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X-ORIGINAL-URL:https://www.fst.um.edu.mo
X-WR-CALDESC:Events for Faculty of Science and Technology | University of Macau
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BEGIN:VTIMEZONE
TZID:Asia/Macau
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:CST
DTSTART:20190101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190703T103000
DTEND;TZID=Asia/Macau:20190703T113000
DTSTAMP:20260611T233740
CREATED:20190703T023021Z
LAST-MODIFIED:20220927T042635Z
UID:5906-1562149800-1562153400@www.fst.um.edu.mo
SUMMARY:Extrapolation multigrid methods for solving large linear system arising from the discretizations of PDEs
DESCRIPTION:Instructors/Speakers\nProf. Kejia PAN\nProfessor\nSchool of Mathematics and Statistics\nCentral South University\nChina \nAbstract\nThe multigrid method is an efficient iterative method for solving discrete partial differential equations. The geometric multigrid method has the nested mesh required for Richardson extrapolation. We propose two extrapolation multigrid methods: extrapolation cascadic multigrid method (EXCMG)\, extrapolation full multigrid method (EXFMG)\, and show the superoptimality of the EXCMG method for solving second-order elliptic problems. Finally\, some numerical experiments include second-order and fourth-order elliptic problems and fractional diffusion equations are given to show the efficiency of the EXCMG method. \nBiography\nProf. Pan is a professor and the vice dean of School of Mathematics and Statistics\, Central South University\, China. He got his PhD degree in Fudan University in 2009. His research area include multigrid method\, finite difference method for solving nonlinear PDEs\, and finite element methods. \n 
URL:https://www.fst.um.edu.mo/event/extrapolation-multigrid-methods-for-solving-large-linear-system-arising-from-the-discretizations-of-pdes/
LOCATION:E11-2027
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190704
DTEND;VALUE=DATE:20190706
DTSTAMP:20260611T233740
CREATED:20190704T095043Z
LAST-MODIFIED:20220927T042635Z
UID:5830-1562198400-1562371199@www.fst.um.edu.mo
SUMMARY:Deep Learning and Computer Vision (DeepVision) Summer Camp 2019
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/deep-learning-and-computer-vision-deepvision-summer-camp-2019/#new_tab
CATEGORIES:activities,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190704
DTEND;VALUE=DATE:20190707
DTSTAMP:20260611T233740
CREATED:20190704T094923Z
LAST-MODIFIED:20220927T042634Z
UID:5827-1562198400-1562457599@www.fst.um.edu.mo
SUMMARY:Sustainable Energy Storage Systems Summer Camp 2019
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/sustainable-energy-storage-systems-summer-camp-2019/#new_tab
CATEGORIES:activities,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190704T100000
DTEND;TZID=Asia/Macau:20190704T110000
DTSTAMP:20260611T233740
CREATED:20190704T025839Z
LAST-MODIFIED:20220927T042634Z
UID:5904-1562234400-1562238000@www.fst.um.edu.mo
SUMMARY:Extremely Low Order Explosive Models from Combustion Process
DESCRIPTION:Instructors/Speakers\nProf. Yufeng XU\nAssociate Professor\nSchool of Mathematics and Statistics\nCentral South University\nChina \nAbstract\nIn this talk\, we will review some explosive models from combustion theory. These models do not satisfy global Lipschitz condition in general\, therefore nonexistence or multiplicity of classic solution are of extensive interests. We shall study a class of generalized explosive model with extremely low order temporal fractional derivative. Blowup phenomenon is theoretically analyzed and numerical simulation is carried out via a mixed numerical method based on adaptive finite difference and discontinuous Galerkin method. It is shown that the size of spatial domain cannot be too small\, for guaranteeing the appearance of explosion of solution\, which coincides with the physical observation. \nBiography\nProf. Xu is an associate professor of School of Mathematics and Statistics\, Central South University\, China. He got his PhD degree in Central South University in 2014. His research area include finite difference method and finite element methods for solving nonlinear fractional PDEs. \n 
URL:https://www.fst.um.edu.mo/event/extremely-low-order-explosive-models-from-combustion-process/
LOCATION:E11-2027
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190708
DTEND;VALUE=DATE:20190710
DTSTAMP:20260611T233740
CREATED:20190708T094721Z
LAST-MODIFIED:20220927T042357Z
UID:5825-1562544000-1562716799@www.fst.um.edu.mo
SUMMARY:Robotics and Artificial Intelligence Summer Camp 2019
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/robotics-and-artificial-intelligence-summer-camp-2019/#new_tab
CATEGORIES:activities,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190710T103000
DTEND;TZID=Asia/Macau:20190710T113000
DTSTAMP:20260611T233740
CREATED:20190710T023045Z
LAST-MODIFIED:20220927T042357Z
UID:5900-1562754600-1562758200@www.fst.um.edu.mo
SUMMARY:CN-WSGD schemes and CN-EWSGD schemes for space-fractional advection-diffusion equations
DESCRIPTION:Instructors/Speakers\nProf. Furong LIN\nProfessor\nDepartment of Mathematics\nShantou University\nChina \nAbstract\nWe consider high order finite difference schemes for one-dimensional space-fractional advection-diffusion equation (SFADE). The temporal derivative is approximated by the Crank-Nicolson (CN) scheme and the space fractional derivatives are approximated by the weighted and shifted Gr\”{u}nwald difference (WSGD) scheme. In general WSGD schemes have second order accuracy\, and by selecting a special parameter\, we get the third order accuracy scheme. However\, the third order scheme may not be stable. In this talk\, some results on the accuracy and the stability of CN-WSGD schemes are reported. \nBiography\nProf. Lin is a professor of Department of Mathematics\, Shantou University\, China. He got his PhD degree in Hong Kong University in 1995. His research area include numerical linear algebra\, fast algorithms for Toeplitz matrix\, numerical methods for PDEs. \n 
URL:https://www.fst.um.edu.mo/event/cn-wsgd-schemes-and-cn-ewsgd-schemes-for-space-fractional-advection-diffusion-equations/
LOCATION:E11-2027
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190713
DTEND;VALUE=DATE:20190720
DTSTAMP:20260611T233740
CREATED:20190713T094533Z
LAST-MODIFIED:20220927T042356Z
