KEYNOTE TALK SERIES

Prof. Shahram Latifi

(University of Nevada, Las Vegas)

Bio: Shahram Latifi received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Louisiana State University in 1986 and 1989, respectively. He is a Professor of Electrical Engineering at the University of Nevada, Las Vegas (UNLV), where he also serves as Co-Director of the Center for Information Technology and Algorithms (CITA). For nearly four decades, Dr. Latifi has designed and taught a wide range of undergraduate and graduate courses spanning Computer Science, Computer Engineering, and Electrical Engineering. He is an internationally recognized educator and researcher who has delivered invited keynotes, plenary lectures, and seminars on Machine Learning, Artificial Intelligence, and Information Technology across the globe. Dr. Latifi has authored more than 300 technical publications in networking, AI/ML, cybersecurity, image processing, biometrics, fault-tolerant computing, parallel processing, and data compression. His research has been supported by major federal agencies and industry leaders, including NSF, NASA, DOE, DoD, Boeing, and Lockheed Martin. He has held several prominent leadership roles, including Associate Editor of the IEEE Transactions on Computers (1999–2006), IEEE Distinguished Speaker (1997–2000), Co-founder and Chair of the IEEE International Conference on Information Technology (2000–2004), and Founder and Chair of the International Conference on Information Technology – New Generations (2005–present). Dr. Latifi is the recipient of numerous research awards, most recently the Barrick Distinguished Research Award (2021). In 2020, he was recognized among the top 2% of researchers worldwide, according to the Stanford/Elsevier global citation database. He is a Fellow of the IEEE (elected 2002) and a Registered Professional Engineer in the State of Nevada.

Title of the Talk: AI at the Crossroads: Power, Risk, and the Path to Responsible Intelligence

Abstract: Over the past two decades, AI has advanced at an extraordinary pace. Breakthroughs in Deep Learning, Generative Adversarial Networks, Transfer Learning, and Large Language Models have accelerated progress and transformed nearly every sector — including education, healthcare, aerospace, manufacturing, security, e-commerce, and the arts. But alongside these achievements come serious concerns. How do we ensure training data is fair and unbiased? How do we protect privacy in increasingly data-driven systems? And most importantly, how do we maintain human control over technologies that are becoming more autonomous? In this talk, I will present a concise overview of AI, Machine Learning (ML), and Deep Learning (DL). I will highlight the challenges not only in building general-purpose AI but, more urgently, in developing AI systems that are safe, transparent, and trustworthy. I will also discuss current national and international initiatives aimed at establishing Responsible AI practices.

Prof. Danijela Cabric

(University of California, Los Angeles)

Bio: Danijela Cabric is a Professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles. She received M.S. from the University of California, Los Angeles in 2001 and Ph.D. from University of California, Berkeley in 2007, both in Electrical Engineering. In 2008, she joined UCLA as an Assistant Professor, where she heads Cognitive Reconfigurable Embedded Systems lab. Her current research projects include novel radio architectures, signal processing, communications, machine learning and networking techniques for spectrum sharing, millimeter-wave, massive MIMO and IoT systems. She is a principal investigator in the three large cross-disciplinary multi-university centers including SRC/JUMP ComSenTer and CONIX, and NSF SpectrumX. Prof. Cabric was a recipient of the Samueli Fellowship in 2008, the Okawa Foundation Research Grant in 2009, Hellman Fellowship in 2012, the National Science Foundation Faculty Early Career Development (CAREER) Award in 2012, and Qualcomm Faculty Awards in 2020 and 2021. Prof. Cabric is an IEEE Fellow.

Title of the Talk : Meeting 6G demands for energy efficiency and access to mid-band spectrum

Abstract: Each generation has taken a big step forward and introduced new technologies in order to increase the performance of networks and devices to support the constantly enriched services. In 5G, the telecommunications industry has been particularly focused on improving user experiences such as data rates and latency. However, 6G key objectives have significantly shifted. Operators are requesting improvement of operating costs, energy efficiency, access to mid-spectrum while embedding and leveraging AI/ML technology. This talk will discuss technologies and architectures for energy-efficient mobile and fixed wireless access using new antenna array designs, beamforming modes, ultra-wideband multiple access, and scalable processing architectures to support different coverage and connectivity requirements in 6G cellular and massive IoT connectivity. It will also explore solutions for enabling spectrum sharing in mid-band spectrum between cellular networks and incumbents including radars and satellites.

Important Deadlines

Full Paper Submission:	21st November 2025
Acceptance Notification:	3rd December 2025
Final/Camera-ready Paper Submission:	22nd December 2025
Early Bird Registration:	10th December 2025
Presentation Submission:	28th December 2025
Conference:	5 - 7 January 2026

Full Paper Submission:	9th November 2023
Acceptance Notification:	30th November 2023
Final Paper Submission:	11th December 2023
Early Bird Registration	16th December 2023
Presentation Submission:	26th December 2023
Conference:	9 - 10th October 2023

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