-
Notifications
You must be signed in to change notification settings - Fork 0
/
index.html
608 lines (504 loc) · 26.1 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
<!DOCTYPE html>
<html lang="en">
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-30941069-2"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-30941069-2');
</script>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Unna:ital,wght@0,400;0,700;1,400;1,700&display=swap" rel="stylesheet">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<title>Raul Castro Fernandez</title>
<link rel="stylesheet" href="styles.css">
</head>
<body>
<div class="container">
<div class="main-title">
<h1>Raul Castro Fernandez</h1>
</div>
<div class="main-title">
<p>Assistant Professor in the Department of Computer Science, Committee for
Data Science, The University of Chicago</p>
</div>
<nav>
<ul class="menu">
<li><a href="#news">NEWS</a></li>
<li><a href="#publications">PUBLICATIONS</a></li>
<li><a href="#students">STUDENTS</a></li>
<li><a href="#teaching">TEACHING</a></li>
<li><a href="#service">SERVICE</a></li>
<li><a href="http://raulcastrofernandez.com/blog">BLOG</a></li>
<li><a href="http://raulcastrofernandez.com/bio">BIO</a></li>
</ul>
</nav>
<p>
I am interested in data; how to think about it, what does it mean to make
good use of it, what theory, algorithms, and systems do we need to exploit it,
and how to leverage it to better our lives. I use a variety of approaches to
study these questions.
</p>
<p>
I am looking for PhD students, postdocs, and research assistants. Take a
look at <a href="http://raulcastrofernandez.com/join.html">this brief writeup if you are interested in working with us</a>.
</p>
<p>
The main area of research my group explores is <a
href="http://raulcastrofernandez.com/ecology.html">data ecology</a>. This is the
name I give to the principles, theory, and methodology that analyze and
synthesize data ecosystems. Here's a one-pager that gives an overview of the
work. And here's a link to a <a
href="https://datascience.uchicago.edu/research/data-ecology/">Research
Initiative on this topic</a>.
</p>
<p>
There are a number of specific areas we are actively interested in:
</p>
<p>
<strong>Data Markets.</strong> We are studying data markets, as these are an
important type of data
ecosystem. You can see our vision for <a href="http://raulcastrofernandez.com/papers/1323-castrofernandez.pdf">internal (read within organizations) data
markets</a>, a <a href="http://raulcastrofernandez.com/papers/7_31CRstatusofDataMarkets_final.pdf">survey about marketplaces</a>, and some <a
href="http://raulcastrofernandez.com/papers/protecting-data-markets-sigmod22.pdf">more</a>
<a
href="http://raulcastrofernandez.com/papers/data-sharing-consortia-escrow.pdf">technical</a>
<a href="https://arxiv.org/abs/2306.02543">work</a>.
</p>
<p>
<strong>Data Sharing.</strong> We work a lot of data-sharing markets. My NSF
CAREER is studying this <a
href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340034&HistoricalAwards=false">type
of ecosystem</a>. We have designed and built a <a href="http://raulcastrofernandez.com/papers/data_station_paper-11.pdf">data
escrow</a>, which permits multiple agnets pools and operate on their data. We have
models for incentivizing the <a href="http://raulcastrofernandez.com/papers/data-sharing-consortia-escrow.pdf">formation of data-sharing consortia</a>. And we are
currently preparing other
interesting work.
</p>
<p>
<strong>Data Discovery</strong> For a number of years I have been interested
in data discovery. I define data discovery as the <a href="http://raulcastrofernandez.com/papers/icde18-aurum.pdf">problem of identifying and
retrieving documents that satisfy an information need</a>. There's a strong
connection to information retrieval but we have concentrated primarily in
tabular data and in data augmentation techniques. This all started with <a
href="http://raulcastrofernandez.com/papers/icde18-aurum.pdf">Aurum</a>.
