Events

The San Francisco Bay Area chapter of the IEEE Computer Society invites to our free and open Virtual Tech Talks (no IEEE membership required): Speaker: Lakshmana Rao Koppada ((https://www.google.com/url?q=https://www.linkedin.com/in/lakshmana-koppada/&sa=D&source=calendar&ust=1781490549069271&usg=AOvVaw11bsPeQi6ednCxDVhnMelu)) Title: GPU-Free Real-Time Utility Asset Anomaly Detection with IBM Granite TSPulse-R1 Abstract: Utility enterprises operate geographically distributed critical assets—such as pumps, tanks, flow meters, and substations that demand continuous health monitoring to avert service disruptions, environmental hazards, and substantial economic losses. Conventional centralized cloud-based Enterprise Asset Management systems incur high latency, excessive bandwidth consumption, and reliance on GPU acceleration, thereby impeding real-time response in connectivity-constrained environments. This paper introduces a GPU-free edge-to-cloud architecture for real-time anomaly detection that exploits the compact IBM Granite TSPulse-R1 time-series foundation model (approximately 1 million parameters). The framework executes lightweight CPU-only inference on edge gateways and transmits only concise anomaly summaries via Message Queuing Telemetry Transport (MQTT), realizing over 99.9% bandwidth reduction relative to raw data transfer. A novel sensor-weighted reconstruction error scoring mechanism prioritizes the most discriminative sensors, enhancing zero-shot multivariate detection performance. Rigorous evaluation on the Water Treatment dataset, a real-world industrial control system benchmark featuring 51 sensors and a 12.53% anomalous timestep ratio, demonstrates an average inference latency of 710.3 ms per 512-timestep chunk, a ROC-AUC of 0.717, and tolerance-adjusted recall exceeding 0.945 at ±20 timesteps. Scalability analysis reveals near-linear latency growth with increasing sensor counts and chunk sizes, confirming feasibility on resource-constrained edge devices. The proposed architecture delivers a scalable, cost-effective, and resilient predictive maintenance solution for critical infrastructure, providing a practical GPU-independent alternative to traditional cloud-centric paradigms while supporting robust operation in distributed utility networks. Bio: Lakshmana Rao Koppada (IETE Fellow, Senior Member IEEE, Professional Member of BCS) is a Digital Transformation Leader and Technical Architect at PwC with over 16 years of experience delivering large-scale enterprise modernization programs. Specializing in IBM Maximo, cloud platforms, and Red Hat OpenShift, he integrates AI/ML, IoT, and predictive analytics to improve asset reliability and operational efficiency. He has led global digital transformation initiatives for Fortune 500 organizations across utilities, pharmaceuticals, manufacturing, energy, and data center industries. A published researcher and certified IBM Maximo/MAS architect, he actively contributes to international technology conferences and innovation initiatives in AI-driven enterprise systems. Speaker(s): Ram Sekhar Bodala Virtual: https://events.vtools.ieee.org/m/563073

30th CASIS Workshop at LLNL

We are thrilled to host LLNL’s 30th Center for Advanced Signal and Image Sciences (CASIS) Workshop.
The workshop returns with a full two-day, in-person schedule on Wednesday and Thursday, June 24–25, 2026.

We encourage a broad range of technical topics at the workshop. Because the workshop is non-archival apart from original work, we are also considering intermediate results from ongoing efforts as well as recently published work for presentation as a talk and/or poster.

The goal of the workshop is to provide a platform for the exchange of ideas and networking with peers across disciplines to foster collaboration and build community.
Please submit your abstract by Friday, May 15, 2026. Authors will be notified of review decisions one week later, on May 22, 2026.

In addition to the regular presentation track, the workshop will feature parallel tutorials, hands-on mini workshops, and a dedicated student track introducing career opportunities at LLNL.

The workshop will be held in person at the
University of California Livermore Collaboration Center
and requires pre-registration by June 18, 2026. As this is a full two-day workshop, coffee and snacks will be provided during morning and afternoon breaks, and lunch will be provided on both days.
As part of the 30th anniversary celebration, a Happy Hour will follow the regular program on Wednesday, June 24, 2026.

Workshop Website:

https://engineering.llnl.gov/centers/casis/workshops

Technical Tracks

• AI / Machine Learning — Phan Nguyen, 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
.

Check out

last year’s event highlights

to see what to expect.

The no-fee CASIS Workshop is sponsored by the
Lawrence Livermore National Laboratory Engineering Directorate
and held at the
University of California Livermore Collaboration Center.
It is organized by the
Center for Advanced Signal and Image Sciences (CASIS),
and is a joint meeting with the local chapters of the
IEEE Signal Processing Society
and
IEEE Computer Society San Francisco Bay Area Chapter,
with support from
IEEE Region 6, East Bay Section.

Co-sponsored by: Lawrence Livermore National Laboratory - Center for Advanced Signal and Image Sciences

Location:
Building 661, L-794
University of California Livermore Collaboration Center
7000 East Ave
Livermore, California 94550
United States