Registered user since Fri 8 Mar 2019
I am passionate about pushing the boundaries of Software Engineering research from India and venturing to create a best research lab at IIT Tirupati, India. Let us see how it goes! The first attempt started with MSR 2019 :) We now have publications in ICSE 2022, ICSE 2021, ICSE 2020, FSE 2020, FSE 2021, ASE 2021, MSR 2021 and we are looking forward for a blast! - Checkout our lab website: https://rishalab.in/
I plan to expand our lab with collaborations across the globe in the broad area of Software Engineering (I also work with siblings - Educational Technologies and Human-Computer Interaction). Happy to chat! Email: firstname.lastname@example.org
Standard Bio: Sridhar Chimalakonda is an Assistant Professor in the Department of Computer Science & Engineering at Indian Institute of Technology Tirupati. He did his PhD and MS by Research in Computer Science & Engineering from IIIT Hyderabad. He is also an ACM India Eminent Speaker. He leads the Research in Intelligent Software and Human Analytics (RISHA) Lab which primarily works in the area of Software Engineering, and specifically AI for SE and SE for AI. We research effective and scalable ways to help developers improve quality of software by qualitatively and quantitatively analyzing a diversified range of software artifacts [such as code, commits, bugs, logs, patterns, designs and so on] in millions of software repositories.
He is passionate about addressing societal challenges through computing research. Recently, he steered the development of an innovative game SurviveCovid-19 for Covid19 awareness and Mood of India portal to gauge the mood of people during Covid19 through Twitter analysis.
He volunteers for ACM SIGSOFT [Special Interest Group in Software Engineering] as Social Media Chair and as Core Committee member of ACM iSIGCSE [Special Interest Group in Computer Science Education]
|EASE 2022|| Towards a Catalog of Energy Patterns in Deep Learning Development|
Exploring Security Vulnerabilities in Competitive Programming: An Empirical Study
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