May 27, 2025
Agentic AI: Shaping the Future of
Autonomy, Innovation, and Society
6:00 PM - 8:00 PM PT
Speaker: Dr. Narayan Srinivasa, IEEE Fellow
Register at: https://events.vtools.ieee.org/m/479545
Agentic AI represents a groundbreaking shift in
artificial intelligence, enabling systems to act
autonomously, make decisions, and achieve goals with
minimal human intervention. This talk explores the concept
of Agentic AI, its various types, adoption trends, and the
transformative impact it is poised to have on industries
and societies. From reshaping workflows to influencing
ethical frameworks, we’ll examine how this emerging
technology drives innovation and redefines humanity’s
relationship with intelligent systems.
About the Speaker
Srinivasa received his Ph. D. from the University of
Florida in Gainesville in 1994 and was Beckman
Post-Doctoral Fellow in the Human Computer Intelligent
Interaction group at the Beckman Institute, University of
Illinois at Urbana Champaign from 1994-1997. Between
1998-2015, he was with HRL Laboratories in Malibu CA where
he became Principal Scientist and the Director for Neural
and Emergent Systems. At HRL, he worked on a wide range of
AI projects for Boeing, GM, and the US Government,
including visual perception and computer vision, signal
processing and sensor fusion, brain-inspired computing,
and robotics. He joined Intel Labs in 2016 as Chief
Scientist to lead the development of neuromorphic
technology and played a key role in the development of the
Loihi neuromorphic chip. He is currently Senior Principal
AI Engineer and Director for Machine Intelligence Research
Programs at Intel Labs. He is responsible for accelerating
Intel Labs research on problems with high risk but also
with a potential for high reward to Intel. He has 126
issued US patents and over 100 articles in journals,
magazines, and conferences. He is a Fellow of the IEEE.
June 3, 2025
Machine Learning in NextG Networks via
Generative Adversarial Networks
7:00 PM - 8:00 PM PT
Speaker: Ender Ayanoglu, Professor, UCI, ayanoglu@uci.edu
Register at: https://events.vtools.ieee.org/m/466461
Generative Adversarial Networks (GANs) implement Machine
Learning (ML) algorithms that can address competitive
resource allocation problems together with detection and
mitigation of anomalous behavior. In this talk, we discuss
their use in next-generation (NextG) communications within
the context of cognitive networks to address i) spectrum
sharing, ii) detecting anomalies, and iii) mitigating
security attacks. GANs have the following advantages.
First, they can learn and synthesize field data, which can
be costly, time consuming, and non-repeatable. Second,
they enable pre-training classifiers by using
semi-supervised data. Third, they facilitate increased
resolution. Fourth, they enable recovering corrupted bits
in the spectrum. The talk will provide basics of GANs, a
comparative discussion on different kinds of GANs,
performance measures for GANs in computer vision and image
processing as well as wireless applications, several
datasets for wireless applications, performance measures
for general classifiers, a survey of the literature on
GANs for i)–iii) above, some simulation results, and
future research directions. In the spectrum sharing
problem, connections to cognitive wireless networks are
established. Simulation results show that a particular GAN
implementation is better than a convolutional auto encoder
for an outlier detection problem in spectrum sensing.
August 19, 2025
The Sketches of Infinite Data and
Algorithms for Real-Time Data Insights
6:00 PM - 8:30 PM PT
Speaker: Dr. Vishnu S. Pendyala, San Jose State
University
Register at: https://events.vtools.ieee.org/m/482936
How are machine learning algorithms able to answer
questions from any nook and corner of the World Wide Web?
How are trending hashtags from the near infinite microblog
posts, unique visitors and other distinct counts in the
near infinite website traffic determined? How do blogging
websites avoid recommending articles a user has previously
read? In general, how can we answer complex queries about
enormous data streams without storing them entirely, in
real-time? The answer often lies in clever approximation
algorithms and data "sketches" that capture essential
properties using vastly reduced space. The relentless flow
of data in modern systems indeed presents significant
challenges. These data streams are often too large to
store and too fast to process exhaustively with
traditional methods. This talk introduces key sketching
and approximation techniques that help generate real-time
data insights by processing data streams.
About the Speaker
Vishnu S. Pendyala, PhD, is a faculty member in Applied
Data Science and an Academic Senator with San Jose State
University, current chair of the Santa Clara Valley
Chapters of IEEE Computer and Computational Intelligence
Societies, Area 4 Coordinator for Region 6, and a
Distinguished Contributor of the IEEE Computer Society. As
a past ACM Distinguished Speaker, researcher, and industry
expert, he gave nearly 100 talks and tutorial sessions in
various forums such as faculty development programs, the
12th IEEE GHTC, IEEE ANTS, 12th IACC, 10th ICMC, IUCEE,
12th ACM IKDD CODS and 30th COMAD to audiences at venues
such as Stanford University, Google, University of Bolton,
Computer History Museum, Universidad de Ingeniería y
Tecnología, Lima, Peru, IIIT Hyderabad, KREA, IIT Jodhpur,
University of Hyderabad, IIT Indore, IIIT Bhubaneswar.
Some of these talks are available on YouTube and IEEE.tv.
He is a senior member of the IEEE and ACM. He has over two
decades of experience in the software industry in the
Silicon Valley, USA. His book, “Veracity of Big Data,” is
available in several libraries, including those of MIT,
Stanford, CMU, the US Congress and internationally. Two
other books on machine learning and software development
that he edited are also well-received and found place in
the US Library of Congress and other reputed libraries.
Dr. Pendyala taught a one-week course sponsored by the
Ministry of Human Resource Development (MHRD), Government
of India, under the GIAN program in 2017 to Computer
Science faculty from all over the country and delivered
the keynote in a similar program sponsored by AICTE,
Government of India in 2022. Dr. Pendyala served on a US
government's National Science Foundation (NSF) proposal
review panel in 2023. He received the Ramanujan memorial
gold medal and a shield for his college at the State Math
Olympiad. He also played an active role in the Computer
Society of India and was the Program Secretary for its
annual national convention.
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