☕️He Wang
Research Associate
International Centre for Theoretical Physics Asia-Pacific
University of Chinese Academy of Sciences
Summary
Hello, I’m He Wang (王赫). Since 2024, I have been working as a Research Associate at the ICTP-AP, University of Chinese Academy of Sciences (UCAS), exploring the fascinating world of gravitational wave physics. My academic voyage took root at Beijing Normal University, where I was honored with a Ph.D. in 2020.
Embarking on the academic path is both challenging and fulfilling. I am firmly set on this path, ready to forge ahead with steadfast determination, undeterred by any obstacles or setbacks that might come my way. The joy I find in disseminating the knowledge I have gathered so far is immeasurable, particularly in the realms of Gravitational-Wave (GW) data analysis and Machine Learning (ML) technology.
Through my blog, I hope to foster a vibrant space of learning and curiosity, offering insights in both Chinese and English. I am in the process of cultivating a rich repository of knowledge, with the hope that, in time, I will carve out a viewpoint that is distinctly my own.
Guided by the profound words of S. Chandrasekhar, who noted, “I have the urge to present my point of view ab initio, in a coherent account with order, form, and structure,” I am navigating the intricate corridors of the academic world. My aim is to remain resilient, sharing my journey and insights, unfazed by potential hurdles or hindrances.
I envision this platform as a living testament to my commitment and evolution in the academic domain, showcasing a relentless pursuit of knowledge, undaunted by any challenges that lie ahead.
My scientific work has followed a certain pattern motivated, principally, by a quest after perspectives.
我的科学研究工作遵循了某种模式，它的动因主要是寻找观点。
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Experience
Engaged in advanced research on AI-based data analysis methodologies tailored for both ground-based and space-based gravitational wave detection.
Specific projects include:
- Developing AI-driven software packages for enhanced data processing in ground-based gravitational wave detection.
- Formulating global fitting algorithms for parameter estimation in space-based gravitational wave detection, utilizing AI techniques.
- Leveraging large language models (LLMs) to optimize algorithms for gravitational wave data analysis, enhancing computational efficiency and analytical accuracy.
Focused on the integration of AI and machine learning technologies for noise reduction and signal detection in gravitational wave research.
Key contributions include:
- Utilizing pre-trained models from natural language processing to expedite signal detection and enhance noise suppression in ground-based gravitational wave detection.
- Developing fast parameter estimation algorithms based on normalizing flow models for space-based gravitational wave projects.
Researched and developed methodologies for the rapid detection and inference of gravitational wave signals using deep learning approaches, focusing on:
- Employing normalizing flow models for efficient and accurate gravitational wave signal inference.
Teaching
Featured Publication
Recent Publication
Github Contribution
License
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