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X-WR-CALDESC:Events for Information Systems Group
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DTSTART;TZID=America/Los_Angeles:20250207T110000
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DTSTAMP:20260504T010122
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SUMMARY:Amr El Abbadi (UCSB): Practical Approaches for Private and Scalable Information Data Management Systems
DESCRIPTION:Practical Approaches for Private and Scalable Information Data Management Systems\n  \nAmr El Abbadi \nProfessor of Computer Science \nUniversity of California at Santa Barbara  \nAbstract.  \nIncreasingly countries and regions have strict laws and regulations to protect the privacy of personal data. For example\, the states of the European Union (EU) enforce the General Data Protection Regulations (GDPR) to protect personal data of individuals living in the EU. Much research has focused on preserving the privacy of data using various advanced cryptographic techniques. However\, and irrespective of the privacy of the data itself\, just the queries requesting the data raise severe privacy concerns owing to numerous attacks and data breaches using access patterns. Our goal in this talk is to demonstrate how private access of data\, using sophisticated\, expensive but secure cryptographic methods can become a practical reality in the near future. Our focus is on supporting oblivious queries and thus hide any associated access patterns on both private and public data.  For private data\, ORAM (Oblivious RAM) is one of the most popular approaches for supporting oblivious access to encrypted data. However\, most existing ORAM datastores are not fault tolerant and hence an application may lose all of its data when failures occur. To achieve fault tolerance\, we propose QuORAM\, the first datastore to provide oblivious access and fault-tolerant data storage using a quorum-based replication protocol.  For public data\, PIR (Private Information Retrieval) is the main mechanism proposed in recent years.  However\, PIR requires the server to consider data as an array of elements and clients retrieve data using an index into the array. This requirement limits the use of PIR in many practical settings\, especially for key-value stores\, where the client may be interested in a particular key\, but does not know the exact location of the data at the server. In this talk we will discuss recent efforts to overcome these limitations\, using Fully Homomorphic Encryption (FHE)\, to improve the performance\, scalability and expressiveness of privacy preserving queries of public data.  \nBiography \nAmr El Abbadi is a Professor of Computer Science. He received his B. Eng. from Alexandria University\, Egypt\, and his Ph.D. from Cornell University. His research interests are in the fields of fault-tolerant distributed systems and databases\, focusing recently on Cloud data management\, blockchain based systems and privacy concerns. Prof. El Abbadi is an ACM Fellow\, AAAS Fellow\, and IEEE Fellow.  He was Chair of the Computer Science Department at UCSB from 2007 to 2011. He served as Associate Graduate Dean at the University of California\, Santa Barbara from 2021–2023.  He served as a journal editor for several database journals\, including\, The VLDB Journal\, IEEE Transactions on Computers and The Computer Journal. He was Program Chair for multiple database and distributed systems conferences\, including most recently SIGMOD 2022. He served on the executive committee of the IEEE Technical Committee on Data Engineering (TCDE) and was a board member of the VLDB Endowment from 2002 to 2008. In 2007\, Prof. El Abbadi received the UCSB Senate Outstanding Mentorship Award for his excellence in mentoring graduate students. In 2013\, his student\, Sudipto Das received the SIGMOD Jim Gray Doctoral Dissertation Award. Prof. El Abbadi is also a co-recipient of the Test of Time Award at EDBT/ICDT 2015.  Recently\, papers he co-authored received an Outstanding paper award in NSDI (Networked System Design and Implementation) 2024 and the Test of Time Award from MDM (Mobile Data Management)2024.   He has published over 350 articles in databases and distributed systems and has supervised over 40 PhD students. \nHost: Faisal Nawab
URL:https://isg.ics.uci.edu/event/amr-el-abbadi-ucsb-practical-approaches-for-private-and-scalable-information-data-management-systems/
LOCATION:DBH 6011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250214T130000
DTEND;TZID=America/Los_Angeles:20250214T140000
DTSTAMP:20260504T010122
CREATED:20250211T005519Z
LAST-MODIFIED:20250225T032621Z
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SUMMARY:Jiadong Bai:  Supporting Data Science Education Using Texera with a Cloud Infrastructure
