Computing’s Top 30: Ming Jin

Ming Jin is one of our "Computing's Top 30 Early Career Professionals" for 2025. This program seeks to highlight an esteemed group of rising stars who earned this honor for their exceptional early-career achievements and role in driving advancements across the computing landscape.

Introduction

I am currently an Assistant Professor at School of Information and Communication Technology (ICT), Griffith University. Prior to this, I obtained my Ph.D. degree in the Faculty of Information Technology at Monash University in 2024.

My expertise lies in time series analytics and spatio-temporal data mining, and I have established a strong track record of publishing high-impact research in top-ranked venues, including NeurIPS, ICLR, ICML, KDD, and TPAMI. My research outputs have been selected as Most Influential & ESI Hot and Highly Cited Papers, with some having become widely used baseline methods such as TimeLLM, gaining substantial recognition in the open-source community.

I am a committee member of IEEE CIS Task Force on AI for Time Series and Spatio-Temporal Data. I also serve as Associate Editor for Neurocomputing (Q1 IF 6.5) and actively contribute as an Area Chair or (Senior) Program Committee member for prestigious AI and data mining conferences.

What inspired you to pursue a career in technology?

My passion for AI has been the driving force behind my journey since my Bachelor's degree. Back then, GenAI wasn't around, and few people even knew what AI was, but I firmly believed it would deeply reshape our future, much like JARVIS from Iron Man (and OpenClaw today after ~10 years). I wished to be part of this golden era of AI and make some tangible contributions. I discovered my specific passion for time series techniques during my research internship at Metso Outotec, and I have been incredibly fortunate to be advised by my life mentor, Dr. Joey Hoang, along with excellent scholars like Prof. Shirui Pan and Prof. Yuan-Fang Li.

What do you consider your highest achievement so far?

While AI encompasses many subfields, I am fortunate to have found a direction I truly enjoy: time series AI. I consider my highest achievement so far to be pioneering the modern development of time series language models (TSLMs). Every day, trillions of timestamped records are generated across transportation, the environment, energy, and healthcare. Time series is essentially the "language" of machines, such as traffic sensors, smart vehicles, and wind turbines. TSLMs build a vital interface between humans and these machines, unlocking massive opportunities like agentic time series analytics for fully autonomous production. My ultimate mission is to enable general-purpose time series AI that automatically coordinates real-world production with human-in-the-loop (HITL) systems.

How do you plan to continue or build on that success?

It is fantastic to see the time series research community growing so fast, and I will continue actively contributing to ensure next-gen time series intelligence becomes a reality soon. My research group is expanding rapidly. Whether it's guiding brilliant students or applying for future funding to expand our capabilities, our mission is to bring innovative ideas to the community that address real-world pain points. We also actively welcome industry partners to reach out so we can define and build the future of time series technology together.

Who do you draw inspiration from and how did that motivate you in your education or career?

I would like to express my deepest gratitude to my life mentors, Dr. Joey Hoang and Robert Matusewicz. During my Master's studies and my internship at Metso Outotec, their unwavering encouragement and support shaped me into who I am today and taught me to persevere in the research I am truly passionate about. Although Dr. Hoang is no longer with us, his spirit continues to inspire and guide me forward.

Is there any emerging technology or industry segment you find exciting or interesting?

I am currently very focused on Agentic AI, which has incredible promise. It has the potential to connect the various time series models and tools we've developed to solve complex, real-world problems for the first time. I am also excited about native multimodal time series foundation models (TSFMs). These models are expected to have all the strengths of modern pretrained LLMs but uniquely excel at processing temporal signals, acting as the "heart" of next-gen production solutions in a way that standard LLMs cannot.

How do you see technology shaping humanitarian efforts or social good in the next 5 years?

AI has fundamentally changed our lifestyle since late 2022, and over the next 5 years, I believe we will witness the emergence of physical AI that brings intelligence directly into the physical world. We will see much more mature industrial solutions built on time series AI applied to smart transport, logistics, and renewable energy. Together, these advancements will elevate production efficiency to unprecedented levels and significantly reduce operational costs, supporting more sustainable and accessible infrastructure globally.

Do you cross-discipline? How did that influence your way of thinking or the way you approach your work?

My lab embraces cross-disciplinary research to ground time series AI into actual production. We work on diverse challenges, from managing bus bunching and electric vehicle battery to optimizing PV and wind power generation and analyzing medical signals. These cross-disciplinary works give me a much deeper understanding of different fields and directly informs the trajectory of where next-gen time series AI needs to go to be genuinely useful.

What advice would you give to young professionals or recent graduates who are trying to enter your field?

First, be confident, bold, and creative. Second, think "how to think", learn from practice, and constantly look for the gaps where you can make an impact. Time series is an enduring research topic that has evolved from early statistical forecasting to today's rapidly advancing universal solutions. My biggest advice to young professionals is to learn to "forecast" the future, trust yourself firmly, and dedicate yourself to work that is genuinely meaningful and valuable.

Make Connections

You can find Ming Jin on LinkedIn.

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