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X-WR-CALDESC:Events for Information Systems Group
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DTSTART;TZID=America/Los_Angeles:20240405T130000
DTEND;TZID=America/Los_Angeles:20240405T140000
DTSTAMP:20260716T103403
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SUMMARY:Lukasz Golab (University of Waterloo): Understanding models and the data they learn from
DESCRIPTION:Lukasz Golab (U. Waterloo) \nUnderstanding models and the data they learn from \nAbstract: The modern world is powered by data. However\, as the capabilities of data-intensive systems grow\, so does their complexity\, making them hard to understand and troubleshoot. I will discuss my lab’s efforts towards understanding models and the data they learn from\, including local and global model explanations as well as model diagnostics for fairness and bias avoidance. \n  \nBio: Lukasz Golab is a Professor and Canada Research Chair at the University of Waterloo. From 2006 to 2011\, he was a Senior Member of Research Staff at AT&T Labs. He obtained a BSc in Computer Science from the University of Toronto (with High Distinction) and a PhD in Computer Science from the University of Waterloo (with Alumni Gold Medal). His long-term research agenda of Data for Good calls for building data-intensive systems with societal impact. His recent projects focus on systems for managing high-speed data events such as data stream engines and blockchains\, understanding complex models and the data they learn from\, and applications including online safety\, education\, and sustainability.
URL:https://isg.ics.uci.edu/event/lukasz-golab-university-of-waterloo-understanding-models-and-the-data-they-learn-from/
LOCATION:DBH 4011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240419T130000
DTEND;TZID=America/Los_Angeles:20240419T140000
DTSTAMP:20260716T103403
CREATED:20240407T214847Z
LAST-MODIFIED:20240407T214847Z
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SUMMARY:Mohammed Al-Kateb (Amazon Redshift): The Evolution of Amazon Redshift
DESCRIPTION:Abstract:\nIn this talk\, we will discuss the evolution of Amazon Redshift over the past 10 years. We’ll discuss the Amazon Redshift architecture. We’ll dive deep in the lifecycle of executing a query in Amazon Redshift. And we’ll examine how Amazon Redshift continues to maintain a leading price/performance in the market. \nBio:\n Mohammed Alkateb leads the Query Optimizer team of Redshift – The Amazon AWS Distributed Cloud Data Warehouse that tens of thousands of customers rely on to gain the insight they need from their most critical data. Prior to joining Amazon\, Mohammed spent over a decade with the Teradata Optimizer team as an individual contributor and engineering manager. Mohammed is also an adjunct professor at Worcester Polytechnic Institute (WPI) and at California State University\, Northridge (CSUN). Mohammed has 16 U.S. patents. And he has publications in research and industrial tracks of premier database conferences including EDBT\, ICDE\, SIGMOD and VLDB. Mohammed holds a Ph.D. degree in Computer Science from The University of Vermont\, and M.Sc. & B.Sc. degrees in Information Systems from Cairo University.
URL:https://isg.ics.uci.edu/event/mohammed-al-kateb-amazon-redshift-the-evolution-of-amazon-redshift/
LOCATION:DBH 4011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240426T130000
DTEND;TZID=America/Los_Angeles:20240426T140000
DTSTAMP:20260716T103403
CREATED:20240424T002712Z
LAST-MODIFIED:20240424T002712Z
UID:1757-1714136400-1714140000@isg.ics.uci.edu
SUMMARY:Xinyuan Lin: Data Science Tasks Implemented with Scripts versus GUI-Based Workflows: The Good\, the Bad\, and the Ugly.
DESCRIPTION:Abstract: As leveraging large-scale data analytics becomes the norm for many applications\, platforms for developing these capabilities have become increasingly important. This work compares the benefits and drawbacks of implementing two commonly used data science platform paradigms: code-based scripts and GUI-based workflows. We implement tasks in both paradigms that provide examples of phases in the typical life cycle of a data science project\, including data wrangling\, machine learning (ML) model training\, and inference. In this talk\, we will examine the relative performance of the implementations under each paradigm in various experimental settings. We will discuss the benefits and drawbacks of each platform implementation and provide a foundation for future work in comparing data science platform paradigms. \nBio: Xinyuan Lin is a third-year Ph.D student in the Computer Science Department at UC Irvine. His research interests include data processing systems and big data analytics.
URL:https://isg.ics.uci.edu/event/xinyuan-lin-data-science-tasks-implemented-with-scripts-versus-gui-based-workflows-the-good-the-bad-and-the-ugly/
LOCATION:DBH 4011
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