IEEE SP Schenectady Chapter (Virtual) Lecture on Signal Processing for the AI Era: A Launchpad for the Next Chapter of SPS and Beyond
IEEE SP Schenectady Chapter (Virtual) Lecture on Signal Processing for the AI Era: A Launchpad for the Next Chapter of SPS and Beyond
Speaker: Hema Achanta
Title: Signal Processing for the AI Era: A Launchpad for the Next Chapter of SPS and Beyond
Abstract:
Artificial intelligence (AI) is reshaping every technical discipline, yet many of its most influential ideas trace directly back to core principles in signal processing. Convolutional architectures, attention mechanisms, spectral methods, and time-series models all carry the imprint of classical Signal Processing concepts only scaled, generalized, and adapted for modern computational environments. In this talk, I will highlight these connections and show how a signal processing perspective provides not only historical context, but practical insight into where AI techniques excel, where they fail, and why.
This session also introduces the direction I intend to set for our IEEE SPS Chapter in the coming year. Our focus will be on bridging foundational signal processing with rapidly evolving areas of machine learning, control, sensing, and high-dimensional data systems, while also keeping an eye on emerging frontiers such as quantum signal processing and quantum-inspired algorithms. These technologies are still early, but they represent the next horizon where Signal Processing principles may again play a defining role.
By examining how signal processing quietly shapes the trajectory of modern AI and how it may influence future quantum frameworks this talk will serve as both a technical deep dive and a preview of the chapter’s priorities rigor, relevance, and cross-disciplinary collaboration. I look forward to working with all of you as we build a chapter that reflects the breadth and ambition of the SPS community today.
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- Starts 21 December 2025 02:00 PM UTC
- Ends 31 December 2025 05:00 PM UTC
- No Admission Charge
Speakers
Hema Achanta
Signal Processing for the AI Era: A Launchpad for the Next Chapter of SPS and beyond
Abstract:
Artificial intelligence (AI) is reshaping every technical discipline, yet many of its most influential ideas trace directly back to core principles in signal processing. Convolutional architectures, attention mechanisms, spectral methods, and time-series models all carry the imprint of classical Signal Processing concepts only scaled, generalized, and adapted for modern computational environments. In this talk, I will highlight these connections and show how a signal processing perspective provides not only historical context, but practical insight.
This session also introduces the direction I intend to set for our IEEE SPS Chapter in the coming year. Our focus will be on bridging foundational signal processing with rapidly evolving areas of machine learning, control, sensing, and high-dimensional data systems, while also keeping an eye on emerging frontiers such as quantum signal processing and quantum-inspired algorithms. These technologies are still early, but they represent the next horizon where Signal Processing principles may again play a defining role.
By examining how signal processing quietly shapes the trajectory of modern AI and how it may influence future quantum frameworks this talk will serve as both a technical deep dive and a preview of the chapter’s priorities: rigor, relevance, and cross-disciplinary collaboration. I look forward to working with all of you as we build a chapter that reflects the breadth and ambition of the SPS community today.
Biography:
Hema Achanta is a Senior Engineer at GE Vernova, working at the intersection of signal processing, estimation, and control for energy and industrial systems. Her work spans data-driven and model-based signal processing and control methods including model predictive control, reinforcement learning, and fault-tolerant architectures with an emphasis on cyber-resilient and safety-critical applications. She holds a Ph.D. in Electrical and Computer Engineering and an M.S. in Mathematics from the University of Iowa, and is an inventor on multiple patents in resilient estimation and secure control. As Chair of the IEEE Signal Processing Society Chapter, she is focused on advancing signal processing as a unifying foundation across AI, control, and emerging computational paradigms.
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