Contrary to popular belief, the so-called 'AI bubble' has not burst in the market - it has only burst in the minds of cynics. AI has transitioned from hype to a new reality, and those who believe in it (paint me a believer) have an opportunity to reshape the world as we know it.
During my time as an AI Automation Engineer, I have actually seen how AI has progressed beyond theory and how it can solve extremely complex, high-stakes problems. Exciting use case scenarios, such as autonomous vehicles and augmented reality, are no longer far-fetched - they are already in motion in our world, proving that AI's promise is not an illusion, but an evolution.
With that being said, I feel I hit a technical ceiling. From May to November 2025, I served as a Research Assistant at GITAM University, focusing on developing AI-driven systems for disease detection and treatment recommendations in agriculture. I also handled data processing and performance optimization of the models. While I excelled in implementing these, I found my process lacking - reliability in unpredictable scenarios and multivariate environments fell just below the acceptable threshold. I was quite surprised. With a 9.0 CGPA and prior success in competitive coding, it was a hard pill to swallow; I lacked the probabilistic and generative grounding needed to move beyond "standard" automations. As I would with any ML model, I yearn to close that gap: to understand the why behind the how, and to transition from an engineer who uses AI to a scientist who advances it - which is why I'm pursuing a Master's degree at the University of Amsterdam.
My conviction in AI's potential was solidified when I saw a model misinterpret a critical scenario, highlighting the need for systems that understand context and reason like humans. If we have to integrate augmented reality and autonomous systems safely into society, models need to show what they think is happening (context), how sure they are (reliability), also why (reasoning), all in a way that humans can trust. My ambition is to build AI models with real-time scene understanding - systems that can interpret data with context, enhanced reasoning, and assist human decision-making under regular as well as uncontrolled settings. This is why I am drawn to the Netherlands because the research culture leans toward rigor with responsibility, and not necessarily commercial applications and hype.
The University of Amsterdam (UvA) is the perfect place for me - for starters, I can bridge my interests across language and vision through the Language Technology Lab and the Computer Vision Research Group. The work associated with Prof. Rijke and Dr. Shaodi tracks close to what I'm aiming for - AI models that interact intelligently as well as accurately. I aim to use mathematical and probabilistic foundations to address the interpretability, and reliability gaps I encountered during my research in Hyderabad, while wishing to be continually challenged to iterate my processes. With a proven strength in Deep Learning and Information Retrieval, and a research culture that pushes beyond 'working demos' toward trustworthy systems - UvA offers the theoretical depth and a systems-thinking approach that I would need to move from simple implementation to a more principled AI development.
The leap from a Bachelor's in computer science at GITAM to a Master's at UvA is not optional for me - it's the step I need. I don't want to keep building systems around ideas I can't fully defend. I want to be in the research community that shapes what AI becomes next. I'm prepared for the program's mathematical intensity, and I'm ready to bring the perspective of someone who has built real systems, hit real limits, and now wants the depth to push past them at the University of Amsterdam.
During my time as an AI Automation Engineer, I have actually seen how AI has progressed beyond theory and how it can solve extremely complex, high-stakes problems. Exciting use case scenarios, such as autonomous vehicles and augmented reality, are no longer far-fetched - they are already in motion in our world, proving that AI's promise is not an illusion, but an evolution.
With that being said, I feel I hit a technical ceiling. From May to November 2025, I served as a Research Assistant at GITAM University, focusing on developing AI-driven systems for disease detection and treatment recommendations in agriculture. I also handled data processing and performance optimization of the models. While I excelled in implementing these, I found my process lacking - reliability in unpredictable scenarios and multivariate environments fell just below the acceptable threshold. I was quite surprised. With a 9.0 CGPA and prior success in competitive coding, it was a hard pill to swallow; I lacked the probabilistic and generative grounding needed to move beyond "standard" automations. As I would with any ML model, I yearn to close that gap: to understand the why behind the how, and to transition from an engineer who uses AI to a scientist who advances it - which is why I'm pursuing a Master's degree at the University of Amsterdam.
My conviction in AI's potential was solidified when I saw a model misinterpret a critical scenario, highlighting the need for systems that understand context and reason like humans. If we have to integrate augmented reality and autonomous systems safely into society, models need to show what they think is happening (context), how sure they are (reliability), also why (reasoning), all in a way that humans can trust. My ambition is to build AI models with real-time scene understanding - systems that can interpret data with context, enhanced reasoning, and assist human decision-making under regular as well as uncontrolled settings. This is why I am drawn to the Netherlands because the research culture leans toward rigor with responsibility, and not necessarily commercial applications and hype.
The University of Amsterdam (UvA) is the perfect place for me - for starters, I can bridge my interests across language and vision through the Language Technology Lab and the Computer Vision Research Group. The work associated with Prof. Rijke and Dr. Shaodi tracks close to what I'm aiming for - AI models that interact intelligently as well as accurately. I aim to use mathematical and probabilistic foundations to address the interpretability, and reliability gaps I encountered during my research in Hyderabad, while wishing to be continually challenged to iterate my processes. With a proven strength in Deep Learning and Information Retrieval, and a research culture that pushes beyond 'working demos' toward trustworthy systems - UvA offers the theoretical depth and a systems-thinking approach that I would need to move from simple implementation to a more principled AI development.
The leap from a Bachelor's in computer science at GITAM to a Master's at UvA is not optional for me - it's the step I need. I don't want to keep building systems around ideas I can't fully defend. I want to be in the research community that shapes what AI becomes next. I'm prepared for the program's mathematical intensity, and I'm ready to bring the perspective of someone who has built real systems, hit real limits, and now wants the depth to push past them at the University of Amsterdam.
