Narges Araboljadidi is a mathematician working in the field of optimization. Her research sits at the meeting point of mathematical theory and practical problem-solving — she is drawn to questions that are difficult precisely because they are nonsmooth or nonconvex, the kind of problems where classical methods reach their limits.

Her path through optimization has been shaped by a curiosity for how abstract algorithms connect to the real world. She has explored convex approximation techniques for energy-efficiency problems in telecommunications, and bundle methods for challenges arising in machine learning. A recurring theme in her work is bridging disciplines: taking an idea developed in one corner of mathematics and asking where else it might be useful.

She values international collaboration as part of how research grows, and has carried out research periods at universities across Europe, working alongside established groups in the optimization community.

I am interested in optimization problems that resist easy treatment — those that are nonsmooth, nonconvex, or large in scale. My aim is to design and analyse algorithms that handle these difficulties efficiently, and to understand both why they work and where they can be applied.

Much of the appeal of optimization, for me, lies in its reach. The same mathematical structures that describe an optimization problem can appear in telecommunications, in machine learning, in data analysis, and far beyond. I enjoy following these connections: studying a method in its pure form, then asking what real problem it might quietly be the answer to. My work combines theoretical investigation with hands-on implementation and numerical experimentation.