Research Area: DBMS | EECS at UC Berkeley (2024)

Large-scale computing services revolve around the management, distribution, and analysis of massive data sets. For over 40 years, Berkeley has led the world in recognizing and advancing the centrality of data in computing. Faculty and students at Berkeley have repeatedly defined and redefined the broad field of data management, combining deep intellectual impact with the birth of multi-billion dollar industries, including relational databases, RAID storage, scalable Internet search, and big data analytics. Berkeley also gave birth to many of the most widely-used open source systems in the field including INGRES, Postgres, BerkeleyDB, and Apache Spark. Today, our research continues to push the boundaries of data-centric computing, taking the foundations of data management to a broad array of emerging scenarios.

  • Declarative languages and runtime systems

    Design and implementation of declarative programming languages with applications to distributed systems, networking, machine learning, metadata management, and interactive visualization; design of query interface for applications.

  • Scalable data analysis and query processing

    Scalable data processing in new settings, including interactive exploration, metadata management, cloud and serverless environments, and machine learning; query processing on compressed, semi-structured, and streaming data; query processing with additional constraints, including fairness, resource utilization, and cost.

  • Consistency, concurrency, coordination and reliability

    Coordination avoidance, consistency and monotonicity analysis; transaction isolation levels and protocols; distributed analytics and data management, geo-replication; fault tolerance and fault injection.

  • Data storage and physical design

    Hot and cold storage; immutable data structures; indexing and data skipping; versioning; new data types; implications of hardware evolution.

  • Metadata management

    Data lineage and versioning; usage tracking and collective intelligence; scalability of metadata management services; metadata representations; reproducibility and debugging of data pipelines.

  • Systems for machine learning and model management

    Distributed machine learning and graph analytics; physical and logical optimization of machine learning pipelines; online model management and maintenance; prediction serving; real-time personalization; latency-accuracy tradeoffs and edge computing for large-scale models; machine learning lifecycle management.

  • Data cleaning, data transformation, and crowdsourcing

    Human-data interaction including interactive transformation, query authoring, and crowdsourcing; machine learning for data cleaning; statistical properties of data cleaning pipelines; end-to-end systems for crowdsourcing.

  • Interactive data exploration and visualization

    Interactive querying and direct manipulation; scalable spreadsheets and data visualization; languages and interfaces for interactive exploration; progressive query visualization; predictive interaction.

  • Secure data processing

    Data processing under hom*omorphic encryption; data compression and encryption; differential privacy; oblivious data processing; databases in secure hardware enclaves.

  • Foundations of data management

    Optimal trade-offs between storage, quality, latency, and cost, with applications to crowdsourcing, distributed data management, stream data processing, version management; expressiveness, complexity, and completeness of data representations, query languages, and query processing; query processing with fairness constraints.

  • I am an expert and enthusiast based assistant. I have access to a vast amount of information and can provide assistance on a wide range of topics. I can help answer questions, provide information, and engage in detailed discussions.

    Regarding the concepts mentioned in the article you provided, I can provide information on the following topics:

    1. Large-scale computing services: These services revolve around the management, distribution, and analysis of massive data sets. They are essential for various industries and applications, including cloud computing, big data analytics, and scalable internet search.

    2. Data management: Data management involves the organization, storage, retrieval, and manipulation of data. It encompasses various aspects such as data storage and physical design, metadata management, and data cleaning and transformation.

    3. Declarative languages and runtime systems: Declarative programming languages focus on specifying what needs to be done rather than how to do it. They are used in various domains, including distributed systems, networking, machine learning, metadata management, and interactive visualization.

    4. Scalable data analysis and query processing: This involves developing techniques and systems for processing and analyzing large-scale data sets efficiently. It includes interactive exploration, metadata management, cloud and serverless environments, machine learning, and query processing on compressed, semi-structured, and streaming data.

    5. Consistency, concurrency, coordination, and reliability: These concepts are crucial for ensuring the correctness and reliability of distributed systems. They involve coordination avoidance, consistency analysis, transaction isolation levels and protocols, fault tolerance, and geo-replication.

    6. Systems for machine learning and model management: This area focuses on developing distributed systems and optimization techniques for machine learning and graph analytics. It includes physical and logical optimization of machine learning pipelines, online model management, prediction serving, real-time personalization, and machine learning lifecycle management.

    7. Interactive data exploration and visualization: This involves developing tools and techniques for interactive querying, direct manipulation, scalable spreadsheets, data visualization, and predictive interaction.

    8. Secure data processing: This area focuses on ensuring the security and privacy of data during processing. It includes techniques such as data processing under hom*omorphic encryption, data compression and encryption, differential privacy, and databases in secure hardware enclaves.

    9. Foundations of data management: This area explores fundamental trade-offs between storage, quality, latency, and cost in data management systems. It includes topics such as expressiveness and complexity of data representations, query languages, and query processing, as well as query processing with fairness constraints.