<a href="https://arxiv.org/abs/2003.09758">ARDA</a> is an application of Aurum to do feature engineering from external
repositories. Continuations to Aurum include <a href="http://raulcastrofernandez.com/papers/ver.pdf">Ver</a>, and continuations to ARDA
include <a
href="http://raulcastrofernandez.com/papers/leva-sigmod22.pdf">Leva</a> and, <a
href="http://raulcastrofernandez.com/papers/metam.pdf">Metam</a>. Right now, Metam says all I want to say about data
augmentation. Ver is still evolving. And while we are at it, we have been
exploring the role of LLMs in this context. First with <a href="http://raulcastrofernandez.com/papers/solo-sigmod24.pdf">Solo</a>, a RAG-style system
that uses a self-supervised approach to train automatically, and more recently
with Pneuma, a work in progress. We have started to explore connections between
data discovery and causal inference (really correlation discovery over large
repositories) with <a href="http://raulcastrofernandez.com/papers/nexus-sigmod24.pdf">Nexus</a>.
</p>
</div>
<div class="container">
<section id="news">
<h2>NEWS</h2>
<p><span style="color: #800000;"><strong>NOVEMBER'24</strong></span>
Talks on data ecology at the Harris School of Public Policy, on data
discovery at GSL@Microsoft, and on data sharing at an <a href="https://www.ideal-institute.org/2024/11/08/fairness-in-generative-ai-protecting-and-compensating-content-producers/">IDEAL
workshop.</a>
</p>
<p><span style="color: #800000;"><strong>OCTOBER'24</strong></span> <a href="https://www.safeinsights.org">New Safeinsights project</a> Safeinsights project kick-off meeting </p>
<p><span style="color: #800000;"><strong>SEPTEMBER'24</strong></span> <a href="">New Members Join the Group</a> Joyce Chen and Hrishee Shastri join the group</p>
<p><span style="color: #800000;"><strong>AUGUST'24</strong></span> <a href="https://vldb.org/2024/">VLDB'24</a> Tapan presents Arachne at VLDB</p>
<p>See a <a href="http://www.raulcastrofernandez.com/log">log</a> of all updates</p>
</section>
<section id="publications">
<h2>PUBLICATIONS</h2>
<p>Here you will find a list of my latest publications.</p>
<div class="year-block">
<h3>2024</h3>
<ul>
<li>
<strong><a href="./papers/arachne-vldb24.pdf">Saving Money for Analytical
Workloads in the Cloud.</a></strong> Tapan Srivastava, Raul Castro Fernandez
<strong>VLDB 2024</strong> <strong> <span
style='color:red;'>(New)</span></strong>
</li>
<li>
<strong><a href="./papers/solo-sigmod24.pdf">Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach.</a></strong> Qiming Wang, Raul Castro Fernandez <strong>SIGMOD 2024</strong>
</li>
<li>
<strong><a
href="./papers/nexus-sigmod24.pdf">Nexus: Correlation Discovery over
Collections of Spatio-Temporal Tabular Data.</a></strong> Yue Gong, Sainyam
Galhotra, Raul Castro Fernandez <strong>SIGMOD 2024</strong>
</li>
<li>
<strong><a
href="./papers/cackle-sigmod24.pdf">Cackle: Analytical Workload Cost and
Peformance Stability with Elastic Pools.</a></strong> Matthew Perron, Raul
Castro Fernandez, David DeWitt, Michael Cafarella, Samuel Madden <strong>SIGMOD 2024</strong>
<li>
<li>
<strong><a
href="./papers/sigmod24-tutorial.pdf">Responsible Sharing of Spatiotemporal Data</a></strong> Raul
Castro Fernandez, Arnab Nandi <strong>SIGMOD 2024 (Tutorial)</strong>
<li>
<li>
<strong><a
href="./papers/Ver_SIGMOD_24_Demo.pdf">Demonstration of Ver: View Discovery in
the Wild</a></strong> Kevin Dharmawan, Chirag Kawediya, Yue Gong, Zaki Indra
Yudhistira, Zhiru Zhu, Sainyam Galhotra, Adila Alfa Krisnadhi, Raul Castro
Fernandez<strong> SIGMOD 2024 (Demo)</strong>
</li>
<li>
<strong><a
href="./papers/Nexus_SIGMOD_24_Demo.pdf">Demonstrating Nexus for Correlation
