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X-WR-CALDESC:Events for Information Systems Group
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DTSTART;TZID=America/Los_Angeles:20241101T130000
DTEND;TZID=America/Los_Angeles:20241101T140000
DTSTAMP:20260427T163032
CREATED:20241017T163643Z
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SUMMARY:Michael Jungmair (TU Munich): A Compiler-Centric Query Engine Design for Mixed Workloads and Modern Hardware
DESCRIPTION:A Compiler-Centric Query Engine Design for Mixed Workloads and Modern Hardware \n11/1/2024\, 1:00 PM 2 PM\, DBH 3011 \nMichael Jungmair\, Technical University of Munich\, Germany \nAbstract: Relational query engines are increasingly expected to handle more than just relational queries and also run on modern hardware that is increasingly parallel and distributed. However\, it is not clear how existing system designs can deal with these two challenges effectively.\nWe propose a holistic\, compiler-centric design for data processing systems that is designed for tightly integrated optimization and execution of relational queries\, non-relational workloads and user-defined functions on modern hardware. \nBio: Michael Jungmair is a third year PhD student at the Technical University of Munich. Supervised by Jana Giceva\, he is performing research in the intersection of database engines and compiler technology. So far\, this research culminated in the design and implementation of LingoDB (lingo-db.com)\, a novel query engine based on the MLIR compiler framework
URL:https://isg.ics.uci.edu/event/michael-jungmair-tu-munich-a-compiler-centric-query-engine-design-for-mixed-workloads-and-modern-hardware/
LOCATION:DBH 3011
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DTSTART;TZID=America/Los_Angeles:20241115T130000
DTEND;TZID=America/Los_Angeles:20241115T140000
DTSTAMP:20260427T163032
CREATED:20241008T012443Z
LAST-MODIFIED:20250211T004236Z
UID:2116-1731675600-1731679200@isg.ics.uci.edu
SUMMARY:Kunwoo Park: CloudMapper: A Pay-as-you-go Solution for Accelerating Genomics Sequence Alignment Using Public Clouds
DESCRIPTION: CloudMapper: A Pay-as-you-go Solution for Accelerating Genomics Sequence Alignment Using Public Clouds \nAbstract: Single-cell RNA sequencing (scRNA-seq) alignment remains a computational bottleneck in bioinformatics data analysis. As datasets grow in size and complexity\, traditional alignment tools\, such as CellRanger\, face significant limitations\, often requiring hours or even days. Furthermore\, setting up the necessary infrastructure frequently demands familiarity with complex tools like Slurm\, creating a barrier for researchers without cluster management expertise. To address these challenges\, we introduce CloudMapper\, a pay-as-you-go solution that simplifies and accelerates scRNA-seq alignment through scalable public cloud resources. Built on the Texera platform\, CloudMapper allows researchers to launch and manage clusters on cloud providers like AWS via an intuitive web interface\, enabling parallel processing of large scRNA-seq datasets. By automating infrastructure setup and providing streamlined resource options\, CloudMapper offers bioinformaticians flexible tools to balance cost and performance\, significantly reducing alignment time and technical overhead. In this talk\, we’ll explore CloudMapper’s problem-centered design\, architectural framework\, and user experience tailored for bioinformaticians. We’ll also discuss ongoing research challenges\, such as minimizing manual configuration for non-expert users\, optimizing resource pre-provisioning to reduce cluster launch times\, and future plans to expand CloudMapper’s capabilities beyond RNA alignment to broader bioinformatics and data-processing tasks. \nBio: Kunwoo Park is a second-year Ph.D. student in the Computer Science Department at UC Irvine\, with research interests in data systems and big data a
URL:https://isg.ics.uci.edu/event/kunwoo-park-talk/
LOCATION:DBH 3011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241122T110000
DTEND;TZID=America/Los_Angeles:20241122T120000
DTSTAMP:20260427T163032
CREATED:20241008T012638Z
LAST-MODIFIED:20260401T210003Z
UID:2118-1732273200-1732276800@isg.ics.uci.edu
SUMMARY:Sainyam Galhotra (Cornell): Context-aware Responsible Data Science
DESCRIPTION:Abstract: Data-based systems are increasingly used in applications that have far-reaching consequences and long-lasting societal impact. However\, the development process remains highly specialized\, tedious\, and unscalable. This produces a manually fine-tuned rigid solution that works only for one specific problem in one specific context. The system fails to adapt to the changing world and severely limits the full utilization of valuable data. \nSo\, how can you avert this fate for your systems? \nIn this talk\, I present my vision of context-aware systems that enable even non-expert users to develop correct\, explainable\, and equitable data-science pipelines. To achieve this\, I will focus on i) re-thinking the design of data science pipelines\, and ii) the importance of causal inference for trustworthy data analysis. I will present a data discovery framework that automatically identifies useful data on behalf of end-users for various tasks. Lastly\, I will discuss my proposal of leveraging counterfactual reasoning and causal inference to quantify the impact of an input on the outcome. These topics are the pieces of the puzzle that come together to create the Data Scientists’ holy grail – an easily deployable\, scalable\, and robust system that you can trust even as everything around it evolves. \n\n\n\n\nBio: Sainyam Galhotra is an Assistant Professor in Computer Science at Cornell University and a field member for Computer Science\, Statistics and Data Science. Previously\, he was a Computing Innovation Fellow pursuing postdoctoral research at the University of Chicago. He received his Ph.D. from the University of Massachusetts Amherst under the supervision of Prof. Barna Saha (currently at UC San Diego). The goal of his research is to lay the foundation of responsible data science\, that enable efficient development and deployment of trustworthy data analytics applications. His research has combined techniques from Data Management\, Probabilistic Methods\, Causal Inference\, Machine Learning\, and Software Engineering. His research has been published in top-tier Data Management (SIGMOD\, VLDB\, PODS\, & ICDE)\, AI (NeurIPS\, AAAI & AIES) and Software Engineering (FSE) conferences. He is a recipient of the Best Paper Award in FSE 2017 and Most Reproducible Paper Award in both SIGMOD 2017 and 2018\, and Best Artifact Paper Honorable Mention Award in SIGMOD 2023. He was recognized as a Data Science rising star\, a DAAD AInet Fellow\, and as the first recipient of the Krithi Ramamritham Award at UMass for contribution to database research. \nhttps://sainyamgalhotra.com/
URL:https://isg.ics.uci.edu/event/sainyam-galhotra-cornell/
LOCATION:DBH 6011
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