This week I had the honour to talk with Petar Veličković, a research scientist at and affiliate lecturer at the University of Cambridge. In this captivating discussion, Petar shares insights into his academic background, his transition into machine learning, the value of pursuing a PhD in the field, and much more, including building Map’s travel time prediction algorithm! DeepMind Google Petar's journey began in Serbia, where he attended a mathematics-oriented high school. With a curriculum focused on mathematics, computer science, and physics, he developed a strong foundation in these subjects. Later, he pursued his undergraduate degree in computer science and his PhD in machine learning at the University of Cambridge. Petar's initial interest was in programming, particularly classical algorithms and competitive programming. However, through his academic journey, he discovered his passion for research. A software engineering internship helped him realize that research allowed him to tackle unsolved problems and make innovative contributions. This realization led him to explore research-oriented internships and ultimately pursue a PhD in machine learning. During his PhD, Petar embraced the opportunity to delve deep into research and push the boundaries of his field. He emphasizes that a successful PhD goes beyond becoming an expert in a specific area—it teaches adaptability and the ability to explore new directions. He also highlights the importance of networking, collaboration, and the lasting connections he made during his doctoral studies. Addressing the concern of specialization, Petar emphasizes that a PhD does not limit one's future prospects. It serves as an entry ticket, demonstrating the ability to persist and create work towards a research objective. He emphasizes that research trends change rapidly, and a PhD equips individuals with the skills to adapt and explore various avenues. Moreover, Petar debunks the misconception that a PhD is a requirement to excel in the industry. While a PhD offers valuable experience for research scientists, engineers, and other similar roles, it is not necessary for everyone. The field of AI welcomes individuals from diverse backgrounds and experiences. With the low barrier to entry and the availability of specialized knowledge, one can engage in cutting-edge research without formal machine learning or computer science education. machine learning Petar Veličković provides valuable insights into his academic journey, the transformative nature of a PhD, and the evolving landscape of AI research. His experiences serve as an inspiration for aspiring researchers, showcasing the importance of curiosity, adaptability, and the pursuit of innovative solutions. To gain further knowledge and insight, be sure to listen to the complete interview with Petar Veličković on , , or . Spotify Apple Podcasts YouTube Explore Petar's fascinating path and gain a deeper understanding of the world of AI research at such a great company like Deepmind. Discover the challenges he faced, the discoveries he made, and the impact he envisions for the future (e.g. AGI!). Watch the Video https://youtu.be/l2cj0-HTCC4?embedable=true&transcript=true
