Murugesh recently won the Best AI/ML Solution award at the MIT (Massachusetts Institute of Technology) Grand Hack 2023 for building DiaQR - a solution focused on detecting early symptoms of diabetes in individuals and preventing limb amputations due to diabetic foot ulcers, which is a major problem in India.

Murugesh’s expertise lies in the efficient, profitable, and secure use of cutting-edge AI and ML technologies to meet the needs of businesses and their end users.

Over his 13-year long journey as a technology leader, Murugesh has mastered a spectrum of skills, including Agile, JavaScript, Python, Node JS, PHP, Framework Concepts, C#, jQuery, Angular, React, JAM Stack, IOT, Serverless Computing, NoSQL, AWS Services, and DevOps.

INDIAai interviewed Murugesapandian to get his perspective on AI.

With your extensive expertise in various technologies, how has your knowledge of AI & ML influenced your approach to product development?

At Siam Computing, I use my expertise in AI and ML to improve product quality and reduce time to market for the digital solutions we build. We spend a significant amount of time understanding user needs and preferences by conducting interviews and extensive market research. Now, AI and ML help us analyze vast amounts of data to predict trends and user behavior better, enabling us to make data-driven decisions and implement proactive product adjustments. This translates to faster product development cycles by prioritizing features that resonate better with the needs of our clients and their end users.

How does Siam Computing leverage AI to simplify the problem-to-product journey for its clients?

We believe the problem-to-product journey should be efficient and user-centric. That's where AI shines. Our AI-driven processes help us identify user pain points and tailor solutions that address specific client needs. AI helps us automate repetitive tasks like testing and code reviews while ensuring code quality. It also helps us with effective peer development. This approach allows us to deliver high-quality products faster while keeping our clients closely involved throughout the product development journey.

In the education and healthcare domains, how has AI-driven digital transformation been achieved through your initiatives at Siam Computing?

Siam Computing is at the forefront of AI-powered digital transformation in education and healthcare. We're developing AI solutions to improve student-tutor, buyer-seller and doctor-patient interactions. For instance, in healthcare, we're building solutions for diagnostics, patient monitoring, and enhancing patient care using predictive analytics. Our diagnostic tools and patient monitoring systems leverage AI for real-time data analysis. This empowers healthcare providers to deliver proactive care and improve patient outcomes. 

What challenges have you encountered in implementing AI in these sectors, and how have you overcome them?

Implementing AI in sensitive sectors like healthcare and education comes with its own set of challenges, like data masking (for students or patients) for privacy, integration and interoperability with disparate or legacy systems, staying updated with the evolving technological landscape, and so on. 

Our well defined processes helped us overcome these hurdles by adopting best-in-class security protocols, flexible integration techniques, implementing ongoing training programs, and fostering clear communication. We also adopted a phased approach, starting with small-scale projects to demonstrate the value proposition, provide clear reports, and gain stakeholder buy-in before scaling up.

How do you ensure that AI solutions developed at Siam Computing are user-centric and address real-world problems?

User-centricity is at the core of everything we do at Siam Computing. To ensure our AI solutions address genuine needs, we employ a rigorous human-centered design process. This involves extensive user research, design thinking, rapid prototyping, and extensive usability testing. We engage with users constantly and gather feedback at every stage to refine our solutions and guarantee they solve real-world problems, not just create a technological illusion.

How do you address ethical considerations in AI development, particularly in sensitive domains like healthcare and education?

Ethical considerations are our top priority, especially with product development in healthcare and education. For instance, in healthcare, we adhere to the strictest data privacy regulations, like HIPAA (in the US) or DISHA (in India). We employ robust security protocols to safeguard sensitive user data using masking techniques and conduct comprehensive testing to ensure the accuracy and reliability of our AI models. Transparency is key here, so we provide clear communication about data usage and empower users with control over their information. 

How do AI and IoT intersect in your projects at Siam Computing, and what synergies have you discovered?

For healthcare providers, we have unlocked exciting possibilities at the intersection of IoT and AI. We've leveraged this synergy to create solutions like remote patient monitoring systems and caregiver notification solutions that use real-time data collection from biosensor-based devices like CGMs (continuous glucose monitors) and smart wearables. AI analyzes the collected data to identify trends and potential issues (predictive analysis). This enables proactive interventions and cost savings for patients and payers (insurance providers). Ultimately, this synergy empowers providers to deliver smarter healthcare and enhance user experiences by empowering systems to make quick decisions and bypass the need for manual analysis. This allows for faster diagnoses, improved care coordination, and better patient outcomes. 

What advice would you give to aspiring AI technologists and developers looking to make an impact in the product development industry?

For aspiring AI professionals, my advice is threefold. First, stay curious and keep learning about AI developments. Gain real-world experience by working on existing use cases and exploring diverse datasets. 

Second, remember that AI is not a magic solution. You must know when AI is NOT required and where other technologies or approaches might be more appropriate. AI and ML adoption should be driven by genuine necessity and not used as a marketing gimmick. Explore areas where AI can impact and transform lives. 

Finally, embrace a growth mindset. Don’t ignore challenges; instead, approach them positively to learn and improve. Get involved in community events, look for mentors, and work with peers.