mexico State University in Las Cruces. Before joining NMSU, he was a post-doc at the Knowledge System Laboratory at Stanford University for almost a year. His main research interests are in knowledge representation and reasoning, especially logic programming and answer set programming and its applications in planning, negotiation, and multi-agent systems.

Answer set programming and its Applications
ASP is an emerging declarative programming paradigm. It has been used in several practical applications. This talk will present the basic idea of ASP and demonstrates its use in several applications such as distributed constraints optimization problems, reasoning about truthfulness of agents’ statements, smart home scheduling etc.
11:00 – 11:45 Kenneth Tran (Microsoft, USA)
Kenneth Tran is a Principal Research Engineer in the Deep Learning Group, Microsoft Research. His research expertise and experience includes Deep Learning, Reinforcement Learning, Optimization, and Distributed Computing. At Microsoft, he led the research and development for strategic AI projects such as Deep Reinforcement Learning for real-world control problems, Project FarmBeats: AI & IoT for Agriculture, and Computer Vision API for Cognitive Services. In addition, Kenneth is also the chief mentor of Microsoft AI School’s advanced projects class. Kenneth received his Ph.D. in Computational & Applied Mathematics from The University of Texas at Austin.

From ML Algorithms to ML Systems
I will present 10 lessons that we’ve learned from building battle-tested machine learning systems at Microsoft.
Assoc. Prof. Bui Thu Lam (Le Quy Don University, Vietnam)
Dr. Lam Thu BUI received the Ph.D. degree in computer science from the University of New South Wales (UNSW), Australia, in 2007. He did
postdoctoral training at UNSW from 2007 until 2009.
He has been involved with academics including teaching and research since 1998. Currently, he is an Associate Professor and Dean of IT Faculty, Le Quy Don Technical University, Hanoi, Vietnam. He is doing research in the field of evolutionary computation, specialized with evolutionary multiobjective optimization. He is the co-editor of the book Multiobjective Optimization in Computational Intelligence: Theory and Practice (IGI Global Information Science Reference Series); and the General Chair of the Ninth International Conference on Simulated Evolution and Learning – SEAL2012.
Dr. Bui is EiC of Journal of Science and Technology: Section on Information and Communication Technology (LQDTU-JICT), a member of the Editorial Board, International Journal of Computational Intelligence and Applications (IJCIA), and was the Vice-Chair of the Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society. He has been a member of the program committees of several conferences and workshops in the field of evolutionary computing, such as the IEEE Congress on Evolutionary Computation and the Genetic and Evolutionary Computation Conference.

Evolutionary computation and its roles in AI
Evolutionary computation is a nature-inspired computing paradigm. It has been a popular research area in AI in the last couple of decades. In this talk, I will cover the recent development in evolutionary algorithms, especially using directional information for guiding evolutionary search. In particular, I will introduce the concept of direction of improvement in both aspects convergence and spreading. Some newly proposed works from our research group related to this will also discussed during the talk.
11:45 – 13:30 Lunch
13:30 – 14:15 Assoc. Prof. Le Thanh Ha (Vietnam National University, Vietnam)
Le Thanh Ha received B.S. and M.S. degrees in Information Technology from the College of Technology, Vietnam National University, Hanoi. In 2005, he received a Korean Government Scholarship for Ph.D program at the Department of Electronics Engineering at Korea University and got Ph.D degree in 2010. After graduation, he joined the Faculty of Information Technology, University of Engineering and Technology, Vietnam National University, Hanoi as an Associate Professor. His research interests are image/video analysis and processing, satellite image processing and computer vision. He has deep experiences in teaching Digital Image Processing, Computer Vision, Multimedia Communication courses for both undergraduate and postgraduate programs. He has also been principle and main investigator of many fundamental research and technology development projects funded by both domestic and international organizations. He also makes contributions in serving many domestic and international ICT academic conferences including KSE, NICS, ATC, SoICT, ICEIC, … In addition, he is a member of the Institute of Electrical and Electronics Engineers (IEEE), The Institude of Electronics, Information and Communication Engineers (IEICE) and The Vietnamese Association for Pattern Recognition (VAPR).

