Jie Han
Jie Han
I am the Principal Investigator (PI) of the Environmental and Public Health Group and a Professor with a 'Young Talent Tier A' appointment at Xi'an Jiaotong University (XJTU), member of the 'C9 league' universities in China, which is ranked among the top 10 in science and engineering in the country.
The overall theme of my research is on environment and public health, and much of my previous work was on emerging contaminants, particularly the migration of trace organic compounds into and out of polymers including membranes, common plastics, and elastomers. I have strong interests in advancing public health including exposure identification, risk assessment, mitigation, and science communication to the general public.
Prior to joining XJTU, I received research training and experience from the University of Auckland (UoA), National University of Singapore (NUS), University of Illinois at Urbana Champaign (UIUC), and University of Massachusetts Amherst (UMass Amherst), and worked with the PIs of several world-renowned laboratories in my fields. Since my Auckland times, I have supervised over 50 students, including undergraduates and Master's students who later developed strong interests in scientific research and pursued their PhDs. In the past six years, I have delivered over a dozen research projects as the PI or co-PI, and received over eight million RMB in research funding.
I have a strong passion in scientific writing, editing, and communication. I hold appointments as the Co-Editor-in-Chief of Environmental Chemistry Letters (Springer Nature, JCR Q1, IF 15.0) and I am a member of the Editorial Board of Chemical Engineering Journal (JCR Q1, IF 13.3), Ecotoxicology and Environmental Safety (JCR Q1, IF 6.8), and Animal Diseases (a new journal launched in spring 2021, indexed by ESCI). I serve as a member of the Council of Science Editors (U.S.) and the China Editology Society of Science Periodicals. I am a Senior Member of the Chinese Society for Enviornmental Sciences and hold long-term memberships of the International Water Association (IWA) and the American Chemical Society (ACS).
Our current research covers the following domains:
(1) Power searching & deep searching
(超级搜索技术)
(2) Data-driven scientometric analysis & knowledge mapping (Python、R、SQL)
(全领域文献分析)
(3) Training Large Language Models for scientific research, writing, and communication (GPTs)
(大语言模型训练与应用开发)
(4) AI-assisted design & synthesis of novel compounds and polymers
(人工智能辅助设计与合成新型化合物与聚合物)
(5) Agile research project management
(敏捷研发项目管理)
We seek students, postdocs, visiting students or scholars, and collaborators from data science, computer science, software engineering, natural language processing, artificial intelligence, and other related disciplines to conduct further work in these research domains.
科研人的外脑 | What do GPTs (and the like) hold for future scientists?
The time has come to test ChatGPT, the new AI application that everyone has been talking about, for something more than recreative fun. Over the past few weeks, it consistently impressed me over numerous random tasks that I demanded, some of which are downright unthinkable for humans, e.g., analyzing a 7,000-word research paper in 30 seconds and listing the findings and methods in bullet points. It is not flawless, but it is indeed powerful.
- Jie Han, at Xi'an Jiaotong University (2023.05.26)
人类世的阿凡达 | The 'Avatar' of Anthropocene
The advancement of sensoring and digital technology has brought us with new capabilities to better build and manage our infrastructures.
These include both natural and built environments, environments we live in and immediately interact with and resources we are constantly in need of, like water and air.
We are partnered with top 2% scientists in this discipline, including researchers at the University of Cambridge, IBM, and Microsoft to bring a set of new, revolutionary concepts and approaches to traditional asset management.
Think of this as environmental engineering on a new level. Assets with sensors and data I/O, interactive models with live inputs and control, responsive infrastructure with algorithms and artificial intelligence—all connected, shared, and digitalized on the scale of a city or nation. Over time, these will save hundreds of millions of costs for tax payers, and improve the quality of living for people living in densely populated areas, like those 'mega cities', by building smarter and more resilient urban infrastructure.
Think of this as David Cameron's "Avatar" for urban infrastructure, in the real world.