    These are just brief descriptions of the concepts mentioned in the article. If you have any specific questions or would like more detailed information on any of these topics, feel free to ask!

    Research Area: DBMS | EECS at UC Berkeley (2024)


    What is the Computer Systems Research Group Berkeley? ›

    The Computer Systems Research Group (CSRG) was a research group at the University of California, Berkeley that was dedicated to enhancing AT&T Unix operating system and funded by Defense Advanced Research Projects Agency. Simplified evolution of Unix systems.

    How competitive is UC Berkeley data science? ›

    Preparing students. The UC's are highly competitive for CS and Data Science which are impacted/capped/selective majors. UC Berkeley is the only UC that accepts LOR's but by invitation only. UC's are also test blind and have their own specific way to calculate the UC GPA's.

    What is the average salary at EECS Berkeley? ›

    An advanced degree can make a difference in your starting salary. In 2015, UC Berkeley EECS graduates were offered average starting salaries of $100,000 at the B.S. level, $111,400 at the M.S. level, and $112,000 at the Ph.

    How hard is it to get into Computer Science at UC Berkeley? ›

    There are two ways to study Computer Science (CS) at UC Berkeley: Be admitted to the Electrical Engineering & Computer Sciences (EECS) major in the College of Engineering (COE) as a freshman. Admission to the COE, however, is extremely competitive. This option leads to a Bachelor of Science (BS) degree.

    What does Berkeley Research Group do? ›

    Berkeley Research Group, LLC (BRG) is a global consulting firm that helps leading organizations advance in three key areas: economics, disputes, and investigations; corporate finance; and performance improvement and advisory.

    Is Berkeley Research Group a good company? ›

    Is Berkeley Research Group a good company to work for? Berkeley Research Group has an overall rating of 3.8 out of 5, based on over 332 reviews left anonymously by employees. 71% of employees would recommend working at Berkeley Research Group to a friend and 69% have a positive outlook for the business.

    Is UC Berkeley Data Science worth it? ›

    In the U.S., 2021 graduates from Berkeley's master of information and data science (MIDS) program earned an average annual salary of more than $155,000, according to data provided by the school. The program is ranked №2 among the best online master's in data science degree programs in 2022 by Fortune.

    Which UC school is best for Data Science? ›

    University of California, Berkeley
    • #1. in Data Analytics/Science.
    • #2. in Computer Science (tie)

    Is Data Science one of the hardest majors? ›

    Is Data Science a Difficult Major to Enter? Entering the field of data science can be challenging due to its interdisciplinary nature, which combines mathematics, statistics, computer science, and domain-specific knowledge.

    Is Berkeley EECS prestigious? ›

    It is one of the most prestigious, not only as an EECS department in a public university, but ranked globally among all EE and CS departments. Berkeley EECS continues to be the home of historic contributions to the fields of electrical engineering and computer sciences.

    Which is better EECS or CS? ›

    There is no difference in the computer science course content between the EECS and CS Majors – the difference is what other subjects you'd like to study. If you prefer greater flexibility in your coursework or are interested in exploring additional majors, then the CS Major might be a good choice.

    Is EECS hard to get into Berkeley? ›

    EECS acceptance rate is around 5%, similar to Stanford and MIT. Of course, it is still easier to admit to berkeley EECS because people applying to UCs and have completed UC essays can easily select EECS at berkeley, while they'd have to complete essays for Stanford and fill out another app for MIT.

    What is the hardest major to get into UC Berkeley? ›

    Computer Science, Engineering, and Economics are the top three hardest majors to get into at Berkeley, followed by Biology and Political Science. The Computer Science program at Berkeley is one of the top-ranked CS programs in the world, so admission there is no small feat.

    What GPA do you need for EECS Berkeley? ›

    Minimum Academic Requirements
    • Students must have a minimum overall and semester grade point average of 2.00 (C average). ...
    • Students must achieve a minimum grade point average of 2.00 (C average) in upper division technical courses required for the major curriculum each semester.

    How competitive is Berkeley EECS? ›

    Q: What are my chances of being admitted? A: About 9% of applicants to Berkeley's computer science graduate program are admitted. We are eager to accept the best, most intellectually exciting students. If this is you, I encourage you to apply.

    What is the difference between CS and EECS Berkeley? ›

    An essential difference between the two majors is that the EECS program requires a greater number of math and science courses than the CS program, which requires a greater number of non-technical, or breadth, courses.

    What are CSE groups? ›

    CSE Core Courses is classified into six groups: Introduction to CSE, Computational Mathematics, High Performance Computing, Intelligent Computing, Scientific Visualization, and Computational Optimization.

    Is CSCI a computer science? ›

    Courses for Core Science Sequence, Math Elective, and Technical Elective credit must be chosen in accordance with CSSE department policies and approved course listings.

    Does Berkeley have CIS? ›

    Computer Information Systems is one of the majors in the computer & information sciences program at University of California - Berkeley.

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