lorr1.github.io/index.html at main · lorr1/lorr1.github.io · GitHub

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        >
          <h1 style>Laurel Orr</h1>
          <address>
            <div></div>
            <div>
              <a>laurel.orr@numbersstation.ai</a>
            </div>
          </address>
          <img
            src="Laurel_Orr_Photo.jpeg"
            alt="Laurel Orr"
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        </div>
      </div>
      <div class="col-md-7 offset4">
        <div class="container-fluid" style="margin-top: 20px">
          <ul class="nav nav-pills">
            <li><a href="#Pubs">Publications</a></li>
            <li><a href="#Blogs">Blogs</a></li>
            <li><a href="#Research">Research and Work Experience</a></li>
            <li><a href="#AboutMe">About Me</a></li>
          </ul>
        </div>
        <div class="container-fluid">
          <h2 id="AboutMe">About Me</h2>
          <p>
            I am currently a Staff Software Engineer at Alation. I was formerly
            a founding engineer and researcher at
            <a href="https://www.numbersstation.ai/">Numbers Station</a> part of
            the
            <a
              href="https://www.numbersstation.ai/post/introducing-numbers-station-labs"
              >Numbers Station Labs</a
            >
            before being acquired by Alation. I think about all things AI
            agents, context management, and structured data tasks.
          </p>

          <p>
            Before Numbers Station, I was a PostDoc at Stanford working with
            <a href="https://cs.stanford.edu/people/chrismre/">Chris Ré</a> in
            the
            <a href="https://hazyresearch.stanford.edu/">Hazy Research Lab</a>.
            In August of 2019, I graduated with a PhD from Paul G Allen School
            for Computer Science and Engineering at the University of Washington
            in Seattle. I was part of the
            <a href="http://db.cs.washington.edu/">Database Group</a> and
            advised by
            <a href="https://homes.cs.washington.edu/~suciu/">Dan Suciu</a> and
            <a href="https://homes.cs.washington.edu/~magda/"
              >Magdalena Balazinska</a
            >.
          </p>

          <p>
            For my undergraduate degree, I went to Carleton College in
            Northfield, MN, where the city's motto is "Cows, Colleges, and
            Contentment" and graduated in 2013 as a Computer Science and
            Mathematics double major.
          </p>

          <h2 id="Research">Research and Work Experience</h2>
          <p>
            My research interests are broadly at the intersection of artificial
            intelligence, foundation models, and data management. I focus on how
            to train, customize, and deploy foundation models to data tasks.
            This includes problems around data curation and management for RAG
            systems, efficient model training and inference for batch workloads,
            and agentic prompting paradigms for end-to-end analytic workflow
            automation.
          </p>

          <p>
            I am a 2020 winner of the IC Postdoc Research Fellowship Program and
            am one of the 2015 winners of the NSF GRFP in Computer Science. In
            the summer of 2016 and 2017, I interned at Microsoft Research as a
            PhD research intern, and in the summer of 2015, I interned at
            Tableau as a software developer. From the summer of 2012 to the
            spring of 2015, I interned at Sandia National Laboratories working
            on high performance computing and image reconstruction.
          </p>

          <h2 id="Projects">Projects</h2>
          <p>
            <em>Meadow: A Framework for Multi-Agent Data Workflows</em><br />
            <a href="https://github.com/NumbersStationAI/meadow">[repo]</a>
          </p>
          <p>
            <em>Manifest: Prompt Programming for Foundation Models</em><br />
            <a href="https://github.com/HazyResearch/manifest">[repo]</a>
          </p>

