Leading international industrial media outlet Control Global has announced that Prof S. Joe Qin, President and Wai Kee Kau Chair Professor of Data Science of Lingnan University, has been inducted into the 2026 Process Automation Hall of Fame in recognition of his long-term outstanding contributions and far-reaching impact in industrial data analytics, process control and automation, the only scholar from the Hong Kong SAR to receive this distinction. Inductees over the years have been key figures driving industrial technological innovation and theoretical breakthroughs, and the accolade is held in very high esteem by both the international academic and industrial communities.
Control Global commended Prof Qin’s academic career for its distinctive interdisciplinary nature, saying that with training spanning electrical engineering, control theory, and chemical engineering, he has demonstrated remarkable versatility across disciplines, and published extensively in process monitoring, system identification, chemometrics, and machine learning.
Prof Qin responded “Being inducted into the Hall of Fame is not the capstone of my academic journey, but rather a prompt for me to share my experiences more openly, including both the right and wrong paths I have taken, so that younger generations may benefit. This spirit of academic inheritance and selfless contribution is a value I hope to carry forward. My best advice to young engineers is to resist the pull of short-term rewards, recognise the full arc you are capable of, and always keep the bigger picture in sight.”
In a feature titled “Engineering a lifetime of reinvention”, Control Global describes Prof Qin’s interdisciplinary academic journey, noting his unusual background. The professor was born in Rizhao, Shandong province, and grew up during a period when formal schooling was limited, yet by the time he was 11 he had already taught himself to make wooden chairs to earn a living. When the higher education system reopened, Prof Qin seized the opportunity to gain admission to Tsinghua University at the age of 16 with the top scores in his cohort to study automatic control, laying the foundation for his engineering career.
Prof Qin recalls in the interview that while he was at Tsinghua University, he met the renowned scholar Prof Harmon Ray, who was visiting the campus and who advised him to pursue a PhD in chemical engineering at the University of Maryland - “life changing” advice. During his doctoral studies, he embarked on early research into how machines learn, examining neural networks’ strengths and limitations from a statistical perspective. After graduation, he became a principal engineer at Emerson Process Management, where he developed two commercial products successfully before returning to academia to teach and conduct research at The University of Texas at Austin and the University of Southern California.
Looking ahead, Prof Qin predicts that while industry has accumulated vast amounts of data over the past decades, its full value has yet to be realised due to previous limitations in computational power. Now although computing capabilities have advanced significantly in recent years, technologies such as artificial intelligence and machine learning remain underutilised in chemical engineering, and Prof Qin believes that applying advanced analytics to process monitoring, control, and optimisation will represent an unprecedented opportunity. He emphasises that the next generation of process engineers will need to be as fluent in data analytics and machine learning as they are in thermodynamics and fluid mechanics.
Prof S. Joe Qin, President of Lingnan University, has been inducted into the Process Automation Hall of Fame
Prof Qin also expresses concern about developments in engineering education, observing that, compared with 30 years ago, mathematical training in engineering programmes is weaker, and it has become more difficult to offer very rigorous courses. This is partly because most people want a programme where even the average student understands most of the concepts and graduates easily, although in the long run this may undermine the cultivation of advanced mathematical talent. Prof Qin suggests that universities create deliberately designed environments for mathematically gifted students to be challenged at an appropriate level, in order to preserve academic depth and international competitiveness.
Established in 2001, the Process Automation Hall of Fame recognises scholars and industry leaders for their outstanding contributions to process automation and control. The other inductees this year are Prof Manfred Morari of the University of Pennsylvania, an eminent international authority in modern systems engineering, and Prof Peter Morgan, longtime process engineer with Syncrude Canada and now an independent consultant.
For the full feature article, please visit: Engineering a lifetime of reinvention: 2026 Process Automation Hall of Fame's S. Joe Qin | Control Global
Does a depressive mood inevitably lead to more pessimistic thinking or over-analysing? A global meta-analysis, the largest of its kind examining the relationship between a depressive mood and reality judgment, co-conducted by the Department of Psychology at Lingnan University has found that the key lies in the nature of the judgment. Overall, individuals in a depressive mood generally make more accurate judgments when handling self-referent tasks or complex issues requiring deep analysis. However, their accuracy is impaired as regards understanding others and interpreting interpersonal relationships. Researchers noted that the findings clarify a decades-long academic debate in psychology regarding whether a depressive mood allows individuals to perceive reality more objectively, and will aid in designing more targeted intervention strategies. The paper was published in Clinical Psychology Review, a top international academic journal in clinical psychology.
A global meta-analysis co-conducted by the Department of Psychology at Lingnan University finds that individuals in a depressive mood can make more accurate judgments in self-referent tasks requiring deep analysis.
