Events

IEEE Computer Society San Francisco Bay Area Chapter Virtual Tech Talk

The San Francisco Bay Area Chapter of the IEEE Computer Society invites you to our free and open
Virtual Tech Talks. No IEEE membership is required.

Speaker: Dr. K. Venkata Nagendra

Title: Emotional Intelligence in AI

Abstract

AI emotional intelligence, also known as emotion AI or affective computing, is the ability of
artificial intelligence to detect, interpret, and simulate human emotions through facial expressions,
body language, voice tone, and other cues.

While AI can analyze emotional signals and generate responses that appear empathetic, it does not possess
true consciousness, empathy, or personal experience, which are essential to genuine emotional intelligence.
Rather than replacing human emotional intelligence, AI can serve as a tool to enhance it by providing
insights, analyzing data at scale, and enabling more natural human-machine interactions.

AI technologies use several methods to recognize and respond to human emotions, including:

• Facial Recognition Technology — used to analyze facial expressions and detect emotions such as
  happiness, sadness, anger, and fear by examining features like the mouth, eyes, and eyebrows.
• Voice Recognition Technology — used to analyze the tone and pitch of a person’s voice to detect
  emotions such as happiness, sadness, and anger through machine learning and speech pattern analysis.

This talk will also discuss the benefits of emotional intelligence in AI, including:

• Creating more meaningful interactions between humans and machines
• Enhancing education
• Helping shape the future of AI

Biography

Dr. Kolluru Venkata Nagendra earned his bachelor’s degree in Computer Science from Sri Venkateswara University,
Tirupati. He later received a master’s degree in Computer Applications from Jawaharlal Nehru Technological University,
Hyderabad, and a Master of Technology in Computer Science and Engineering from Acharya Nagarjuna University, Guntur.
He also earned a Ph.D. in Computer Science from Vikrama Simhapuri University, Nellore, Andhra Pradesh, and completed
a Post Doctoral Fellowship (PDF) in CSE from Srinivasa University, Mangalore.

Dr. Nagendra is the author of numerous computer science textbooks and more than 70 research papers published in
UGC- and Scopus-indexed journals. He has also served as a judge and reviewer for many conferences and journals.
With more than 17 years of experience at several engineering colleges, he is currently working at
SRKR Engineering College, Bhimavaram, Andhra Pradesh. His research interests include Machine Learning,
Deep Learning, and Data Mining.

Speaker(s): Dr. K. Venkata Nagendra

Virtual Event Link:

https://events.vtools.ieee.org/m/538538

IEEE Computer Society San Francisco Bay Area Chapter Virtual Tech Talk

The San Francisco Bay Area Chapter of the IEEE Computer Society invites you to our free and open
Virtual Tech Talks. No IEEE membership is required.

Event Page:

Register on Eventbrite

Speaker:

Jyotheeswara Reddy Gottam

Title: The Triple Threat: Transforming Enterprise Quality Engineering Practices with Generative AI, Predictive Analytics, and Self-Healing Automation

Abstract

Modern software teams face mounting pressure to release high-quality applications faster while managing increasing system complexity and continuous delivery expectations within modern CI/CD pipelines. Traditional testing approaches often struggle to keep pace with rapid code changes, expanding regression suites, and the rising cost of maintaining automation frameworks. These challenges frequently lead to delayed releases, increased testing costs, and defects escaping into production.

This presentation explores how the “Triple Threat” of AI-driven testing technologies—generative AI for test script creation, machine learning-based predictive defect analytics, and self-healing automation frameworks—is transforming enterprise quality engineering practices.

First, generative AI accelerates test development by automatically generating test cases, scripts, and data from requirements, user stories, or code changes, significantly reducing manual scripting effort. Second, predictive defect analytics powered by machine learning analyzes historical defect patterns, code churn, and previous test outcomes to identify high-risk components and prioritize testing efforts where failures are most likely to occur. Third, self-healing automation frameworks intelligently adapt to UI or API changes, minimizing brittle test failures and reducing the costly maintenance typically associated with large automated test suites.

