[12/2021] Dr. Liping Yang, director of the GeoAIR Lab, will be giving an invited talk from the Department of Geography, University of Zurich (Dec. 14). The talk topic is, “Advancing GIScience and Remote Sensing Using GeoAI and Geovisualization”.
[08/2021] Dr. Liping Yang, director of the GeoAIR Lab, will be giving an invited talk early this month from the Department of Infrastructure Engineering, The University of Melbourne. The talk topic is, “Advancing Computer Vision and Machine Learning Using Topological Graph-Based Representations and Methods”.
[12/2020] Dr. Liping Yang, director of the GeoAIR Lab, will give the oral presentation for the (active) deep learning paper , titled “Geographical feature classification from text using (active) convolutional neural networks” at the IEEE International Conference on Machine Learning and Applications (ICMLA) 2020 on Dec. 14! Check out the pre-recorded video HERE.
[12/2020] Dr. Liping Yang, director of the GeoAIR Lab, will give an invited talk from CMU, titled “Advancing Patent Image Data Analysis Using Topological Graph-Based Representations and Methods” at the AI & Patent Data Workshop on Dec. 9!
- The pre-recorded very inspiring talk video can be found at HERE, where Dr. Yang introduces her recently proposed very intuitive but very powerful new image representation to advance machine vision, followed with three applications that serve as effective demonstration of the usage of the novel image representation.
[06/2020] The DIRA workshop at CVPR 2020 that Liping is primary organizing will take place on June 14! We have 7 fantastic keynotes given by top computer vision and machine learning researchers from USA and UK (including MIT, Stanford, Georgia Tech, IBM, Facebook, U of Pittsburgh and U of Edinburgh). Check out the detailed schedule at HERE.
[06/2020] Liping is co-organizing ICMLA2020 Special Session: “AI with Geographic Information Systems for Social Good“.
[05/2020] GeoAIR Lab (Geospatial Artificial Intelligence Research and Visualization Lab), directed by Liping, has launched!
[04/2020] Liping Yang has two papers (one led by Liping and one advised by Liping) accepted at IEEE CVPR 2020 Workshop on Diagram Image Retrieval and Analysis!
[03/2020] Liping gave an invited talk, titled “Advancing Machine Learning and Machine Vision Using Topological Graph-Based Representations, Methods, and Algorithms”, Computer Science Seminar, Department of Computer Science, University of New Mexico, Albuquerque, NM !
[01/2020] Liping has started her professorial career journey.
[01/2020] Liping’s CVPR 2020 DIRA workshop calls for papers.
[11/2019] Liping is offering PhD and Master positions in GIScience and GeoAI, check out HERE for more details and how to apply.
[10/2019] Liping’s CVPR 2020 workshop proposal on image retrieval and analysis has been accepted!
[10/2019] Liping gave an invited talk, titled “Advancing Machine Learning and Machine Vision Using Topological Graph-Based Representations, Methods, and Algorithms”, at the New Mexico Big Data and Analytics Summit Conference in Albuquerque, NM !
[09/2019] Our paper (advised by Liping), titled “A topological graph-based representation for denoising low quality binary images“, has been accepted by the ICCV 2019 SGRL workshop !
[07/2019] Liping has received an exciting research grant about computer vision and machine learning funded by DOE LDRD program!
[06/2019] Our paper (led by Liping), titled “Image classification using topological features automatically extracted from graph representation of images” , has been accepted by the KDD 2019 MLG (the 15th International Workshop on Mining and Learning with Graphs) Workshop !
[04/2019] Our paper (led by Liping) , titled “A novel algorithm for skeleton extraction from images using topological graph analysis”, has been accepted by the CVPR 2019 Workshop Deep Learning for Geometric Shape Understanding!
[04/2019] Liping has accepted an offer — a tenure-track assistant professor at University of New Mexico — received in February 2019!
[03/2019] A paper led by Liping on using (deep) machine learning paper for flood detection, titled “Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event”, has been published in the Springer journal Soft Computing (indexed by Science Citation Index)!