Discovery over Collections of Spatio-Temporal Tabular Data</a></strong> Yue
Gong, Raul Castro Fernandez <strong>SIGMOD 2024 (Demo)</strong>
</li>
</ul>
</div>
<div class="year-block">
<h3>2023</h3>
<ul>
<li>
<strong><a href="./papers/llm_db_vision_vldb23-11.pdf">How Large Language Models Will Disrupt Data
Management.</a></strong> Raul Castro Fernandez, Aaron Elmore, Michael Franklin,
Sanjay Krishnan, Chenhao Tan. <strong>VLDB 2023</strong>
</li>
<li>
<strong>Data and AI Model Markets: Grand Opportunities for
Data and Model Sharing, Discovery, and Integration.</a></strong> Jian Pei, Raul
Castro Fernandez, Xiaohui Yu. <strong>VLDB 2023 (Tutorial)</strong>
</li>
<li>
<strong><a href="./papers/saibot_vldb_2023.pdf">Saibot: A Differentially Private Data
Search Platform.</a></strong> Zezhou Huang, Jiaxiang Liu, Daniel Gbenga Alabi,
Raul Castro Fernandez, Eugene Wu. <strong>VLDB 2023</strong>
</li>
<li>
<strong><a href="https://arxiv.org/abs/2306.02543">Addressing Budget Allocation and Revenue
Allocation in Data Market Environments Using an Adaptive Sampling
Algorithm.</a></strong> Boxin Zhao, Boxiang Lyu, Raul Castro Fernandez, Mladen
Kolar. <strong>ICML 2023</strong>
</li>
<li>
<strong><a href="./papers/data-sharing-consortia-escrow.pdf">Data-Sharing Markets: Model, Protocol,
and Algorithms to Incentivize the Formation of Data-Sharing
Consortia.</a></strong> Raul Castro Fernandez. <strong>SIGMOD 2023</strong>
</li>
<li>
<strong><a href="./papers/metam.pdf">Metam: Goal-Oriented Data
Discovery.</a></strong> Sainyam Galhotra, Yue Gong, Raul Castro Fernandez.
<strong>ICDE 2023</strong>
</li>
<li>
<strong><a href="./papers/ver.pdf">Ver: View-Discovery in the
Wild.</a></strong> Yue Gong, Zhiru Zhu, Sainyam Galhotra, Raul Castro Fernandez.
<strong>ICDE 2023</strong>
</li>
</ul>
</div>
<div class="year-block">
<h3>2022</h3>
<ul>
<li>
<strong><a href="./papers/data_station_paper-11.pdf">Data Station: Delegated, Trustworthy, and
Auditable Computation to Enable Data-Sharing Consortia with a Data
Escrow.</a></strong> Siyuan Xia, Zhiru Zhu, Chris Zhu, Jinjin Zhao, Kyle Chard,
Aaron Elmore, lan Foster, Michael Franklin, Sanjay Krishnan, Raul Castro
Fernandez. <strong>VLDB 2022</strong>
</li>
<li>
<strong><a href="./papers/7_31CRstatusofDataMarkets_final.pdf">Revisiting Online Data Markets in 2022. A
Seller and Buyer Perspective.</a></strong> Javen Kennedy, Pranav Subramaniam,
Sainyam Galhotra, Raul Castro Fernandez. <strong>SIGMOD Record</strong>
</li>
<li>
<strong><a href="./papers/19130-Final PDF-23138-1-10-20220414.pdf">Enabling Al Innovation via Data and Model
Sharing: An Overview of the Nsf Convergence Accelerator Track D.</a></strong>
Several authors <strong>Al Magazine</strong>
</li>
<li>
<strong><a href="./papers/protecting-data-markets-sigmod22.pdf">Protecting Data Markets from Strategic
Buyers.</a></strong> Raul Castro Fernandez. <strong>SIGMOD 2022</strong>
</li>
<li>
<strong><a href="./papers/leva-sigmod22.pdf">Leva: Boosting Machine Learning
Performance with Relational Embedding Data Augmentation.</a></strong> Alex Zhao,
Raul Castro Fernandez. <strong>SIGMOD 2022</strong>
</li>
</ul>
</div>
<div class="year-block">
<h3>2020</h3>
<ul>
<li>
<strong><a href="./papers/1323-castrofernandez.pdf">Data Market Platforms: Trading Data
Assets to Solve Data Problems.</a></strong> Raul Castro Fernandez, Pranav
Subramaniam, Michael Franklin. <strong>VLDB 2020</strong>
</li>
<li>
<strong><a href="https://arxiv.org/abs/2003.09758">ARDA: Automatic Relational Data
Augmentation for Machine Learning.</a></strong> Nadiia Chepurko, Ryan Marcus,
Emanuel Zgraggen, Raul Castro Fernandez, Tim Kraska, David Karger.