When deployed together, these technologies reinforce one another: generative AI expands test coverage, predictive analytics focuses testing on the most critical risk areas, and self-healing automation ensures test suites remain resilient despite frequent application updates. Applied across API testing, functional testing, integration testing, and end-to-end testing, this integrated approach enables organizations to modernize their testing strategy while improving reliability.

The combined impact allows enterprises to reduce testing costs by up to 40%, accelerate release cycles by 30%, and improve defect detection rates by over 50%, demonstrating how AI-driven testing can deliver measurable improvements in both quality and delivery speed across enterprise software systems.

Biography

Jyotheeswara Reddy Gottam is a Software Engineering Leader with over a decade of experience in the retail and e-commerce industry. Currently a Senior Software Engineer at Walmart Global Tech, he leads end-to-end testing strategies for high-traffic marketplace platforms, driving scalability, reliability, and performance for systems handling millions of daily transactions.

He specializes in Gen AI, machine learning, AI agents, RAG, test automation, performance engineering, and CI/CD enablement. Throughout his career, he has architected scalable automation frameworks, significantly reduced regression cycles, improved release velocity, and ensured platform stability during peak traffic events. He has also led cross-functional initiatives across payments, inventory, personalization, and mobile platforms.

Speaker(s): Jyotheeswara Reddy Gottam

Virtual Event Link:

https://events.vtools.ieee.org/m/548913

We are thrilled to host LLNL’s 30th Center for Advanced Signal and Image Sciences (CASIS) workshop. The workshop returns with a full 2-day in-person schedule on Wednesday and Thursday, June 24-25, 2026. We encourage a broad range of technical topics at the workshop and being non-archival apart from original work, we are also considering intermediate results from ongoing efforts as well as recently published publications for presentation as a talk and/or a poster. The goal of the workshop is to provide a platform for the exchange of ideas and network with peers across disciplines to foster collaboration and build community. Please submit your abstract by Friday, May 15, 2026. Authors will be notified of the review decisions one week later on May 22, 2026. Apart from the regular presentation track we will feature parallel tutorials, hands-on mini workshops and a dedicated student track to introduce career opportunities at LLNL. The workshop will be held in-person at the (https://uclcc.org/) and requires pre-registration until June 18, 2026. As this is a 2-day whole-day workshop, we will provide coffee and snacks in morning and afternoon breaks as well as a lunch on both days. As this is our 30th anniversary, we will also host a Happy Hour following the regular program on Wednesday, June 24, 2026. (https://engineering.llnl.gov/centers/casis/workshops) This year’s workshop features presentations in the following tracks, moderated by the Program Chairs: - AI/Machine Learning (PhanNguyen, Kowshik Thopalli) - National Ignition Facility (Eugene Kur, Christopher Miller) - Non-Destructive Evaluation (Seemeen Karimi, Harry Martz) - Quantum Sensing & Quantum Computing (Kristi Beck) - Remote Sensing, Non-Invasive Imaging & Inverse Problems (Sean Lehman, Viacheslav Li) - Robotics & Automation (Aldair Gongora, Abhik Sarkar) - Student Track: All topics (Poster only) (Ted Bauman, Min Priest) Become part of this great experience and submit your talk proposal at https://engineering.llnl.gov/centers/casis/workshops before May 15, 2025! Check out (https://www.llnl.gov/article/53041/annual-workshop-brings-together-signal-image-science-community) for last year’s amazing event to see what to expect! The no-fee CASIS Workshop is sponsored by the (https://engineering.llnl.gov/) and held at the (https://uclcc.org/). It is organized by the (https://engineering.llnl.gov/centers/casis), and is a joint meeting with the local chapters of the (https://www.ewh.ieee.org/r6/oeb/SigProc/sigproc.html) and (https://r6.ieee.org/sfoeb-cs/). supported by the (https://r6.ieee.org/oeb/). Co-sponsored by: Lawrence Livermore National Laboratory - Center for Advanced Signal and Image Sciences Bldg: Building 661 L-794, University of California Livermore Collaboration Center, 7000 East Ave, Livermore, California, United States, 94550