Best Place To BuildProf. Prabhu Rajagopal l"Brain drain isn't about salary. We want to be challenged"| Ep. 3
CHAPTERS
- 0:00 – 2:32
From “Intelligent Manufacturing” to Industry 4.0: why the curriculum was ahead of its time
Amrit and Prof. Prabhu connect over being alumni of the Intelligent Manufacturing dual degree and unpack what “intelligent” meant back then versus today’s Industry 4.0. Prabhu explains the early vision: sensors, live feedback, connected machines, and optimization across the manufacturing process.
- •Industry 4.0 as connected, networked, sensor-rich manufacturing
- •Intelligent manufacturing anticipated real-time monitoring and optimization
- •Machine-to-machine communication as a core idea
- •How this framing shaped Prabhu’s later research interests
- 2:32 – 4:44
Winning the Shanti Swarup Bhatnagar Prize: personal roots and translational impact
Prabhu reflects on receiving the Bhatnagar Prize—both as a career milestone and a personal moment tied to his father’s CSIR background. He emphasizes that the award recognized not only academic publications but also field-deployed, industry-relevant innovation.
- •Bhatnagar Prize significance and family connection to CSIR
- •Seeing his name alongside India’s scientific luminaries
- •Recognition under “technology and innovation” category
- •Pride in translating research into real-world deployments via industry/startups
- 4:44 – 6:05
What “science administration” means at IITM: building mission-driven innovation infrastructure
The conversation demystifies “science administration” as creating enabling systems that push mission-oriented scientific work into reality. Prabhu frames IITM’s innovation bodies (CFI, NIRMAN, etc.) as institutional foundations that could become historically significant over time.
- •Science administration as administrative force behind mission science
- •Analogies to institution-building by Bhabha/Sarabhai
- •Nurturing student innovation and product creation
- •Helping ideas move into practical solutions, startups, and products
- 6:05 – 9:58
The IIT Madras innovation stack: CFI → NIRMAN → Incubation (and how research commercializes)
Prabhu draws a clear “innovation and development stack” showing how students and researchers move from hands-on building to pre-incubation and then to full incubation. The ecosystem combines student maker culture, institutional support, and pathways for research commercialization.
- •CFI as the hands-on maker core transforming student culture
- •IC&SR as foundational industry-interface and support layer
- •Feeders like TechSoc/E-Cell and commercialization via GDC
- •NIRMAN as a rare ‘pre-incubator’ bridging idea-to-startup
- •Multiple pathways: student projects to incubation; lab research to commercialization
- 9:58 – 12:33
Changing academic culture: PhDs thinking like founders, and why “brain drain” isn’t just salary
They discuss how motivations have shifted: fewer students automatically go abroad, and more stay for meaningful challenges and impact. Prabhu notes that many incoming PhD applicants now explicitly ask about startup potential, reversing older academic norms.
- •PhD interviews now include startup ambitions and market-oriented topic choice
- •Earlier era: DRDO/TCS/abroad as dominant options; now broader opportunities
- •Shift from ~70% going abroad to more staying back (as a trend)
- •Brain drain reframed: desire for challenging, high-impact problems
- •Ecosystem and opportunity density as retention factors
- 12:33 – 16:41
Startup story #1—Planys: underwater robotics for inspections and ‘Internet of Underwater Things’
Prabhu narrates how Planys emerged from CFI robotics teams and a master’s project, catalyzed by industry interest (e.g., Reliance). The company grew into a pioneer of commercial underwater robotic inspection, with global operations and defense prospects.
- •Origin in CFI teams (AUV/ROV competitions) and a pipeline-inspection thesis project
- •Industry pull: early traction from Reliance leading to product direction
- •Applications: oil tanks, bridge river crossings, dams/hydel infrastructure
- •Structural Health Monitoring (SHM) as continuous/operational infrastructure monitoring
- •‘Internet of Underwater Things’: acoustic beacons + vehicles + satellite uplink for data transfer
- 16:41 – 20:12
Startup story #2—Solinas and a primer on NDE: seeing inside structures without damaging them
The discussion moves to Solinas, rooted in lab work and a dual-degree project, now deployed across Indian cities for water/sewer maintenance and anti-manual-scavenging robotics. Prabhu then explains NDE (Non-Destructive Evaluation) using medical X-ray/ultrasound analogies and ties NDE to both Planys and Solinas workflows.
- •Solinas deployments in Smart Cities and sewer/water network maintenance
- •Robotics aimed at eliminating manual scavenging
- •NDE explained: inspection without damage to prevent catastrophic failures
- •Historical tools: X-ray; modern tools: ultrasonics and other sensing methods
- •NDE in practice: Planys carries sensors underwater; Solinas uses internal laser-based pipeline inspection
- 20:12 – 21:47
Startup story #3—XYMA and waveguide (fiber) acoustics: sensing in extreme industrial temperatures
Prabhu introduces XYMA as deep-tech commercialization from PhD-level research: transmitting sound through “waveguides” to sense conditions in high-temperature processes where conventional sensors fail. He highlights industrial adoption across refining, petrochemicals, mining, and process industries, and ongoing material challenges for steel temperatures.
- •Waveguide acoustics as ‘sending sound through a wire’ (analogous to fiber optics)
- •Enabling sensing at ~1500°C for smelting, vitrification, and nuclear contexts
- •Outputs: temperature, viscosity, rheology inferred remotely
- •Commercializing ‘core science’ into products used across many process industries
- •Frontier challenge: steel at ~1800°C still drives ongoing research
- 21:47 – 25:42
TRL and the ‘valley of death’: why labs + startups together solve industry-oriented problems
They break down Technology Readiness Levels and explain why academia traditionally stops at TRL 1–3 (proof-of-concept), leaving a gap before field deployment. Prabhu describes CNDE’s model: industry supplies problem statements, the lab builds deep IP, and startups execute commercialization and deployment.
