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Xiaohua Huang 

Xiaohua (IEEE Senior Member, CCF Member, ACM Member) is a Professor in Nanjing Institute of Technology. He was a visiting post-doctoral researcher in University of Cambridge in 2018. He had worked as a senior researcher with Professor Matti Pietikäinen (IEEE Fellow, IAPR Fellow) and Professor Guoying Zhao (IEEE Fellow), at the Center for Machine Vision and Signal Analysis (CMVS), Faulty of Information Technology and Electrical Engineering (ITEE) in University of Oulu. He had been a visiting scholar in University of Canberra in 2015.

In 2017-2018, his research on emotion recognition based on deep learning has been supported by Jorma Ollila Grant 2017 of Nokia Foundation. Recently, he has received two research fundings from Finnish Cultural Foundation (Central Fund) and Kaute Foundation (AI Fund) for supporting his research on micro-expression analysis.. He has participated into two Tekes funded projects ("Facial Behavior Analysis 'in-the-wild'" and "Quantifying human experience for increased intelligence within work teams and in the customer interface"). 

He was a research assistant in Southeast University (China), supervised by Professor Wenming Zheng, and then became a PhD student, supervised by Professor Matti Pietikäinen (IEEE Fellow, IAPR Fellow) and Professor Guoying Zhao (IEEE Fellow) in the Center for Machine Vision Research (CMV) at University of Oulu. During his doctoral studying, he was financially supported by the Senior Research Fellow Start-up package (Infotech Oulu, Principle Investigator: Prof. Zhao), Computer Vision for Continuous Emotion State Analysis (Academy of Finland, Principle Investigator: Prof. Zhao) and Tekes Project (AFFECT: multimodal emotion recognition for affective computing, 2011.08-2013.07). He was also awarded external funding from Nokia Scholarship by Nokia Foundation (2012, 2013),  IEEE Biometric Travel Grant 2013 and Infotech Oulu Doctoral Scholarship (2014),. 

His current research focus on human behavior analysis (facial expression, micro-expression, group-level emotion recognition and pain intensity estimation), affective computing (speech, multi-modal emotion recognition, cross-database emotion recognition), face recognition and texture analysis. He has also focused on machine learning, computer vision and deep learning with applications to affective computing and human behavior analysis.