ECDN Rep for the School of Computer Science 
What is your academic background and your current research field?
I have been a Computer Scientist all my life, but always with an interdisciplinary view. Throughout my career I worked on a range of topics, for example, wireless networking, compiler optimization, statistical privacy, computational geometry, algebraic topology, data mining, and machine learning. I have worked with graphs, time series, images, audio, and data generated from human mobility. Currently my interest is in mobile-health (collecting data from mobiles and wearables and doing machine learning for health applications), and decentralised deep learning like Federated Learning. I love thinking and discussing opportunities for Computer Science in interdisciplinary domains.
What is your role as a ECDN Rep and how can postdocs in your school contact you?
I am a friendly point of contact for all the post-docs and early career academics within the School of Computer Science. Please reach out in case you have any issues or ideas that could enhance your experience at the University. Please contact me via email - a.ghosh.1@bham.ac.uk / teams / in person. I’m in 211 at the School of Computer Science most of the days.
If you are non-CS and want to explore computer science or collaborate, please reach out. I’ll do my best to direct you to the right experts.
What do you feel are the benefits of the ECDN programme for early career academics and researchers?
The ECDN committee is a great resource for early career researchers to form new connections and engage with development issues that are needed for further career progression. It is also great to build a network in and outside the school. It is a wonderful platform to learn from peers and also exchange ideas for collective development.
What do you feel have been the benefits to you from being a postdoc rep?
With my limited exposure to the activities (~4 months) I met many like-minded people from different institutes. This gave me a great opportunity to start building both a research and a social network.