UID:5822-1562976000-1563580799@www.fst.um.edu.mo
SUMMARY:科技學院『第一屆內地優秀大學生夏令營交流活動』
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/ugsummercamp2019/#new_tab
CATEGORIES:activities,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190715
DTEND;VALUE=DATE:20190718
DTSTAMP:20260611T233740
CREATED:20190715T094004Z
LAST-MODIFIED:20220927T042356Z
UID:5815-1563148800-1563407999@www.fst.um.edu.mo
SUMMARY:Climate Change and Civil Engineering Summer Camp 2019
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/climate-change-and-civil-engineering-summer-camp-2019/#new_tab
CATEGORIES:activities,cee_events,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190716T110000
DTEND;TZID=Asia/Macau:20190716T120000
DTSTAMP:20260611T233740
CREATED:20190716T040859Z
LAST-MODIFIED:20190716T040859Z
UID:6515-1563274800-1563278400@www.fst.um.edu.mo
SUMMARY:The Stator-PM Brushless Machines and Potential Applications
DESCRIPTION:Instructors/Speakers\nProf. Wei HUA\nProfessor\nSchool of Electrical Engineering\, Southeast University\nNanjing\, China \nAbstract\nThe stator-PM brushless machines employ both permanent magnets (PMs) and armature windings in stator\, and the rotor is only made of salient iron laminations. Hence\, this type of machine has attracted considerable interests due to the advantages of brushless\, simple and robust structure\, high torque (power) density\, strong anti-demagnetization capability\, high efficiency\, and flexible topologies. In this presentation\, an overview of stator-PM brushless machines\, including basic topology\, operation principle\, performance analysis\, and control strategies\, especially for the potential applications. \nBiography\nProf. Wei HUA was born in Taizhou\, China\, in 1978. He received the B.Sc. and Ph.D. degrees in electrical engineering from Southeast University\, Nanjing\, China\, in 2001 and 2007\, respectively. From September 2004 to August 2005\, he visited the department of Electronics and Electrical Engineering\, The University of Sheffield\, UK\, as a Joint-Supervised Ph. D student. Since 2007\, he has been with Southeast University\, where he is currently a Professor with the School of Electrical Engineering. Prof. Wei HUA is an expert in the area of design\, analysis\, and control of electrical machines. He is the recipient of National Distinguished Youth Science Foundation\, Changjiang Scholars Program (Youth Scholars) of MOE and National Excellent Youth Science Foundation. He is the author or coauthor of over 150 technical papers\, and he is the holder of 50 patents. \n 
URL:https://www.fst.um.edu.mo/event/the-stator-pm-brushless-machines-and-potential-applications/
LOCATION:N21-2006
CATEGORIES:ece_events
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190724
DTEND;VALUE=DATE:20190726
DTSTAMP:20260611T233740
CREATED:20190724T092853Z
LAST-MODIFIED:20220927T042356Z
UID:5804-1563926400-1564099199@www.fst.um.edu.mo
SUMMARY:3D Reconstruction with Quadcopters Summer Camp 2019
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/3d-reconstruction-with-quadcopters-summer-camp-2019/#new_tab
CATEGORIES:activities,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190724T163000
DTEND;TZID=Asia/Macau:20190724T173000
DTSTAMP:20260611T233740
CREATED:20190724T040553Z
LAST-MODIFIED:20190724T040553Z
UID:6513-1563985800-1563989400@www.fst.um.edu.mo
SUMMARY:Cognitive Radios: A New Solution to a Not So New Problem
DESCRIPTION:Instructors/Speakers\nProf. Kai-Kit WONG\nChair Professor\nDepartment of Electronic and Electrical Engineering\nUniversity College London\nUK \nAbstract\nFor mobile communications networks\, there is a need for spectrum regulators to find ways to more intelligently and flexibly utilise the precious spectrum. Indeed\, the concept of cognitive radio which was first introduced by Joseph Mitola III in 1998\, aimed to do exactly that. Over the past decade since his work\, scientists and engineers attempted to adopt a game-theoretic approach to tackle the problem of autonomous spectrum sharing by cognitive radios. The results are unfortunately mostly negative reconfirming the fact that competition among selfish individuals is bound to result in the tragedy of the commons\, with “cognitive” radios still excessively interfering with each other without a proper reconciliation mechanism. In this talk\, we consider the autonomous resource allocation problem for the orthogonal frequency-division multiple-access (OFDMA) interference channel where each user (i.e.\, cognitive mobile radio) can freely occupy any of the subcarriers in the network\, and is required to decide its own subcarrier allocation\, with only local channel state information. We use this network as an example\, to show that collective intelligence for a group of self-organising cognitive radios is possible\, and that cognitive radios can be empowered with forward-looking ability to negotiate with each other for the benefits of not only individuals but all. Simulation results demonstrate that in the example of autonomous OFDMA\, the proposed approach can achieve the network sum-rate that is extremely close to the optimal centralised solution. The talk is concluded by a discussion of extending the use of such approach to a hierarchical primary-secondary spectrum sharing network. \nBiography\nKai-Kit Wong received the BEng\, the MPhil\, and the PhD degrees\, all in Electrical and Electronic Engineering\, from the Hong Kong University of Science and Technology\, Hong Kong\, in 1996\, 1998\, and 2001\, respectively. After graduation\, he took up academic and research positions at the University of Hong Kong\, Lucent Technologies\, Bell-Labs\, Holmdel\, the Smart Antennas Research Group of Stanford University\, and the University of Hull\, UK. He is Chair in Wireless Communications at the Department of Electronic and Electrical Engineering\, University College London\, UK. \nHis current research centers around 5G and beyond mobile communications\, including topics such as massive MIMO\, full-duplex communications\, millimetre-wave communications\, edge caching and fog networking\, physical layer security\, wireless power transfer and mobile computing\, V2X communications\, and of course cognitive radios. There are also a few other unconventional research topics that he has set his heart on\, including for example\, fluid antenna communications systems\, remote ECG detection and etc. He is a co-recipient of the 2013 IEEE Signal Processing Letters Best Paper Award and the 2000 IEEE VTS Japan Chapter Award at the IEEE Vehicular Technology Conference in Japan in 2000\, and a few other international best paper awards. \nHe is Fellow of IEEE and IET and is also on the editorial board of several international journals. He has served as Senior Editor for IEEE Communications Letters since 2012 and also for IEEE Wireless Communications Letters since 2016. He had also previously served as Associate Editor for IEEE Signal Processing Letters from 2009 to 2012 and Editor for IEEE Transactions on Wireless Communications from 2005 to 2011. He was also Guest Editor for IEEE JSAC SI on virtual MIMO in 2013 and currently the Guest Editor for IEEE JSAC SI on physical layer security for 5G. \n 