          <h2 id="Blogs">Blogs</h2>
          <p>
            <em>Introducing Meadow: LLM Agents for Data Tasks.</em>
            <strong>Laurel Orr</strong>, Ines Chami, and Chris Aberger.<br />
            <em>Team:</em> Numbers Station Labs<br />
            <a
              href="https://numbersstation.ai/introducing-meadow-llm-agents-for-data-tasks/"
              >[blog]</a
            >
          </p>
          <p>
            <em>Text-to-SQL That Isn’t.</em> <strong>Laurel Orr</strong> and
            Chris Aberger.<br />
            <em>Team:</em> Numbers Station Labs<br />
            <a href="https://numbersstation.ai/text-to-sql-that-isnt/"
              >[blog]</a
            >
          </p>
          <p>
            <em>DuckDB-NSQL: How to Quack in SQL.</em>
            <strong>Laurel Orr</strong> and Sen Wu.<br />
            <em>Team:</em> Numbers Station Labs<br />
            <a href="https://numbersstation.ai/duckdb-nsql-how-to-quack-in-sql/"
              >[blog]</a
            >
          </p>
          <p>
            <em
              >Introducing NSQL: Open-source SQL Copilot Foundation Models.</em
            >
            Sen Wu, <strong>Laurel Orr</strong>, and Manasi Ganti.<br />
            <em>Team:</em> Numbers Station Labs<br />
            <a
              href="https://numbersstation.ai/introducing-nsql-open-source-sql-copilot-foundation-models/"
              >[blog]</a
            >
          </p>
          <p>
            <em>SQL Coding Assistants Customized to Enterprise Logs.</em>
            <strong>Laurel Orr</strong>, Xiao Ling, and Vishal Motwan.<br />
            <em>Team:</em> Numbers Station Labs<br />
            <a
              href="https://www.numbersstation.ai/post/numbers-station-sql-transformation"
              >[blog]</a
            >
          </p>
          <p>
            <em
              >Misral-A Journey Torwards Reproducible Language Model
              Training.</em
            >
            <strong>Laurel Orr*</strong> and Siddharth Karamcheti*.<br />
            <em>Team:</em> Jason Bolton, Tianyi Zhang, Karan Goel, Avanika
            Narayan, Rishi Bommasani, Deepak Narayanan<br />
            <em>Advisors</em>: Tatsunori Hashimoto, Dan Jurafsky, Christopher D.
            Manning, Christopher Potts, Christopher Ré, Percy Liang<br />
            <a href="https://crfm.stanford.edu/2021/08/26/mistral.html"
              >[blog]</a
            >, <a href="https://youtu.be/du1YiytHwXs?t=3294">[talk]</a>
          </p>
          <p>
            <em
              >Bootleg: Chasing the Tail with Self-Supervised Named Entity
              Disambiguation.</em
            >
            <strong>Laurel Orr</strong>, Megan Leszczynski, Simran Arora, Neel
            Guha, Xiao Ling, Sen Wu, and Christopher Ré<br />
            <a href="https://hazyresearch.stanford.edu/blog/2020-11-10-bootleg"
              >[blog]</a
            >
          </p>