The research team, comprising scholars from Lingnan University, the Polish Academy of Sciences in Poland, and The Chinese University of Hong Kong, aggregated psychological and clinical studies published globally between 1971 and November 2025 from three leading international academic databases: Web of Science, PsycINFO, and PubMed. Synthesising empirical data from 32,914 participants, the study examined the relationship between a depressive mood and judgmental accuracy across three distinct groups: non-depressed healthy controls, individuals with a self-reported depressive mood via questionnaires, and clinically diagnosed depressed patients, using known objective outcomes as the baseline for comparison.
The team integrated multiple classic psychological behavioural experiments in the study. The first type of experiment was the "green light test", which assessed judgment of control. Participants sat in front of a computer and chose whether or not to press a button to see if a green lightbulb would light up. In reality, the light was entirely randomised by a computer programme. The results showed that the healthy control group tended to believe they had a significant ability to control the light, exhibiting an optimistic bias. Conversely, individuals in a depressive mood understood that they had absolutely no control over the outcome.
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The second type of experiment was the "deception detection task" to test complex analytical capabilities. Participants watched multiple video clips of real people speaking and had to identify who was telling the truth and who was lying. Spotting deception requires multi-step logical deconstruction, representing a complex issue that demands deep analysis. The results indicated that in these complex tasks, individuals in a depressive mood achieved a higher level of analytical accuracy compared to the healthy control group.
The third type of experiment evaluated "other-referent tasks" testing the participants' ability to observe and decode the behaviours, emotional states, or social interactions of others, such as evaluating the actual emotional states of individuals in audio or video clips. The results revealed that the judgmental accuracy of individuals in a depressive mood lagged significantly behind. The study suggested that depressed individuals are more prone to misinterpret others' behaviour and reactions.
The research team explained that the first and second types of experiments involved self-referent judgments, such as evaluating one's own performance, assessing one's ability to influence outcomes, or facing complex tasks requiring multi-step analysis. Individuals in a depressive mood made slightly more accurate judgments than healthy controls because the non-depressed control group commonly exhibited an "optimistic bias". This bias acts as a healthy psychological defence mechanism that maintains self-esteem through over-optimism, causing people to overestimate the extent to which they can control outcomes.
However, the third type of experiment involved other-referent tasks, such as understanding the behaviour of others and interpreting interpersonal relationships. In these scenarios, participants with severe but not moderate or mild depressive symptoms were more prone to judgmental bias and demonstrated lower accuracy. This shows that the relationship between a depressive mood and judgmental accuracy varies significantly depending on the task and context; hence, a blanket assumption that a "depressive mood allows people to see reality more objectively" is inaccurate, especially for those in severe emotional distress, or with sleep problems, difficulty concentrating, or fatigue – all symptoms of clinical depression.
Prof Hodar Lam, lead and corresponding author of the study and Research Assistant Professor at Lingnan University.
Prof Hodar Lam, lead and corresponding author of the study and Research Assistant Professor of the Department of Psychology and Associate Programme Director of the MSc in Work and Organisational Psychology Programme at Lingnan University, stated that this global big-data study spanning nearly half a century provides a vital reference for Hong Kong citizens who face a fast-paced and stressful lifestyle. He said "From an evolutionary perspective, all emotions, positive and negative, help humans to survive. A depressive mood could trigger more analytical, problem-solving rumination and learnings from the negative emotions. A transient depressive mood in daily life is fundamentally different from clinical depression. Experiencing mild, short-term depressive or negative emotions in daily life does not necessarily mean a decline in cognitive capabilities. In tasks involving self-assessment, deep analysis, or complex judgments, individuals in a depressive mood are actually less susceptible to the ‘optimistic bias’ common to the healthy public, allowing them to make a more objective appraisal of their own situation and capabilities."
Prof Lam went on to explain "Society should avoid stereotyping and categorising all depressive moods as a lack of rational judgment. Equally, we must not misunderstand a depressive mood as an inherent advantage, thereby ignoring its potential risks. Since research shows that a depressive mood impairs accuracy in understanding others and interpreting interpersonal relationships, the judgmental bias of participants with more severe symptoms will increase. Therefore, people must take emotional health seriously. This area could become a key focus for future psychological interventions to design more targeted treatment and support strategies."
Prof Lam emphasised that to help others experiencing persistent emotional distress, first show empathy and validation instead of asking them to “think positively or rationally”, because their perceptions could be right. People with deteriorating depressive symptoms, or who find that their work, interpersonal relationships, or daily lives are being affected, are encouraged to seek professional help as a brave and responsible act of self-care.
The study was co-first authored by Dr June Yeung of the Polish Academy of Sciences and an alumna of Lingnan University. To read the full research paper, please visit: Depression and accuracy of judgment: A meta-analysis – ScienceDirect
