Amin Assareh
Part-Time Lecturer
Research interests
- Knowledge graphs
- Generative AI
- Machine learning
- Artificial intelligence
- Natural language processing
- Meta learning
- Transfer learning
- Semi-supervised learning
- Weak supervision
Education
- PhD in Computer Science, Kent State University
- MS in Biomedical Engineering, AmirKabir University of Technology
- BS in Biomedical Engineering, AmirKabir University of Technology
Biography
Amin Assareh is a part-time lecturer at the Khoury College of Computer Sciences at Northeastern University. He earned his bachelor’s and master’s degrees in biomedical engineering from AmirKabir University of Technology, and his PhD in computer science from Kent State University. His teaching focuses on machine learning and data mining, and he is research active in several areas including machine learning, natural language processing, and transfer learning.
Assareh has patented the Systems and Methods for Data Exfiltration Detection.
He is currently also a vice president of data science at Fidelity Investments. Prior to joining Northeastern, Assareh was a senior manager of analytics and insights at Fidelity Investment.
Outside of work, he practices Brazilian Jiu Jitsu and has competed in multiple tournaments.
Recent publications
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Interaction Trees: Optimizing Ensembles of Decision Trees for Gene-Gene Interaction Detections
Citation: Assareh, Amin & Volkert, L.G. & Li, Jing. (2012). Interaction Trees: Optimizing Ensembles of Decision Trees for Gene-Gene Interaction Detections. Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012. 1. 616-621. 10.1109/ICMLA.2012.114. -
Feature selections using AdaBoost: Application in gene-gene interaction detection
Citation: Assareh, Amin & Volkert, L.G. & Li, Jing. (2012). Feature selections using AdaBoost: Application in gene-gene interaction detection. Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012. 831-837. 10.1109/BIBMW.2012.6470248. -
Evolutionary Selection of Regressional Predictors to Enhance the Performance of Microfossil-Based Paleotemperture Proxies
Citation: Assareh, Amin & Volkert, L.G. & Ortiz, Joseph. (2011). Evolutionary Selection of Regressional Predictors to Enhance the Performance of Microfossil-Based Paleotemperture Proxies. Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010. 379 - 385. 10.1109/ICMLA.2010.63.