Recent & Upcoming Talks

I was selected to participate in ASEAN Young Scientists Connect (AYSC) 2026 at Dusit Thani Bangkok, a forum for young researchers across ASEAN to connect, exchange ideas, build collaborations, and explore opportunities for joint research and funding. In the morning, I attended funding-focused sessions, and in the afternoon, I represented my team as a speaker, delivering a 3-minute pitch on Trans-GMS LLM.

I was invited to speak at AutoTech Aftermarket Summit 2026 at BITEC Bangkok, where I shared perspectives on how AI, computer vision systems, and intelligent infrastructure are transforming the future of the automotive aftermarket industry. The session explored the convergence of research, production AI systems, operational deployment, and next-generation automotive technologies.

I was honored to be selected—among only 8 individuals—for an oral presentation at the 14th Critical Care Conference. My talk focused on CU-ICU, a Thai-language instruction-tuned model designed to assist ICU practitioners. Built on the T5 architecture, the model was fine-tuned using efficient techniques such as LoRA, AdaLoRA, and IA3. CU-ICU integrates evidence-based clinical guidelines, including those from the Surviving Sepsis Campaign, to support clinical reasoning.

I was invited to give a talk on the transformative impact of Vision Transformers (ViTs) in the car insurance industry. The session explored the mathematical foundations of ViTs, highlighting how the self-attention mechanism enables precise visual data analysis. I also examined the use of custom loss functions to enhance claim prediction accuracy and discussed the integration of Large Language Models (LLMs) to further advance AI capabilities.

I was invited by NSTDA to teach a session focused on advanced artificial intelligence and computer vision, using the iconic “Where’s Waldo” problem as a practical case study. The session explored how AI can be trained to detect complex visual patterns—not just identifying a character in a busy scene, but demonstrating the power of modern deep learning techniques.

In this quick pitch, I’m thrilled to introduce KAO—a Kernel-Adaptive Optimization framework designed for high-fidelity inpainting of very high-resolution satellite imagery.

I had the opportunity to give a final orientation speech to the undergraduate students of the Department of Electrical and Computer Engineering at KMUTNB. The focus of my speech was on the transformative impact of AI, particularly highlighting the advancements in Large Language Models (LLMs) like ChatGPT. I discussed how these models have revolutionized natural language processing, enabling sophisticated interactions and problem-solving capabilities.

I was invited to present on advanced geospatial data analytics, focusing on how spatial and geographic information can be transformed into actionable insights. Leveraging PySpark for distributed computing significantly accelerates data processing, allowing efficient handling of large-scale geospatial datasets. The course also introduced distributed machine learning techniques to build scalable, high-performance predictive models.

My AI research was proudly featured in Techsauce News, spotlighting breakthrough advancements in deep tech and AI innovation. This recognition not only highlights the significant impact and transformative potential of my work in advancing artificial intelligence technologies but also underscores its role in shaping the future of smart automation. By pushing the boundaries of AI research, my efforts aim to drive cutting-edge solutions that address real-world challenges, foster industry innovation, and contribute to a smarter, more connected world.

I was invited to Italy to present my research, “MARS, Mask Attention Refinement with Sequential Quadtree Nodes,” at the ICIAP 2023 Workshop. This prestigious conference, organized biennially by CVPL under the International Association for Pattern Recognition (IAPR), will unite experts to discuss advancements in car insurance and computer vision. My research addresses the challenge of accurately evaluating car damages using MARS, which enhances instance segmentation through self-attention mechanisms and quadtree transformers.

I was invited to speak at the “Achieve Data Science First Meet” for a MOOC student project event, where I highlighted the growing recognition of data science, AI, and machine learning’s importance across various industries. I advised that organizations, regardless of their size or sector, must effectively develop and implement data science capabilities to stay competitive in the era of big data, or risk falling behind.

I was invited to teach a course on distributed machine learning to the Geo-Informatics and Space Technology Development Agency (GISTDA). The curriculum covered fundamental concepts of PySpark, basic deep learning techniques, and practical applications of distributed training using TensorFlow.

I recently had the opportunity to speak with high school students about ‘Career Paths for AI Research Scientists.’ During the talk, I shared my experiences as a postdoctoral researcher in AI, diving into the exciting world of artificial intelligence. I discussed the various career opportunities in the field, from academic research to industry roles, and highlighted how my own journey has shaped my understanding of Generative AI.

This research was featured on the official website of the College of Computing, Khon Kaen University. As part of advancing AI for healthcare, Dr. Teerapong Panboonyuen developed LymphAware, an intelligent framework designed to improve the reliability of lymphoma cancer diagnosis from histopathology images by reducing shortcut bias and focusing on clinically meaningful patterns. The work contributes to the development of trustworthy medical AI that can better support physicians and improve patient care.

Let’s meet Kao, a Senior Research Scientist at MARSAIL and a C2F High-Potential Postdoc at Chulalongkorn University. Since earning his Ph.D. in 2020, he’s been pushing the limits of AI—using transformers to unlock new insights from satellite images and geospatial data. By combining deep learning with smart optimization, Kao tackles some of the toughest challenges in remote sensing. Beyond his research, he’s passionate about inspiring and mentoring the next generation of AI innovators.

This dissertation introduces a new architecture for remote sensing, featuring Global Convolutional Network (GCN), channel attention, domain-specific transfer learning, Feature Fusion (FF), and Depthwise Atrous Convolution (DA). Tests on Landsat-8 and ISPRS Vaihingen datasets show that this model significantly outperforms the baseline.

I was honored to be invited as the Head TA for the Data Science Pathway course organized by CHULA MOOC Achieve. In this role, I guided students through the practical applications of data science using RapidMiner and machine learning, along with Python programming. The curriculum encompassed both supervised and unsupervised learning labs, featuring tasks such as Customer Retention Prediction and Boston Housing Price Prediction, among others.