Monday March 31, 2025, 1:30pm - 6:00pm (Coffee break 3:30pm - 4:00pm) (Tentative)
For questions or inquiries about the ACM SAC 2025 tutorials, please contact the Tutorials Chair(s):
Alessandro Ortis
University of Catania
Catania, Italy
alessandro.ortis@unict.it
Presenter:
Engin Zeydan
- Senior Researcher, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Abdullah Aydeger
- Department of Electrical Engineering and Computer Science, Florida Institute of Technology
Abstract:
The tutorial proposal focuses on the convergence of quantum threats in the domain of 6G networks. It aims to provide an in-depth study of this convergence, starting with background information on quantum attacks, post-quantum cryptography, and quantum key distribution. It will then explore its execution to 6G networks and their quantum-based threats. The tutorial will include a step-by-step demonstration of two of the demos to illustrate the practical implementation of these concepts. The tutorial is designed for participants with no prerequisite knowledge and aims to introduce them to the application of post-quantum cryptography and quantum key distribution to protect the 6G networks. As this topic is gaining significance and relevance in the telecommunications industry, the tutorial offers attendees the opportunity to learn about cutting-edge security issues for 6G networks and their specific applications from the cybersecurity perspective.
Bios:
Engin Zeydan received a PhD degree in February 2011 from the Department of Electrical
and Computer Engineering at Stevens Institute of Technology, Hoboken, NJ, USA. Since
November 2018, he has been with the Services as Networks (SaS) Research Unit of the
CTTC working as a Senior Researcher. He was a part-time instructor at Electrical and
Electronics Engineering department of Ozyegin University Istanbul, Turkey between
January 2015 and June 2018. His research areas include data engineering/science for
telecommunication networks and network security.
Dr. Abdullah Aydeger is currently an assistant professor at the Electrical Engineering
and Computer Science Department at FIT. Prior to joining FIT in August 2022, he was an
assistant professor at the School of Computing at Southern Illinois University, Carbondale,
since 2020. Dr. Aydeger obtained a Ph.D. Degree in Computer and Electrical Engineering
from Florida International University in 2020. His research interests are post-quantum
cryptography, network security, and virtualization.
Presenter:
Shiwei Liu
- University of Oxford
Arijit Ukil
- TCS Research
Abstract:
Large language models (LLMs) have become pivotal in modern deep learning, making it essential to understand the underlying patterns, particularly as these models scale exponentially. With parameter counts skyrocketing from billions to trillions in recent years, the associated computational costs and energy consumption for training and fine-tuning have escalated dramatically. This rapid expansion has sparked a growing interest in techniques that can achieve more compact model paradigms without sacrificing performance. Sparsitybased model compression has emerged as one of the most promising approaches for optimizing model efficiency, yet its application to LLMs has lagged behind other compression strategies. To address this gap, we revisit the existing landscape of model compression, focusing specifically on sparse neural networks, and offer a clear categorization of the methodologies employed in this space. We then explore recent advancements in sparsity for LLM compression and the caveat of sparsity in LLMs. The tutorial will conclude with identifying key challenges and opportunities in this field. Ultimately, this tutorial provides a comprehensive roadmap tracing the evolution of model compression and sparsity from traditional models to large-scale LLMs, emphasizing its critical importance in the ongoing development of efficient and scalable AI systems.
Bios:
Shiwei Liu is a Royal Society Newton International Fellow at the University of Oxford and a Junior Research Fellow (JRF) at Somerville College. He was a
Postdoctoral Fellow at the University of Texas at Austin. He obtained his Ph.D. with
the Cum Laude from the Eindhoven University of Technology in 2022. He has received two Rising Star Awards from KAUST and the Conference on Parsimony and
Learning (CPAL). His Ph.D. thesis received the 2023 Best Dissertation Award from
Informatics Europe. He has co-organized several tutorials in ICASSP'24, IJCAI'23,
and ECML-PKDD'22, as well as the Edge-LLM challenge/workshop in NeurIPS'24.
