Bhanu Prakash Reddy Rella 

Senior Data Engineer at Meta 

IEEE Senior Member

About Bhanu:

Bhanu Prakash Reddy Rella is a Senior Data Engineer at Meta and an IEEE Senior Member, recognized for his leadership in sustainable AI and energy-efficient signal processing systems. At Meta, he advances real-time data and signal pipelines that process billions of user interactions daily, focusing on efficient signal representation, streaming computation, and adaptive architectures. His work ensures that modern AI platforms scale with accuracy, resilience, and reduced environmental cost. Previously, at Walmart Global Tech, Rella designed cloud-native pipelines for advertising and retail analytics, where he introduced energy-aware approaches to signal extraction and feature computation that transformed noisy, high-volume data into actionable insights. Rella also contributes to the IEEE DataPort Site Enhancement Subcommittee, helping shape the platform’s usability, AI/ML innovations, and next-generation data science features — strengthening IEEE DataPort as a premier global research data platform. He is the author of Energy-Efficient Computing for Modern AI, inventor of patented sparse neural network techniques, and founder of The Green AI Initiative, a global community platform promoting responsible and sustainable practices in AI and signal processing. He has contributed as peer reviewer for leading journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Elsevier JPDC, and PLOS ONE. He has also served as Session Chair, Advisory Board Member, or Program Committee Member for multiple international IEEE and Springer conferences, and as a judge for various global hackathons and innovation competitions across the USA, Europe, Middle East, and Asia. Through his patents, publications, books, and community leadership, Rella continues to advance Green Signal Processing as both a technical discipline and a global responsibility. He holds a Master’s in Management Information Systems and a B.Tech in Electrical and Electronics Engineering, bridging the worlds of engineering, computing, and strategic leadership.

1. Why did you choose the field of Signal Processing as your career?

For me, signal processing was the “hidden language” behind everything I encountered as an engineer. During my undergraduate studies, I realized that whether it is speech, images, medical scans, or data logs — all are signals that carry structure waiting to be uncovered. When I entered industry, this fascination deepened.

At Walmart, I worked with massive volumes of advertising data that behaved like high-dimensional signals — noisy, redundant, and yet incredibly valuable once processed correctly. At Meta, the challenge is even greater: billions of behavioral, transactional, and contextual signals flow across platforms daily. To me, signal processing is the discipline that allows us to extract meaning from this ocean of data while ensuring the process is efficient and sustainable.

It is this conviction — that signal processing can be the bridge between theory, AI, and global responsibility — that inspired me to launch The Green AI Initiative, bringing together researchers and engineers worldwide to embed sustainability into every layer of computation.

2. How does your work affect society?

At Meta, I work on real-time signal processing frameworks that sustain one of the world’s largest digital ecosystems. Every day, billions of multimodal signals — interactions, behaviors, and contextual data — flow through our platforms. My role is to ensure that these signals are processed with precision, efficiency, and responsibility. By advancing methods such as sparse representation, adaptive streaming architectures, and federated signal processing, I help create systems that are faster, more accurate, and far less energy-intensive.

At Walmart, I gained the foundation for this journey by redesigning retail and advertising pipelines, embedding efficiency into signal extraction and feature computation. That experience showed me that signal processing is not only about delivering insights, but also about minimizing redundancy and resource consumption.

The societal impact of this work is twofold. First, billions of people experience reliable, personalized AI-driven services that enrich their daily lives. Second, the platforms delivering those services operate with Green AI principles, demonstrating that progress in signal processing can align with environmental stewardship. In short, my work ensures that technology does not just scale, but scales responsibly.

3. What challenges have you had to face to get where you are today?

A recurring challenge has been balancing innovation with sustainability. In large-scale industry, the pressure is often on achieving maximum accuracy or performance — sometimes at the expense of efficiency. Early in my career, I realized that this mindset leads to resource-heavy systems that are hard to sustain. At Walmart, I had to push for redesigning pipelines in a way that proved efficiency was not a compromise but a competitive advantage. At Meta, I faced similar questions: can we serve billions of users with less compute overhead? My answer was to pioneer approaches such as sparse neural networks, streaming optimization, and federated learning with pruning, which delivered accuracy while reducing waste.

Another challenge has been advocacy. Convincing teams and stakeholders that Green AI is not just ethical but also good engineering and good business requires persistence, clear results, and data-driven evidence. Looking back, I see these challenges not as obstacles but as opportunities to prove that sustainability and performance can go hand in hand.

4. What advice would you give to scientists/engineers in signal processing?

Signal processing is evolving faster than ever. It is no longer confined to audio or image compression; it now underpins AI, cloud data systems, healthcare analytics, and immersive computing. My advice to young engineers is to think beyond accuracy metrics. Ask yourself: How much energy does this system consume? Can we design it better without compromising quality? That mindset will set you apart.

Also, embrace interdisciplinarity. Signal processing today sits at the crossroads of mathematics, AI, distributed systems, and even sustainability science. The more connections you can make across domains, the more impactful your work will be. And finally, engage with the community — whether publishing, mentoring, or contributing to IEEE. Signal processing is a field built on collaboration, and your career will grow as much through service as through technical achievements.

5. Anything else that you would like to add?

I believe AI and signal processing are at an inflection point. On one hand, they can accelerate global challenges like rising energy consumption; on the other, they can provide tools to solve them. My work — through patents, publications, and sustainable pipeline design at Meta and Walmart — has always leaned toward the latter. I see Green AI and sustainable signal processing as not just technical goals but global responsibilities.