I am currently a post-doctoral researcher at Max-Planck Institute for Software Systems (MPI-SWS). My research interests lie in building distributed, networked and privacy-aware systems, focused on problems at the boundary of information technology and society. I have built systems for road traffic monitoring, human mobility measurements, public policy audit and privacy enhancement in ubiquitous systems, among others.
I like to work on problems where I can apply inter-disciplinary CS techniques and the solutions might potentially have societal impact, especially in developing regions. I like the tension created by methods with conflicting requirements, when incorporated in the same system. E.g. some sensing task might give best results with high-dimensional deep learning based features extracted from the data. But if those features need to be securely transmitted to a remote server for privacy reasons, cryptography will pose a conflicting requirement for the plaintext message to be small, to minimize network bandwidth.
Building prototypes for real world problems also poses interesting trade-offs between performance vs. practical issues. Processing high definition (HD) videos at powerful GPU servers might give the best performance in road traffic monitoring. But the poor broadband infrastructure in developing countries might prohibit real time streaming of HD videos from the roads to the servers. In-situ processing might mandate using mobile and embedded platforms, but their processor and battery constraints can conflict with heavy computation and low latency requirements.
Sometimes trade-offs come from conflicting interests of different stake holders. Individuals might have their personal opinions on privacy, while retailers might want to profile all customers alike for targeted advertising and financial profits. Balancing such conflicts between CS techniques, or a CS method and a real world constraint, or among different stake holders in an application scenario, gets me excited.