<strong>VLDB 2020</strong>
</li>
<li>
<strong><a href="https://arxiv.org/abs/1911.11727">Starling: A Scalable Query Engine on
Cloud Function Services.</a></strong> Matt Perron, Raul Castro Fernandez, David
DeWitt, Samuel Madden. <strong>SIGMOD 2020</strong>
</li>
<li>
<strong><a href="./papers/cidr2020.pdf">A System for Studying Deep Network
Training.</a></strong> Raul Castro Fernandez <strong>CIDR’20 (Abstract)</strong>
</li>
<div class="year-block">
<h3>2019</h3>
<ul>
<li>
<strong><a href="./papers/lazo_icde19_cr_verified.pdf">Lazo A Cardinality-Based Method for
Coupled Estimation of Jaccard Similarity and Containment.</a></strong> Raul
Castro Fernandez, Jisoo Min, Demitri Devada, Samuel Madden.
<strong>ICDE’19</strong>
</li>
<li>
<strong><a href="https://arxiv.org/abs/1903.05008">Termite: A System for Tunneling Through
Heterogeneous Data.</a></strong> Raul Castro Fernandez, Samuel Madden.
<strong>AIDM@SIGMOD’19</strong>
</li>
<li>
<strong><a href="./papers/raha.pdf">Raha: A Configuration-Free Error
Detection System.</a></strong> Mohammad Mahdavi, Ziawasch Abedjan, Raul Castro
Fernandez, Sam Madden, Mourad Ouzzani, Michael Stonebraker, Nan Tang
<strong>SIGMOD’19</strong>
</li>
<li>
<strong><a href="https://dl.acm.org/citation.cfm?id=3226631">Aurum: A Story About Research
Taste.</a></strong> Raul Castro Fernandez. <strong>Making Databases Work. ACM
Morgan & Claypool. 2019</strong>
</li>
</ul>
</div>
<div class="year-block">
<h3>2018</h3>
<ul>
<li>
<strong><a href="./papers/icde18-aurum.pdf">Aurum: A Data Discovery
System.</a></strong> Raul Castro Fernandez, Ziawasch Abedjan, Famien Koko, Gina
Yuan, Samuel Madden, Michael Stonebraker. <strong>ICDE’18</strong>
</li>
<li>
<strong><a href="./papers/icde18-seeping.pdf">Seeping Semantics: Linking Datasets using
Word Embeddings for Data Discovery.</a></strong> Raul Castro Fernandez, Essam
Mansour, Abdulhakim Qahtan, Ahmed Elmagarmid, Ihab Ilyas, Samuel Madden, Mourad
Ouzzani, Michael Stonebraker, Nan Tang. <strong>ICDE’18</strong>
</li>
<li>
<strong><a href="./papers/sigmod18-mdf.pdf">Meta-Dataflows: Efficient Exploratory
Dataflow Jobs.</a></strong> Raul Castro Fernandez, William Culhane, William
Culhane, Pijika Watcharapichat, Matthias Weidlich, Victoria Lopez Morales, Peter
Pietzuch. <strong>SIGMOD’18</strong>
</li>
<li>
<strong><a href="./papers/icde18-xtructure.pdf">Extracting Syntactical Patterns from
Databases.</a></strong> Andrew Ilyas, Joana M. F. da Trindade, Raul Castro
Fernandez, Samuel Madden. <strong>ICDE’18</strong>
</li>
<li>
<strong><a href="./papers/kdd18-fahes.pdf">FAHES: A Robust Disguised Missing Values
Detector.</a></strong> Mourad Ouzzani, Nan Tang, Ahmed Elmagarmid, Raul Castro
Fernandez, Abdulhakim A. Qahtan. <strong>KDD’18</strong>
</li>
<li>
<strong><a href="./papers/icde2018-demo-civilizer.pdf">Building Data Civilizer Pipelines with an
Advanced Workflow Engine.</a></strong> Essam Mansour, Dong Deng, Raul Castro
Fernandez, Abdulhakim Qahtan, Wenbo Tao, Ziawasch Abedjan, Ahmed Elmagarmid,
Ihab Ilyas, Samuel Madden, Mourad Ouzzani, Michael Stonebraker, Nan Tang.
