基本信息
浏览量:2769
职业迁徙
个人简介
My research is motivated by applying Machine Learning in practice. My approach is to work on challenging applications that help my students and me to identify gaps in the literature or assumptions in the state-of-the-art that do not hold for our applications. This research approach often leads to contributions both in Computer Science as well as the application areas.
One instance of such an approach is the challenge of incorporating classification algorithms on embedded devices. For example, I have developed lightweight models that can run in environments with severe power restrictions such as satellites and sensors. One notorious application is the development of sensors to classify insects in flight automatically, allowing the creation of surveillance systems for disease vectors, invasive species and pests. I have also developed EmbML, a Machine Learning tool to convert sickit-learn and Weka classifiers into C++ code crafted to run into low-power microcontrollers, such as ones found in the Arduino family.
In the last years, I have actively worked in the area of Machine Learning Quantification, developing new algorithms to count events accurately. These recent developments have led to the proposal of a novel Data Mining task known as One-class Quantification as well as a family of efficient quantification algorithms.
The impact of my research can be measured by the number of recent papers citing my research articles. According to Google Scholar, my paper have more than 9,000 citations, with more than 1,000 citations in 2020.
One instance of such an approach is the challenge of incorporating classification algorithms on embedded devices. For example, I have developed lightweight models that can run in environments with severe power restrictions such as satellites and sensors. One notorious application is the development of sensors to classify insects in flight automatically, allowing the creation of surveillance systems for disease vectors, invasive species and pests. I have also developed EmbML, a Machine Learning tool to convert sickit-learn and Weka classifiers into C++ code crafted to run into low-power microcontrollers, such as ones found in the Arduino family.
In the last years, I have actively worked in the area of Machine Learning Quantification, developing new algorithms to count events accurately. These recent developments have led to the proposal of a novel Data Mining task known as One-class Quantification as well as a family of efficient quantification algorithms.
The impact of my research can be measured by the number of recent papers citing my research articles. According to Google Scholar, my paper have more than 9,000 citations, with more than 1,000 citations in 2020.
研究兴趣
论文共 177 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
ICRA 2024 (2024)
IEEE transactions on knowledge and data engineeringpp.1-13, (2024)
CoRR (2024)
AINTEC '24: Proceedings of the Asian Internet Engineering Conference 2024pp.18-25, (2024)
Shayan Azizi,Norihiro Okui, Masataka Nakahara, Ayumu Kubota,Gustavo Batista,Hassan Habibi Gharakheili
ACM SIGCOMM Conferencepp.25-27, (2024)
DSpp.3-17, (2023)
SDMpp.622-630, (2023)
加载更多
作者统计
#Papers: 177
#Citation: 10650
H-Index: 34
G-Index: 102
Sociability: 5
Diversity: 2
Activity: 23
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn