<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.10.0">Jekyll</generator><link href="edgan8.github.io/feed.xml" rel="self" type="application/atom+xml" /><link href="edgan8.github.io/" rel="alternate" type="text/html" /><updated>2025-06-29T21:16:58+00:00</updated><id>edgan8.github.io/feed.xml</id><title type="html">Edward Gan</title><subtitle>Software Engineer at Scale AI</subtitle><entry><title type="html">CoopStore: Optimizing Precomputed Summaries for Aggregation</title><link href="edgan8.github.io/paper/2020/07/01/coopstore.html" rel="alternate" type="text/html" title="CoopStore: Optimizing Precomputed Summaries for Aggregation" /><published>2020-07-01T00:00:00+00:00</published><updated>2020-07-01T00:00:00+00:00</updated><id>edgan8.github.io/paper/2020/07/01/coopstore</id><content type="html" xml:base="edgan8.github.io/paper/2020/07/01/coopstore.html"><![CDATA[<p><a href="https://github.com/stanford-futuredata/sketchstore">[Code]</a>
<!-- [[ArXiv]](https://arxiv.org/abs/2002.03063) --></p>]]></content><author><name></name></author><category term="paper" /><summary type="html"><![CDATA[[Code]]]></summary></entry><entry><title type="html">Approximate Selection with Guarantees using Proxies</title><link href="edgan8.github.io/paper/2020/06/10/supg.html" rel="alternate" type="text/html" title="Approximate Selection with Guarantees using Proxies" /><published>2020-06-10T00:00:00+00:00</published><updated>2020-06-10T00:00:00+00:00</updated><id>edgan8.github.io/paper/2020/06/10/supg</id><content type="html" xml:base="edgan8.github.io/paper/2020/06/10/supg.html"><![CDATA[]]></content><author><name></name></author><category term="paper" /><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Data summaries for scalable, high-cardinality analytics</title><link href="edgan8.github.io/paper/2020/02/10/phdthesis.html" rel="alternate" type="text/html" title="Data summaries for scalable, high-cardinality analytics" /><published>2020-02-10T00:00:00+00:00</published><updated>2020-02-10T00:00:00+00:00</updated><id>edgan8.github.io/paper/2020/02/10/phdthesis</id><content type="html" xml:base="edgan8.github.io/paper/2020/02/10/phdthesis.html"><![CDATA[<p><a href="/assets/papers/thesis-slides.pdf">[Slides]</a></p>]]></content><author><name></name></author><category term="paper" /><summary type="html"><![CDATA[[Slides]]]></summary></entry><entry><title type="html">CrossTrainer: Practical Domain Adaptation with Loss Reweighting</title><link href="edgan8.github.io/paper/2019/05/07/crosstrainer.html" rel="alternate" type="text/html" title="CrossTrainer: Practical Domain Adaptation with Loss Reweighting" /><published>2019-05-07T00:00:00+00:00</published><updated>2019-05-07T00:00:00+00:00</updated><id>edgan8.github.io/paper/2019/05/07/crosstrainer</id><content type="html" xml:base="edgan8.github.io/paper/2019/05/07/crosstrainer.html"><![CDATA[]]></content><author><name></name></author><category term="paper" /><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">DIFF: A Relational Interface for Large-Scale Data Explanation</title><link href="edgan8.github.io/paper/2018/12/01/diff.html" rel="alternate" type="text/html" title="DIFF: A Relational Interface for Large-Scale Data Explanation" /><published>2018-12-01T00:00:00+00:00</published><updated>2018-12-01T00:00:00+00:00</updated><id>edgan8.github.io/paper/2018/12/01/diff</id><content type="html" xml:base="edgan8.github.io/paper/2018/12/01/diff.html"><![CDATA[]]></content><author><name></name></author><category term="paper" /><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries</title><link href="edgan8.github.io/paper/2018/08/27/moments.html" rel="alternate" type="text/html" title="Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries" /><published>2018-08-27T00:00:00+00:00</published><updated>2018-08-27T00:00:00+00:00</updated><id>edgan8.github.io/paper/2018/08/27/moments</id><content type="html" xml:base="edgan8.github.io/paper/2018/08/27/moments.html"><![CDATA[<p><a href="/assets/papers/moments-slides.pdf">[Slides]</a>
<a href="https://github.com/stanford-futuredata/msketch">[Code]</a></p>]]></content><author><name></name></author><category term="paper" /><summary type="html"><![CDATA[[Slides] [Code]]]></summary></entry><entry><title type="html">Scalable Kernel Density Classification via Threshold-Based Pruning</title><link href="edgan8.github.io/paper/2017/05/18/tdkc.html" rel="alternate" type="text/html" title="Scalable Kernel Density Classification via Threshold-Based Pruning" /><published>2017-05-18T00:00:00+00:00</published><updated>2017-05-18T00:00:00+00:00</updated><id>edgan8.github.io/paper/2017/05/18/tdkc</id><content type="html" xml:base="edgan8.github.io/paper/2017/05/18/tdkc.html"><![CDATA[<p><a href="/assets/papers/tkdc-slides.pdf">[Slides]</a>
<a href="https://github.com/stanford-futuredata/tkdc">[Code]</a></p>]]></content><author><name></name></author><category term="paper" /><summary type="html"><![CDATA[[Slides] [Code]]]></summary></entry><entry><title type="html">MacroBase: Prioritizing Attention in Fast Data</title><link href="edgan8.github.io/paper/2017/05/17/macrobase.html" rel="alternate" type="text/html" title="MacroBase: Prioritizing Attention in Fast Data" /><published>2017-05-17T00:00:00+00:00</published><updated>2017-05-17T00:00:00+00:00</updated><id>edgan8.github.io/paper/2017/05/17/macrobase</id><content type="html" xml:base="edgan8.github.io/paper/2017/05/17/macrobase.html"><![CDATA[<p><a href="/assets/papers/macrobasetods-paper.pdf">[Extended Journal]</a></p>]]></content><author><name></name></author><category term="paper" /><summary type="html"><![CDATA[[Extended Journal]]]></summary></entry><entry><title type="html">Prioritizing Attention in Fast Data: Principles and Promise</title><link href="edgan8.github.io/paper/2017/01/08/mbcidr.html" rel="alternate" type="text/html" title="Prioritizing Attention in Fast Data: Principles and Promise" /><published>2017-01-08T00:00:00+00:00</published><updated>2017-01-08T00:00:00+00:00</updated><id>edgan8.github.io/paper/2017/01/08/mbcidr</id><content type="html" xml:base="edgan8.github.io/paper/2017/01/08/mbcidr.html"><![CDATA[]]></content><author><name></name></author><category term="paper" /><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Type Classes for Lightweight Substructural Types</title><link href="edgan8.github.io/paper/2014/07/14/clamp.html" rel="alternate" type="text/html" title="Type Classes for Lightweight Substructural Types" /><published>2014-07-14T00:00:00+00:00</published><updated>2014-07-14T00:00:00+00:00</updated><id>edgan8.github.io/paper/2014/07/14/clamp</id><content type="html" xml:base="edgan8.github.io/paper/2014/07/14/clamp.html"><![CDATA[<p><a href="/assets/papers/clamp-thesis.pdf">[Thesis]</a> <a href="/assets/papers/clamp-slides.pdf">[Slides]</a></p>]]></content><author><name></name></author><category term="paper" /><summary type="html"><![CDATA[[Thesis] [Slides]]]></summary></entry></feed>