Muhammed Adil Yatkın is a doctoral student at Kuressaare College, whose academic journey began in mathematics but has since led him into the field of engineering.
From Mathematics to Engineering
He began his Bachelor’s studies in mathematics, but his growing interest in computational methods and their applications guided him towards a Master’s in Computer Science at the University of Tartu. There, he developed a deeper fascination with machine learning and artificial intelligence – fields that enable complex engineering problems to be solved more efficiently and effectively. He later pursued his Ph.D. at Tallinn University of Technology under the supervision of Professor Mihkel Kõrgesaar, focusing on the intersection of machine learning and engineering.
"I consider myself fortunate to have started my Ph.D. at a time when machine learning and artificial intelligence are advancing rapidly. This perfectly aligns with my interests," says Yatkın. "My supervisor, Mihkel, has been an incredible support throughout this journey," he adds.
The Role of Machine Learning in Engineering
Yatkın’s research focuses on applying machine learning models to accelerate and simplify complex engineering simulations. By replacing parts of traditional computational methods with machine-learning-based solutions, it is possible to significantly reduce computing time and enhance the efficiency of simulations.
"This journey has been a truly enriching experience," he reflects. Under Mihkel Kõrgesaar’s guidance, he has published several research papers on his work and has also applied for a research grant.
Collaboration from Research to Entrepreneurship
However, the collaboration with supervisor Kõrgesaar extends beyond the university. Together, they founded Euler Analytics, a company focused on machine learning research and development, providing solutions for traditional engineering fields. The company aims to bridge the gap between scientific research and practical applications, working with both the private sector and publicly funded R&D projects.
"We focus on how machine learning can enhance and accelerate engineering computations and processes," Yatkın explains. The merging of machine learning and engineering keeps opening new opportunities in both research and industry. Yatkın’s work demonstrates that machine-learning-based solutions can help reduce computational load and improve the efficiency of complex engineering processes.
As the field keeps evolving, Yatkın is committed to keeping pace with its advancements. Whether in research, entrepreneurship, or the development of new solutions, his goal remains clear: to use machine learning to make engineering more precise, faster, and more efficient.
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