Evan Markou
Australian National University
Evan Markou is a PhD candidate in machine learning at ANU, where he explores the mathematical underpinnings of artificial intelligence. He earned his Bachelor’s (Honours) in Computer Science from Harokopio University of Athens and completed a Master’s in Machine Learning and Computer Vision at ANU. Evan’s research spans machine learning, nonconvex optimisation, high-dimensional statistics and geometry, and deep learning theory. His interdisciplinary work seeks to uncover the complex principles driving deep neural networks, aiming to create more robust, efficient, and insightful AI systems.
Can you give me a quick rundown about the type of mathematics you are studying and its potential impacts for the broader community (think how you would explain your work and studies to others who don’t study maths)
I work in applied mathematics with a focus on theoretical deep learning. This involves using tools from mathematical analysis, probability theory, optimization, and statistics to rigorously study deep neural networks—specifically their convergence, optimality, and generalisation properties.
How did you get into mathematics/statistics/data science? Was there someone or something that inspired you to this field?
I’ve always been driven by a natural curiosity about how the world works and a love for solving puzzles. Early on, a great teacher showed me that mathematics wasn’t just about numbers—it was a way to describe and predict real-world phenomena. That early inspiration sparked my interest in both statistics and data science, as I saw how they could transform data into meaningful insights. Over time, this passion evolved into a focus on the theoretical foundations of deep learning, where I now explore how neural networks learn and generalise.
You received a Travel Grant to attend AMSI Summer School 2025. How important was this in terms of your ability to attend, fully participate in the program and meet others studying in similar fields? Do you think it was an advantage to attend the program in-person?
The Travel Grant was essential for my participation at the AMSI Summer School 2025. It eased the financial burden, allowing me to focus on engaging fully with the program rather than worrying about travel costs. Attending in-person was a significant advantage—it fostered spontaneous discussions, meaningful networking with peers and experts, and a deeper, more immersive learning experience that I believe would have been hard to replicate virtually.
What was the most valuable part of the program for you?
The most valuable part of the program was the opportunity to engage deeply with both peers and experts. The interactive sessions and discussions not only enriched my understanding of advanced topics in deep learning but also fostered meaningful connections and collaborations that will significantly influence my future research.
In the long-term, what do you think are the benefits of having attended Summer School?
The fresh perspectives and collaborations I’ve formed continue to shape my research trajectory and open up future opportunities, both academically and professionally.
Summer School included a special Careers Day program which aims to help give students an idea of the kinds of career paths available to maths graduates in industry and private sector research areas. Do you feel better equipped to explore career options in the mathematical sciences after attending AMSI Summer School?
Yes
What advice would you give to someone who is considering applying for Summer School in 2026? Should they apply and why?
I’d strongly encourage you to apply for Summer School 2026. It’s a unique opportunity to immerse yourself in a stimulating academic environment where you can engage with leading researchers and passionate peers. The in-person experience is particularly valuable—you gain not only advanced insights into your field but also lasting connections and practical skills that can propel your career forward. If you’re serious about deepening your knowledge and building a robust professional network, this is an opportunity you shouldn’t miss.
What are your current career ambitions in the mathematical sciences sector?
My current career ambitions center on advancing the theoretical foundations of deep learning while bridging the gap between abstract mathematics and practical applications. I aim to pursue a research-focused career—ideally starting with a postdoctoral position and eventually moving into a faculty role—where I can continue exploring interesting topics in AI.
How did connecting with the community at AMSI Summer School support your experience?
Connecting with the AMSI Summer School community was one of the most enriching aspects of the experience. It wasn’t just about absorbing lectures or attending sessions—it was the chance to exchange ideas, ask questions, and receive diverse perspectives on complex topics. Informal discussions during breaks or group work often sparked new insights, and the supportive atmosphere encouraged honest dialogue about challenges in our research. This network of peers and mentors has not only boosted my current understanding but is also laying the groundwork for future collaborations and a sustained academic community in the mathematical sciences.