Masoud Makrehchi, PhD
Associate Professor
Electrical, Computer and Software Engineering
Faculty of Engineering and Applied Science
Electrical, Computer and Software Engineering
Faculty of Engineering and Applied Science
Leading social media research analytics to predict trends in behaviour for safer communities
Full biography
Social media yields constant online chatter and offers up unparalleled access to society’s diverse views and beliefs. Leading an emerging field, Masoud Makrehchi, Ph.D., Associate Professor in the Faculty of Engineering and Applied Science, is one of Canada’s first researchers to mine and analyze social media data to help solve real-world problems. Working with law enforcement data, Dr. Makrehchi’s novel research uses social media to predict the crime rate direction in neighbourhoods. Extracting gold from an abundance of social media babble, he is developing unique algorithms and predictors for analytics to build better, safer communities. Along those lines, his research also focuses on modelling bullying behaviour in small communities. He aims to create a universal tool for modelling and simulation of dynamic social agent networks; allowing analysts, teachers, and law enforcement officers to analyze bullying behaviour, impact and consequences; and generate prediction models across ethnicity, gender, and disability. In collaboration with the University of Ottawa, Dr. Makrehchi is using social media to understand the stock market movement, particularly during initial public offering (IPO). He is analyzing Twitter feeds to determine public sentiment for pricing IPOs, and identifying this correlation to stock market success. A staunch advocate for social justice, Dr. Makrechi joined Ontario Tech University in July 2012, following his role as a senior research scientist in Research and Development for Thomson Reuters in Minneapolis-St. Paul, Minnesota, where he served as a consultant in 2013. From 2002 to 2007, he was a research assistant in the Pattern Analysis and Machine Intelligence Lab, and the Learning Objects Repositories NETwork (LORNET) a five-year, NSERC-funded project; both in the Department of Electrical and Computer Engineering at the University of Waterloo in Waterloo, Ontario. While there, he earned his Doctorate in Electrical and Computer Engineering in 2007. Dr. Makrehchi gained international industry experience as a systems analyst and project manager for ICT projects in various organizations including Irankhodro Automotive Industries in Tehran, Iran. In 1994, he received his Master of Science in Computer Engineering from Shiraz University in Shiraz, Iran, and in 1991, he obtained his Bachelor of Science in Electrical and Computer Engineering from the Iran University of Science and Technology.
Areas of expertise
Courses
- SOFE 3770UDesign and Analysis of AlgorithmsDesigning and analyzing algorithms; asymptotic notation; recurrences and recursion; probabilistic analysis and randomized algorithms; sort algorithms; priority queues; medians and order statistics; data and advanced data structures; augmenting data structures for custom applications; dynamic programming; greedy algorithms; graph algorithms; sorting networks; matrix operations; linear programming; number-theoretic algorithms; string matching; NP-completeness and approximation algorithms; object libraries.
- SOFE 3720UIntroduction to Artificial IntelligenceThis course introduces students to basic concepts and methods of artificial intelligence from a software engineering perspective. The emphasis of the course will be on the selection of data representations and algorithms useful in the design and implementation of intelligent systems. Knowledge representation methods, state-space search strategies, and use of logic for problem-solving. Applications are chosen from among expert systems, planning, natural language understanding, uncertainty reasoning, machine learning, and robotics. The course will contain an overview of one AI language and discussion of important applications of artificial intelligence methodology.
- SOFE 4830UReal-Time Systems and ControlComputing systems design for real-time applications in control, embedded systems and communications; microcontrollers; data acquisition in robotics and manufacturing, file management, memory management and multitasking in a real-time environment; object-oriented design principles for real-time systems. Robustness
- ENGR 5775GKnowledge Discovery and Data MiningThis course covers the discovery of new knowledge using various data mining techniques on real-world datasets, and the current research directions represent the foundation context for this course. This course utilizes the latest blended learning techniques to explore topics in foundations of knowledge discovery and data mining; data mining approaches; and the application of data mining within such diverse domains as health care, business, supply chain and IT security. Current research directions, trends, issues and challenges are also explored.
Education
- 2007PhD - Electrical and Computer EngineeringUniversity of Waterloo
- 1994MSc - Computer EngineeringShiraz University
- 1991BSc - Software EngineeringIran University of Science and Technology
Speaking Engagements
- Washington, D.C. December 31, 1969How to Predict Social Trends by Mining User SentimentsInternational Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP-2015)
- Warsaw, Poland November 8, 2014The Correlation Between Language Shift and Social Conflicts in Polarized Social MediaIEEE/WIC/ACM International Conference on Web Intelligence
- Atlanta, Georgia December 31, 1969Stock Prediction Using Event-based Sentiment AnalysisIEEE/WIC/ACM International Conference on Web Intelligence
- Chicago, Illinois January 1, 2011Social Links Recommendation by Learning Hidden TopicsAssociation for Computing Machinery (ACM) Conference on Recommender Systems
- Thunder bay, Ontario May 7, 2010Query-relevant Document Representation for Text ClusteringFifth International Conference on Digital Information Management
- Sydney, Australia September 12, 2008Text Clustering Using Taxonomy2008 IEEE/WIC/ACM International Conference on Web Intelligence
- Glasgow, Scotland December 31, 1969Automatic Extraction of Domain-Specific Stopwords from Labeled Documents2008 European Conference on Information Retrieval
- Omaha, Nebraska December 31, 1969A Text Classification Framework with a Local Feature Ranking for Learning Social Networks2007 IEEE International Conference on Data Mining
Affiliations
- Institute of Electrical and Electronics Engineers (IEEE)
- Professional Engineers Ontario