<strong>(Demo) ICDE’18</strong>
</li>
</ul>
</div>
<div class="year-block">
<h3>2017</h3>
<ul>
<li>
<strong><a href="./papers/vldb17-quill.pdf">Quill: Efficient, Transferable, and Rich
Analytics at Scale.</a></strong> Badrish Chandramouli, Raul Castro Fernandez,
Jonathan Goldstein, Ahmed Eldawy, Abdul Quamar. <strong>VLDB’17</strong>
</li>
<li>
<strong><a href="./papers/cidr17-civilizer.pdf">The Data Civilizer System.</a></strong>
Dong Deng, Raul Castro Fernandez, Ziawasch Abedjan, Sibo Wang, Michael
Stonebraker, Ahmed Elmagarmid, Ihab Ilyas, Samuel Madden, Mourad Ouzzani, Nan
Tang. <strong>CIDR’17</strong>
</li>
<li>
<strong><a href="./papers/sigmod2017-demo-civilizer.pdf">A Demo of the Data Civilizer
System.</a></strong> Raul Castro Fernandez, Dong Deng, Essam Mansour, Abdulhakim
A Qahtan, Wenbo Tao, Ziawasch Abedjan, Ahmed Elmagarmid, Ihab Ilyas, Samuel
Madden, Mourad Ouzzani, Michael Stonebraker, Nan Tang. <strong>(Demo)
SIGMOD’17</strong>
</li>
</ul>
</div>
<div class="year-block">
<h3>2016</h3>
<ul>
<li>
<strong><a href="./papers/socc16-ako.pdf">Ako: Decentralised Deep Learning with
Partial Gradient Exchange.</a></strong> Pijika Watcharapichat, Victoria Lopez
Morales, Raul Castro Fernandez, Peter Pietzuch. <strong>SOCC’16</strong>
</li>
<li>
<strong><a href="./papers/vldb16-detectingerrors.pdf">Detecting Data Errors: Where are we and
what needs to be done?.</a></strong> Ziawasch Abedjan, Xu Chu, Dong Deng, Raul
Castro Fernandez, Ihab F. Ilyas, Mourad Ouzzani, Paolo Papotti, Michael
Stonebraker, Nan Tang. <strong>VLDB’16</strong>
</li>
<li>
<strong><a href="./papers/sigmod16-workshop-aurum.pdf">Towards Large-Scale Data
Discovery.</a></strong> Raul Castro Fernandez, Ziawasch Abedjan, Samuel Madden,
Michael Stonebraker. <strong>ExploreDB@SIGMOD’16</strong>
</li>
<li>
<strong><a href="./papers/sigmod16-saber.pdf">SABER: Window-Based Hybrid Stream
Processing for Heterogeneous Architectures.</a></strong> Alexandros Koliousis,
Matthias Weidlich, Raul Castro Fernandez, Paolo Costa, Alexander Wolf, Peter
Pietzuch. <strong>SIGMOD’16</strong>
</li>
<li>
<strong><a href="./papers/icde16-demo-java2sdg.pdf">Java2SDG: Stateful Big Data Processing
for the Masses.</a></strong> Raul Castro Fernandez, Panagiotis Garefalakis,
Peter Pietzuch. <strong>(Demo) ICDE’16</strong>
</li>
</ul>
</div>
<div class="year-block">
<h3>2015</h3>
<ul>
<li>
<strong><a href="./papers/cidr15-liquid.pdf">Liquid: Unifying Nearline and Offline
Big Data Integration.</a></strong> Raul Castro Fernandez, Peter Pietzuch, Joel
Koshy, Jay Kreps, Dong Lin, Neha Narkhede, Jun Rao, Chris Riccomini, Guozhang
Wang. <strong>CIDR’15</strong>
</li>
</ul>
</div>
<div class="year-block">
<h3>2014</h3>
<ul>
<li>
<strong><a href="./papers/atc14-sgd.pdf">Making State Explicit for Imperative Big