- •TRL spectrum: TRL 3 proof-of-concept; 4–6 mockups; 7–9 field studies
- •Academia’s traditional focus on TRL 1–3 and why momentum/funding often stops
- •The ‘valley of death’ between prototype and deployment
- •CNDE as deep R&D + infrastructure; startups as delivery/commercialization arm
- •Industry-linked research pipeline across oil & gas, nuclear, defense, aerospace
- 25:42 – 28:06
CNDE’s startup ecosystem and the pivot to data, AI, and cybersecurity
Prabhu maps the expanding set of CNDE spinouts beyond Planys/Solinas/XYMA, showing how a large lab can continuously generate ventures. He explains how robotics and sensors created massive data streams, driving adoption of AI for summarization/analysis and raising cybersecurity concerns—eventually leading to blockchain and healthcare work.
- •CNDE scale: large multi-level team enabling sustained R&D and IP creation
- •Additional spinouts: Dhvani (split into multiple companies), Maximal Labs, Detec, Plenome
- •Data explosion from robots (gigabytes/terabytes) pushes automation and AI analytics
- •Manual analysis replaced by custom AI summarization and interpretation
- •Cybersecurity motivation: data transmission vulnerabilities from industrial sensors
- 28:06 – 30:31
Why blockchain matters for AI: data fidelity, privacy, and healthcare interoperability (Plenome)
Prabhu argues that trustworthy AI requires trusted data pipelines, and positions blockchain as a way to preserve data authenticity and enable privacy/anonymization. The COVID-era challenge of moving medical test data across geographies becomes the catalyst for Plenome’s healthcare cybersecurity and interoperability direction.
- •Blockchain as protection for data integrity used in AI models
- •Privacy/anonymization as a prerequisite for large-scale analytics
- •COVID revealed practical pain: repeated tests due to poor interoperability
- •Plenome’s focus: healthcare data security with interoperability as a side effect
- •AI + data systems as a scalable next frontier for engineering-led innovation
- 30:31 – 32:57
Engineering has always been cross-disciplinary: from NDE roots to ‘general engineering’ mindset
Responding to whether this work is ‘outside mechanical engineering,’ Prabhu describes how NDE inherently spans mechanics, electronics, instrumentation, signal processing, and AI. He argues disciplines still matter for deep domain expertise, but at sufficient depth they converge—especially through data-centric thinking.
- •NDE as inherently multi-disciplinary from the start
- •Early precedent: neural networks applied to ultrasonics decades ago
- •Debates on creating ‘general engineering’ and changing boundaries
- •Value of going deep in one domain while integrating across fields
- •Data/AI as a unifying meta-layer across disciplines
- 32:57 – 35:08
Student-to-startup pipeline example: IITM’s blockchain elections (club → startup)
Prabhu recounts how the WebOps & Blockchain Club built a distributed-node voting system used in multiple election cycles. The project later transitioned into repeated deployments via Plenome, illustrating IITM’s pathway from student experimentation to entrepreneurial execution.
- •Blockchain framed as a tool for ‘trust’ in multi-party verification systems
- •Elections as the ‘mother of all trust problems’
- •Prototype: student-ID-based authentication and distributed counting/results
- •Three election cycles completed; later run twice by Plenome
- •Repeatable pattern: club project → productization → startup deployment
- 35:08 – 41:23
Life at IIT Madras: nicknames, alumni bonds, and the Vivekananda Study Circle
The conversation turns personal—nickname culture, lifelong alumni networks, and formative communities on campus. Prabhu shares how the Vivekananda Study Circle helped students reconnect with Indian philosophical roots and encouraged a broader, multi-narrative way of understanding history and self.
- •Nickname culture as a shared identity system at IITM
- •Alumni hospitality and long-term bonding across geographies
- •Vivekananda Study Circle’s role in exploring Indian/Hindu philosophy
- •Learning to seek multiple narratives and primary-source thinking
- •How campus communities shaped values beyond academics
- 41:23 – 44:05
Poetry and philosophy: beauty as a guiding principle in science, innovation, and life
Prabhu explains why “poet” sits alongside his technical identity: for him, poetry is the search for beauty in everything, including research and building. He links this to philosophical ideas like Satyam-Shivam-Sundaram and frames innovation as a pursuit of truth that must also be auspicious and beautiful.
- •Writes poetry under a different name; treats it as a parallel life
- •Poetry as ‘finding beauty’—not limited to rhyme/rhythm
- •Satyam-Shivam-Sundaram as an integrated worldview
- •Innovation framed as a search for truth with societal goodness
- •Surrealism/magical realism influences; hint at a future poetry-focused episode
- 44:05 – 51:11
New venture directions and IIT Madras as the ‘best place to build’: the three-part formula
Prabhu outlines newer ventures (AI for 3D printing, AR/VR marketplaces, foundational robotics models, ML water metering) and discusses whether startup pressure exists (he says it’s still fringe but growing). He closes with why IITM stands out: strong industry interactions, a student making culture, and a mature startup pipeline supported by long-standing institutions and flagship success stories.
- •New ventures: Matterize (AI visual models for 3D printing), Zeroscape (AR/VR marketplace), Botforge (robot foundation models), TerraClime (ML water metering)
- •Startup pressure vs excitement: entrepreneurship still not the default path
- •IITM’s replicable template: industry interactions + making culture + startup culture
- •Chennai’s industrial density as a force-multiplier for problem access
- •Legacy ecosystem pillars: NSRP/IC&SR, CFI, NIRMAN, Incubation Cell; examples like Agnikul, GalaxEye, Ather, Modularz
