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Zhichen Lai - Postdoctoral Researcher - Roskilde University

Published online: 28.03.2025

Zhichen Lai's PHD study "Efficient Deep Learning for Correlated Time Series Analytics" focus on the development of computationally efficient and adaptive solutions for Correlated Time Series (CTS) analytics. The interest within CTS originates in his broader exploration of spatio-temporal data and its critical role in real-world applications like healthcare, transportation, and industrial monitoring.

Portrait

Zhichen Lai - Postdoctoral Researcher - Roskilde University

Published online: 28.03.2025

Zhichen Lai's PHD study "Efficient Deep Learning for Correlated Time Series Analytics" focus on the development of computationally efficient and adaptive solutions for Correlated Time Series (CTS) analytics. The interest within CTS originates in his broader exploration of spatio-temporal data and its critical role in real-world applications like healthcare, transportation, and industrial monitoring.

About Zhichen Lai

  • Age and place of birth: 1995, China
  • Nationality: Chinese
  • Title of Ph.d. dissertation: Efficient Deep Learning for Correlated Time Series Analytics
  • Department at AAU: Department of Computer Science
  • Faculty at AAU: THE TECHNICAL FACULTY OF IT AND DESIGN
  • Campus at AAU: Campus Aalborg
  • Year of Ph.d. graduation: 2025

 

Current employment and workplace

Postdoctoral Researcher, Roskilde University

How did I become interested in my field of research?

I became interested in Correlated Time Series (CTS) research through my broader exploration of spatio-temporal data and its critical role in real-world applications like healthcare, transportation, and industrial monitoring. The increasing deployment of sensors in Cyber-Physical Systems (CPSs) generates vast amounts of temporally and spatially correlated data, but existing deep learning models struggle to balance accuracy and efficiency, especially in resource-constrained environments. Recognizing this challenge, I was drawn to developing computationally efficient and adaptive solutions for CTS analytics. The intersection of deep learning, efficiency optimization, and real-world applicability continues to drive my passion for this field.

What am I most passionate about in my work?

I am most passionate about developing efficient and adaptive deep learning models for CTS analytics, particularly in resource-constrained environments. The challenge of balancing accuracy with computational efficiency drives my research, as real-world applications¡ªranging from healthcare monitoring to smart transportation¡ªdemand practical and scalable solutions. I find great satisfaction in designing lightweight models. Additionally, I am motivated by the potential impact of my work, ensuring that deep learning-based CTS methods can be effectively deployed in real-world CPSs, where sensor data plays a crucial role in decision-making. Pushing the boundaries of efficiency and adaptability in CTS analytics continues to inspire my research.

What made the strongest impression on me during my PhD defense?

The first year of your PhD is crucial¡ªmake the most of it by exploring different research directions and identifying the one that best aligns with your strengths, interests, and the feasibility of completing your PhD. Don¡¯t rush into a specific topic too early; instead, take the time to survey the field, read broadly, and engage in discussions with advisors and peers. A well-chosen direction will not only make your research more impactful but also make the entire PhD journey smoother and more fulfilling.

In the long term, what impact can my research have on society?

In the long term, my research on efficient and adaptive CTS analytics can enhance real-time decision-making in healthcare, transportation, and industry. By optimizing deep learning models for efficiency, my work supports sustainable AI development, reduces energy consumption, and makes advanced analytics more accessible, ultimately improving the safety, efficiency, and sustainability of CPSs.

What piece of advice would I offer current PhD fellows who want a career outside academia?

For PhD fellows considering careers outside academia, my key advice is to start preparing early by developing transferable skills and expanding your professional network. While deep research expertise is valuable, industries prioritize problem-solving, communication, and adaptability. Engage in industry-relevant projects, learn practical tools beyond your academic work, and seek internships or collaborations if possible. Networking is crucial¡ªconnect with alumni, attend industry events, and explore career paths that align with your expertise. Most importantly, recognize that a PhD equips you with critical thinking and analytical skills that are highly valuable beyond academia, so confidently leverage your strengths in any career transition.

A little bit about the person behind the researcher?

Beyond research, I have a deep love for music, especially singing Jay Chou¡¯s songs, which serve as both a passion and a way to unwind. I also enjoy cooking, particularly Chinese cuisine¡ªa skill I honed during my studies in Denmark, where I found the local food too bland for my taste. Cooking became more than just a necessity; it turned into a creative outlet and a connection to home. On a personal level, I share my life with my wife, a PhD in dentistry who works in China. We got married during my PhD journey, overcoming the challenges of a long-distance relationship. These experiences have shaped me beyond academia, adding balance, resilience, and joy to my daily life.

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