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International Training Workshop on Big Data for Developing Countries 2019
Speakers

Yongchang Hui, PhD

Associate Professor in Statistics, Head of Department of Statistics,

School of Mathematics and Statistics

Xi’an Jiaotong University

Email: huiyc180@xjtu.edu.cn

Dr. Hui got his PhD from Northeast Normal University in 2013, and was research assistant at Hong Kong Baptist University and Chinese University of Hong Kong. He is now working on statistical inference in big data, medical data analysis and financial econometrics.

Data analysis: Organizing, visualizing, and modeling

In this short course we first introduce some challenges in the time of Big Data. After then we look though some basic statistical minds and methods for analyzing financial data. Real data analysis based on R will show the process of digging data including data organizing, visualizing and modeling

Big data analysis (Hands-on)

Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools. Big data technologies describe a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery, and/or analysis. In this short course we will introduce some technics for big data analysis based on Spark R.

 

Buyang Cao, PhD.

Professor

College of Architecture and Urban Planning, Tongji University

Dr. Buyang Cao is a professor and Ph. D student supervisor at College of Architecture and Urban Planning, Tongji University. He earned doctor degree in Operations Research. Before he joined Tongji University, he had been with Esri, Inc., the world largest GIS service/product provider, for decades. Dr. Cao led and was involved in multiple national key projects and designing/developing application systems for Fortune 500 enterprises. He possesses multi-year experiences in problem analysis, algorithm design/development, software engineering project management, etc. He was awarded by INFORMS (Institute for Operations and Management Science) due to excellence in applying optimization techniques for solving complicated decision support problems from the real world. Numerous research papers by Dr. Cao appeared in some top-tier international journals.  Currently, his research is focusing on applying AI, big data, data analytical, and algorithm development technologies to the problems from Smart City and Logistics arenas.

Big data and machine learning techniques: theories and applications in smart cities (1): theory

Big data and machine learning techniques: theories and applications in smart cities (2): practice

This course will provide an overview of big data and machine learning technologies as well as their applications in smart city arena. The basic big data concepts and techniques will be introduced and some their application examples shall be explained. The basic but yet widely applied machine learning methodologies are introduced. The examples of machine learning will be explained and discussed to help comprehending the theories.

The course is divided into two parts: one of them is theoretical teaching while the other is to use Python and machine learning approaches to solve come small but practical problems.

The students shall be able to understand the basic big data and machine learning principles, their applicable areas, and how to use Python and its embedded machine learning library to solve the problems encountered in smart city applications, after they complete this course.

 

Siliang Tang, PhD.

Associate Professor

College of Computer Science, Zhejiang University.

Email: siliang@zju.edu.cn

 

Dr. Siliang Tang is currently an associate professor at the College of Computer Science, Zhejiang University. His research interests include Information Extraction, Knowledge-base construction, and Cross-modal computing. So far, he has published more than 50 papers in the top-tier scientific conference/journal such as ACL, EMNLP, NAACL, SIGIR, AAAI, IJCAI, TKDE, INS (NLP and Information Retrieval); ACM MM, IEEE Trans. Multimedia (Multimodal Understanding and Retrieval); IEEE Trans. on Image Processing, IEEE Trans. on Circuits and Systems for Video Technology (Image Processing and Understanding); IEEE VIS, IEEE Trans. on Visualization and Computer Graphics (Data Visualization). At present, he is mainly working for CKCEST on the projects of automatic knowledge base construction from semi-structured and unstructured text. He and his team participated in the NIST TAC (https://tac.nist.gov/) competition for knowledge base population since 2015 and won the top three places in some tracks (English EDL 2016 1st place; TEDL 2017 2nd place; DDI 2018,task1&2, 1st place) several times. 

Large Scale Knowledge Graph Construction

Constructing a large scale machine-readable knowledge graph from unstructured natural language text, and furthermore, to teach machines to learn to reason and complete the extracted knowledge is a grand challenge to both industry and research communities. Deep learning is a powerful approach for automatic knowledge graph construction and population. In this tutorial, we will introduce some basic concepts and the current state-of-the-art methods of deep learning based information extraction, entity/relation embedding, as well as knowledge base completion and reasoning.  

