I have a background in Software Engineering and started my first job as a developer in 2002. Later on, I co-founded a startup focusing on web-based learning platforms and multimedia content for e-learning. I finished college and continued working in the startup two years, collaborating with a number of major e-learning institutes in Iran. Then, I went to graduate school and got my Master's Degree with a focus on Business Intelligence and Decision Support Systems. Graduate school was a transition for me from Software Engineering to Data Mining and Artificial Intelligence. After graduating, I decided to pursue my passion for machine learning and data mining as a PhD student in the US. Currently, I am data scientist at LegalSifter, focused on Information Extraction from legal documents. You can find more about me in my resume.
May 2016 – August 2017
Worked as a part-time intern during Summer 2016, and Spring 2017, and started full-time in Summer 2017 as a Machine Learning lead, working on ContractSifter and LegalSifter. During my intenship days, I was working with a sizable corpus of contracts and applying unsupervised / semi-supervised methods to facilitate information extraction. Currently, I am a Data Scientist with the following responsibilities:
August 2009 – August 2011
Led a team of engineers in migrating to a new online learning platform that supports online monitoring of student interactions.
Created an online analytical processing (OLAP) framework using Microsoft SQL Server Analysis Service (SSAS).
March 2008 – August 2009
Performed technical software and server support for online learning environment. Provided help and supplementary materials for learners and instructors to use the environment effectively, assisting with critical system outages, software upgrades, and capacity planning, maintenance, and support, in a 24x7 availability environment.
March 2006 – March 2008
Led a team of engineers to develop a PHP based online learning platform that served 4 major e-learning institutes with more than 10,000 online students.
Led a team of content developers to produce we-based, interactive, multimedia courseware using Adobe Flash, Presenter, and Captivate.
August 2004 – March 2006
Lead designer and developer of software applications in oil, gas and petrochemical industry, including Oilfield Development Industry Feasibility Analysis (ODIFA), Thermodynamic Properties Calculations (Thermoprop), and Pipeline Transformation Simulation (PipeSim), using Borland C++ Builder, Microsoft Visual C++ and Visual C#.Net.
March 2002 – December 2004
Cooperated in team Projects, to develop web-based, network and windows-based applications, including Network Surveillance and Control (3rd Eye), IP Based Camera Surveillance System (Varadid), using Borland C++ Builder and Delphi.
August 2012 – Present
Working with Kevin Ashley, Chris Schunn, and Diane Litman on ArgumentPeer project, an NSF funded project for Teaching Writing and Argumentation with AI-Supported Diagramming and Peer Review. Developing computational linguistic model that identifies argumentative elements from student essays and provides feedback and scaffolding based on the identified elements.
May 2015 – September 2015
June 2012, 2013, and 2014
Worked under the mentorship of Geoff Gordon and Zach Pardos to develop a spectral learning method for inferring student mastery in a Java self-assessment tool.
Worked with Michael Yudelson to design a new model for inferring student mastery on the data from KDD Cup 2010, Educational Data Mining Challenge with 20 million transactions belonging to over 6 thousand students working on nearly 150 sections of mathematics curriculum practicing around 1650 skills.
Mentored a summer school student to use different text similarity metrics in order to cluster reading comprehension passages in a language tutoring system.
September 2011 - Present
Main research is focused on the application of Artificial Intelligence in Education. More specifically, improving writing and argumentation using computer and AI mediated tools. I also have publications in Adaptive Educational Hypermedia, Intelligent Tutoring Systems, Student Modeling, and Peer-Review.
Advisor: Prof. Kevin Ashley
September 2008 – September 2010
My main studies were focused on Business Intelligence and Decision Support Systems, Machine Learning and Data Mining Concepts and Applications. My final project was An Investigation of Data Mining Methods in Educational Systems along side with applying several data mining methods on the web-usage logs of online students.
