I have wide interests in AI research and applications plus computer programming in general. What I'm working towards: explainable AI, responsible and ethical use of automation, especially in the biomedical field. 

Graduated with BSc in physics and mathematics and PhD (IGS and computer science department) from Nanyang Technological University (Alibaba-NTU Talent). I was also a CN Yang Scholar.

Other things about me: (1) have experiences in web development (2) am always looking for opportunity to start a business with novel products (3) love to learn different languages.

Stanford Advisors

Current Research and Scholarly Interests

I'm working on explainable artificial intelligence (explainable AI, XAI), healthcare analytics and machine learning in general.

I design and study deep learning models with humanly understandable concepts. Large models are powerful, enabling large scale transformations in many aspects of the society. In particular, we aim to improve the efficiency and availability of healthcare to the public. However, large models can be difficult to understand, and we are trying to improve their transparency one part at a time.

I also conduct research on understanding these complex models. With various post-hoc methods and probes, we seek to understand the inner working of an AI model. With better understanding, we can better align our priorities when we consider the decisions made by an AI system.

Objectives: to achieve transparency and responsible use of automated systems.

Lab Affiliations

All Publications

  • Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator's publication trends. Heliyon Ghanem, M., Espinosa, C., Chung, P., Reincke, M., Harrison, N., Phongpreecha, T., Shome, S., Saarunya, G., Berson, E., James, T., Xie, F., Shu, C. H., Hazra, D., Mataraso, S., Kim, Y., Seong, D., Chakraborty, D., Studer, M., Xue, L., Marić, I., Chang, A. L., Tjoa, E., Gaudillière, B., Tawfik, V. L., Mackey, S., Aghaeepour, N. 2024; 10 (7): e29050


    Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field.The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test.The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, "Cells & Proteomics" had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000-2022. However, when focusing on the period from 2015 to 2022, topics "Cells & Proteomics" and "Pulmonology" exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in "Aneurysms", "Covid 19 Pandemic", and "Artificial intelligence & Machine Learning".Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.

    View details for DOI 10.1016/j.heliyon.2024.e29050

    View details for PubMedID 38623206

    View details for PubMedCentralID PMC11016610

  • Enhancing the confidence of deep learning classifiers via interpretable saliency maps NEUROCOMPUTING Tjoa, E., Khok, H., Chouhan, T., Guan, C. 2023; 562
  • Self reward design with fine-grained interpretability SCIENTIFIC REPORTS Tjoa, E., Guan, C. 2023; 13 (1): 1638


    The black-box nature of deep neural networks (DNN) has brought to attention the issues of transparency and fairness. Deep Reinforcement Learning (Deep RL or DRL), which uses DNN to learn its policy, value functions etc, is thus also subject to similar concerns. This paper proposes a way to circumvent the issues through the bottom-up design of neural networks with detailed interpretability, where each neuron or layer has its own meaning and utility that corresponds to humanly understandable concept. The framework introduced in this paper is called the Self Reward Design (SRD), inspired by the Inverse Reward Design, and this interpretable design can (1) solve the problem by pure design (although imperfectly) and (2) be optimized like a standard DNN. With deliberate human designs, we show that some RL problems such as lavaland and MuJoCo can be solved using a model constructed with standard NN components with few parameters. Furthermore, with our fish sale auction example, we demonstrate how SRD is used to address situations that will not make sense if black-box models are used, where humanly-understandable semantic-based decision is required.

    View details for DOI 10.1038/s41598-023-28804-9

    View details for Web of Science ID 000984271700048

    View details for PubMedID 36717641

    View details for PubMedCentralID PMC9886969