URL:https://www.fst.um.edu.mo/event/cognitive-radios-a-new-solution-to-a-not-so-new-problem/
LOCATION:E11-G015
CATEGORIES:ece_events
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190725
DTEND;VALUE=DATE:20190727
DTSTAMP:20260611T233740
CREATED:20190725T092107Z
LAST-MODIFIED:20220927T042355Z
UID:5795-1564012800-1564185599@www.fst.um.edu.mo
SUMMARY:Smart transportation summer camp 2019
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/smart-transportation-summer-camp-2019/#new_tab
CATEGORIES:activities,eme_events,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190726T150000
DTEND;TZID=Asia/Macau:20190726T160000
DTSTAMP:20260611T233740
CREATED:20190726T070004Z
LAST-MODIFIED:20220927T042355Z
UID:5896-1564153200-1564156800@www.fst.um.edu.mo
SUMMARY:Computational protein design by accommodating flexibility and binding free energy in improving the affinity
DESCRIPTION:Instructors/Speakers\nProf. Vannajan Sanghiran Lee\nUniversity of Malaya\nMalaysia \nAbstract\nComputational structure-based protein design programs are becoming an increasingly important tool in molecular biology. The talk will review recent developments in algorithms for protein design\, emphasizing how novel algorithms enable the use of more accurate biophysical models. The focus is on the relationship between protein flexibility and binding free energy and some useful hints for understanding when\, and to what extent\, flexibility. Lessons learned using molecular dynamics simulations and gaussian network model in designing DARPins (designed ankyrin repeat proteins)\, a genetically engineered antibody mimetic proteins\, in HIV\, dengue\, and cancer targets will be discussed and concluded with a list of algorithmic challenges in computational protein design that we believe will be especially important for the design of therapeutic proteins. \nBiography\nAssoc. Prof. Dr. Vannajan Sanghiran Lee received her BSc (1994) in Chemistry from Chiang Mai University\, Thailand and PhD (2001) in Pharmaceutical Sciences and Physical Chemistry from University of Missouri-Kansas City\, USA under the scholarship from the Institute of Promotion and Development Science and Technology Project\, Thailand. After that she received the Post Doctoral Scholarship (2002) from the Thailand Research Fund and worked at the Computational Chemistry Unit Cell (CCUC)\, Chulalongkorn University\, Thailand. She worked as a lecturer and researcher in Computational Simulation and Modeling Laboratory (CSML)\, Department of Chemistry and Center for Innovation in Chemistry\, Chiang Mai University\, Chiang Mai\, Thailand from 2001-2011. In 2010\, she joined the school of pharmaceutical sciences\, University Sains Malaysia as a visiting researcher. She presently works as a Assoc. Prof. at Department of chemistry\, University of Malaya and as deputy head of Center of Theoretical and Computational Physics (TCP). Her present research interest includes computer-aided molecular modeling and computational chemistry using Molecular Dynamics (MD)\, Monte Carlo Simulations (MC)\, Quantum Mechanics (QM)\, Data Analytics and Machine Learning in diverse research and development fields such as biomolecular/material design. \n 
URL:https://www.fst.um.edu.mo/event/computational-protein-design-by-accommodating-flexibility-and-binding-free-energy-in-improving-the-affinity/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190728
DTEND;VALUE=DATE:20190802
DTSTAMP:20260611T233740
CREATED:20190728T090127Z
LAST-MODIFIED:20220927T042355Z
UID:5785-1564272000-1564703999@www.fst.um.edu.mo
SUMMARY:科技學院『第一屆內地優秀大學生夏令營交流活動』
DESCRIPTION:
URL:/academics/summer-camp/summer-camp-2019/ugsummercamp2019/#new_tab
CATEGORIES:activities,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190809T163000
DTEND;TZID=Asia/Macau:20190809T173000
DTSTAMP:20260611T233740
CREATED:20190809T083024Z
LAST-MODIFIED:20220927T042355Z
UID:5889-1565368200-1565371800@www.fst.um.edu.mo
SUMMARY:Computer Vision++: The Next Step Towards Big AI
DESCRIPTION:Instructors/Speakers\nProf. Jiebo LUO\nUniversity of Rochester\, the USA \nAbstract\nWith the huge successes of deep learning in computer vision\, many computer vision problems are seemingly being solved. Where do we go from here? We will discuss a few directions where computer vision can be either further pushed to deal with data scarcity and data noise\, or synergistically integrated with other disciplines such as NLP and data mining\, to continue to advance the frontiers of artificial intelligence. \nBiography\n\n\n\nProfessor Jiebo Luo joined the University of Rochester in 2011 after a prolific career of fifteen years at Kodak Research Laboratories. He has been involved in numerous technical conferences\, including serving as the program co-chair of ACM Multimedia 2010\, IEEE CVPR 2012\, ACM ICMR 2016\, and IEEE ICIP 2017. He has served on the editorial boards of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)\, IEEE Transactions on Multimedia (TMM)\, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)\, IEEE Transactions on Big Data (TBD)\, ACM Transactions on Intelligent Systems and Technology (TIST)\, Pattern Recognition\, Knowledge and Information Systems (KAIS)\, Machine Vision and Applications\, and Journal of Electronic Imaging. He is a Fellow of the ACM\, AAAI\, IEEE\, SPIE and IAPR. He is a Data Science Distinguished Researcher with the New York State CoE Goergen Institute for Data Science. \n\n\n\n 
URL:https://www.fst.um.edu.mo/event/computer-vision-the-next-step-towards-big-ai/
LOCATION:E12-G004
