Azgar Ali N
Azgar Ali N is a Technical Program Manager leading critical network deployment programs in North and South Americas for Google Enterprise Network under Global Networking and Infrastructure. Partnering with Google Research and DeepMind, he contributes in Responsible AI development of products such as NotebookLM, Magic Editor for Google Photos and Gemini multimodal models through evaluations around highly sensitive areas for launch readiness.
Azgar holds a MS in Engineering Management from Northwestern University, BE in Electronics and Communication Engineering from R V College of Engineering, Bangalore and a PG Diploma in Cyber Laws and Cyber Forensics from the National Law School of India University (NLSIU). He is an elected Fellow of the Institution of Electronics and Telecommunication Engineers (IETE) and a Senior Member of IEEE. He has represented IEEE-USA in the US Congress, advocating for policy positions in AI, technology infrastructure, skilled workforce development, funding of federal research institutions and programs.
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Project and task scheduling under uncertainty remains a fundamental challenge in program and project management, where accurate estimation of task durations and dependencies is critical for delivering complex, multi project systems. The Program Evaluation and Review Technique provides a probabilistic framework to model task variability and critical paths. In this paper, the author presents a novel formulation of PERT scheduling as an energy minimization problem within a Hopfield neural network architecture. By mapping task start times and precedence constraints into a neural computation framework, the networks inherent optimization dynamics is exploited to approximate globally consistent schedules. The author addresses key theoretical issues related to energy function differentiability, constraint encoding, and convergence, and extends the Hopfield model for structured precedence graphs. Numerical simulations on synthetic project networks comprising up to 1000 tasks demonstrate the viability of this approach, achieving near optimal makespans with minimal constraint violations. The findings suggest that neural optimization models offer a promising direction for scalable and adaptive project tasks scheduling under uncertainty in areas such as the agentic AI workflows, microservice based applications that the modern AI systems are being built upon.
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Record Number of Members Visit U.S. Congress to Talk Tech Policy
IEEE Spectrum (2025)
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This IEEE Spectrum article reflects on advocacy for U.S. technological leadership during my Congressional visit through IEEE-USA. Leading an expert group of other distinguished IEEE members, we urged lawmakers to support critical initiatives. Key priorities included sustained funding for federal research institutions like NIST, NASA, and the NSF, reauthorizing the SBIR/STTR programs vital for small business innovation, and passing the CREATE AI Act to democratize AI resources by establishing the National AI Research Resource (NAIRR).
We also emphasized strengthening the STEM talent pipeline through the CHIPS and Science Act and expanding high-skilled immigrant visas. We highlighted rapid AI advancements, such as autonomous vehicles, the surge in FDA-approved AI based medical devices, as underscoring the need for these strategic investments and policy actions. The article conveys a sense of urgency, calling for concrete congressional action to ensure the U.S. maintains its technological edge while also sharing my personal experiences.
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Beyond The Code: AI Regulations As The Secret Compass Of Engineering Managers
Proceedings of the American Society for Engineering Management 2024 International Annual Conference (2024)
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Technology is a product of society. As technology evolves, the norms governing it have to mature for enabling its proper use within the society. The interest in Artificial Intelligence (AI) has surged following the introduction of chatGPT. Firms, both large and small, are competing to develop new products and solutions involving AI. Amidst these developments, leading corporations such as Google and Microsoft have proactively committed to upholding responsible innovation in AI development. Governments worldwide are responding with the creation of guidelines and regulations in the field. Notably, in March 2024, the United Nations General Assembly (UNGA) adopted landmark regulation on AI.
At the heart of these developments in AI are engineering managers who leverage technical advances to build products and services that create value. To effectively harness AI for human benefit, engineering managers must be aware of these evolving regulations governing AI. Some regulations such as Digital Markets Act (DMA) and General Data Protection Regulations (GDPR) have far reaching consequences for organizations globally. Having a working knowledge of these statutory requirements will enable engineering managers to identify the opportunities and constraints in leveraging AI technology while building products and services. It will allow them to make informed decisions about data collection methods, model training processes, the deployment of AI systems and metrics for their evaluation. At scale, it can become a competitive advantage for the firms they work in, as explored through real-world examples in this paper.
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Google Earth Engine for Astronomy Sites
Google Earth Outreach, Google (2020)
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This poster, "Google Earth for Astronomy Sites," by Azgar Ali N. was selected and presented at the Geo for Good Summit 2020. It details an outreach activity co-organized with Dr. B S Shylaja that used tools like Google Earth, Google Earth Engine, and OpenSpace to analyze ancient astronomical structures, with a focus on the Indus Valley Civilization. The project showcased how these mapping tools can visualize alignments (e.g., Stonehenge ), analyze site plans (e.g., Mohenjodaro ), and perform calculations on ancient sites (e.g., Dholavira ). The approach was well-received by a diverse audience and highlighted the research potential of these tools for astronomers studying ancient sites.
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