I am a software architect and research scientist with a background spanning industrial software engineering, applied artificial intelligence, and theoretical computer science.
My work in industry has covered a broad range of domains, from banking and financial systems to large-scale optimization and algorithmic decision-making. I have designed and developed complex software systems, bringing algorithmic ideas from research and experimentation into reliable, production-scale applications.
More recently, my focus has expanded to predictive modeling, applied machine learning, and agentic AI systems, particularly the design of intelligent systems capable of reasoning, planning, and solving complex problems.
I am also actively involved in exploring the capabilities and limitations of frontier AI models. This includes collaborating with leading AI labs to develop challenging tasks and benchmarks that probe the boundaries of current models' reasoning, problem-solving, and software engineering capabilities. Drawing on my experience in algorithm design, theoretical computer science, and software engineering, I focus on problems that require deep reasoning and sophisticated technical approaches.
Alongside my industry work, I remain actively engaged in fundamental research in theoretical computer science. My research focuses on distributed computing, graph algorithms, and network algorithms, with particular emphasis on understanding the computational limits of distributed systems and developing efficient algorithms for large scale networks.
My academic work has resulted in more than 20 publications in leading journals and conferences. I served on the Program Committee of SPAA 2020 and have reviewed research papers for several leading conferences, including STOC, FOCS, SODA, ICALP, and PODC. I have also taught university courses in various subjects, including distributed computing, graph algorithms, machine learning, and automata theory.
My interests lie at the intersection of theory and practice: using mathematical and algorithmic insights to solve real-world problems, while drawing inspiration from practical challenges to explore new research directions.