Primary Research Interests Research Articles

Can Artificial Intelligence Assist in Delivering Continuous Renal Replacement Therapy?

Continuous renal replacement therapy (CRRT) is widely utilized to support critically ill patients with acute kidney injury. Artificial intelligence (AI) has the potential to enhance CRRT delivery, but evidence is limited. We reviewed existing literature on the utilization of AI in CRRT with the objective of identifying current gaps in evidence and research considerations. We conducted a scoping review focusing on the development or use of AI-based tools in patients receiving CRRT. Ten papers were identified; 6 of 10 (60%) published in 2021, and 6 of 10 (60%) focused on machine learning models to augment CRRT delivery. All innovations were in the design/early validation phase of development. Primary research interests focused on early indicators of CRRT need, prognostication of mortality and kidney recovery, and identification of risk factors for mortality. Secondary research priorities included dynamic CRRT monitoring, predicting CRRT-related complications, and automated data pooling for point-of-care analysis. Literature gaps included prospective validation and implementation, biases ascertainment, and evaluation of AI-generated health care disparities. Research on AI applications to enhance CRRT delivery has grown exponentially in the last years, but the field remains premature. There is a need to evaluate how these applications could enhance bedside decision-making capacity and assist structure and processes of CRRT delivery.

Using data citation to define a knowledge domain: A case study of the Add‐Health dataset

AbstractTo date, most studies in scientometric map and track the main topics in a knowledge domain by measuring publications in core journals or keyword searches in databases. The present study instead proposes a novel metrics in which a knowledge domain is mapped and tracked via articles that cite the same openly accessible dataset. We retrieved 1,537 journal articles citing the National Longitudinal Study of Adolescent to Adult Health (Add‐Health) as the basis for an investigation of the major research topics associated with this dataset and how they evolved over time. To identify the primary research interests associated with the dataset, co‐word network modularity analysis was used. Another novel aspect of this study is that it juxtaposes the research topics identified by the co‐word approach with those generated by topic modeling: an approach that complements network modularity analysis, and allows for cross‐referencing between the results of these two methods. Keyness analysis was also performed to identify significant keywords in different time periods, which enables tracing of research interests in Add‐Health as they evolve. The methodological implications of using data citation as the basis for delineating a knowledge domain and techniques for its mapping are also discussed.

Notes on Contributors

Notes on Contributors

The Technical Challenges for Applying Unsaturated Soil Sensors to Conduct Laboratory-Scale Seepage Experiments.

Although many unsaturated soil experiments have successfully delivered positive outcomes, most studies just concisely illustrated sensor techniques, because their main objectives focused on bridging research gaps. Inexperienced research fellows might rarely follow up those techniques, so they could encounter very trivial and skill-demanding difficulties, undermining the quality of experimental outcomes. With a motivation to avoid those, this work introduces technical challenges in applying three sensor techniques: high precision tensiometer, spatial time-domain reflectometry (spatial TDR) and digital bench scales, which were utilized to measure three fundamental variables: soil suction, moisture content and accumulative outflow. The technical challenges are comprehensively elaborated from five aspects: the functional mechanism, assembling/manufacturing approaches, installation procedure, simultaneous data-logging configurations and post data/signal processing. The conclusions drawn in this work provide sufficient technical details of three sensors in terms of the aforementioned five aspects. This work aims to facilitate any new research fellows who carry out laboratory-scale soil column tests using the three sensors mentioned above. It is also expected that this work will salvage any experimenters having troubleshooting issues with those sensors and help researchers bypass those issues to focus more on their primary research interests.

Transmission Line Dynamic Circuit Model for Effective Length of Ground Electrode Under Lightning Transients

The effective length of the ground electrode is one of the primary research interests of lightning grounding system design. In this article, using a practical numerical analysis, a new expression for estimating the effective length of the simple ground electrode is proposed. A new dynamic circuit model based on the transmission line is developed to derive the expression. The model considers the soil ionization, frequency-dependent soil parameters, mutual reactance, and skin effect. Several rigorous impulse test simulations are performed on different electrode lengths, soil properties, and lightning currents to estimate the effective length of ground electrode numerically. A new expression is arrived at using the curve-fitting and optimization technique from the relationships of the effective length with soil resistivity, impulse rise time, and current. The proposed dynamic model and the expression to obtain the effective length are validated by published results.