          <h2 id="Pubs">Publications</h2>
          <p class="pub">
            <em
              >Ask Me Anything: A simple strategy for prompting language
              models.</em
            >
            Simran Arora, Avanika Narayan, Mayee F. Chen,
            <strong>Laurel Orr</strong>, Neel Guha, Kush Bhatia, Ines Chami,
            Frederic Sala, Christopher Ré. arXiv 2022.<br />
            <a href="https://arxiv.org/pdf/2210.02441.pdf">[paper]</a>
          </p>
          <p class="pub">
            <em>Can Foundation Models Wrangle Your Data?</em> Avanika Narayan,
            Ines Chami, <strong>Laurel Orr</strong>, Christopher Ré. arXiv
            2022.<br />
            <a href="https://arxiv.org/pdf/2205.09911.pdf">[paper]</a>
          </p>
          <p class="pub">
            <em>Data Management Opportunities for Foundation Models.</em>
            <strong>Laurel Orr</strong>, Karan Goel, Christopher Ré. CIDR
            2022.<br />
            <a href="http://cidrdb.org/cidr2022/papers/p51-orr.pdf">[paper]</a>
          </p>
          <p class="pub">
            <em
              >On the Opportunities and Risks of Foundation Models (Lead of Data
              Section).</em
            >
            <strong>Laurel Orr</strong>, Simran Arora, Karan Goel, Avanika
            Narayan, Michael Zhang, Christopher Ré. arXiv 2021.<br />
            <a href="https://arxiv.org/abs/2108.07258">[paper]</a>
          </p>
          <p class="pub">
            <em
              >Cross-Domain Data Integration for Named Entity Disambiguation in
              Biomedical Text.</em
            >
            Maya Varma, <strong>Laurel Orr</strong>, Sen Wu, Megan Leszczynski,
            Xiao Ling, Christopher Ré. EMNLP 2021.<br />
            <a href="https://aclanthology.org/2021.findings-emnlp.388/"
              >[paper]</a
            >
          </p>
          <p class="pub">
            <em
              >Managing ML Pipelines: Feature Stores and the Coming Wave of
              Embedding Ecosystems (Tutorial).</em
            >
            <strong>Laurel Orr</strong>, Atindriyo Sanyal, Xiao Ling, Karan
            Goel, Megan Leszczynski. VLDB 2021.<br />
            <a href="https://arxiv.org/abs/2108.05053">[paper]</a>,
            <a href="https://vldb.org/2021/files/slides/tutorial/tutorial4.pdf"
              >[slides]</a
            >
          </p>
          <p class="pub">
            <em
              >Goodwill Hunting: Analyzing and Repurposing Off-the-Shelf Named
              Entity Linking Systems.</em
            >
            Karan Goel, <strong>Laurel Orr</strong>, Nazneen Fatema Rajani,
            Jesse Vig, Christopher Ré. NAACL Industry 2021.<br />
            <a href="https://aclanthology.org/2021.naacl-industry.26.pdf"
              >[paper]</a
            >
          </p>
          <p class="pub">
            <em
              >Bootleg: Chasing the Tail with Self-Supervised Named Entity
              Disambiguation.</em
            >
            <strong>Laurel Orr*</strong>, Megan Leszczynski*, Simran Arora, Sen
            Wu, Neel Guha, Xiao Ling, Christopher Ré. CIDR 2021.<br />
            <a href="https://arxiv.org/abs/2010.10363">[paper]</a>,
            <a href="https://www.youtube.com/watch?v=dO7nVXTvepg">[talk]</a>
          </p>
          <p class="pub">
            <em
              >Mosaic: A Sample-Based Database System for Open World Query
              Processing.</em
            >
            <strong>Laurel Orr</strong>, Samuel Ainsworth, Walter Cai, Kevin
            Jamieson, Magda Balazinska, Dan Suciu. CIDR 2020.<br />
            <a href="https://arxiv.org/abs/1912.07777">[paper]</a>
          </p>
          <p class="pub">
            <em>Sample Debiasing in the Themis Open World Database System.</em>
            <strong>Laurel Orr</strong>, Magdalena Balazinska, and Dan Suciu.
            SIGMOD 2020.<br />
            <a href="https://arxiv.org/abs/2002.09799">[paper]</a>
          </p>
          <p class="pub">
            <em
              >Pushing Data-Induced Predicates Through Joins in Big-Data
              Clusters.</em
            >
            Srikanth Kandula, <strong>Laurel Orr</strong>, and Surajit
            Chaudhuri. VLDB 2019.<br />
            <a href="http://www.vldb.org/pvldb/vol13/p252-orr.pdf">[paper]</a>
          </p>
          <p class="pub">
            <em
              >EntropyDB: A Probabilistic Approach to Approximate Query
              Processing.</em
            >
            <strong>Laurel Orr</strong>, Magdalena Balazinska, and Dan Suciu.
            VLDB Journal 2019.<br />
            <a href="https://arxiv.org/abs/1911.04948">[paper]</a>
          </p>
          <p class="pub">
            <em
              >Probabilistic Database Summarization for Interactive Data
              Exploration.</em
            >
            <strong>Laurel Orr</strong>, Magdalena Balazinska, and Dan Suciu.
            VLDB 2017.<br />
            <a href="https://arxiv.org/abs/1703.03856">[paper]</a>
          </p>
          <p class="pub">
            <em>Explaining Query Answers with Explanation-Ready Databases.</em>
            Sudeepa Roy, <strong>Laurel Orr</strong>, and Dan Suciu. VLDB
            2015.<br />
            <a href="https://www.vldb.org/pvldb/vol9/p348-roy.pdf">[paper]</a>
          </p>
          <p class="pub">
            <em
              >Big-Data Management Use-Case: A Cloud Service for Creating and
              Analyzing Galactic Merger Trees.</em
            >
            S. Loebman, J. Ortiz, L. Choo, L. Orr, L. Anderson, D. Halperin, M.
            Balazinska, T. Quinn, F. Governato. SIGMOD Workshop on Data
            Analytics in the Cloud (DanaC) 2014.<br />
            <a href="https://dl.acm.org/doi/pdf/10.1145/2627770.2627774"
              >[paper]</a
            >
          </p>
          <p class="pub">
            <em
              >Cluster-Based Approach to a Multi-GPU CT Reconstruction
              Algorithm.</em
            >
            <strong>Laurel J. Orr</strong>, Edward S. Jimenez, Kyle R. Thompson.
            Conference Proceedings for the IEEE Nuclear Science Symposium and
            Medical Imaging Conference 2014.
          </p>
          <p class="pub">
            <em
              >Preparing for the 100-Megapixel Detector: Reconstruction a
              Multi-Terabyte Computed Tomography Dataset.</em
            >
            <strong>Laurel J. Orr</strong>, and Edward S. Jimenez. Conference
            Proceedings for the Penetrating Radiation Systems and Applications
            XIV Workshop at the SPIE International Symposium on SPIE Optical
            Engineering+Applications 2013.
          </p>
        </div>
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