• SIAT  Global Center for Big Mobile Intelligence,Executive Director | 2016.10-Until Now

• Innopolis University  Institute of Information Systems,Assistant Professor | 2015.2-2016.12

• ETH Zurich  Computer Science,Visiting Scientist | 2014.10-2015.1

• Carnegie Mellon University  Computer Science,Visiting Scholar | 2013.11-2014.4

• Singapore Management University  Information Systems,Visiting Scholar | 2013.7-2013.10

• Nanyang Technological University  Computer Science,Visiting Scholar | 2012.5-2012.7

• Singapore Management University  Information Systems,Research Fellow | 2009.12-2010.12

1. COURSES FOR 2015-2016 FALL SEMESTER AT INNOPOLIS UNIVERSITY

DATA MODELLING AND DATABASES (6 ECTS)

The course presents an introduction to data modelling & database management systems (DBMSs). Most commercial applications involve the use of a database management system to store information. A bank ATM has access to customer balance stored in a database. When you use a credit card, information about your card and each transaction is stored in a database. The state Department of Motor Vehicles keeps track of your drivers license and your car in databases. This course will cover the design of database systems, important database theories, SQL, programming and relational databases, and logical, object -oriented, and XML databases. The course will also involve projects and exercises using PostgreSQL, an SQL database, and the web.

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DATA SCIENCE PROJECT MODULE 1 (10 ECTS)

This course is designed for data science master students. Data Science Project Module I exposes the most recent topics in Data Science especially in very large scale data management, data mining, and data analysis to students. The topics will be selected from recent conferences and journals, building up interest and basics for students on a wide range of data science topics. The output is that each student at the end of the semester is able to understand the basics and the state-of-the-art results of a concrete data science topic, presenting scientifically comparable analysis.

INFORMATION RETRIEVAL (6 ECTS)

The course presents an introduction to the field of information retrieval and discusses automated techniques to effectively handle and manage unstructured and semi-structured information. This includes methods and principles that are at the heart of various systems for information access, such as Web or enterprise search engines, categorization and recommended systems, as well as information extraction and knowledge management tools.

2. COURSES FOR 2014-2015 SPRING SEMESTER

MACHINE LEARNING (6 ECTS)

This course review the necessary statistical preliminaries and provide an overview of commonly used machine learning methods. Our focus is on real understanding of supervised learning, unsupervised, learning theory, and reinforcement learning and adaptive control. The course will also cover recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

INFORMATION RETRIEVAL (6 ECTS)

The course presents an introduction to the field of information retrieval and discusses automated techniques to effectively handle and manage unstructured and semi-structured information. This includes methods and principles that are at the heart of various systems for information access, such as Web or enterprise search engines, categorization and recommended systems, as well as information extraction and knowledge management tools.

DATA MINING (6 ECTS)

Data Mining is an analytic process, which explores large data sets (also known as big data) to discover consistent patterns. This computational process involves a use of methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. This course will discuss such algorithms for analyzing large amount of data sets. The topics include: Clustering, Link Analysis, Mining the Web Structured Data, Text Mining, etc.

SOCIAL NETWORK ANALYSIS (6 ECTS)

A social network is a social structure made up of a set of social actors and a set of the dyadic ties between these actors. Social network analysis studies the relationships of the nodes (actors) in the network, represented by edges (ties). The course will cover theoretical aspects of the structure as well as practical application of popular social networks. In this course, you will learn various topics including diffusion of information, community detection, and visualization. You will have a hands-on experience of acquiring social network data and analyzing it with different algorithms.