Editorial introductions.

Editorial introductions.

Editorial introductions.

Current Opinion in Lipidology was launched in 1990. It is part of a successful series of review journals whose unique format is designed to provide a systematic and critical assessment of the literature as presented in the many primary journals. The field of lipidology is divided into six sections that are reviewed once a year. Each section is assigned a Section Editor, a leading authority in the area, who identifies the most important topics at that time. Here we are pleased to introduce the Section Editors for this issue. SECTION EDITORS Frank M. SacksFrank M. SacksDr Sacks is Professor of Cardiovascular Disease Prevention in the Nutrition Department of Harvard Chan School of Public Health; Professor of Genetics & Complexes Diseases; and Professor of Medicine at Harvard Medical School. Dr Sacks led the panel designing the DASH Study, which crafted a healthful eating pattern and demonstrated that it lowered blood pressure more effectively than any previous dietary treatment. Subsequently Dr Sacks led the DASH-Sodium study, which determined the dose-response effect of dietary sodium on BP. These multi-center National Heart Lung and Blood Institute trials found major beneficial additive effects of low salt and a dietary pattern rich in fruits and vegetables on blood pressure. He also led the seminal PoundsLost trial which showed the percentage of calories from fat or carbohydrate had no effect on long term weight loss. Currently, he is co-investigator of the MIND study, a randomized trial of a diet that combines features of the Mediterranean and DASH diets to preserve cognition in older people, funded by the National Institute on Aging. Dr Sacks's laboratory has studied the role of apolipoproteins in atherogenic lipoprotein metabolism in obesity and hypertriglyceridemia. He authored over 30 research papers on apoC-3 and/or apoE. Recently he discovered that a type of HDL that contains apolipoprotein C-III predicted higher rates of heart disease and diabetes, the opposite to the protective relation for the total HDL; and impaired HDL function in reverse cholesterol transport. Dr Sacks teaches at Harvard School of Public Health as course director for nutritional metabolism and biochemistry. Dr Sacks received the 2011 Research Achievement Award of the American Heart Association for lifetime research accomplishment. Majken K. JensenMajken K. JensenMajken K. Jensen, PhD, is Associate Professor of Nutrition and Epidemiology at Harvard T. H. Chan School of Public Health. Dr Jensen's primary research interests include novel metabolic markers, nutrition, and genetics, in relation to chronic lifestyle diseases. In ongoing collaborations with Dr Frank Sacks, they investigate how the presence of a small proinflammatory protein (apolipoprotein C-III) on HDL particles marks a potentially dysfunctional type of HDL that is not inversely associated with risk of cardiometabolic health, such as IMT, diabetes and cardiovascular disease. Other currently funded work is focused on lipoprotein metabolism and Alzheimer's disease and stroke. Her research predominantly utilizes large-scale observational studies (including the Health Professionals Follow-Up Study, the Nurses’ Health Study, the Danish Diet, Cancer and Health study, the Multi-Ethnic Study of Atherosclerosis, and the Cardiovascular Health Study) where cost-efficient prospective case-control studies are nested within for purpose of measuring novel biomarkers or genome-wide markers. Dr Jensen's research has been published in leading scientific journals and has been recognized with numerous awards. She received the Trudy Bush Fellowship for Cardiovascular Disease Research in Women's Health from the American Heart Association in 2011 and originally came for research studies at Harvard T. H. Chan School of Public Health as a Fulbright scholar. Dr Jensen has published over 80 original research articles and 5 reviews, editorials, and letters.

