Keynote Speakers
Prof. Rajkumar Buyya
IEEEFelow, ACM Fellow
The University of Melbourne, Australia
Bio: Dr. Rajkumar Buyya is a Redmond Barry
Distinguished Professor and Director of the Quantum
Cloud Computing and Distributed Systems (qCLOUDS)
Laboratory at the University of Melbourne,
Australia. He is also serving as the founding CEO of
Manjrasoft, a spin-off company of the University,
commercializing its innovations in Cloud Computing.
He has authored over 850 publications and seven
textbooks including "Mastering Cloud Computing"
published by McGraw Hill, China Machine Press, and
Morgan Kaufmann for Indian, Chinese and
international markets respectively. Dr. Buyya is one
of the highly cited authors in computer science and
software engineering worldwide (h-index=178,
g-index=394, i10-index=854, and 168,400+
citations). A bibliometric study by Stanford
University and Elsevier since 2019, Dr. Buyya is
recognized as the Highest-Cited author in the
Distributed Computing field worldwide. He graduated
60+ PhD students who are working in world-leading
research universities and high-tech companies such
as Microsoft, Google, and IBM. He has been
recognised as an ACM Fellow, IEEE Fellow, a "Web of
Science Highly Cited Researcher" for seven times
since 2016, the "Best of the World" twice for
research fields (in Computing Systems in 2019/2024
and Software Systems in 2021/2022/2023) as well as
"Lifetime Achiever" and "Superstar of Research" in
"Engineering and Computer Science" discipline twice
(2019 and 2021) by the Australian Research Review.
Software technologies for Grid, Cloud, Fog, Quantum
computing developed under Dr.Buyya's leadership have
gained rapid acceptance and are in use at several
academic institutions and commercial enterprises in
60+ countries around the world. Manjrasoft's Aneka
Cloud technology developed under his leadership has
received "Frost New Product Innovation Award". He
served as founding Editor-in-Chief of the IEEE
Transactions on Cloud Computing. He is currently
serving as Editor-in-Chief of Software: Practice and
Experience, a long-standing journal in the field
established in 1970. He has presented over 750
invited talks (keynotes, tutorials, and seminars) on
his vision on IT Futures, Advanced Computing
technologies, and Spiritual Science at international
conferences and institutions in Asia, Australia,
Europe, North America, and South America. He has
recently been recognized as a Fellow of the Academy
of Europe. For further information on Dr.Buyya,
please visit his cyberhome: www.buyya.com.
Speech Title: Neoteric Frontiers in Cloud and
Quantum Computing
Abstract: The twenty-first-century digital
infrastructure and applications are driven by Cloud
computing, Internet of Things (IoT), Artificial
Intelligence (AI), and Quantum computing paradigms.
The Cloud computing paradigm has been transforming
computing into the 5th utility wherein "computing
utilities" are commoditized and delivered to
consumers like traditional utilities such as water,
electricity, gas, and telephony. It offers
infrastructure, platform, and software as services,
which are made available to consumers as
subscription-oriented services on a pay-as-you-go
basis over the Internet. Its use is growing
exponentially with the continued development of new
classes of applications such as AI-powered models
(e.g., ChatGPT) and the mining of crypto currencies
such as Bitcoins. To make Clouds pervasive, Cloud
application platforms need to offer (1) APIs and
tools for rapid creation of scalable and elastic
applications and (2) a runtime system for deployment
of applications on geographically distributed Data
Centre infrastructures (with Quantum computing
nodes) in a seamless manner.
This keynote presentation will cover (a) 21st
century vision of computing and identifies various
emerging IT paradigms that make it easy to realize
the vision of computing utilities; (b) innovative
architecture
for creating elastic Clouds integrating edge
resources and managed Clouds, (c) Aneka 6G, a 6th
generation Cloud Application Platform, for rapid
development of Big Data/AI applications and their
deployment on private/public Clouds driven by user
requirements, (d) experimental results on deploying
Big Data/IoT applications in engineering, health
care (e.g., COVID-19), deep learning/Artificial
intelligence (AI), satellite image processing, and
natural language processing (mining COVID-19
literature for new insights) on elastic Clouds, (e)
QFaaS: A Serverless Function-as-a-Service Framework
for Quantum Computing; and iQuantum Simulation
Toolkit, and (f) new directions for emerging
research in Cloud and Quantum computing.