Advisor: Prof. Jafar Habibi
September 2001 – September 2006
My major was Software Engineering and Computer Programing with beneficial courses including Computer Architecture, Object Oriented Programing, Programing Languages, Network and Web Programing. As a B.S. final project I was part of a team to develop an Online Learning Environment for the e-learning center at our university.
|Skill||Platform||Skill Level (Years)|
|MS SQL Server, MySQL||Expert (5)|
Windows ProgramingC, C++, C#
|Borland C++ Builder, MS Visual Studio .NET||Expert (5)|
Software Development ProcessRational Unified Process (RUP)
|IBM Rational Rose||Expert (4)|
|Borland C++ Builder||Expert (3)|
|Atlassian JIRA||Intermediate (1)|
|Skill||Platform||Skill Level (Years)|
Data MiningFeature Engineering, Clustering, Classification, Anomaly Detection, Association Rule Mining
|Weka, RapidMiner, Python||Expert (5)|
Business IntelligenceData Warehousing, Online Analytical Processing, Reporting, Visualization
|MS SQL Server BI Studio, Pentaho, JasperSoft||Expert (3)|
Statistical AnalysisHypothesis Testing, Analysis of Variance and Covariance, Predictive Analysis
|Stata, Excel, R||Intermediate (2)|
|Skill||Platform||Skill Level (Years)|
Natural Language ProcessingDiscourse Analysis, Argument Mining, Automatic Summarization
|Python (nltk, gensim, spacy)||Expert (4)|
Machine LearningHidden Markov Models, Support Vector Machines, Representation Learning, Neural Networks
|Python (scikit-learn, keras, tensorflow)||Expert (4)|
Recommender SystemsCollaborative Filtering, Matrix Factorization, Tensor Decomposition
|Python (graphlab)||Intermediate (2)|
Falakmasir, M. H., Gonzalez-Brenes J. P., S., Gordon, G. J., & DiCerbo, K. E. (2016) A Data-Driven Approach for Inferring Student Proficiency from Game Activity Logs. In 3rd ACM Conference on Learning@ Scale (L@S 2016), Edinburgh, Scotland, (pp.341-349). [PDF]
Falakmasir, M. H., Yudelson, M., Ritter, S., & Koedinger, K. (2015) Spectral Bayesian Knowledge Tracing. In 8th International Conference on Educational Data Mining (EDM 2015), Madrid, Spain, (pp.360-363). [PDF]
Falakmasir, M. H., Pardos, Z. A., Gordon, G. J. & Brusilovsky, P. (2013) A Spectral Learning Approach to Knowledge Tracing. (Best Student Paper) In 6th International Conference on Educational Data Mining (EDM 2013), Memphis, TN, US, (pp. 28–35). [PDF]
Falakmasir, M. H., Hsiao, I. H., Mazzola, L., Grant, N. & Brusilovsky, P. (2012) The Impact of Social Performance Visualization on Students. In 12th International Conference of Advanced Learning Technologies (ICALT 2012), Rome, Italy, (pp. 565-569). [PDF]
Falakmasir, M. H. & Habibi, J. (2010) Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in e-Learning. In 3rd International Conference on Educational Data Mining (EDM 2010), Pittsburgh, PA, US, (pp. 241-248). [PDF]
Falakmasir, M. H., Habibi, J., Moaven S., & Abolhassani, H. (2010) Business Intelligence in e-Learning: (Case Study of Iran University of Science and Technology). In 2nd International Conference on Software Engineering and Data Mining (SEDM 2010), Chengdu, China (pp. 473-477). [PDF]
Jabbari F., Falakmasir, M. H., Ashley, K. D., (2016) Identifying Thesis Statements in Student Essays: The Class Imbalance Challenge and Resolution. In the 29th International Florida Artificial Intelligence Research Society Conference (FLAIRS-29), Key Largo, Florida, USA, (pp. 220-225). [PDF]
Falakmasir, M. H., Ashley, K. D., Schunn, C. D., & Litman, D. J. (2014) Identifying Thesis and Conclusion Statements in Student Essays to Scaffold Peer Review. In 12th International Conference on Intelligent Tutoring Systems (ITS 2014), Honolulu, HI, US (pp.254-259). [PDF]
Lynch, C., Falakmasir, M. H., Ashley, K. D., (2014) Matching Hypothesis Text in Diagrams and Essays. In 7th International Conference on Educational Data Mining (EDM 2014), London, UK, (pp. 383-384). [PDF]
Falakmasir, M. H., Ashley, K. D., & Schunn, C. D. (2013) Using argument diagramming to improve peer grading of writing assignments. In 1st Workshop on Massive Open Online Courses (moocshop) at the 16th Annual Conference on Artificial Intelligence in Education (AIED 2013), Memphis, TN, USA, (pp. 41-48). [PDF]
Lynch, C., Ashley, K. D., Falakmasir, M. H. (2012) Comparing Argument Diagrams. In 27th Annual Conference on Legal Knowledge and Information Systems (JURIX), Amsterdam, Netherlands, (pp. 81-90). [PDF]