A Framework for Urban Parks

Jessica Fernandez, Yang Song, Mary Padua and Pai Liu Dr. Jessica Fernandez is an assistant professor at the University of Georgia. Her research and teaching incorporate contemporary approaches to the creation and assessment of place through emerging applications such as big data and VR/AR. As a licensed landscape architect, she has over a decade of experience working on a variety of nationwide awardwinning projects. She owns ALPHA Design Studio, where she applies her teaching and research in the design and building industry. Dr. Yang Song is assistant professor of landscape architecture at Texas A & M University and works at the intersection of landscape architecture, community planning, and urban design. His teaching and research activities have a strong focus on the role of public placemaking to community health and resiliency. He has a long-standing interest in the application of digital technology in research and design, especially in the area of social media and the built environment. His research examines the usage of urban parks and streets through the engagement patterns depicted in Instagram, Tripadvisor, and Twitter. Dr. Mary Padua is a licensed landscape architect with decades of experience in the public and private sectors, including running her practice, MGP Studio ART DESIGN RESEARCH. Simultaneously, she is a design educator and professor at Clemson University, where she served four years as chair of the Department of Landscape Architecture. Her practice and research activities focus on human-centered outdoor restorative environments that also intersect with technology. She is internationally recognized as a thought leader, visual artist, and writer who has authored works on China’s vast urban experiment, sociocultural theory, and the meaning of place. Dr. Pai Liu is a lecturer at Dalian University of Technology who taught previously at the University of Oklahoma and Clemson University. Dr. Liu obtained her PhD in Planning, Design, and the Built Environment from Clemson University. Her primary research interests are in health care environments, with an emphasis on design for the aging. Her current research projects include comparing elder care environments in the United States and China, investigating the application of cultural design in health care environments, and developing analytical tools for behavior mapping.

The Use of Corpora in the Teaching of ESP (the Sphere of Pedagogy and Psychology)

Specialized language from the spheres of pedagogy and psychology constitutes a fundamental aspect within the teacher – student communication. The submitted paper handles lexical and morphological features of compound nouns containing the nouns amnesia, memory and recall. The primary research interest focuses on collocations and concordances in which they appear on a regular basis; we also deal with the grammatical relations and elements of meaning that have an impact on the discourse characteristics. The studied lexical units are elaborated within English Web 2015 utilizing Sketch Engine. In the research, both quantitative (statistical methods) and qualitative (observation, comparison, generalization, data-driven research data modelling) methods were employed. To begin with, we provide the frequency list of the studied words within the text corpus, and we categorize them from the morphological perspective as the two aspects influence their lexical behaviour and employment for teaching purposes. The research outcomes indicate that the analysed lexical units exist in a broad discourse scope; moreover, they appreciably grant profitable acts of communication in the pedagogical settings. Eventually, we outline feasible pedagogical inferences of the inquiry outcomes in the teaching of English and we suggest a set of corpus-driven exercises on the professionally-oriented language from the fields of pedagogy and psychology. To sum up, the outcomes confirm that the lexical and semantic characteristics of specialized language underline social aspects of communication between experts (and public too). Further, we intend to elaborate words belonging to other spheres of professional communication (medicine, sociology, etc.) in the framework of the corpus examination.

Queer in Translation: Sexual Politics under Neoliberal IslamLGBTI Rights in Turkey: Sexuality and the State in the Middle East

Queer in Translation: Sexual Politics under Neoliberal IslamLGBTI Rights in Turkey: Sexuality and the State in the Middle East

Application of Artificial Neural Networks in Construction Management: Current Status and Future Directions

Artificial neural networks (ANN) exhibit excellent performance in complex problems and have been increasingly applied in the research field of construction management (CM) over the last few decades. However, few papers draw up a systematic review to evaluate the state-of-the-art research on ANN in CM. In this paper, content analysis is performed to comprehensively analyze 112 related bibliographic records retrieved from seven selected top journals published between 2000 and 2020. The results indicate that the applications of ANN of interest in CM research have been significantly increasing since 2015. Back-propagation was the most widely used algorithm in training ANN. Integrated ANN with fuzzy logic/genetic algorithm was the most commonly employed way of addressing the CM problem. In addition, 11 application fields and 31 research topics were identified, with the primary research interests focusing on cost, performance, and safety. Lastly, challenges and future directions for ANN in CM were put forward from four main areas of input data, modeling, application fields, and emerging technologies. This paper provides a comprehensive understanding of the application of ANN in CM research and useful reference for the future.

Editorial introductions.

Editorial introductions.

