International Conference on Internet of Things, Computing, and AI Applications (ICIoTCAA-2026)

About the Conference Keynote Speakers Conference Chairs Important Dates and Registration Fees Instructions Guidelines for Authors Plagiarism Keynote Invitation Downloads

Keynote Speakers

Saibabu Merakanapalli

Independent Researcher, United States.

Title: AI-Enhanced Safety Architectures for Software-Defined Vehicles: Advancing Fault-Tolerant Steering and Braking Systems.

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Sakthi Sankara Balaji Sathyamurthy

Sakthi Sankara Balaji Sathyamurthy is a globally recognized Technology Executive and AI Transformation Leader with more than 20 years of experience delivering enterprise-scale digital transformation, artificial intelligence, data engineering, cloud modernization, and enterprise data governance initiatives across the banking, financial services, healthcare, pharmaceutical, and technology industries.

Throughout his career, Sakthi has successfully led complex technology programs for globally recognized organizations including Truist Bank, Intercontinental Exchange (ICE), Discover Financial Services, Merck, Pfizer Pharmaceuticals, the Port Authority of New York & New Jersey (NYPD), and other Fortune 500 enterprises. His expertise spans Artificial Intelligence, Machine Learning, Enterprise Data Platforms, Responsible AI, Cloud Architecture, Financial Technology, Regulatory Data Management, and Large-Scale Digital Transformation.

As a technology leader, Sakthi has directed multidisciplinary engineering teams responsible for enterprise modernization initiatives that have improved platform reliability to 99.9%, generated multi-million-dollar operational savings, accelerated cloud adoption, strengthened enterprise data governance, and enabled scalable AI-driven decision-making across mission-critical financial platforms.

Sakthi is an active contributor to the global research and professional community. He serves as an Editorial Reviewer for the Journal of Recent Trends in Computer Science and Engineering (JRTCSE) and regularly reviews scholarly research in Artificial Intelligence and Machine Learning. He has authored peer-reviewed publications in AI, enterprise data engineering, and financial technology, and has received international recognition for his research contributions, including a Best Paper Award and the Global Recognition Award for Innovation and Leadership.

Senior Technology Program Manager, Truist Bank, United States.

Title: Responsible Enterprise AI: Building Trusted, Scalable, and Governed AI Systems for the Future of Financial Services.

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Prakasam Venkatachalam

AVP Technology, LPL Financial, USA.

Title: Engineering Fair and Trustworthy AI: Runtime Architectures for Security, Accountability, and Assurance

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Prosenjit Saha

Engineering Leader — Manager, System Development, Amazon Web Services (AWS), USA.

Title: Loop Engineering at Scale: How Autonomous AI Systems Are Replacing Workflows and Accelerating Business

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Harsh Verma

Harsh Verma is a Principal Software Engineer in AI at Palo Alto Networks, focused on advancing autonomous agent systems and intelligent AI strategies. Harsh is also a Forbes Technology Council member and, as a speaker, mentor, and advisor, he brings both technical depth and strategic guidance to leaders adopting next-generation AI capabilities. He drives responsible, scalable AI adoption. His work influences how enterprises innovate, secure, and operationalize the next generation of intelligent systems. Harsh specializes in offering scalable AI solutions, mentoring aspiring entrepreneurs, delivering impactful conference talks, and advising startups on their AI strategies. Whether it's navigating AI implementation or fostering innovation with AI, Harsh is committed to delivering meaningful results in the tech industry.

His contributions have been recognized through globally respected awards and institutions, reinforcing both impact and influence. He is a recipient of the Global Recognition Award (2026) for measurable advances in enterprise AI and cybersecurity, and also the Globee Awards, which recognize excellence & innovation across global business leadership. He acts as a Senior Member of IEEE, which reflects peer-reviewed recognition of his sustained technical and professional contributions to the field, an honor reserved for individuals demonstrating significant performance and impact.