Deep Learning for Task-oriented Conversational AI

Dialogue systems have shown great potential in improving human-machine interactions, especially when they meet with recent advanced deep learning technologies. The deep learning has revolutionized the paradigm of the standard pipeline of a dialogue system and brings substantial development in creating such complex systems. In this tutorial, we will introduce audiences the pipeline framework for modeling task-oriented dialogue systems, which includes three key components: 1) Spoken Language Understanding; 2) Dialogue Management; 3) Natural Language Generation. For each key component, we will define the research problem, provide a brief literature review and introduce the current state-of-the-art approaches.

 

Chen Li, PhD.

Professor

Xi’an Jiaotong University

 

 

Professor Chen Li received the PhD from the University of Cambridge and accomplished postdoctoral research in the Massachusetts Institute of Technology (MIT) before becoming a senior research scientist in MIT. He was awarded the Cambridge Overseas Fellowship and European Molecular Biology Laboratory Fellowship. He was the principle investigator of a number of the international projects with millions-dollars support. His research focuses on designing data-driven models to syntactically and semantically analyze data and developing the applications of biomedical interests.

Natural language processing concepts and tasks

Big data is likely the most discussed technological word nowadays. Merrill Lynch cited a rule of thumb that somewhere around 80-90% of all potentially usable business information may originate in unstructured data. Within those, text is the main form of unstructured data. Natural language processing (NLP) is a research and technology to let computers to be able to understand texts as humans do. The lecture will introduce the basic concepts and tasks of NLP.

Advanced natural language processing in biomedicine

The simplist biomedical process could be a very complicated network illustrating relationships between symptoms, medicines and proteins, genes etc. The knowledge of the relationships is largely distributed in vast number of scientific literatures, clinical records and knowledgebases. The natural language processing has been desperatedly called to assist efficient information retrieval and knowledge discovery in biomedical domain. The lecture will introduce the advanced NLP techniques and tasks in the biomedical domain.

 

Dr. Permanand Mohan

Senior Lecturer
Department of Computing and Information Technology

The University of the West Indies

Dr. Emilie Ramsahai

Data Science Consultant

 

Introduction to Big Data and Machine Learning

Abstract: This session provides a context for all the topics to be covered in the workshop. In the first part of the session, we will give an introduction to big data concepts and terminology. We will explain why big data matters to business and other organizations and show how big data can be applied to improve business effectiveness. We will also discuss typical applications of big data such as text analytics, classification, recommendation systems, user categorization, and explore popular big data visualization options. In the second part of the session, we will discuss the fundamental concepts of machine learning, since machine learning is a key technology used in big data analysis. We will showcase several examples of machine learning applications and explain how machine learning is different from traditional computer programming and other technologies such as expert systems.

Speaker Bio (P. Mohan): Permanand Mohan is an Executive Member of CARISCIENCE. He is a Senior Lecturer in Computer Science at the St. Augustine campus of University of the West Indies in Trinidad and Tobago where he has been teaching full-time for almost 25 years. He has a Ph.D. in Computer Science from the University of the West Indies, an M.Sc. in Computer Science from the University of Saskatchewan, and a B.Sc. in Computer Science from the University of the West Indies.

Permanand was previously a Visiting Professor at the Laboratory for Advanced Research in Intelligent Educational Systems (ARIES) at the University of Saskatchewan in Canada and a Fulbright Visiting Scholar to the School of Information Sciences at the University of Pittsburgh in the USA. He served as the Chief Examiner for the Caribbean Examinations Council’s CAPE Examinations in Computer Science in the first twelve years that it was offered. More recently, he served for six years as the Head of the Department of Computing and Information Technology at the University of the West Indies.

His research interests lie in the field of artificial intelligence and education, advanced technologies for learning, mobile learning and mobile health, the Semantic Web, robotics, and data science.