Maria O. Longas

Maria O. Longas, 76, died Jan. 7 in Hobart, Indiana. “Maria, a biochemist and native of Colombia, studied in New York and worked at the NYU Medical Center, Sloan Kettering Institute, Columbia University College of Physicians and Surgeons, and the Mount Sinai School of Medicine. She started her teaching career in 1987 at Purdue University Calumet (now Purdue University Northwest) in Hammond, Indiana. She was promoted to full professor in 1996 and retired in 2019. Her primary research interests were hyaluronic acid, aging effects of the skin, and Alzheimer’s disease. She was a long-time supporter of charities and the arts. She self-published a book of poems, Reminiscences, in 1997.”—Kay Rowberg and Hal Pinnick, colleagues Most recent title: Professor emerita of Chemistry, Purdue University Northwest Education: BA, chemistry, Hunter College, 1971; MA, chemistry, New York University, 1973; PhD, chemistry, New York University, 1978 To recognize your late loved one or colleague,

Understanding environmental epigenomics in autism spectrum disorder: an interview with Farah RZahir.

In this interview, Dr Farah RZahir speaks with Storm Johnson, Commissioning Editor for Epigenomics, on her work to date in the field of epigenomics, autism and intellectual disability. Dr Farah RZahir specializes in the identification of novel genetic and epigenetic causes for neurodevelopmental diseases. Her PhD, awarded in 2011 by the University of British Columbia (UBC), resulted in the characterization of new intellectual disability (ID) syndromes, as well as discovery of several new causative genes for the disorder. She was awarded the prestigious James Miller Memorial Prize for integrating basic and clinical science in 2010. Her PhD dissertation was nominated for the Governor General's gold medal - the highest possible accolade at UBC for doctoral research work. She then completed a postdoctoral tenure in Canada's premier Michael Smith Genome Sciences Centre, where she used whole-genome-sequencing methods to comprehensively assess genetic, molecularand structural causes for ID, employing several firsts for bioinformatic data mining in the field. During her postdoctorate she won three distinguishedawards and was a fellow of the Canadian Institute of Health Research, ranking in the top 2% nationally. Dr Zahir was appointed as an Assistant Professor at the Hamad Bin Khalifa University in 2016, where she led a group focused on neurogenomics and neuroepigenomics research. She was a foundingmember of the Precision and Genomics Medicine graduate program there. Currently she has rejoined UBC's department of Medical Genetics. Among her most significant achievements is the establishment of the novel Zahir Friedman syndrome, an intellectual disability/autism spectrum disorder syndrome that is caused by a major epigenomic regulator. Her current primary research interest is how epigenomics can be changed by environmental impactsand how these effects may be harnessed for neurodevelopmental disorders' prophylaxis and therapeutics.

DeepSperm: A robust and real-time bull sperm-cell detection in densely populated semen videos

Background and ObjectiveObject detection is a primary research interest in computer vision. Sperm-cell detection in a densely populated bull semen microscopic observation video presents challenges that are more difficult than those presented by other general object-detection cases. These challenges include partial occlusion, vast number of objects in a single video frame, tiny size of the object, artifacts, low contrast, low video resolution, and blurry objects because of the rapid movement of the sperm cells. This study proposes a deep neural network architecture, called DeepSperm, that solves the aforementioned problems and is more accurate and faster than state-of-the-art architectures. MethodsIn the proposed architecture, we use only one detection layer, which is specific for small object detection. For handling overfitting and increasing accuracy, we set a higher input network resolution, use a dropout layer, and perform data augmentation on saturation and exposure. Several hyper-parameters are tuned to achieve better performance. Mean average precision (mAP), confusion matrix, precision, recall, and F1-score are used to measure accuracy. Frame per second (fps) is used to measure speed. We compare our proposed method with you only look once (YOLO) v3 and YOLOv4. ResultsIn our experiment, we achieve 94.11 mAP on the test dataset, F1-score of 0.93, and a processing speed of 51.9 fps. In comparison with YOLOv4, our proposed method is 2.18 x faster on testing, and 2.9 x faster on training with a small dataset, while achieving comparative detection accuracy. The weights file size was also reduced significantly, with one-twentieth that of YOLOv4. Moreover, it requires a 1.07 x less graphical processing unit (GPU) memory than YOLOv4. ConclusionsThis study proposes DeepSperm, which is a simple, effective, and efficient deep neural network architecture with its hyper-parameters and configuration to detect bull sperm cells robustly in real time. In our experiments, we surpass the state-of-the-art in terms of accuracy, speed, and resource needs.