Arijit Ukil (TCS Research) is having more than 20 years of research experience in different capacities. He is working as Principal Scientist in TCS Research,
Tata Consultancy Services, India with expertise in the areas of tinyML and DNN
optimization. Previously, he worked as Scientist in Defence Research and Development Organization, India. He has published more than 60 research papers in
distinguished conferences and journals. He has filed more than 50 patents with more
than 35 patent grants in different geographies. He holds PhD (Cum Laude) from
University of Murcia, Spain. He is a Senior Member, IEEE. He served as the general chair in KDAH workshop of ACM CIKM and ICASSP 2024 workshop of Deep
Neural Network Model Compression. He has presented number of tutorials in different venues including invited talks in University of Hertfordshire, UK, University
of Glasgow, UK, ICASSP, tinyML Forum.
Presenter:
JJ Merelo-Guerv'os
- Department of Computer Engineering, Automatics and Robotics, University of Granada, Spain
Abstract:
Green computing is a general term that describes a host of techniques that
try to minimize the carbon footprint of software applications. As such, it is
not a single body of knowledge, but a series of best practices that help reduce
energy consumption relying on the features of any of the different layers that are
exercised by software applications. This represents a challenge at the time of
designing a comprehensive syllabus that would help students develop the series
of skills needed to identify energy bottlenecks and eliminate them. In this poster
we will describe the different concepts involved, and how they will be delivered
to guarantee the achievement of learning objectives. Green computing [2] deals,
in general, with reducing the environmental impact of the creation and use of
computing resources. From the software perspective, it proposes maximizing
the amount of work done for every unit of energy spent. But to achieve that,
how energy is spent across all the different computing layers need to be assessed
and understood.
This is why getting the student to achieve a certain amount of understanding
of the different process involved, methodologies needed to carry out that
assessment, and eventually design your code from the ground up or refactoring
it to make it greener is a challenge.
Bios:
JJ Merelo obtained a degree in theoretical physics at the University of Granada,
where he obtained a PhD in 1994 and is currently a full professor at the department of Computer Engineering, Automatics and Robotics. Besides being a
frequent open-source contributor, he has been working as a senior software engineer. His GitHub profile is https://github.com/JJ. His Google Scholar profile
https://scholar.google.com/citations?user=gFxqc64AAAAJ shows several hundred publications, and close to 10000 citations.
For the last 3-4 years, he has been PI of grants related to energy optimization, focused on evolutionary algorithms, and has published several papers on
the subject [3]. He has also coordinated a summer school on green software
development in summer 2024, within the framework of the summer schools of
the University of Granada.
He has ample experience in coordinating summer schools, such as the two
SigEVO summer schools that happened in Osaka and later in Prague. As a
teacher, he has got 36 years experience, including tutorials and presentations in
conferences such as GECCO, PPSN or CEC/WCCI.
Presenter:
Mike Mannion
- Department of Computing, Glasgow Caledonian University, Glasgow, UK
Hermann Kaind
- Institute of Computer Technology, TU Wien, Vienna, Austria
Abstract:
The volume, variety and velocity of products in software-intensive systems product lines is increasing. One challenge is to understand the range of similarity between products. Reasons for product comparison include (i) to decide whether to build a new product or not (ii) to evaluate how products of the same type differ for strategic positioning or branding reasons (iii) to gauge if a product line needs to be reorganized (iv) to assess if a product falls within the national legislative and regulatory boundaries. We will discuss two different approaches to address this challenge. One is grounded in feature modelling, the other in case-based reasoning. We will also describe a specific product comparison approach using similarity matching, in which a product configured from a product line feature model is represented as a weighted binary string, the overall similarity between products is compared using a binary string metric, and the significance of individual feature combinations for product similarity can be explored by modifying the weights. We will illustrate our ideas with a mobile phone example, and discuss the benefits and limitations of this approach.
Bios:
Mike Mannion is Professor of Software Engineering at Glasgow Caledonian University, UK. He has
several years' software engineering industry experience. His research interests include software
engineering and artificial intelligence. He is a Chartered Engineer, a member of IEEE and ACM, and
a Fellow of the British Computer Society. He has published 80+papers and delivered 40+ tutorials.
Hermann Kaindl joined the Institute of Computer Technology at TU Wien in Vienna, Austria, in early
2003 as a full professor. Before moving to academia, he was a senior consultant with the division of
program and systems engineering at Siemens Austria. He has published seven books and more than
250 papers in refereed journals, books and conference proceedings. He is a Senior Member of the
IEEE and a Distinguished Scientist Member of the ACM. He has previously delivered 60+ tutorials.