Hamidreza HosseinkhaniSharif University of Technology | SHARIF · Department of Computer Engineering
Hamidreza Hosseinkhani
Master of Science
About
1
Publication
26
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Introduction
Experienced software engineer, mentor, and research enthusiastic with a demonstrated history of working for the top-ranked startups in the country. Skilled in Machine Learning, Data Science, Mobile Software Development, Leadership, and Teaching. Strong education professional with a Master of Science (M.Sc.) focused in Artificial Intelligence from IAU, Science And Research Branch.
Now interested in Deep Reinforcement Learning and Explanation in Recommender Systems.
Additional affiliations
November 2020 - February 2021
Position
- Lecturer
Description
- I teach the "Introduction to Computer Science" course to freshmen at the Sharif University of Technology. You can find my profile at http://ce.sharif.edu/~hosseinkhani/ You can join my virtual class at https://vc.sharif.edu/ch/hosseinkhani ِِYou can see the assignments at https://quera.ir/overview/add_to_course/course/6978 You can watch the lecture videos at https://www.aparat.com/v/2clRF?playlist=634448
January 2020 - February 2021
Maktabkhooneh
Position
- Instructor
Description
- Maktabkhooneh partners with more than 200 leading universities and companies to bring flexible, affordable, job-relevant online learning to individuals and organizations worldwide. It offers a range of learning opportunities—from hands-on projects and courses to job-ready certificates, career credentials, and degree programs. My profile in Maktabkhooneh: https://maktabkhooneh.org/teacher/%D8%AD%D9%85%DB%8C%D8%AF%D8%B1%D8%B6%D8%A7-%D8%AD%D8%B3%DB%8C%D9%86-%D8%AE%D8%A7%D9%86%DB%8C-tch575/
June 2019 - present
Inpin
Position
- Chief Technology Officer
Education
September 2014 - August 2021
September 2009 - August 2014
Publications
Publication (1)
Recommender systems try to discover some latent features of users and items by looking at the available information such as users' history of ratings to items and then use these latent factors to estimate users' interest level in a particular item. Traditional methods such as standard matrix factorisation rely on the ratings that users have submitt...
