Dear all, The next talk in the IARCS Verification Seminar Series will be given by Umang Mathur, an Assistant Professor in the School of Computing at the National University of Singapore. The talk is scheduled on Tuesday, January 3, at 1900 hrs IST (add to Google calendar <https://calendar.google.com/calendar/event?action=TEMPLATE&tmeid=MWVtZzZuZnNydWNzbDYxOGFmdnZmdmRia2IgdnNzLmlhcmNzQG0&tmsrc=vss.iarcs%40gmail.com> ). The details of the talk can be found on our webpage ( https://fmindia.cmi.ac.in/vss/), and also appended to the body of this email. The Verification Seminar Series, an initiative by the Indian Association for Research in Computing Science (IARCS), is a monthly, online talk-series, broadly in the area of Formal Methods and Programming Languages, with applications in Verification and Synthesis. The aim of this talk-series is to provide a platform for Formal Methods researchers to interact regularly. In addition, we hope that it will make it easier for researchers to explore newer problems/areas and collaborate on them, and for younger researchers to start working in these areas. All are welcome to join. Best regards, Akash, Deepak, Madhukar, Srivathsan ============================================================= Title: Dynamic Data Race Prediction: Fundamentals and Advance Meeting Link: https://us02web.zoom.us/j/89164094870?pwd=eUFNRWp0bHYxRVpwVVNoVUdHU0djQT09 (Meeting ID: 891 6409 4870, Passcode: 082194) Abstract: Concurrent programs are notoriously hard to write correctly, as scheduling nondeterminism introduces subtle errors that are both hard to detect and to reproduce. Data races are arguably the most insidious amongst concurrency bugs and extensive research efforts have been dedicated to effectively detect them. A data race occurs when memory-conflicting actions are executed concurrently. Consequently, considerable effort has been made towards developing efficient techniques for race detection. The preferred approach to detect data races is through dynamic analysis, where one observes an execution of a concurrent program and checks for the presence of data races in the execution observed. Traditional dynamic race detectors rely on Lamport's happens-before (HB) partial order, which can be conservative and are often unable to discover simple data races, even after executing the program several times. Dynamic data race prediction aims to expose data races, that can be otherwise missed by traditional dynamic race detectors (such as those based on HB), by inferring data races in alternate executions of the underlying program, without re-executing it. In this talk, I will talk about the fundamentals of and recent algorithmic advances in data race prediction. Bio: Umang Mathur is an Assistant Professor at the National University of Singapore. He received his PhD from the University of Illinois at Urbana Champaign in 2021 and was an NTT Research Fellow at the Simons Institute for the Theory of Computing at Berkeley. His research interests lie in the use of formal methods and logic for answering design, analysis and implementation questions in programming languages, software engineering and systems. He is a recipient of a Google Research Award (2022), Google PhD Fellowship (2019), an ACM SIGSOFT Distinguished Paper Award at ESEC/FSE'18, a Best Paper Award at ASPLOS'22 and was invited as a Young Researcher at the 8th Heidelberg Laureate Forum.