Headquartered at the University of Cambridge, the United Kingdom has taken a lead in building a national digital twin, the first of its kind. But there are plenty to do toward that goal. Both the scale and complexity of this work require collaborative efforts by engineering and IT experts from multiple disciplines.
Yet, the change is inevitable, and it is only a matter of time to unlock the potential of digtal and information technologies in the design, construction, and managment of urban infrastructure. We are excited to be part of this transformational change in the world’s most populous nation.
- Jie Han, at Xi'an Jiaotong University (2022.01.16, updated on 2022.01.18)
数据里的科学 | What are 'Big Science Data' and why do we research it?
We all heard about Big Data. The explosive amount of information published in scientific literature makes 'big data' also a reality for scientists to munch on, day in and day out. For just about every research discipline, there are a countless number of publications. If you are stepping into a well-established research domain, even combing through the existing findings and gaps becomes a daunting task that can take weeks to months to accomplish. To put things into perspective, the number of scientific publications indexed in Clarivate® Web of Science Core Collection in the six environmental science categories has grown from 22,000 in 1990 to an astonishing 252,000 in 2022, an increase of 11.5-fold. And the number keeps growing.
On the bright side, we have many, many peer-reviewed scientific references that are available and, with the advancement of information technology and mobile computing, they are literally at our fingertips. Having gone through the peer review process, these represent - at least at the time of their publication – new discoveries, fresh opinions, and critical assessments carried out by researchers all around the globe. The downside, however, is that it becomes increasingly difficult for one to quickly grasp the essence of the current data and knowledge – especially when you are under pressure or running against time.
The classic wisdom, of course, is to read review papers. This approach has served us well and I was among one of those who strongly advocate reading reviews before narrowing down your research focus on any particular subject. The body of existing literature, however, keeps growing bigger but there is only so much you can convey in a 10,000-word article, the ‘typical’ length limit of reviews published in the environmental science discipline. To add to that challenge, review articles are narrowing their focuses to deliver more critical, in-depth insights into the subfields of a wider research domain. This approach helps readers gain invaluable insights into particular subject areas, but it is not helpful for one to quickly gain an overall understanding and state-of-the-art knowledge over the whole subject area, a task that is often faced by industrial scientists, engineers, decision-makers to develop new technologies and prioritize future research efforts, as well as young students and scholars who are about to start their research career in a particular field of study.
There are many questions we wish to answer - and many things we want to achieve - through the use of data science. Below are some true stories drawn from my personal experience over the years working as an environmental scientist and an enthusiast in advancing public health. Data science is exciting and rapidly evolving as an emerging research discipline in its own right. There are just so many things for us to explore. We are continuing to explore and will have other stories to share with you. Come back and check for our updates.
- Jie Han, at Xi'an Jiaotong University (2021.06.21; updated 2023.12.23)
选择你要走的路 | Choosing a path you are about to step onto
First I wish to share a story of my once awkward conversation with a senior person.
About ten years ago, in a nicely decorated meeting room at a prestigious university, a senior person from the university's management told me “You can spend your time on anything." It was not meant to inspire or motivate me. Rather, what it actually meant was that "...Make sure you spend your time on things that are worthwhile". This was the whole point why we had that conversation.
That conversation stayed with me. Ten years later, I still ask myself this hypothetical question, sometimes as early as when I get up in the morning. It just popped into my mind.
It is a hard question to answer after all. And it can be even harder for someone who just began their postgraduate study or launched their independent research career. There are so many things we don’t know yet, and scientific literature looks like a gigantic, expanding ocean for us to navigate through in pitch dark.
Yet we must face this question head on, and put serious thoughts into it, even though the answer may not be obvious and can take us a while to find out.
Those who have been through the process would know that, the real first step of starting your postgraduate study or chart a research career - after you have made up your mind to do so - is to choose a topic that you are passionate about and can benefit the society or humanity in some way - to answer the whole "worthwhile" question and even to gain some recognition later from your achievements, provided that you do get there eventually.