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190810
DTEND;VALUE=DATE:20190817
DTSTAMP:20260611T233740
CREATED:20190810T072151Z
LAST-MODIFIED:20220927T042354Z
UID:5723-1565395200-1565999999@www.fst.um.edu.mo
SUMMARY:The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)
DESCRIPTION:
URL:https://www.ijcai19.org/#new_tab
CATEGORIES:conferences,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190812T103000
DTEND;TZID=Asia/Macau:20190812T113000
DTSTAMP:20260611T233740
CREATED:20190812T023002Z
LAST-MODIFIED:20220927T042354Z
UID:5885-1565605800-1565609400@www.fst.um.edu.mo
SUMMARY:On the Normalized Similarity and Distance Metrics
DESCRIPTION:Instructors/Speakers\nProf. Kaizhong ZHANG\nUniversity of Western Ontario\, London\, Ontario\, Canada \nAbstract\nSimilarity and distance metrics are widely used in many research areas and applications. In some applications\, similarity or distance metrics normalized with the “size” of the objects being measured are required. In this talk\, we will first present a formal definition of similarity metric and then show general solutions to normalize a given similarity or distance metric. Examples and applications of the general solutions will also be presented. \nBiography\nK. Zhang received the M.S. degree in mathematics from Peking University\, Beijing\, China\, in 1981\, and the M.S. and Ph.D. degrees in computer science from the Courant Institute of Mathematical Sciences\, New York University\, New York\, USA\, in 1986 and 1989\, respectively. \nHe is currently a professor in the Department of Computer Science\, University of Western Ontario\, London\, Ontario\, Canada. His research interests include bioinformatics\, algorithms\, image processing and databases. \n  \n 
URL:https://www.fst.um.edu.mo/event/on-the-normalized-similarity-and-distance-metrics/
LOCATION:E11-1006 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190812T110000
DTEND;TZID=Asia/Macau:20190812T120000
DTSTAMP:20260611T233740
CREATED:20190812T030053Z
LAST-MODIFIED:20220927T042354Z
UID:5872-1565607600-1565611200@www.fst.um.edu.mo
SUMMARY:Aspect-level Sentiment Analysis: Techniques and Datasets
DESCRIPTION:Instructors/Speakers\nProf. Min YANG\nShenzhen Institutes of Advanced Technology\nChinese Academy of Sciences\nChina \nAbstract\nMin Yang is currently an assistant professor at Shenzhen Institutes of Advanced Technology\, Chinese Academy of Sciences. She received her Ph.D. degree from the department of computer science\, the University of Hong Kong in 2017. Her current research interests include natural language processing\, data mining\, recommendation systems. Dr. Yang has more than 70 international\, peer-reviewed publications on top-tier conferences or journals\, such as ACL\, SIGIR\, WWW\, KDD\, AAAI\, IJCAI\, TKDE\, TMM\, etc. \nBiography\na \n 
URL:https://www.fst.um.edu.mo/event/aspect-level-sentiment-analysis-techniques-and-datasets/
LOCATION:E11-1009 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190814T090000
DTEND;TZID=Asia/Macau:20190814T094500
DTSTAMP:20260611T233740
CREATED:20190814T010017Z
LAST-MODIFIED:20220927T042353Z
UID:5868-1565773200-1565775900@www.fst.um.edu.mo
SUMMARY:Neural Graph Matching and Beyond
DESCRIPTION:Instructors/Speakers\nProf. Junchi YAN\nShanghai Jiao Tong University\nChina \nAbstract\nIn this talk\, I will first give a brief introduction on graph matching\, which is a combinatorial problem in nature. Then we will show two deep network based pipelines for addressing the graph matching problem via deep learning. The models involve learning of the association based graph node embedding\, cross-graph affinity learning\, and a Sinkhorn layer for solving the linear assignment task\, etc. We will also discuss some works on joint matching and link prediction among two or multiple graphs. In the end\, some discussion will be given on the future work and outlook for connecting graph matching with machine learning. \nBiography\nDr. Junchi Yan is currently an Independent Research Professor (PhD Advisor) with the Department of Computer Science and Engineering\, Shanghai Jiao Tong University. He is also affiliated with The Artificial Intelligence Institute of SJTU and an adjunct professor with the School of Data Science\, Fudan University. Before that\, he was a Research Staff Member with IBM Research – China where he started his career since April 2011. He obtained a Ph.D. at the Department of Electronic Engineering from Shanghai Jiao Tong University\, China. His work on graph matching received the ACM China Doctoral Dissertation Nomination Award and China Computer Federation Doctoral Dissertation Award. His research interests are machine learning\, data mining and computer vision. He serves as an Associate Editor for IEEE ACCESS\, (Managing) Guest Editor for IEEE Transactions on Neural Network and Learning Systems\, Pattern Recognition Letters\, Pattern Recognition\, Vice Secretary of China CSIG-BVD Technical Committee\, and on the executive board of ACM China Multimedia Chapter. He has published 50+ peer reviewed papers in top venues in AI and has filed 20+ US patents. He has won the Distinguished Young Scientist of Scientific Chinese for year 2018. \n 
URL:https://www.fst.um.edu.mo/event/neural-graph-matching-and-beyond/
LOCATION:E11-G015 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190814T094500
DTEND;TZID=Asia/Macau:20190814T103000
DTSTAMP:20260611T233740
CREATED:20190814T014539Z
LAST-MODIFIED:20220927T042353Z
UID:5866-1565775900-1565778600@www.fst.um.edu.mo
SUMMARY:Learning to Build a New Reality