Contributors

Previous articleNext article FreeContributorsFull TextPDF Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreLaura K. Nelson is assistant professor of sociology at the University of British Columbia. She uses computational methods to study social movements, gender, culture, and institutions, and has published in outlets such as Sociological Methods and Research, Mobilization, Poetics, and Gender and Society.Yang Zhang is assistant professor at the School of International Service at American University in Washington, D.C., and a sociologist of history, politics, culture, and knowledge. He is currently finishing a book entitled Empire and Its Enemies: Religion, Ethnicity, and Rebellions in Mid-19th-Century China.Laura Hamilton is professor and chair of the Department of Sociology at the University of California, Merced, where she researches the organizational production of inequality in higher education. Her books include Broke: The Racial Consequences of Underfunding Public Universities (with Kelly Nielsen) and Parenting to a Degree: How Family Matters for College Women’s Success. She is cofounder (with Charlie Eaton) of the Higher Education Race and the Economy (HERE) Lab, which is currently working on projects addressing the unequal racial and class distribution of resources and risk in higher education.Elizabeth A. Armstrong is Sherry B. Ortner Collegiate Professor of Sociology at the University of Michigan. She is the coauthor, with Laura T. Hamilton, of Paying for the Party: How College Maintains Inequality (Harvard University Press, 2013). She and a team of collaborators at the University of Michigan are in the midst of a project investigating university responses to sexual misconduct.Sarah Ashwin is professor of industrial relations in the Department of Management at the London School of Economics. She has a longstanding research interest in Russia’s stalled gender revolution, which she interrogates to develop different aspects of gender theory.Katherine Keenan is a lecturer (assistant professor) at the University of St Andrews, United Kingdom. Her work focuses on inequalities in population health and wellbeing across the life course. She has conducted mixed method research in Post-Soviet contexts for over a decade.Irina M. Kozina is professor of sociology at the National Research University Higher School of Economics (HSE University) Moscow. Her research interests include labor markets, industrial relations, gender, and qualitative methodology.Ellis P. Monk Jr. is associate professor of sociology at Harvard University. His research interests include ethnoracial categorization and inequality in comparative perspective, the social determinants of health, sociology of the body, and social psychology. Currently he is continuing his research on skin tone stratification across multiple domains—such as health, the criminal justice system, and so on—and further developing his research on bodily capital. His work has appeared in the American Journal of Sociology, Social Forces, and Social Problems, among other publications.Michael Esposito is assistant professor of sociology at Washington University in St. Louis. His primary research interests include population health demography; racial and ethnic disparities in well-being; and statistical methodology for the social sciences.Hedwig (Hedy) Lee is professor of sociology and codirector of the Center for the Study of Race, Ethnicity, and Equity at Washington University in St. Louis. Previous articleNext article DetailsFiguresReferencesCited by American Journal of Sociology Volume 127, Number 1July 2021 Article DOIhttps://doi.org/10.1086/715716 Views: 1202 © 2021 by The University of Chicago. All rights reserved. Crossref reports no articles citing this article.

A Qualitative Research of Perception Investigations in Caregivers Under the COVID-19 Pandemic

We enthusiastically read the article entitled “The experiences of health-care providers during the COVID-19 crisis in China: a qualitative study” by Liu and colleagues [1]. Although this study highlights the support needed to protect the physical and mental wellness of caregivers in the COVID-19 pandemic, the potential limitations that existed in its qualitative research methods may undermine the accuracy of data collection indicated by our previous work [2,3]. First, the information gleaned from the participants could have been more productive if the authors used focus group as it leverages the power of the group dynamics to encourage back and forth conversation leading to fully exploring and expanding participant’s true feelings, thoughts, and experiences toward the COVID-19 compared to the in-depth interviews [4]. Even though some researchers claim that in-depth interviews (e.g., phone interview) are better to investigate a sensitive subject like the COVID-19 crisis that participants may feel uncomfortable to share their thoughts in front of a group of people, the recent study confirms that 1) sensitive and intimate disclosures are more likely to occur in the focus group; 2) some certain sensitive themes only happen in the focus group; and 3) neither sensitive themes emerged exclusively nor frequently in indepth interviews compared to focus group [5]. Second, ensuring the homogeneity of interest is way more critical than the general heterogeneity like the demographics of the recruited participants [6,7]. Specifically, the desirable combination of interest consensus and common ground with diverse experience is important to generate rich information. However, the perceptions of the COVID-19 pandemic may be different between physicians and nurses based on the duty of these two different professional roles. This is because nurses are more likely to interact with COVID-19 patients directly and more frequently than physicians. Therefore, according to the primary research interest that investigates the experiences of fighting the COVID-19 pandemic, the participants should have been limited to nurses as the frontline caregivers to maintain the homogeneity of interest. Third, considering that all participants were recruited from one local hospital, convenient sampling appears better than snowball sampling as the authors can recruit participants based on their availability and accessibility [8]. This is because scheduling participants at a time when they have more energy may yield richer information data than reaching out to them through phone interviews. These suggested changes in data collection and participant enrollment should be implemented in future qualitative research for the investigation of global pandemic crisis to reach more convincing conclusions.

