MARS
Research Field
I received the Ph.D. degree in computer science from National Tsing Hua University (Taiwan). Before I worked in Academia Sinica and National Chiayi University. Now I join National Chung Cheng University. My research interests include information retrieval, multimedia indexing, data mining and computer vision.
Our lab name is "Multimodal Analysis, Retrieval, and Search (MARS)." MARS focuses on advancing technologies that enable machines to understand, index, and retrieve information from diverse data sources. Our research integrates methods from information retrieval, multimedia indexing, data mining, and computer vision to address the growing challenges of managing large-scale, heterogeneous, and multimodal datasets.
We explore innovative solutions for effective and efficient retrieval across multiple data modalities, such as text, images, audio, and video. By combining deep learning techniques with traditional retrieval models, our work aims to bridge the semantic gap between different data types and improve user-centric search experiences. Our current projects span from cross-modal retrieval and recommender systems to large-scale data mining and visual understanding tasks.
Through interdisciplinary collaboration and applied research, our lab strives to develop scalable and robust algorithms that push the boundaries of multimodal information processing, contributing to both academic advancements and real-world applications.
Our research focuses on developing advanced algorithms and systems in the areas of approximate nearest neighbor search, recommender systems, and cross-modal retrieval. These topics are closely interconnected, addressing the fundamental challenges of efficiently processing, understanding, and retrieving relevant information from large-scale and diverse datasets. We aim to contribute to both theoretical advancements and practical solutions for next-generation information systems in various applications, such as agriculture and manufacturing.
Council of Agriculture Smart Agriculture Digital Twin Innovation Competition, 2022, Finalist Award.
National Chiayi University College of Science and Engineering Creative Project Competition, 2022, Gold Award.
Chiayi City AIoT Makerthon Design Application Competition, 2020, Second Place.
Image Processing and Pattern Recognition Society (IPPR) of the Republic of China, 2019, Ph.D. Honorable Mention Paper Award.
Computer Graphics Workshop (CGW), 2018, Honorable Mention Paper Award.
Outstanding Part-time Administrative Faculty Award, National Chiayi University, 2018.
Ministry of Science and Technology (MOST) Excellent Young Scholar Research Project, 2015-2017.
Industrial Technology Research Institute (ITRI) Quality Patent Award, 2009.
D. E., Computer Science, National Tsing Hua University, 2004.
M. E., Computer Science and Information Engineering, National Taiwan University, 1999.
B. B. A., Information Management, National Taiwan University, 1997.
2 Vacancies
Job Description
- Conduct research on Approximate Nearest Neighbor (ANN) search algorithms and tools, including graph-based methods, quantization techniques, learning-to-index approaches, FAISS, and Weaviate.
- Develop and optimize ANN-based recommender systems, improving personalized recommendations for large-scale datasets.
- Explore cross-modal retrieval techniques, enabling efficient matching between different modalities (e.g., text-image, audio-video, or genetic-phenotypic data).
- Apply deep learning and machine learning techniques to improve retrieval accuracy and search efficiency.
- Publish research findings in top-tier conferences and journals in machine learning, information retrieval, and data mining (e.g., NeurIPS, ICML, CVPR, SIGIR, WWW, KDD).
- Develop open-source libraries and software tools for large-scale ANN search.
- Participate in grant writing, research proposals, and academic collaborations.
Preferred Intern Education Level
Undergraduate, Master or Ph.D. students in Computer Science, Machine Learning, Data Science, Information Retrieval, or a related field.
Skill sets or Qualities
- Proficiency in machine learning and deep learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).
- Experience with ANN search techniques, such as LSH, HNSW, IVF-PQ, or deep learning-based indexing.
- Ability to work independently and in a team-oriented research environment.