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What can we do better to make POPL/PLDI more relevant f or the next generation?

What can we do better to make POPL/PLDI more relevant f or the next generation?. With inputs from:. Ras Bodik (UC-Berkeley) Swarat Chaudhuri (Penn State) Sumit Gulwani (MSR Redmond). Questions. How to make PL relevant 30 years from now?

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What can we do better to make POPL/PLDI more relevant f or the next generation?

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  1. What can we do better to make POPL/PLDI more relevant for the next generation? With inputs from: Ras Bodik (UC-Berkeley) Swarat Chaudhuri (Penn State) Sumit Gulwani (MSR Redmond)

  2. Questions • How to make PL relevant 30 years from now? • What problems will programming in 2040 be solving? • Are we laying the foundations for 6 Turing awards from PL over the next 30 years? To think about these questions, let us consider: • What are current technology trends? • What are the unique strengths of PL as an area?

  3. Technology Trends • Computational devices getting cheaper • Thousands of super-computer programmers • Millions of traditional software developers • Billion end-users!! • What is the programming model for these folks? • Has mostly remained an HCI topic! • Cloud Computing • Computing as a commodity • Has mostly remained architecture/systems topic! • .... Are we keeping an eye on these trends?

  4. Unique Strengths of PL • Intersects with most areas in computer science. • Healthy mix of theory and practice. • Can be a breeding ground for inter-disciplinary disruptive innovation. What have we done to foster this?

  5. Interesting inter-disciplinary areas • Human Computer Interaction • Visualization • Natural Language Processing • Cognitive science, Education • Systems biology, Social science • Computational Thinking in Sciences • Gaming • …

  6. Computational Thinking in Sciences Computational thinking is entering biology, social science, economics • example: agent-based generative social science Theory, algorithms, simulation already export C.S. ideas to sciences • Q: what will happen once the computational thinking takes foothold? • A: some notion of programming will follow Programming may be the tool for modeling and synthesis • example: understand and defend against biological attacks • growth of popularity: from the hands of researchers to practitioners • growth of scope: build large-scale models by composition Goal: develop languages for thinking and doing in sciences • Role of programming languages: bridge thinking and machines; make computational thinking accessible to masses; enable large models • State of the art: languages for sciences are “decades old”, did not receive the attention of mainstream programming languages: • support for reuse and modularity, higher-level abstractions, static typing, program analysis, model checking.

  7. Call for Action • Bring to attention technology trends. • Call for papers can include new futuristic topics. • Start accepting (short) papers that bring in new problem definitions or ad-hoc solutions. • Encourage inter-disciplinary work. • Make it easy for outsiders to get in. • Have a separate category and/or use a different criterion (as for pearl papers). • Invited talks from experts in other communities.

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