DESCRIPTION:Abstract\nIn this talk\, we will first present our paper at the DSE-K12 conference with the title “DS4ALL: Teaching High-School Students Data Science and AI/ML Using the Texera Workflow Platform as a Service.” Traditional data science education often requires students to have programming experience and install local software. It also makes collaboration inefficient and slows down the feedback loop when students need help from TAs. To address these challenges\, we developed a new teaching paradigm using Texera\, and successfully hosted DS4ALL summer programs in 2023 and 2024. As a result\, students with no prior coding experience were able to perform data analysis using AI/ML techniques on the platform.The second part of the talk will focus on how we are scaling to a broader audience based on the success of the Texera system used in DS4ALL\,. There are several challenges of building such a cloud  infrastructure\, such as handling versatile service traffic\, supporting concurrent isolated workflow executions\, and managing diverse data storage needs. To address these challenges\, we develop Texera to be able to run using Kubernetes to achieve scalable service management; For workflow execution\, we run each workflow in an isolated Kubernetes pod to ensure performance and security. On the data storage side\, we design a storage layer that integrates LakeFS for managing versioned datasets and Apache Iceberg for handling versioned tables with support for concurrent read/write operations. These solutions allow Texera to be deployed as a cloud service to provide a scalable\, secure\, and efficient environment for data science workflows.Bio\nJiadong Bai is a second-year Ph.D. student in the Computer Science Department at UC Irvine\, with research interests in data systems\, data science\, and big data analysis. He’s supervised by Prof. Chen Li.Shengquan Ni is a sixth-year Ph.D. student in the Department of Computer Science advised by Professor Chen Li. His research interests include big data processing\, distributed systems\, data analytics\, and data science.
URL:https://isg.ics.uci.edu/event/jiadong-bai-tbd/
LOCATION:DBH 3011
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DTSTART;TZID=America/Los_Angeles:20250221T130000
DTEND;TZID=America/Los_Angeles:20250221T140000
DTSTAMP:20260504T010122
CREATED:20250211T005602Z
LAST-MODIFIED:20250225T040933Z
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SUMMARY:Ketan C Maheshwari (Oak Ridge National Laboratory): Enacting Distributed HPC Workflows: Opportunities and Challenges
DESCRIPTION:Abstract: The Dept of Energy (DOE) complex comprises of many science facilities that could be classified as data producing (eg. the Advanced Photon Source at Argonne National Laboratory) and consuming (eg. the Leadership Class Computing Facilities at the Oak Ridge National Laboratory) facilities. Modern science campaigns often require extensive usage of more than one such facilities which may be located remote from each other and administered separately. This presents opportunities for the scientific computational workflows to aid in the process. At the same time\, there are equally daunting challenges faced to successfully and smoothly accomplish these workflows. Our talk will dive into these opportunities and challenges and dive into proposed solutions and path forward. \nBio: Dr. Ketan Maheshwari is a Senior Linux Systems Engineer within the NCCS Division at the Oak Ridge National Laboratory. He has over 15 years of experience working with HPC systems with over 10 years with the leadership class systems at ALCF and OLCF. He is interested in science applications porting to large scale computing infrastructures and has a hands-on expertise in workflows\, parallelization and HPC. He has given several talks on technical topics at local as well as international venues\, most notably on GNU Parallel (eScience’23\, CUG’24\, PEARC’24\, SC’24)\, Linux Terminal Tools (USENIX/LISA’19\, LOPSA’18) and Swift Workflows (CCGrid’13). Ketan received his PhD in the area of Scientific Workflows from University of Nice and a Masters in Grid Computing from University of Amsterdam. \n  \nHost: Chen Li
URL:https://isg.ics.uci.edu/event/ketan-c-maheshwari-oak-ridge-national-laboratory/
LOCATION:DBH 3011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250228T110000
DTEND;TZID=America/Los_Angeles:20250228T120000
DTSTAMP:20260504T010122
CREATED:20250211T005638Z
LAST-MODIFIED:20250225T032827Z
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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 helps users identify useful data for various tasks like hypothesis generation\, fact checking and causal inference. Lastly\, I will discuss my proposal of leveraging causal reasoning 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  \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-tbd/
LOCATION:DBH 3011
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