Sweden
I am a Mathematician and Data Scientist with a PhD in Geometric Optimization. During my academic journey I polished my expertise in geometric analysis, combinatorial optimization, and algorithm design, the foundations I continue to apply daily in industry.Since completing my PhD, I have worked as a Data Scientist and R&D Consultant, contributing to projects where mathematics, optimization, and machine learning converge. I helped build a digital twin of a real-world warehouse and developed an optimized bin-packing solution tailored to the facility’s physical constraints. I am also part of a fusion stellarator project, applying machine learning techniques to explore optimal designs for what could become a key energy technology of the future. Currently, I’m working on assignment optimization in logistics settings, once again integrating mathematical rigor with practical warehouse operations. During my doctoral research, I explored rich geometric concepts—such as Voronoi diagrams, a cornerstone of self-driving navigation—these concepts continue to influence the way I design and reason about algorithms. I enjoy diving into machine learning methods, expanding my knowledge, and combining theory with real-world applications. Python remains my main tool for turning mathematical ideas into robust, scalable solutions.I always welcome thoughtful discussions on innovative problem-solving and exchanging ideas.
- Warehouse Bin-Packing Optimization: Modeled a real-world warehouse bin-packing problem and developed a high-fidelity digital twin to generate synthetic data. Designed and implemented an effective mathematical optimization solution to improve packing efficiency. - Fusion Industry Collaboration: Working with one of Europe’s leading fusion energy companies on data-driven optimization solutions. Applying advanced machine-learning methods—such as UMAP, Gaussian Processes, and other optimization algorithms—to extract insights, model complex plasma-related data, and find optimal configurations. - Staff Scheduling Optimization: Developing data-driven scheduling models to optimize workforce allocation for real-life operational environments. Focus on improving planning accuracy, efficiency, and constraint handling using ML-assisted optimization techniques. - Technical Training: Teaching and designing industry-focused courses in supervised and unsupervised machine learning for professional upskilling and applied R&D contexts.
- Conducted research on geometric optimization problems across Euclidean, spherical, and hyperbolic spaces and published high impact factor papers in peer reviewed journals. - Worked on topics including tiling, spherical packing, Dowker-type problems, and Steiner symmetrization in multiple dimensions. - Developed both theoretical insights and computational techniques relevant to optimization, modeling, and algorithmic geometry.
Developed an AI chatbot process using HubSpot. Analyzed and optimized business processes by developing high-level requirements and mapping workflows. Simplified complex technical documents, identifying key issues and recommending data-driven solutions. Strengthened cross-functional collaboration between clients and technical teams to improve project efficiency.
Taught calculus and linear algebra courses to undergraduate students.