Enhancing continuous control of mobile robots for end-to-end visual active tracking

In the last decades, visual target tracking has been one of the primary research interests of the Robotics research community. The recent advances in Deep Learning technologies have made the exploitation of visual tracking approaches effective and possible in a wide variety of applications, ranging from automotive to surveillance and human assistance. However, the majority of the existing works focus exclusively on passive visual tracking, i.e., tracking elements in sequences of images by assuming that no actions can be taken to adapt the camera position to the motion of the tracked entity. On the contrary, in this work, we address visual active tracking, in which the tracker has to actively search for and track a specified target. Current State-of-the-Art approaches use Deep Reinforcement Learning (DRL) techniques to address the problem in an end-to-end manner. However, two main problems arise: (i) most of the contributions focus only on discrete action spaces, and the ones that consider continuous control do not achieve the same level of performance; and (ii) if not properly tuned, DRL models can be challenging to train, resulting in considerably slow learning progress and poor final performance. To address these challenges, we propose a novel DRL-based visual active tracking system that provides continuous action policies. To accelerate training and improve the overall performance, we introduce additional objective functions and a Heuristic Trajectory Generator (HTG) to facilitate learning. Through extensive experimentation, we show that our method can reach and surpass other State-of-the-Art approaches performances, and demonstrate that, even if trained exclusively in simulation, it can successfully perform visual active tracking even in real scenarios.

The Book of Mormon for the Least of These. Vol. 1: 1 Nephi–Words of Mormon

Book Review| April 01 2021 The Book of Mormon for the Least of These. Vol. 1: 1 Nephi–Words of Mormon Fatima Salleh, with Margaret Olsen Hemming. The Book of Mormon for the Least of These. Vol. 1: 1 Nephi–Words of Mormon. Salt Lake City: BCC Press, 2020. Daniel Becerra Daniel Becerra Reviewed by Daniel Becerra is an assistant professor of ancient scripture at Brigham Young University and a scholar of early Christianity. He received a PhD in Religion (Early Christianity) from Duke University and an MTS in New Testament/Early Christianity from Harvard Divinity School. His primary research interests concern moral formation in Late Antiquity, particularly within Christian ascetic contexts. He also researches topics relating to theology and ethics in the Book of Mormon. He is the author of 3rd, 4th Nephi: A Brief Theological Introduction (2020). Search for other works by this author on: This Site Google Journal of Book of Mormon Studies (2021) 30: 262–270. https://doi.org/10.5406/jbookmormstud2.30.2021.0262 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Daniel Becerra; The Book of Mormon for the Least of These. Vol. 1: 1 Nephi–Words of Mormon. Journal of Book of Mormon Studies 1 April 2021; 30 262–270. doi: https://doi.org/10.5406/jbookmormstud2.30.2021.0262 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressJournal of Book of Mormon Studies Search Advanced Search The text of this article is only available as a PDF. Copyright 2021 by the Board of Trustees of the University of Illinois2021 Article PDF first page preview Close Modal You do not currently have access to this content.

Reaction: Engineer Biology for Uranium

Fei Sun is currently an associate professor at the Department of Chemical and Biological Engineering of the Hong Kong University of Science and Technology (HKUST). He obtained his BS and PhD degrees from Peking University and the University of Chicago, respectively. Before joining HKUST, he worked as a postdoctoral researcher at the California Institute of Technology. His primary research interests include protein engineering, synthetic biology, and biomaterials. Chuan He is currently the John T. Wilson Distinguished Service Professor in the Department of Chemistry at the University of Chicago. His research interests include topics in chemical biology, bioinorganic chemistry, and protein engineering.