Prof. Shahram Latifi
IEEE Fellow, AAIS Fellow
University of Nevada, USA
Bio: Shahram Latifi, received the Master of
Science and the PhD degrees both in Electrical and
Computer Engineering from Louisiana State
University, Baton Rouge, in 1986 and 1989,
respectively. He is currently a Professor of
Electrical Engineering at the University of Nevada,
Las Vegas. Dr. Latifi is the co-director of the
Center for Information Technology and Algorithms
(CITA) at UNLV. He has designed and taught
undergraduate and graduate courses in the broad
spectrum of Computer Science and Engineering in the
past four decades. An internationally recognized
researcher and speaker, Dr. Latifi has delivered
keynote talks and seminars on AI, Machine Learning,
and emerging technologies worldwide. He has authored
over 400 technical articles in the areas of
networking, AI/ML, cybersecurity, image processing,
biometrics, fault tolerant computing, parallel
processing, and data compression. His research has
been funded by NSF, NASA, DOE, DoD, Boeing, and
Lockheed. Dr. Latifi was an Associate Editor of the
IEEE Transactions on Computers (1999-2006), an IEEE
Distinguished Speaker (1997-2000), Co-founder and
Chair of the IEEE Int'l Conf. on Information
Technology (2000-2004) and founder and Chair of the
International Conf. on Information Technology-New
Generations (2005-Present) . Dr. Latifi is the
recipient of several research awards, the most
recent being the Barrick Distinguished Research
Award (2021). Dr. Latifi was recognized to be among
the top 2% researchers around the world in December
2020, according to Stanford top 2% list (publication
data in Scopus, Mendeley). He is an IEEE Fellow
(2002), an AAIS Fellow (2025) and a Registered
Professional Engineer in the State of Nevada.
Prof. Huiyu Zhou
University of Leicester,
UK
Bio: Dr. Huiyu Zhou received a Bachelor of
Engineering degree in Radio Technology from Huazhong
University of Science and Technology of China, and a
Master of Science degree in Biomedical Engineering
from University of Dundee of United Kingdom,
respectively. He was awarded a Doctor of Philosophy
degree in Computer Vision from Heriot-Watt
University, Edinburgh, United Kingdom. Dr. Zhou
currently is a full Professor at School of Computing
and Mathematical Sciences, University of Leicester,
United Kingdom. He has published over 600
peer-reviewed papers in the field. His research work
has been or is being supported by UK EPSRC, ESRC,
AHRC, MRC, EU, Innovate UK, Royal Society, British
Heart Foundation, Leverhulme Trust, Puffin Trust,
Alzheimer’s Research UK, Invest NI and industry.
Homepage: https://le.ac.uk/people/huiyu-zhou
Speech Title:
Title: Image Completion with Context-Adaptive
Diffusion
Abstract:
Image completion is a challenging task, particularly
when ensuring that generated content seamlessly
integrates with existing parts of an image. While
recent diffusion models have shown promise, they
often struggle with maintaining coherence between
known and unknown (missing) regions. This issue
arises from the lack of explicit spatial and
semantic alignment during the diffusion process,
resulting in content that does not smoothly
integrate with the original image. Additionally,
diffusion models typically rely on global learned
distributions rather than localized features,
leading to inconsistencies between the generated and
existing image parts. In this work, we propose
ConFill, a novel framework that introduces a
Context-Adaptive Discrepancy (CAD) model to ensure
that intermediate distributions of known and unknown
regions are closely aligned throughout the diffusion
process. By incorporating CAD, our model
progressively reduces discrepancies between
generated and original images at each diffusion
step, leading to contextually aligned completion.
Moreover, ConFill uses a new Dynamic Sampling
mechanism that adaptively increases the sampling
rate in regions with high reconstruction complexity.
This approach enables precise adjustments, enhancing
detail and integration in restored areas. Extensive
experiments demonstrate that ConFill outperforms
current methods, setting a new benchmark in image
completion.