Hey there, fellow researchers! If you’re reading this, chances are you’re considering pursuing a PhD in machine learning, just like me. I’m currently in a master’s program in statistics, and I’m eager to dive deeper into the theoretical foundations of machine learning and deep neural networks. But with so many fascinating areas to explore, it can be tough to decide on the perfect PhD topic. That’s why I’m seeking advice from the community.
My primary interest lies in statistical learning theory, but I’m also drawn to optimization as a potential research area. My ultimate goal is to work as a researcher at an AI research lab, focusing on theoretical and foundational work on neural networks. So, I want to make sure I choose a topic that aligns with my goals and sets me up for success.
If you’re in a similar situation or have experience in this field, I’d love to hear your thoughts. What areas of research would you recommend for someone interested in doing fundamental research in machine learning and deep neural networks? What are some popular and promising techniques and mathematical frameworks used by researchers working on the theoretical foundations of deep learning?
Thanks in advance for your advice and insights!