Students

• Postdoc, Dr. Ghadeer Abuoda (co-supervised with Marin Litoiu)
• PhD, Jing Li
• PhD, Shaikh Quader (co-supervised with Marin Litoiu)
• MSc, Ali Faraji
• MSc, Amirhossein Nadiri
• MSc, Nina Yanin
• UG, Pranav Gupta

• Jing Li (MSc, 2023, Trajectory Prediction Learning Using Deep Generative Models) [ thesis | slides ]
• Gian Alix (MSc, 2023, Leveraging Deep Learning Methods for Trajectory Similarity Learning and Trajectory Pathlet Dictionary Construction) [ thesis | slides ]
• Khoa Tran (MScAI, 2023, Text Classification for Skewed Label Sets using Modified TextGCN and Human Pseudo-Labels) [ report ]
• Andrew Jaramillo (MSc, 2023, co-supervised with Marin Litoiu, Batch Query Memory Prediction Using Deep Query Template Representations) [ thesis | slides ]
• Mahmoud Alsaeed (MSc, 2023, Trajectory-User Linking using Higher-order Mobility Flow Representations) [ thesis | slides ]
• Ali Nematichari (MSc, 2022, Evaluating and Forecasting the Operational Performance of Road Intersections) [ thesis | slides ]
• Mahta Shafieesabet (MSc, 2022, Efficient Mining of Active Components in a Network of Time Series) [ thesis | slides ]
• Fazel Arasteh (MASc, 2021, Network-aware Multi-agent Reinforcement Learning for Adaptive Navigation of Vehicles in a Dynamic Road Network) [ thesis | slides ]
• Hoorieh Marefat (MSc, 2021, co-supervised with Aijun An, Fast Similarity Graph Construction via Data Sketching Techniques) [ thesis | slides ]
• Nastaran Babanejad (PhD, 2020, co-supervised with Aijun An, Enriching Word Representation Learning for Affect Detection and Affect-aware Recommendations) [ thesis | slides ]
• Saim Mehmood, (MSc, 2020, Learning Semantic Relationships of Geographical Areas based on Trajectories) [ thesis | slides ]
• Eric Niloy Costa, (MSc, 2020, Effective Density Visualization of Multiple Overlapping Axis-aligned Objects) [ thesis | slides ]
• Tilemachos Pechlivanoglou, (MSc, 2019, Sweep-line Extensions to the Multiple Object Intersection Problem: Methods and Applications in Graph Mining) [ thesis | slides ]
• Farzaneh Heidari, (MSc, 2019, Evolving Network Representation Learning Based on Random Walks) [ thesis | slides ]
• Mahmoud Alsaeed (UG, LURA, Summer 2019)
• Jing Li (UG, LURA, Summer 2020)
• Kenneth Tjhia (UG, USRA, Summer 2019, 2020)
• Gian Alix (UG, USRA, Summer 2021)
• Nina Yanin (UG, USRA, Summer 2021, LURA FW 2022/23, USRA Summer 2023)

Currently looking to hire bright and hard-working domestic or international students at all levels (PhD, M.Sc, Undergrad). If you are interested in conducting research in any of the following areas please email me your most recent CV, transcript and experience at papaggel@eecs.yorku.ca to express your interest to work with me.

• Graph Mining
• Data Mining
• Machine Learning
• Big Data and Knowledge Discovery

In principle, we are interested in innovative research on all aspects of data science, knowledge discovery and data mining, ranging from theoretical foundations to novel models and algorithms for data mining problems in science, business, medicine, and engineering. More applied research that makes innovative technical contributions or sheds light to different aspects or dimensions of a research topic are also welcome.

Doing research in these areas is a challenging but rewarding experience. We are looking for originality of ideas, and we welcome students who are passionate and inspired about research and system-building in this area.

• Trajectory Data Mining
• Network Representation Learning
• Streaming and Dynamic Graph Mining

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