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I will always remember Dr. Priya Ranjan not only as the Chairman of JK Monitor, but as a mentor, guide and elder brother whose kindness shaped my life and work. Write a tribute at NEW DELHI: Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), in collaboration with the Indian affiliate of Marvell Technology, Inc., has successfully launched Networks for AI/ML Systems, an advanced academic-industry course focused on the systems layer of modern AI—where networking fabrics, memory hierarchies and distributed execution determine performance at scale.
As AI models grow to trillions of parameters, the dominant challenges are no longer confined to compute, but lie in data movement, interconnects and coordination across distributed systems. The course was jointly conceived to educate students on these realities as they appear in production AI infrastructure.
The course was co-designed and is co-taught by Dr. Rinku Shah (IIIT-Delhi) and Abed Mohammad Kamaluddin, director at Marvell, with ongoing technical mentoring from Marvell engineers and architects. In parallel, IIIT-Delhi and Marvell are collaborating on research in AI networking and systems, with the course reinforcing and feeding into these joint research directions.
“Networks for AI/ML Systems is India’s first and the first known course of its kind globally. By integrating AI networking, CXL-based memory systems and AI-scale simulation, the course exposes students to system-level challenges rarely addressed in traditional curricula,” said Prof. Pushpendra Singh, Head, Department of Computer Science and Engineering, IIIT-Delhi.
Nearly 45 students from B.Tech., M.Tech. and Ph.D. programs participated in the initial course, which followed a project-first, peer-driven pedagogy. A post-course survey showed a clear increase in student confidence in reasoning about AI systems, fabrics and distributed machine-learning workflows.
Student projects spanned AI fabrics and transports, CXL-aware training and inference, distributed training and serving pipelines, in-network compute, telemetry-driven optimization and ML-based security and observability. Students consistently identified the open-ended, systems-heavy nature of the projects as the most influential aspect of their learning, enabling them to explain complex system behavior, rather than just implementing tools.
Dr. Rinku Shah noted, “Students rarely see how AI systems behave at scale. This course exposes fabric-level and memory-level challenges that matter in modern AI clusters. The collaboration with Marvell was instrumental in making this possible.”
During his visit to IIIT-Delhi, Prasun Kapoor, AVP at Marvell, interacted with students and reviewed project work. He noted, “The course exposes students to the cutting-edge ML network fabric technologies as they evolve in the industry, reflected in their systems thinking around scale, data movement and real infrastructure constraints.”
Abed Mohammad Kamaluddin added, “Modern AI systems demand engineers who can reason across fabrics, memory and execution. This course was designed to cultivate that end-to-end systems mindset.”
The course demonstrates the impact of deep, voluntary industry–academia collaboration in emerging systems domains. It builds one of India’s earliest cohorts trained specifically in AI/ML systems and fabrics, aligns with national priorities in AI compute and semiconductor capability, and positions IIIT Delhi’s role in next-generation AI systems education and research.
IIIT-Delhi and Marvell plan to deepen this collaboration through joint research initiatives, advanced coursework and continued mentoring—strengthening India’s role in next-generation AI systems and fabrics.