Speaker Bio (E. Ramsahai): Dr Ramsahai is a consulting Data Scientist, with more than 20 years industry experience.  She is currently a SimpliLearn reseller, offering much needed short-term online training courses to help professionals get certified and get ahead.  Some of these include training in Apache Spark, Hadoop, Storm and Cassandra.  She has held Information Resources Manager positions at PowerGen, Bermudez Group and Tucker Energy Services.    She completed her PhD in Statistics and a Masters in Computer Science, both at the University of the West Indies, where she has also lectured the Big Data and Visualisation course from the Masters in Data Science, offered by the Department of Computing and Information Technology, St Augustine Campus.  She has worked at the International Centre for Genetic Engineering and Biotechnology (ICGEB) in New Delhi, India and continues to publish and collaborate with a number of researchers in this area. 

 

 

Dr. Yufei Wu

Associate Professor
Centre for Information and Communication Technology

The University of Trinidad and Tobago

 

Data Privacy and Trust Issues in Caribbean Organizations

Abstract: Data privacy and consumer trust is still in its early development stage in Caribbean in spite of gaining tremendous momentum recently. Lack of regional data legal frameworks and public awareness is an obstacle for cultivating the business opportunities and the social wellbeing from the 4th industrial revolution technologies such as the Big Data, IoT (Internet of Things), cloud computing etc. in the Caribbean.

This presentation highlights the major data privacy and trust issues in Caribbean organizations, and discuss the technical, legal and policy challenges which includes: (a) overview of the relevant laws and regulations of security, privacy and trust issues in EU, USA, Singapore and Kenya; and (b) the privacy preservation mechanisms in data/big data; and c) what Caribbean organizations can do to satisfy the data regulatory requirements, improve consumer confidence, and help to create Caribbean big data markets.

Speaker Bio: Dr. Wu received his Ph.D. in Computer Engineering from the University of Montreal, and an MBA from McGill University. Dr. Wu worked as an investment banking manager for Peregrine Investments Holdings (a subsidiary of Morgan Stanley) and then a manager for State Power Corporation in Hong Kong. His teaching and research interests are in the areas of cybersecurity management, auditing and hacking.  Dr. Wu teaches courses on cryptography, data security and privacy, network security, hacking and penetration testing.

Dr. Wu has spearheaded the establishment of Caribbean Institute for Cybersecurity, which is the first of its kind in the Caribbean. The Institute started a MSc cybersecurity degree program in January 2019. The degree offers two tracks, with one track focusing on hacking and the other on management, laws, and governance.

Dr. Wu is a recipient of 2018 International Visitor Leadership Program (IVLP). The International Visitor Leadership Program is the U.S. Department of State’s premier professional exchange program. Dr. Wu has CISSP (Certified Information Security Systems Professional) and CEH (Certified Ethical Hacker) certifications, and is a senior member of IEEE.

 

Big Data – The Regional Experience

(Examples of Big Data Applications in the Caribbean)

Speakers & Lectures

Prof. Patrick Hosein

Professor,

University of the West Indies

 

UWI/Industry Collaborations in Data Science and Operations Research

Patrick attended the Massachusetts Institute of Technology (MIT) where he obtained five degrees including a PhD in Electrical Engineering and Computer Science. He has worked at Bose Corporation, Bell Laboratories, AT&T Laboratories, Ericsson and Huawei. He has published extensively with over 100 refereed journal and conference publications. He holds 40 granted and 42 pending patents in the areas of telecommunications and wireless technologies. He was nominated for the Ericsson Inventor of the Year award in 2004, was the Huawei US Wireless Research Employee of the year for 2007 and is a 2015 Anthony Sabga Caribbean Laureate for Science and Technology. Patrick is presently the administrative and technical contact for the TT top level domain, CEO of the TTNIC and a Professor of Computer Science at the University of the West Indies. His present areas of research include radio resource management, QoS and pricing for 5G cellular networks.