Answering this question or rather - shedding some light onto it - was the very first thing we hope to answer with data science. In science, we often do not know where it is going to lead us (and in many cases this is the whole point of why we do it in the first place), but we do need to know what the current focus is, what has been done, what are the consensuses, and perhaps most importantly, what hasn't been done and how it intersects with our passion because, for young students and scholars, that is where the real opportunities are.
- Jie Han, at Xi'an Jiaotong University (2021.06.21; updated 2023.12.12)
韩杰 等.《融合ChatGPT进行科学研究与写作:初学者指南》
(New Book) ChatGPT in Scientific Research and Writing: A Beginner's Guide. Springer Nature Switzerland AG 2024. https://link.springer.com/book/10.1007/978-3-031-66940-8 (Published 14 September 2024)
作者注 | Authors' Note: Most scientists are constantly under pressure for reading essential literature, designing new experiments, writing successful proposals and papers, and meeting deadlines. However, imagine that your brain is connected to the entire human knowledge and can extract instantly essential information for discovery. Imagine that research tasks that took days to months can now be done within few seconds. This is not science fiction anymore since the onset of generative artificial intelligence tools such as ChatGPT. This book explains concisely and simply how to use ChatGPT for identifying new results, crafting titles, editing language, interpreting figures, creating visuals, and refining methods. ChatGPT even allows for brainstorming, designing experiments, writing proposals, responding to reviewers, and evaluating research papers. Written for researchers with no background in coding or prompt engineering, this book provides the skills necessary to navigate the changing landscape of scientific research. In particular, you will learn how to leverage ChatGPT’s unique capabilities to generate ideas, streamline literature reviews, and craft compelling narratives. In short, this book empowers you to unlock the potential of ChatGPT, boosting productivity, and take your scientific research and writing to new heights.
韩杰 等. AI如何评价我的论文?
(图片由DALL E2模型生成)
(Preprint version) What does AI think of my paper? Available at SSRN. http://dx.doi.org/10.2139/ssrn.4525950 (Date Written: 26 May 2023) PDF download
作者注 | Authors' Note: The time has come to test ChatGPT, the new AI application that everyone has been talking about, for something more than recreative fun. Over the past few weeks, it consistently impressed me over numerous random tasks that I demanded, some of which are downright unthinkable for humans, e.g., analyze a 7,000-word research paper in 30 seconds and list the findings and methods in bullet points. It is not flawless, but it is indeed powerful.
Eric Lichtfouse博士 等. 科研人员的“外脑”:科研评价与论文写作
(图片来自: AJ Cann/Flickr)
Exobrains for research evaluation and paper writing. Environmental Chemistry Letters. https://doi.org/10.1007/s10311-023-01672-5 (Date Written 30 July 2023, Published 10 December 2023)
韩杰 等. 大数据揭示环境领域新的研究前沿和当下热点
(Preprint version) COVID-19 big data highlight new environmental research frontiers and current spotlights. Available at SSRN. http://ssrn.com/abstract=4033554 (Date Written: 13 February 2022) PDF download
作者注 | Authors' Note: In the past two years, environmental scientists around the world have put tremendous efforts into understanding the environmental persistence and transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the infectious agent of COVID-19, and the reverse impact on the natural and living environments by the global pandemic. Many innovative, forward-thinking thoughts and methods have been put forward by environmental scientists. There are now more than 10,000 articles related to COVID-19 published in scholarly journals in environmental research disciplines, representing about 5% of scientific publications on COVID-19 from all research disciplines and showcasing the tireless efforts and swift actions taken by environmental researchers around the globe. This vast and growing body of scientific literature provides invaluable references for decision-makers, yet it also presents a daunting task for one to acquire the essential knowledge for decision-making or to find major gaps for prioritizing future research efforts. Data science offers efficient tools for scientists and policy-makers to cope with the need to harness the wealth of information from what is becoming the largest collection of scientific publications on a human pandemic in history.