DESCRIPTION:Instructors/Speakers\nProf. Jingyi YU\nShanghaiTech University\nChina \nAbstract\nThere have been tremendous advances on applying deep learning techniques for 2d image understanding. In contrast\, very little work has focused on employing deep learning for modeling datasets beyond 2D such as 3D geometry and 4D light fields. In this talk\, I present several latest works from our group on in this exciting new arena\, with a focus on their applications to virtual and augmented reality and computational photography. I first present a novel deep surface light field (DSLF) technique. A surface light field represents the radiance of rays originating from any points on the surface in any directions. Traditional approaches require ultra-dense sampling to ensure the rendering quality. Our DSLF works on sparse data and automatically filling in the missing data by leveraging different sampling patterns across the vertices and at the same time eliminates redundancies due to the network’s prediction capability. For real data\, we address the image registration problem as well as conduct texture-aware remeshing for aligning texture edges with vertices to avoid blurring. Next\, I present an end-to-end deep learning scheme to establish dense shape correspondences and subsequently compress 3d dynamic human bodies. Our approach uses sparse set of “panoramic” depth maps or PDMs\, each emulating an inward-viewing concentric mosaics (CM). We then develop a learning-based technique to learn pixel-wise feature descriptors on PDMs. The results are fed into an autoencoder-based network to achieve ultra-high compression ratio. \nBiography\nJingyi Yu is currently a Full Professor and Associate Dean of the School of Information Science and Technology at ShanghaiTech University. He is also affiliated with the Department of Computer and Information Sciences at University of Delaware. He received B.S. from Caltech in 2000 and Ph.D. from MIT in 2005. He has published over 120 papers at highly refereed conferences and journals\, and holds over 10 international patents on computational imaging. His research interests span a range of topics in computer vision and computer graphics\, especially on computational photography and non-conventional optics and camera designs. He is a recipient of the NSF CAREER Award and the AFOSR YIP Award\, and has served as an area chair of many international conferences including CVPR\, ICCV\, ECCV\, ICCP and NIPS. He is currently an Associate Editor of IEEE TPAMI\, IEEE TIP\, and Elsevier CVIU\, and will be program chair of ICPR 2020 and IEEE CVPR 2021. \n 
URL:https://www.fst.um.edu.mo/event/learning-to-build-a-new-reality/
LOCATION:E11-G015 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190814T103000
DTEND;TZID=Asia/Macau:20190814T111500
DTSTAMP:20260611T233740
CREATED:20190814T023011Z
LAST-MODIFIED:20220927T042352Z
UID:5862-1565778600-1565781300@www.fst.um.edu.mo
SUMMARY:Towards Intelligent Perception of Facial Images from End to End
DESCRIPTION:Instructors/Speakers\nProf. Junliang XING\nInstitute of Automation\, Chinese Academy of Sciences\nChina \nAbstract\nFace is perhaps the most important visual object in computer vision\, with extensive studies in the past decades. In the deep learning era\, the performances of computer vision problems related to faces have been significantly boosted\, many of which have already met the requirements in real-world applications. In the talk\, I will first make some basic introductions on the face vision problems\, including face detection\, face alignment\, face tracking\, face attribute analyses and face recognition. Then I will introduce some of our previous works related to this topic. At last\, I will point out some future trends in this direction. The main objective of this talk is to help the audiences get a comprehensive understanding of this relatively mature yet still hot research direction in computer vision. \nBiography\nDr. Junliang XING received his dual B.E. degrees in Computer Science and Applied Mathematics from Xi’an Jiaotong University\, 2007\, and his Ph.D. degree in Computer Science and Technology from Tsinghua University\, 2012. After that\, he became an assistant professor within the National Laboratory of Pattern Recognition\, Institute of Automation\, Chinese Academy of Sciences\, where he is now a Professor and master student supervisor. Dr. Xing has published over 100 papers in peer-reviewed international conferences like ICCV\, CVPR\, ECCV\, ACM Multimedia\, AAAI\, IJCAI\, and journals like TPAMI\, IJCV\, TIP\, PR. He has translated two books in computer vision and wrote one book on deep learning. Dr. Xing was the recipient of Google PhD Fellowship in 2011\, the Best Paper Award of ACM International Conference on Multimedia in 2013\, and the champions of many international AI technical competitions in face recognition\, pose estimation\, etc. His main research areas lie in pattern recognition\, computer vision\, and machine learning\, with a main focus on vision problems related to human faces and bodies. \n 
URL:https://www.fst.um.edu.mo/event/towards-intelligent-perception-of-facial-images-from-end-to-end/
LOCATION:E11-G015 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190814T103000
DTEND;TZID=Asia/Macau:20190814T113000
DTSTAMP:20260611T233740
CREATED:20190814T023049Z
LAST-MODIFIED:20220927T042352Z
UID:5864-1565778600-1565782200@www.fst.um.edu.mo
SUMMARY:Error convolution structure in discrete Caputo derivatives and global consistency analysis
DESCRIPTION:Instructors/Speakers\nProf. Jiwei ZHANG\nSchool of Mathematics and Statistics\nWuhan University\nChina \nAbstract\nNonuniform time-stepping methods are promising for Caputo reaction sub-diffusion problems because they would be simple and effectiveness in resolving the initial singularity and other nonlinear behaviors occurred away from the initial time. Compared with traditional local methods for the first-order derivative\, the numerical analysis for nonlocal time-stepping schemes on non-uniform time meshes are challenging due to the convolution integral (nonlocal) form of fractional derivative. We develop a general framework for the stability and convergence analysis with three tools: a family of complementary discrete convolution kernels\, a discrete fractional Gronwall inequality (DFGI) and a global (convolutional) consistency analysis\, which is not limited to a specific time mesh by building a convolution structure of local truncation error. It seems that the present techniques are extendable to the variable-order\, distributed-order diffusion equations and other nonlocal-in-time diffusion problems. \nBiography\nProf Zhang is a Professor of School of Mathematics and Statistics\, Wuhan University. He got his PhD degree in Hong Kong Baptist University in 2009. His research area include fast algorithms for fractional PEDs and numerical methods for PDEs. \n 
URL:https://www.fst.um.edu.mo/event/error-convolution-structure-in-discrete-caputo-derivatives-and-global-consistency-analysis/
LOCATION:E11-1006
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190814T111500
DTEND;TZID=Asia/Macau:20190814T120000
DTSTAMP:20260611T233740