 

Mr. Ian John

Chief Executive Officer

Massy Technology InfoCom

 

Data Science Initiatives within the Massy Group of Companies

Ian John is the CEO of Massy Technology InfoCom – the leading ICT company in the Caribbean.

He considers himself a technological futurist with specific interest in attempting to systematically explore predictions and possibilities about the future and how they can emerge from the present to impact our peoples, communities, societies and ultimately our way of life.

At Massy Technologies InfoCom (Trinidad) Ltd., Ian leads a high performance team of over 300 world class knowledge workers with subject matter expertise in Digital Transformation, Data Science, IOT, Telecommunications, Software Development, Mobile Technologies and IT Systems Integration.

Notably, in 2006 he started iMedia Caribbean, a New Media company providing Mobile SMS aggregation across North America and Caribbean on Major Mobile carriers. The company pioneered the integration of Social Media with live TV and Radio broadcast and provided real-time voting platforms combing multiple platforms.

John believes The Fourth Industrial Revolution presents a tremendous platform to boosting local and regional economic productivity, unleashing the true wealth of Caribbean nations, shifting labor from routine tasks to higher-skilled and meaningful work, opening up the middle class to many more people around the Caribbean.

 

Dr. Gunjan Mansingh (UWI)

Data Analytics in Jamaica – Implications

for the Caribbean Countries

 

Gunjan Mansingh is Head of the Department and a Senior Lecturer at the Department of Computing, The University of the West Indies (U.W.I), Mona, Jamaica. She obtained a PhD. in Information Systems and an M.Phil. in Computer Science from U.W.I. and a B.Sc. from St Xavier’s College, Bombay University, India. She worked for a few years in the IT industry in Jamaica before moving to academia. She teaches various courses at the undergraduate and the graduate level in Computer Science and Information Systems. She has conducted workshops in Jamaica on data mining and business intelligence.

She is a co-author of the book “Business Intelligence for SMEs: An Agile Roadmap for Sustainability.” She is a co-editor of an edited book titled “Knowledge Management for Development: Domains, Strategies and Technologies for Developing Countries”, Springer Integrated Series in Information Systems. She also serves on the international editorial review board of International Journal of Knowledge Management. Her research interests are in the following areas; Business Intelligence, Data Mining, Machine Learning, Decision Support Systems, Knowledge Management and Knowledge Management Systems, Expert Systems and Technology Adoption. In her research she has worked in different domains in Jamaica such as healthcare, crime, agriculture, e-commerce and financial services. Her research focus has been on harnessing data, information and knowledge in the various sources to assist in the decision making process. She has over 50 publications in international journals, proceedings of several international conferences and as book chapters.

 

Mr. Pernel Roberts (TSTT)

Senior Manager Strategic Analytics and Performance

Telecommunications Services of Trinidad and Tobago (TSTT)

Data Governance in a Big Data and Advanced Analytics Environment

Pernel is the Senior Manager Strategic Analytics and Performance at the Telecommunications Services of Trinidad and Tobago (TSTT). He leads a department that provides actionable intelligence to the corporate leadership team and lines of businesses by deploying various analytics solutions across several use cases including churn, supply chain management, order management and fraud. Pernel has been working in the IT industry for the past 26 years across several sectors including telecommunications, public sector, military and education. He has been involved in the field of data analytics since 2008.

Mr. Roberts is also a military officer at the rank of Major and holds the positions of Information Technology Officer and Officer Commanding Bravo Company at the Trinidad and Tobago Defence Force Reserves. Over the past 18 years he has designed and implemented several ICT projects at the Trinidad and Tobago Defence Force.

Complementing his BSc, MSc and Postgraduate Certificate in Higher Education are the following professional certifications: Program Management Professional (PgMP), Project Management Professional (PMP), Certified Business Intelligence Professional (CBIP) and Fellow of The Higher Education Academy of the United Kingdom. Pernel is currently pursuing a Doctor of Education in Organizational Leadership.

 
 
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