Deepak Ajwani

Dr. Deepak Ajwani is an Assistant Professor at the School of Computer Science, University College Dublin. His research focuses on machine learning techniques for solving combinatorial optimisation problems. His research interests include design and analysis of algorithms, algorithm engineering, combinatorial optimisation and machine learning. He has more than 50 peer-reviewed top-tier conference and journal publications in the design and analysis of algorithms, algorithm engineering, combinatorial optimisation and machine learning areas. He is on the editorial board of Machine Learning journal (Springer) and regularly serves as a senior programme committee member/programme committee member of top conferences (WWW, IJCAI, AAAI, ECML-PKDD, ALENEX and CP) in the areas of machine learning, algorithm engineering and optimisation. He is a funded investigator with the SFI Centre for Research Training in Machine Learning (ML-Labs).

In teaching and student supervision, he has been nominated for the UCD Teaching and Learning Award in 2019/2020, 2021/22 and 2023/24. His supervised undergraduate students won the Franz Geiselbrechtinger medal in 2023 and 2024 for the best final-year project in theoretical computer science.

Dr. Ajwani has a B.Tech and M.Tech degrees from the Indian Institute of Technology, Delhi in India. He received his Ph.D. from Max Planck Institute for Informatics (Germany) in 2008, for his work on I/O-efficient graph traversal algorithms. Thereafter, he worked as a Postdoctoral Researcher at MADALGO - Centre for Massive Data Algorithms (Aarhus University in Denmark), where he developed shared memory multicore algorithms for discrete optimisation problems. In 2010, he was awarded a grant from the Irish Research Council for Science, Engineering and Technology (IRCSET) and IBM Research for a project on designing graph partitioning and repartitioning techniques in the context of Exascale stream computing systems. As part of this Postdoctoral work carried out at University College Cork, he proposed a methodology to co-optimise the inter-related hard problems of interconnect topology configuration, graph partitioning and routing, that provided fairly good solutions in very little time. From 2012 to 2018, he worked as a research scientist at Nokia Bell Labs, where he and his team designed and developed a complex learning system for interpreting, organising and navigating unstructured content (natural language text).