CREATED:20190814T031550Z
LAST-MODIFIED:20220927T042352Z
UID:5859-1565781300-1565784000@www.fst.um.edu.mo
SUMMARY:Multi-View Fusion and Representation for Image Analysis
DESCRIPTION:Instructors/Speakers\nProf. Zhe XUE\nBeijing University of Posts and Telecommunications\nChina \nAbstract\nReal-world data can be described from multiple views. For instance\, an image can be described by color histogram\, SIFT\, HOG and other features. The content of a Web page can be described by texts\, images and videos. Describing an object from multiple perspectives constitutes multi-view data. Multi-view learning is to use the complementary nature of different views to improve the learning performance than using a single view. In this talk\, I will first briefly introduce some basic concepts and issues in multi-view learning. Then I will introduce some of our recent multi-view learning works for image analysis including multi-view dimensionality reduction\, multi-view clustering and incomplete multi-view classification. This report aims to help the audience understand the basic tasks and latest developments of multi-view learning. \nBiography\nDr. Zhe Xue received his Ph\,D. degree in Computer Science from school of computer and control engineering\, University of Chinese Academy of Sciences (UCAS) in 2017. After that\, he became an assistant professor at school of computer science\, Beijing University of Posts and Telecommunications. His research interest is generally in machine learning and data mining\, and particularly in multi-view learning and image analysis. Dr. Xue has published over 20 papers in international conferences and journals such as AAAI\, IJCAI\, IEEE TCSVT\, CVIU\, Information Sciences. He has undertook and participated in many projects\, including National Key R&D Program of China\, 973 Program\, National Natural Science Foundation of China and so on. He is the reviewer of ACM MM\, DASFAA\, IEEE TIFS\, Multimedia Tools and Applications and other international journals and conferences. He is also a member of the Intelligent Service Committee of the Chinese Association for Artificial Intelligence. \n 
URL:https://www.fst.um.edu.mo/event/multi-view-fusion-and-representation-for-image-analysis/
LOCATION:E11-G015 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190814T151500
DTEND;TZID=Asia/Macau:20190814T160000
DTSTAMP:20260611T233740
CREATED:20190814T071525Z
LAST-MODIFIED:20220927T042351Z
UID:5854-1565795700-1565798400@www.fst.um.edu.mo
SUMMARY:Integrating Learning with Game Theory for Societal Challenges
DESCRIPTION:Instructors/Speakers\nProf. Fei Fang\nInstitute for Software Research\nSchool of Computer Science\nCarnegie Mellon University\nUSA \nAbstract\nThere is a rising interest in developing artificial intelligence-based tools to help address societal challenges. Motivated by these challenges\, we have proposed game theory and machine learning/reinforcement learning-based models and algorithms for problems with strategic interactions among agents. In this talk\, I will introduce our models and algorithms that have led to two successfully deploy applications: one used by US Coast Guard for protecting the Staten Island Ferry in New York City since April 2013\, the other used in multiple conservation areas around the world for anti-poaching effort. In addition\, I will highlight our most recent advances in integrating deep learning with game theory\, including computing equilibrium by learning from self-play and end-to-end learning of game parameters. \nBiography\nFei Fang is an Assistant Professor at the Institute for Software Research in the School of Computer Science at Carnegie Mellon University. Before joining CMU\, she was a Postdoctoral Fellow at the Center for Research on Computation and Society (CRCS) at Harvard University. She received her Ph.D. from the Department of Computer Science at the University of Southern California in June 2016. She received her bachelor degree from the Department of Electronic Engineering\, Tsinghua University in July 2011. Her research lies in the field of artificial intelligence and multi-agent systems\, focusing on integrating game theory and mechanism design with machine learning. Her work has been motivated by and applied to security\, sustainability\, and mobility domains\, contributing to the theme of AI for Social Good. Her work has won the Distinguished Paper at the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI’18)\, Innovative Application Award at Innovative Applications of Artificial Intelligence (IAAI’16)\, the Outstanding Paper Award in Computational Sustainability Track at the International Joint Conferences on Artificial Intelligence (IJCAI’15). Her dissertation is selected as the runner-up for IFAAMAS-16 Victor Lesser Distinguished Dissertation Award\, and is selected to be the winner of the William F. Ballhaus\, Jr. Prize for Excellence in Graduate Engineering Research as well as the Best Dissertation Award in Computer Science at the University of Southern California. Her work has been deployed by the US Coast Guard for protecting the Staten Island Ferry in New York City since April 2013. Her work has led to the deployment of PAWS (Protection Assistant for Wildlife Security) in multiple conservation areas around the world\, which provides predictive and prescriptive analysis for anti-poaching effort. \n 
URL:https://www.fst.um.edu.mo/event/integrating-learning-with-game-theory-for-societal-challenges/
LOCATION:N1-1004\, Centre for Innovation and Entrepreneurship\, Guest House\, University of Macau
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190814T163000
DTEND;TZID=Asia/Macau:20190814T173000
DTSTAMP:20260611T233740
CREATED:20190814T040035Z
LAST-MODIFIED:20250813T043827Z
UID:6508-1565800200-1565803800@www.fst.um.edu.mo
SUMMARY:AI: A Networking and Communication Perspective
