Recent & Upcoming Talks

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. I explored cutting-edge techniques like Vision Transformers to develop models with exceptional precision in image recognition, showcasing how these advanced architectures are redefining the limits of AI perception and understanding.

In this quick pitch, I’m thrilled to introduce MeViT—a medium-resolution Vision Transformer developed for high-precision semantic segmentation of Landsat satellite imagery.

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.

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.

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 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 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.

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.

Hey everyone! I’m Teerapong Panboonyuen, but you can call me Kao. I’m passionate about creating impactful AI. While working on my Ph.D. in Computer Engineering at Chula, I’ve taught, tackled exciting data science projects, and assisted in machine learning classes. My goal? To elevate AI and leverage it for a better world. My journey has been a thrilling exploration of advanced technologies, from diffusion models and generative adversarial networks (GANs) to self-supervised learning and probabilistic modeling.