From Human Machine Interaction to Human Machine Intelligence
The levels of cooperation between HUMANS and MACHINES are in forms of Interaction, Integration, and Intelligence: Interaction can be described as stimulus-response which implies the Machines are just wait and do what Humans order; Integration implies partnership between the human and computer in which information is exchanged for physically WORKING together; Intelligence implies partnership between the human and computer in which information is exchanged for THINKING together. There is a continuum from Interaction to Integration then Intelligence. Doing research in HMI extends the level of human and machine cooperation from Interaction to Integration then Intelligence. I devote this talk in the discussion about the transformation from Interaction to Integration then Intelligence.
Assoc. Prof. Huynh Thanh Binh (Hanoi University of Science and Technology, Vietnam)Huynh Thi Thanh Binh is Associate Professor and Vice Dean of the School of Information and Communication Technology (SoICT), Hanoi University of Science and Technology (HUST). She is Head of Modeling, Simulation and Optimization Lab (MSO).
Her current research interests include – Computational Intelligence, Artificial Intelligence, Memetic Computing, Evolutionary Multitasking. She has published more than 80 refereed academic papers/articles, 2 Books; Editor 1 Book. She is Associate Editor of the International Journal of Advances in intelligent Informatics, VNU Journal Computer Science Communication Engineering; Editor Board of Journal of Computer Science and Cybernetics,. She has served as a regular reviewer, a programme committee member of numerous prestigious academic journals and conferences, such as IEEE Transactions on Vehicular Technology, Journal of Information Science and Engineering, IES, SoICT…
She is member of IEEE Computational Intelligence Society – Women in Computational Intelligence Committe (2017, 2018); Chair of IEEE Computational Intelligent Society Vietnam Chapter (IEEE Vietnam CIS). She is member of some committee of IEEE Asia Pacific: Strategic Planning, Membership Development, Humanitarian Technology Activities.

Evolutionary Multitasking – A New Paradigm
We are in era where many methods of computational problem-solving methodologies are being developed to address the diverse issues that researchers are intersted. Traditional methods for optimization, including the population-based search algorithms of Evolutionary Computation (EC), have generally been focused on efficiently solving only a single optimization task at a time. In fact, the variety and volume of incoming information streams that must be absorbed and appropriately processed, the need to multitask are unprecedented. Recently, Multifactorial Optimization (MFO) has been developed to explore the potential for evolutionary multitasking. The pursuit of intelligent systems and algorithms that are capable of efficient multitasking is rapidly gaining importance among contemporary scientists who are faced with the increasing complexity of real-world problems. We introduce the characteristic of population-based search algorithms, i.e., their inherent ability (much like the human mind) to handle multiple optimization tasks at once. Most notably, it shows that multi-tasking allows a person to automatically promote common ground between different optimization tasks, thus providing a significant scope for improvement in problem solving in the real world.
14:15 – 15:00 Dr. Nguyen Tuan Duc (Alt Plus, Vietnam)
Dr. Nguyen Tuan Duc received his BE, MS and PhD degrees in Information Science and Technology from the University of Tokyo. He joined Alt Inc (Japan) from 2016 and is currently the Chief Representative of Alt Vietnam. His research interests include Information Retrieval, Information Extraction, Natural Language Understanding and Dialogue Systems. He is in charge of building the core NLP components of the Personal Artificial Intelligence (P.A.I.) software al+.

Dialogue Engine Algorithms for Personal Artificial Intelligence (P.A.I.)
In this presentation, we introduce a new paradigm of Artificial Intelligence called Personal Artificial Intelligence (P.A.I.). P.A.I creates a digital clone of the user: it copies all aspects of human personalities including thoughts, behaviors, appearances, and voices. This digital version of the user can act on behalf of the user and can even make decisions as if the user would. We will introduce the overall architecture of the P.A.I. system that we are building at al+ Inc. Moreover, we will explain in details some important components of the core NLP infrastructure of the al+ P.A.I. software, such as the named entity recognition system, the relation extraction system and the dialogue engine.
Dr. Dao Duc Minh (Viettel R&D Institue, Vietnam)
Dr. Minh Dao received the B.Sc. degree in Electrical Engineering from Hanoi University of Technology, Vietnam in 2007, the double Master degree in Information and Communication Technologies from Polytechnic University of Turin, Italy, and Karlsruhe Institute of Technology, Germany in 2009, and the Ph.D.in Electrical and Computer Engineering from The Johns Hopkins University, Baltimore MD, USA, in 2015. From 2015 to 2017, he worked as a Research Scientist in the U.S. Army Research Laboratory (ARL) in Maryland, USA. In June of 2017, he joined Viettel R&D Institute, where he currently leads the Image Processing R&D team. His research interests are broadly in the areas of signal/image/video processing, statistical machine learning, computer vision and artificial intelligence.