Beyond the enterprise, Harsh plays a visible role in shaping the broader Data & AI ecosystem. Through mentorship at leading accelerators UC Berkeley Skydeck, judging global AI innovation forums, and publishing thought leadership in Forbes and Hackernoon as a Technical Council Member, he contributes to defining how AI leadership itself is evolving. His work influences not only the organizations he serves but also the next generation of AI systems, leaders, and standards.

Principal Software Engineer in AI/ML, Palo Alto Networks, USA.

Title: Building an AI-Enhanced Enterprise Security Solution Using Artificial Intelligence in Cybersecurity

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Vinaykumar Chitukoori

Senior Technical Sales Engineer, ConsultNet, New Jersey, United States.

Title: Digitization’s Toll on Talent: Bridging Technology, Human Capital, and Strategic Hiring

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The automotive industry is undergoing a major transformation from hardware-centered vehicle development toward software-defined vehicle architectures. In this emerging model, vehicle functions are increasingly implemented, updated, and improved through software running on centralized or zonal computing platforms. This transition enables greater scalability, faster feature deployment, improved connectivity, and more intelligent vehicle behavior, but it also introduces significant challenges involving system complexity, functional safety, cybersecurity, communication reliability, and validation. This keynote explores the engineering principles and enabling technologies behind safe, intelligent, and connected software-defined vehicles. It examines the evolution from distributed electronic control units to centralized computing architectures and discusses the roles of AUTOSAR Classic, AUTOSAR Adaptive, service-oriented communication, vehicle middleware, and standardized software interfaces. Particular attention will be given to the integration of safety-critical technologies such as steer-by-wire, brake-by-wire, electronic power steering, advanced driver-assistance systems, and intelligent chassis control. The presentation will also discuss how artificial intelligence can support vehicle perception, predictive diagnostics, fault detection, energy-efficient control, and decision-making under changing road, weather, and operating conditions. Because AI-supported functions must operate dependably in real-world environments, the keynote will address functional-safety considerations, fault-tolerant design, degraded operating modes, cybersecurity protection, and verification through model-based development, simulation, and hardware-in-the-loop testing. In addition, the keynote will highlight practical engineering challenges such as managing complex interfaces, maintaining end-to-end communication integrity, coordinating functions across vehicle domains, and designing reusable architectures for multiple vehicle platforms. The session will conclude with a forward-looking perspective on how modular software, intelligent control, continuous updates, and safety-focused system engineering can work together to build the next generation of dependable, connected, and automated mobility.

Fairness, privacy, security, and accountability are often engineered as separate concerns, evaluated by different teams using different tools. In deployed AI systems, however, these properties interact—and can conflict. Data minimization may remove attributes required to assess disparate outcomes. Federated learning in non-IID environments can lead to uneven influence among participating nodes. Security and privacy controls that reduce observability can also restrict the evidence needed for fairness assessment, provenance, and accountability. Trustworthiness, therefore, cannot be treated as a post-deployment compliance layer; it must be designed into the system architecture from the outset. This keynote presents an architectural approach to engineering trustworthy AI around four interdependent foundations: fairness, privacy, security, and accountability. Privacy is addressed through federated and privacy-preserving learning across distributed and partially trusted environments. Security incorporates multimodal and context-aware authentication, neuro-symbolic attack forecasting, and provenance mechanisms that trace decisions across agents, models, data, and software dependencies. The keynote further introduces an intent-aware governance framework that shifts fairness evaluation from a one-time pre-deployment checkpoint to continuous runtime assurance. A three-plane architecture—policy, enforcement, and evidence—translates governance requirements into executable controls, observable decisions, and auditable evidence. These principles are illustrated through TrustFeed, a deployed multi-agent human-AI system for content assessment and disclosure. Across 51 evaluation sessions, participants achieved 54.8% accuracy in identifying machine-generated content, demonstrating the limitations of relying on human detection alone and motivating provenance, disclosure, and evidence-based trust mechanisms. Attendees will leave with a practical reference architecture for building trustworthy AI systems and a clearer understanding of where fairness, privacy, security, and accountability reinforce one another, where they conflict, and how those trade-offs can be governed at runtime.