Mate KOVACS Assistant Professor
- 教員データベース
- イー・ソサイエティ研究室
- 所属学部
- 情報理工学部
- 職位
- Assistant Professor
- 専門
- Deep Learning, Natural Language Processing, Computational Business Intelligence, Big Data
- 主な担当科目
- Artificial Intelligence
- 研究分野・テーマ
- Data Science, Deep Learning, Natural Language Processing
- 過去の部活動
- Swimming, Mountain Climbing, Rock band (Bass)
- 得意な科目
- Biology, Chemistry
- おすすめの書籍
The Name of the WindPatrick Rothfuss
Message
What are the appealing and interesting points of Information Science and Engineering?
Information Science and Engineering is an exciting and growing discipline that involves theories, methods, and technologies from various domains such as Computer Science, Information Theory, Mathematics, Linguistics, etc. I found it fascinating how much impact Information Systems and new technologies have on our everyday lives.
How will the knowledge gained in the College of Information Science and Engineering be useful for students after graduation?
Students who successfully graduate from the Collage of Information Science and Engineering will have the necessary knowledge to solve real-world problems, and will have the ability to function as team players within a multi-cultural environment.
Please describe your major research, activities, and current research themes.
Online reviews on e-commerce websites (such as Rakuten, Amazon) can provide valuable information for companies who seek to improve their products and services based on the needs of the customers. However, the huge amount of reviews available on web platforms can be overwhelming for both the customers and the companies. Therefore, companies need systems and computational tools to analyze the reviews and obtain valuable information from them. My current research focuses on developing theoretical and computational models to help assess the quality and helpfulness of online customer reviews.
Please describe technologies that you find interesting now.
I am particularly interested in transformers-based language models such as GPT-2, and few-shot learning applications in Computer Vision.