AI in Electro-Optical/Infrared camera surveillance systems
Nowadays, Electro-Optical and Infrared (EO/IR) technology plays a critical role in many military, defense, security and industry applications; as it provides the day-night and long-range visualization capability, improves the user's ability to automatically identify targets, performs threat assessment, raises situational awareness, as well as supports weapons engagement through automatic surveillance and fire control solutions through line-of-sight. In this talk, we will introduce several modern EO/IR camera systems that are currently being developed in Viettel R&D Institute. Furthermore, we will propose a number of artificial intelligence applications that equip our EO/IR camera surveillance systems with the ability to automatically perform detection, localization, recognition, identification and tracking of all ground/air/maritime target types in real time. Our solutions are based on some most advanced machine-learning and deep-learning models trained on large-scale data. Several techniques of online learning and multi-sensor fusion will also be proposed to provide our system with high-performance accuracies and low false alarm rates, even in bad-seeing conditions or complex backgrounds.
15:00 – 15:15 Coffee Break
15:15 – 16:00 Prof. Nguyen Kim Khoa (University of Québec, Canada)
Kim Khoa Nguyen is Associate Professor in the Department of Electrical Engineering at the University of Quebec’s Ecole de technologie supérieure (ETS), Montreal, Canada. He has a PhD from Concordia University in Electrical and Computer Engineering. He served as CTO of Inocybe Technologies, a leading company in software-defined networking (SDN) solutions. He was the architect of the Canarie’s GreenStar Network and also involved in establishing CSA/IEEE standards for green ICT. He has led R&D in large-scale projects with Ericsson, Ciena, Telus, and InterDigital. He published extensively, and holds several industrial patents. His expertise includes smart city, cloud computing, IoT, big data, data center, network optimization, high speed networks, and green ICT.

AI in smart city infrastructure management
Today, smart city technologies are driving new solutions to tackle emerging challenges in urbanization such as traffic congestion, air and noise pollution, safety and crime, emergency response, climate change, economic growth, and delivery of city services. These technologies are relying heavily on a highly complex smart city network infrastructure, including multiple layers, multiple communication technologies, multiple vendor equipment, and multiple traffic patterns. Such an infrastructure enables real-time situational awareness in the urban system by its ability to gather and integrate data at scale, securely and privately, from environmental, critical infrastructure, health and personal sensors. A significant amount of effort has been invested on architecting agile and adaptive management solutions in support of autonomic, self-managing smart city networks. Recent advances in network softwarization and programmability through Software-Defined Networking (SDN) and Network Functions Virtualization (NFV), the proliferation of new sources of data, and the availability of low-cost and seemingly infinite storage and compute resources from the cloud are paving the way for the adoption of machine learning (ML) and artificial intelligence (AI) to realize cognitive network management in support of autonomic networking in smart city. In this talk, we will review challenges and issues when applying ML and AI in smart city network management, with a focus on infrastructure orchestration. We will also present a use-case of a smart city model built at the heart of Montreal, Canada, in collaboration with global players, including Ericsson, Ciena, Videotron, and Telus.
Prof. Massimo Piccardi (University of Technology Sydney, Australia)
Massimo Piccardi (M.Eng., 1991, Ph.D. Bologna, 1995) is a professor at University of Technology Sydney (UTS) where he serves as a discipline leader for the School of Electrical and Data Engineering and as a program leader for the Global Big Data Technology Centre, a University-supported task force in communications and big data analytics. His main research areas are computer vision, multimedia, machine learning, and, more recently, natural language processing. Prof. Piccardi has published over a hundred and fifty papers to date, is a Senior Member of the IEEE and serves as an Associate Editor for journal “Computer Vision and Image Understanding.