DESCRIPTION:Instructors/Speakers\nProf. Quee Seng QUEK\nTenured Associate Professor\nSingapore University of Technology and Design (SUTD)\nSingapore \nAbstract\nRecent breakthroughs in artificial intelligence and machine learning\, including deep neural networks\, the availability of powerful computing platforms and big data are providing us with technologies to perform tasks that once seemed impossible. In the near future\, autonomous vehicles and drones\, intelligent mobile networks\, and intelligent internet-of-things (IoT) will become a norm. At the heart of this technological revolution\, it is clear that we will need to have artificial intelligence over a massively scalable\, ultra-high capacity\, ultra-low latency\, and dynamic new network infrastructure. In this talk\, we will provide a simple overview of AI for the perspective of networking and communications and share some interesting applications. In addition\, we will also share some of our preliminary works in this area. \nBiography\nTony Quek is IEEE Fellow\, IEEE ComSoc Distinguished Lecturer. He is also the Acting Head of Information System Technology and Design Pillar\, Sector Lead for SUTD AI Program\, and the Deputy Director of SUTD-ZJU IDEA. Singapore University of Technology and Design. His current research topics include wireless communications and networking\, big data processing\, network intelligence\, URLLC\, and IoT. \nTony Quek received the B.E. and M.E. degrees in Electrical and Electronics Engineering from Tokyo Institute of Technology\, Tokyo\, Japan\, respectively. At Massachusetts Institute of Technology (MIT)\, Cambridge\, MA\, he earned the Ph.D. in Electrical Engineering and Computer Science. Currently\, he is a tenured Associate Professor with the Singapore University of Technology and Design (SUTD). \nDr. Quek has been actively involved in organizing and chairing sessions and has served as a TPC member in numerous international conferences. He is serving as the General Chair for IEEE ICCC 2020. He is currently serving as the Chair of IEEE VTS Technical Committee on Deep Learning for Wireless Communications as well as an elected member of the IEEE Signal Processing Society SPCOM Technical Committee. He was an Executive Editorial Committee Member of the IEEE Transactions on Wireless Communications\, an Editor of the IEEE Transactions on Communications\, and an Editor of the IEEE Wireless Communications Letters. He is a co-author of a few books published by Cambridge University Press. \nDr. Quek received the 2008 Philip Yeo Prize for Outstanding Achievement in Research\, the 2012 IEEE William R. Bennett Prize\, the 2016 IEEE Signal Processing Society Young Author Best Paper Award\, 2017 CTTC Early Achievement Award\, 2017 IEEE ComSoc AP Outstanding Paper Award\, and 2016-2018 Clarivate Analytics Highly Cited Researcher. \n 
URL:https://www.fst.um.edu.mo/event/ai-a-networking-and-communication-perspective/
LOCATION:E11-1006
CATEGORIES:ece_events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190816T111500
DTEND;TZID=Asia/Macau:20190816T121500
DTSTAMP:20260611T233740
CREATED:20190816T031526Z
LAST-MODIFIED:20220927T040651Z
UID:5852-1565954100-1565957700@www.fst.um.edu.mo
SUMMARY:Gap probability in critical random matrix ensembles and the coupled Painleve II system
DESCRIPTION:Instructors/Speakers\nDr. Shuai-Xia XU\nLecturer\nSun Yat-sen University\nChina \nAbstract\nWe study the gap probabilities in the critical unitary invariant random matrix ensembles\, where the Painleve II and Painleve XXXIV kernels arise. By studying the Fredholm determinants of the Painleve II and Painlev XXXIV kernels\, we obtain integral expression of the gap probabilities by using solutions to the coupled Painleve II system. Moreover\, the large gap asymptotics are derived with the constant terms given explicitly in terms of the Riemann zeta-function. This talk is based on a joint work with Dan Dai from City University of HongKong. \nBiography\nProf. Xu Shuaixia got his PhD degree from Sun Yat-sen University in 2011. From 2011 to 2013\, he worked as a postdoctoral fellow at Sun Yat-sen and is currently a lecturer at Sun Yat-sen University. His research areas include nuclear science and technology-other disciplines of nuclear science and technology. \n 
URL:https://www.fst.um.edu.mo/event/gap-probability-in-critical-random-matrix-ensembles-and-the-coupled-painleve-ii-system/
LOCATION:E11-1006
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190909T103000
DTEND;TZID=Asia/Macau:20190909T233000
DTSTAMP:20260611T233740
CREATED:20190909T023011Z
LAST-MODIFIED:20220927T040651Z
UID:5849-1568025000-1568071800@www.fst.um.edu.mo
SUMMARY:History of the Generalized Inverse & Linear Least Squares
DESCRIPTION:Instructors/Speakers\nProf. Yimin WEI\nProfessor\nSchool of Mathematical Sciences\nFudan University\nChina \nAbstract\nIn this talk\, we will introduce the history of the generalized inverse and linear least squares problem. Some recent research in this area will be given. Possible future work will also be discussed. \nBiography\nProf. Yimin WEI is a professor in Fudan University\, China. He has published over 100 papers in mathematics. His recent interest includes matrix analysis\, least squares problems and study of multilinear systems. \n 
URL:https://www.fst.um.edu.mo/event/history-of-the-generalized-inverse-linear-least-squares/
LOCATION:E11-1035
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190911T143000
DTEND;TZID=Asia/Macau:20190911T153000
DTSTAMP:20260611T233740
CREATED:20190911T063058Z
LAST-MODIFIED:20220927T040650Z
UID:5823-1568212200-1568215800@www.fst.um.edu.mo
SUMMARY:Security design of IoT products
DESCRIPTION:Instructors/Speakers\nMr. Zexin JIANG\nGuangzhou Bonson Information System Co. Ltd. \nAbstract\n\n簡單自我介紹\n\n平凡的我出生在不平凡的地方\n不吃辣椒，卻孤身一路向北\n明明可以安穩舒適，卻非要折騰\n\n\n大四之後的求學\n\n沒有畢業合照的大四\n我是如何保送到清華進修的\n國家高鐵系統真的運行著我寫的代碼嗎\n深深地遺憾\n\n\n職場折騰\n\n內地壟斷國企是一種什麼樣的存在\n獨檔一面是一種什麼樣的感受\n如何避免當背鍋俠\n我在電網工作5年獲得的5個第一\n辭職之後我拿著兩份工資\n民企與國企的差異極大\n一年時間從產品經理到CTO\n技術、產品、工程有什麼區別，變現的誤區\n\n\n幾點感悟分享\n\n選擇比努力重要\n多做事少計較得失\n適當包裝自己\n風險管理與自我保護\n\n\n\nBiography\n廣東省普寧人，高級工程師职称，（ISC）2會員、全國電力運行與控制標準委員會標準工作組成員、廣東省電機工程學會成員、廣州市科技評審專家，獲得國家授權專利56項，發表論文12篇，出版專著1本，獲省級科技進步壹等獎、獲2018年廣州市“珠江科技新星”專項資助、2019年廣州市“產業急需緊缺人才”補貼。擁有國際註冊信息安全專家和國家重要信息系統保護人員等職業資格。 \n2004年-2008年，南京理工大學自動化專業本科學習（專業第1名，GPA 3.9/4.0）。參加全國大學生電子設計大賽獲國家二等獎、全國大學生智能汽車比賽獲得國家二等獎、江蘇省大學生機器人比賽獲得壹等獎、江蘇省優秀本科畢業論文壹等獎、獲得學校最高榮譽獎學金以及每年特等獎學金。 \n2008年-2011年，清華大學（免試）控制科學與工程專業碩士研究生。期間參與“十壹五”國家重大科技攻關（中國高鐵國產化裝備研發）項目以及和諧號CRH5動車組網絡控制系統的引進消化吸收創新的產品研發工作。研發了支持硬件在環的大規模分布式半實物仿真引擎，研發成果支撐應用於武廣、京津、北京地鐵等線路設備的上線前驗證。 \n2011年至2016年，廣東電網電力科學研究院（南方電網自動化重點實驗室），歷任助理專責、專責、技術專家、電力機器人攻關團隊組長。從事電力物聯網和電力監控安全防護體系領域的研究工作，主筆編制了南方電網電力監控系統最頂層安全防護技術規範和管理規範等多項企業標準，參與電力行業信息安全標準編制2項，推動商用密碼技術在電力行業的應用，相關成果於2012年獲得廣東省電力行業協會科技創新成果獎、於2013年南方電網青工創新創效成果壹等獎、2014年獲得南方電網科技進步壹等獎。2015年在南方電網全網信息安全攻防競賽獲第1名。 \n2016年至今，廣州邦訊信息系統有限公司（深交所股票代碼：300366），歷任產品經理、產品副總監、CTO。主要從事工業物聯網和物聯網安全相關產品研發管理工作。創建了公司商用密碼產品線，帶領團隊研發多款物聯網及安全產品，獲得國家密碼局型號認證和公安部檢測認證。 \n 
