Invited Keynote Speakers
Professor, Institute of Computing Technology
Chinese Academy of Sciences, China
Keynote Title: FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
Abstract: With the rapid development of computing technology, wearable devices such as smart phones and wristbands make it easy to get access to people’s health information including activities, sleep, sports, etc. Smart healthcare achieves great success by training machine learning models on large quantity of user data. However, there are two critical challenges. Firstly, user data often exists in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Secondly, the models trained on the cloud fail on personalization. In this talk, I will introduce FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds personalized models by transfer learning. It is able to achieve accurate and personalized healthcare without compromising privacy and security. Experiments demonstrate that FedHealth produces higher accuracy (5.3% improvement) for wearable activity recognition when compared to traditional methods. FedHealth is general and extensible and has the potential to be used in many healthcare applications.
Speaker’s Bio: Dr. Yiqiang Chen received the BS and MS degrees in computer science from Xiangtan University, Xiangtan, China, in 1996 and 1999, respectively, and the PhD degree in computer science from the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, in 2003. In 2004, he was a visiting scholar researcher with the Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST), Hong Kong. He is currently a professor and the director of the Pervasive Computing Research Center, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS). His research interests include artificial intelligence, pervasive computing, wearable computing, and human computer interaction. He is a senior member of the IEEE. He has a lot of publications on several top journals and conferences, including Science (supplementary), TKDE, TNN, Scientific Reports, Scientific Data, IJCAI, AAAI UbiComp, ACM MM, ICDM. His work on wearable and transfer learning was awarded the best paper of GameNets 2014, PlatCon 2015, and ICCSE 2018. Dr. Chen is the associate editor (AE) of several journals such as IEEE Trans. On Emerging Trend on Computational Intelligence, International Journal of Machine Learning and Cybernetics, and IEEE Access. He is also the TPC chair (member) of IJCAI 2019, ISWC 2018, PerCom 2017, AAAI 2015 TPC Member, ICCSE 2017, and ICAA 2018. He is the chair of IEEE UIC 2019, PCC 2010, PCC 2017, and CSCC 2019.
Associate Professor, School of Social Sciences
Nanyang Technological University, Singapore
Keynote Title: Behavioral and Cognitive Neuroscience of Parkinson’s Disease
Abstract: Parkinson’s Disease (PD) is a neurodegenerative disease that is characterized by movement disorders. It is clinically defined by the presence of bradykinesia (slowness in movement), at least one additional cardinal motor feature (rigidity or rest tremor), and additional criteria. It is prevalent in aged populations, more than 3% in those above 80 years old globally. As the society is approaching an aged population, we will expect more social and family stress brought by this disease. What are the early diagnostic signals? What are the mechanisms? What are the treatments and preventions? Supported by the prevalent evidence of intraneuronal protein aggregation, α Synuclein, there are different hypothesis targeting to reveal the mechanism of PD, such as α Synuclein proteostasis, Prion-like propagation of α synuclein, Mitochondrial dysfunction, Oxidative stress, Neuroinflammation, and Motor circuit pathophysiology. Large genome-wide association studies have revealed some genes in heritable or sporadic PD. These might indicate an interplay among genetic, environment and personal responses. The diagnostic tests include imaging and genetic screenings, cerebrospinal fluid and blood tests. Early screening and prevention are in the demand of the current research. It has been suggested that there are non-motor symptoms before the onset of motor disorders. There are multiple treatments that include the Dopaminergic pharmacological targets, e.g. L DOPA, and Non-dopaminergic targets, e.g. deep brain stimulation for drug-resistant tremor patients and exercise-based treatments. In addition to these, nutrient, cognitive and stress management might be possible other supplementary treatments for early stage of PD. On the other hand, PD is one form of broad neurological diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and Hungtinton’s diseases (HD), spinocerebellar ataxias, amyotrophic lateral sclerosis and prion diseases, that affect movements, feelings, cognition and memory. Compelling evidence suggests that these diseases may share a common cause – the accumulation of misfolded protein aggregation triggers the synaptic and dendritic loss, neuronal apoptosis, and atypical brain inflammation. Therefore, it suggests common therapeutic strategies to be developed in the current and future research.
Speaker’s Bio: Dr. Hong Xu graduated from Peking University with a B.S in Psychology in 2000, the University of Chicago with a Master’s in Statistics in 2005 and a Ph.D in Psychology in 2007. After her postdoctoral training in Columbia University, she started the Visual Cognitive Neuroscience Lab at Nanyang Technological University in Singapore in 2009. The lab studies the perceptual, behavioral and neural response patterns of different age groups, including the aged populations. She started a new line of research on linking the neural mechanisms of visual perception and action, its applications in virtual reality and real environments, and implications for artificial intelligence. Her research is highly interdisciplinary: electrophysiology, psychophysics, behavioral and cognitive neuroscience, engineering, statistical and computational modeling.
Professor, School of Computer Science
University of Science & Technology of China, China
Keynote Title: Big-data Knowledge Engineering and Its Applications
Abstract: In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This presentation discusses big-data knowledge engineering, a knowledge engineering method that handles fragmented knowledge modeling and knowledge extraction, fusion on fragmented knowledge, and its applications on medical applications.
Speaker’s Bio: Dr. Huanhuan Chen received the BSc degree from the University of Science and Technology of China (USTC), Hefei, China, in 2004, and the PhD degree in computer science from the University of Birmingham, Birmingham, United Kingdom, in 2008. He is currently a professor in the School of Computer Science and Technology, USTC. His research interests include neural networks, Bayesian inference, and evolutionary computation. He received multiple academic rewards including the 2015 International Neural Network Society Young Investigator Award, the 2012 IEEE Computational Intelligence Society Outstanding PhD Dissertation Award, the 2009 IEEE Transactions on Neural Networks Outstanding Paper Award (bestowed in 2012), and the 2009 British Computer Society Distinguished Dissertations Award. He is an associate editor of the IEEE Transactions on Neural Networks and Learning Systems, and the IEEE Transactions on Emerging Topics in Computational Intelligence.