Structured prediction for the summarisation and alignment of videos
In this talk, I present two approaches for the automated summarisation and the automated alignment of videos based on structured prediction. The first approach aims to summarise an action video by a selection of its frames while simultaneously recognising its main action. The second approach aims to find the best alignment of two given videos and can be used as a generalised distance for video classification. Both approaches are similar in spirit and leverage structured prediction and the structural SVM framework for inference and for training. Potentially, they could be coupled with deep learning layers to capture the best of both worlds.
16:00 – 16:45 Assoc. Prof. Ta Cao Minh (Hanoi University of Science and Technology, Vietnam)Ta Cao Minh graduated with Red Diploma (First Class Honors) from Institute of Technology in Pilsen (Univ. of West Bohemia now), Czech Republic in 1986 and received Ph.D. degree in Electrical Engineering at Laval University, Canada in 1998. He spent 6 years working in Japan (1998 – 2004), at Kyushu University, The University of Tokyo and at NSK Steering Systems Ltd. Co. He became Associate Professor at the Department of Industrial Automation, Hanoi University of Science and Technology (HUST), Vietnam in 2009. Besides, he has been the Founder and Director of the Center for Technology Innovation, HUST since 2009.
He has been appointed several visiting professorship positions: at NTUST, Taiwan (2010), UTS, Australia (2012), University Lille 1, France (2015, 2016) and at TUM, Germany (2017). His research interest is focused on Advanced Control of Electric Motor Drives, Control of Electric Vehicles, Power Electronics Converters for Renewable Energy/ Smart Grid applications.
Dr. Minh is the author/co-author of 14 Japanese patents (12 of which also granted in US/Europa), 39 international peer-reviewed journal/conference papers and 21 national peer-reviewed journal/conference papers. He was recipient of the Second Prize Paper Award of the IEEE Industrial Drives Committee in 2000, the 2012 Patent Business Contribution Reward of NSK Ltd. Co., Japan, and the 2017 Nagamori Award for Innovative Control Techniques of Electric Motor Drives.
Prof. Ta has been IEEE Senior Member since 2004 and served as IEEE Vietnam Section Chair during 2008 – 2011. He is presently the Vice-president and General Secretary of the Vietnam Automation Association (VAA).

Performance Improvement of Industrial Motor Drives using Artificial-Intelligence based Control Techniques
Motors and motor-driven systems account for as much as 55% of all global electricity consumption and 70% of all electricity used by industry. We can use the control engineering approach to achieve two goals: improve the performance of the motor drives and reduce the energy consumption. In the last 3 decades, Artificial Intelligence (AI) has been very much developed and found a wide range of applications, including the control of motor drives. Artificial-Intelligence based Control Techniques can be considered as powerful tools to deal with the process of which the parameters and/or operation condition vary largely. In this paper, the control techniques based on Fuzzy Logic and Artificial Neural Network – a form of AI – are applied for various kind of motor drives: Induction Motor, PM Synchronous Motor, BLDC Motor and Switched Reluctance Motor (SRM). The simulation and experimental results show that performance of IA-based technique are superior, as compared with of the conventional control techniques.
Prof. Do Van Tien (Budapest University of Technology and Economics, Hungary)Tien Van Do received the M.Sc. and Ph.D. degrees in telecommunications engineering from the Technical University of Budapest, Hungary, in 1991 and 1996, respectively. He is a professor in the Department of Telecommunications of the Budapest University of Technology and Economics, and a leader of Communications Network Technology and Internetworking Laboratory. He habilitated at BME, and received the DSc from the Hungarian Academy of Sciences in 2011. He has participated and lead work packages in the COPERNICUS-ATMIN 1463, the FP4 ACTS AC310 ELISA, FP5 HELINET, FP6 CAPANINA projects funded by EC (where he acted as a work package leader). He led various projects on network planning and software implementations that results are directly used for industry such ATM & IP network planning software for Hungarian Telekom, GGSN tester for Nokia, performance testing program for the performance testing of the NOKIA’s IMS product, automatic software testing framework for Nokia Siemens Networks. His research interests are queuing theory, telecommunication networks, cloud computing, performance evaluation and planning of ICT Systems. He is also a board member of Discrete Dynamics in Nature and Society, Hindawi.
AI in 5G networksThe telecommunications industry have pursued efforts to realize the idea of 5G networks. Besides increasing speeds and more efficient utilization of spectrum, a main goal is to establish the foundation and the common framework of new innovative network services.
5G operators may create new revenue streams from hosting 3rd party applications in their infrastructure in addition to the provisioning of their own services.
To achieve the aims of 5G, the development of scalable self-managed software and cloud platforms that supports the rapid deployment of services are needed. European Telecommunications Standards Institute (ETSI) has specified the Network Functions Virtualisation concept where Network Services are constructed by an appropriate chaining of
Network Functions (either physical network functions or virtualized network functions -- VNF). The NFV initiative transformed the way telecom network operators architect their networks. It embraced virtualization techniques widely used in the IT industry and introduced the Infrastructure-as-a-Service cloud computing model into the Telco world.Some use cases of 5G applications have strict requirements (i.e., high availability and low latency for VR, AR, V2X, etc.) that can be achieved by the careful engineering, the new operating rules of cloud platforms and the optimal placement of application components. In the talk, the application possibilities of AI will be outlined for the efficient operation and management of 5G networks.