Special Session
The 27th International Conference on Neural Information Processing(ICONIP2020)
November 18 - November 22, 2020
Bangkok, Thailand
Session Organisers
Prof Saeid Nahavandi, Deakin University, saeid.nahavandi@deakin.edu.au
Saeid Nahavandi received a Ph.D. from Durham University, U.K. in 1991. He is an Alfred Deakin Professor, Pro ViceChancellor, Chair of Engineering, and the Founding Director of the Institute for Intelligent Systems Research andInnovation at Deakin University. His research interests include modelling of complex systems, robotics and haptics.
Professor Nahavandi is Editor-In-Chief: IEEE SMC Magazine, the Senior Associate Editor: IEEE Systems Journal,Associate Editor of IEEE Transactions on Systems, Man and Cybernetics: Systems, and IEEE Press Editorial Board
member. Professor Nahavandi is a Fellow of IEEE (FIEEE), Engineers Australia (FIEAust), the Institution ofEngineering and Technology (FIET). Saeid is a Fellow of the Australian Academy of Technology and Engineering(ATSE). He has published more than 800 journal and conference papers.
A/Prof Abbas Khosravi, Deakin University, abbas.khosravi@deakin.edu.auAbbas Khosravi received his PhD from Deakin University in 2010. He is currently an associate professor with theInstitute for Intelligent Systems Research and Innovation at Deakin University. His current research interests include
machine learning, artificial intelligence, and their applications for data mining, computer vision, optimization, andoperation planning. He has received several prestigious grants to conduct fundamental and applied research in the fieldof AI-based uncertainty quantification. He has published more than 200 journal and conference papers and his h-index
is 37 based on Google Scholar.
Prof Amir F Atiya, Cairo University, Egypt, amir@alumni.caltech.edu
Amir Atiya received his B.S. and M.S. degrees from Cairo University, and the M.S. and Ph.D. degrees from Caltech(California Institute of Technology), all in electrical engineering. Amir Atiya is currently a Professor at the Departmentof Computer Engineering, Faculty of Engineering, Cairo University, where he has been a faculty member since 1993.
He received recently the Egyptian State Appreciation Award for 2017, the Egyptian State Distinction Award for 2010,
the highly regarded Kuwait Prize for 2005, and the Young Investigator Award from the International Neural Network
Society in 1996. Amir Atiya has had research contributions in the areas of in machine learning, neural networks, timeseries forecasting, natural language processing, and pattern classification. His work has been cited over 7800 times andhas an H-index of 41. He is currently on the editorial board of several scientific journals.

Special Session Title
Uncertainty Estimation: Theories and Applications

Special Session Description
How confident is a neural network model about its prediction? How much can one trust predictions of neural networks
for new samples? How can one develop neural networks that know when they do not know? Answering these questions
is a prerequisite for widespread deployment of neural networks in safety critical applications. The field of uncertainty
quantification of neural networks has received huge attention in recent years from both academia and industry. Several
methods and frameworks have been proposed in literature to generate predictive uncertainty estimates using neuralnetworks. There are currently theoretical gaps and practical issues with proposed frameworks for uncertainty estimation
using neural networks. Also, the research on the application of predictive uncertainty estimates for developing
uncertainty-aware systems is still rare.
The goal of this special session is to provide an in-depth discussion of the latest academic and industrial research findingsof uncertainty estimation using neural networks. The session will get together prominent and upcoming scientists
conducting research in this field. Topics of interest include, but are not limited to:-Uncertainty estimation for different neural network types (feedforward, CNN, recurrent, LSTM, encoders,etc)
Probabilistic forecasting
Bayesian methods for uncertainty quantification
Ensemble-based uncertainty estimation
Sampling-free methods for uncertainty estimation
Objective evaluation of uncertainty estimates
Design of uncertainty-aware/averse systems
Applications of predictive uncertainty estimate (industry, engineering, robotics, medical, finance, etc)

Potential Contributors-Prof Nahavandi and A/Prof Khosravi from Deakin University
Prof Dipti Srinivasan from National University of Singapore
A/Prof Yarin Gal from Oxford University
Dr Hao Quan, Nanjing University of Science and Technology
Researchers from Google conducting research on deep uncertainty quantification
Researchers from University of Queensland (Australia) conducting research on renewable energies
Researchers conducting research in the area of probabilistic forecasting (University of Montreal)
Researcher conducting research in the area of probabilistic classification (University of Cambridge)