URL:https://www.fst.um.edu.mo/event/security-design-of-iot-products/
LOCATION:E11-1006 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190911T150000
DTEND;TZID=Asia/Macau:20190911T160000
DTSTAMP:20260611T233740
CREATED:20190911T070002Z
LAST-MODIFIED:20220927T040650Z
UID:5814-1568214000-1568217600@www.fst.um.edu.mo
SUMMARY:Image Authentication and Tamper Localization
DESCRIPTION:Instructors/Speakers\nProf. Yulin WANG\nFull Professor of Wuhan University\nChina \nAbstract\nImage authentication can be used in many fields\, including e-government\, e-commerce\, national security\, news pictures\, court evidence\, medical image\, engineering design\, and so on. Since some content-preserving manipulations\, such as JPEG compression\, contrast enhancement\, and brightness adjustment\, are often acceptable—or even desired—in practical application\, an authentication method needs to be able to distinguish them from malicious tampering\, such as removal\, addition\, and modification of objects. Therefore\, the traditional hash-based authentication is not suitable for the application. As for the semi-fragile watermarking technique\, it meets the requirements of the above application at the expense of severely damaging image fidelity. In this talk\, we propose a hybrid authentication technique based on what we call fragile hash value. The technique can blindly detect and localize malicious tampering\, while maintaining reasonable tolerance to conventional content-preserving manipulations. The hash value is derived from the relative difference between each pair of the selected DCT coefficient in a central block and its counterpart which is estimated by the DC values of the center block and its adjacent blocks. In order to maintain the relative difference relationship when the image undergoes legitimate processing\, we make a pre-compensation for the coefficients. Finally\, we point out the direction using deep leaning technique for image authentication. \nBiography\nProf. Yulin Wang is a full professor and PhD supervisor in the School of Computer Science\, Wuhan University\, China. He got PhD degree in 2005 from University of London\, UK. Before that\, he had worked in National Institute of Research and Huawei@ for more than ten years. He has involved more than 20 national and international research projects\, and hold 8 patents. He got his master and bachelor degree in 1990 and 1987 respectively from Xi-Dian University\, and Huazhong University of Science and Technology\, both in China. His research interests include image and video processing\, software engineering\, information security and artificial intelligence. In recently 10 years\, Prof. Wang has published as first author 3 books\, 40 conference papers and 45 journal papers\, including in IEEE Transactions and ACM Transactions journals. Prof. Wang served as editor-in-chief for International Journal of Advances in Multimedia in 2010. He served as reviewer for many journals\, including IEEE Transactions on Image Processing\, IEEE Transactions on CSVP and IEEE Transactions on Multimedia. He served as reviewer for many research funds\, including National High Technology Research and Development Program of China (‘863’ project). Prof. Wang was the external PhD adviser of Dublin City University\, Ireland during 2013-2016. He has served as chairman and keynote speaker at more than 30 international conferences since 2008. He bas been listed in Marcus “who’s who in the world” since 2008. He was selected as scientific and technological innovative talents in Hubei Province in 2014. Since 2012\, he has served as Deputy Director of Hubei Science and Technology Commission of China Association for the Promotion of Democracy (CAPD). \n 
URL:https://www.fst.um.edu.mo/event/image-authentication-and-tamper-localization/
LOCATION:E11-4045
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190919T143000
DTEND;TZID=Asia/Macau:20190919T153000
DTSTAMP:20260611T233740
CREATED:20190919T063006Z
LAST-MODIFIED:20220927T040650Z
UID:5801-1568903400-1568907000@www.fst.um.edu.mo
SUMMARY:Sinotools: a computational framework to analyze cancer genomic sequencing data for precision oncology
DESCRIPTION:Instructors/Speakers\nProf. Bingding HUANG\nCollege of Big Data and Internet\nShenzhen University \nAbstract\nWe have developed a computational framework called Sinotools to analyze cancer genomic sequencing data for precision oncology. First of all\, Sinotools can identify very low-frequency variants in ctDNA samples from cancer patients using duplex barcode sequencing technology. Sinotools also includes a novel computational algorithm to identify actionable gene fusion events from targeted sequencing data. Sinotools is available at https://github.com/SinOncology. \nBiography\nProf. Bingding Huang received his BSc in Cell Biology from University of Science and Technology of China in 2002. Then he received his Master degree in Computer Science from Saarland University and Max Planck Institute for Informatics under Scholarship from International Max Planck Research School in 2004. Afterwards\, he joined the Bioinformatics group in Technical University of Dresden where he received his PhD (Dr.ret.nat) in Computer Science from the Computer Science department in 2007. Then he joined the Molecular and Cellular Modeling group in Heidelberg Institute for Theoretical Studies for one year postdoc training. From 2009 to 2012 he was a Adjunct Associate Professor in Division of Systems Biology\, Zhejiang-California International NanoSystems Institute\, Zhejiang University China. From 2012 to 2015 Dr. Huang worked as a Bioinformatics Scientist in German Cancer Research Center Heidelberg. Then from 2015 to 2017 he worked as a Senior Computational Biologist in Neo New Oncology AG (Part of Siemens Healthineers). Afterwards\, he joined Sinotech Genomics Inc. Shanghai as Vice President of Research and Development and Head of Bioinformatics. Recently Dr. Huang joined the College of Big Data and Internet\, Shenzhen University as a full Professor in Bioinformatics and Big data. \n 
URL:https://www.fst.um.edu.mo/event/sinotools-a-computational-framework-to-analyze-cancer-genomic-sequencing-data-for-precision-oncology/
LOCATION:E12-G004
CATEGORIES:event_list,seminarslectures
END:VEVENT
END:VCALENDAR