Data Processing.</a></strong> Raul Castro Fernandez, Matteo Migliavacca,
Evangelia Kalyvianaki and Peter Pietzuch. <strong>USENIX ATC’14</strong>
</li>
<li>
<strong><a href="./papers/debs14_seep_gc14.pdf">Grand Challenge Scalable Stateful Stream
Processing for Smart Grids.</a></strong> Raul Castro Fernandez, Matthias
Weidlich, Peter Pietzuch and Avigdor Gal. <strong>DEBS’14</strong>
</li>
</ul>
</div>
<div class="year-block">
<h3>2013</h3>
<ul>
<li>
<strong><a href="./papers/sigmod13-seep.pdf">Integrating Scale Out and Fault Tolerance in Stream
Processing using Operator State Management.</a></strong> Raul Castro Fernandez,
Matteo Migliavacca, Evangelia Kalyvianaki and Peter Pietzuch. <a href="https://sigmod.org/sigmod-awards/sigmod-test-of-time-award/"><span
style="color:red;">SIGMOD’13 (SIGMOD’23 Test of Time
Award)</span></a>
</li>
<li>
<strong><a href="">Towards Low-Latency and In-Memory
Large-Scale Data Processing.</a></strong> Raul Castro Fernandez and Peter
Pietzuch. <strong>PhD Workshop@DEBS’13</strong>
</li>
</ul>
</div>
</section>
<section id="students">
<h2>STUDENTS</h2>
<p class="introtxt">Below I include Postdocs, PhD, and Master students. In addition to these, I’m fortunate to work with great undergraduate students and occasionally with external students.</p>
<div class="year-block">
<h3>Postdocs and PhD Students</h3>
<ul>
<li>Qiming Wang</li>
<li>Yue Gong</li>
<li>Zhiru Zhu</li>
<li>Tapan Srivastava</li>
<li>Steven Xia</li>
<li>Chris Zhu</li>
<li>Hrishee Shastri</li>
</ul>
</div>
<div class="year-block">
<h3>Master and Undergraduate Students</h3>
<ul>
<li>Joyce Chen</li>
<li>Alena Zeng</li>
<li>Chirag Kawediya</li>
</ul>
</div>
<div class="year-block">
<h3>Alumni</h3>
<ul>
<li>Kevin Dharmawan (external collaborator, to SBU PhD
program)</li>
<li>Zach Hempstead (to Anthropic) </li>
<li>Sainyam Galhotra (to Cornell (assistant professor))</li>
<li>Stanley Zhu (to Google)</li>
<li>Alex Zhao (to Citadel)</li>
<li>Jenny Long</li>
<li>Yintong Ma (to ByteDance)</li>
<li>Ipsita Mohanty (to UWaterloo MSC program)</li>
<li>Ryan Wong (to UMichigan Undegraduate program)</li>
</ul>
</div>
</section>
<section id="teaching">
<h2>TEACHING</h2>
<ul>
<li>The Value of Data (Fall’20, Fall’21, Fall'22,
Fall'23, Spring'24, Fall'24)</li>
<li>Ethics, Fairness, Responsibility, and Privacy in
Data Science (Spring’20, Spring’21, Spring'22, Spring'23, Spring'24)</li>
<li>Introduction to Databases (Winter’20, Winter’21,
Winter'22, Winter'23)</li>
</ul>
</section>
<section id="service">
<h2>SERVICE</h2>
<ul>
<li>VLDB’25 Metareviewer</li>
<li>SIGMOD’25 PC Member</li>
<li>CIDR’25 PC Member</li>
<li>Tabular Repr Workshop at Neurips 25</li>
<li>SIGMOD’24 PC Member</li>
<li>CIDR’24 PC Member</li>
<li>SIGMOD’23 PC Member</li>
<li>SIGMOD’23 Mentorship Co-Chair</li>
<li>VLDB’23 PC Member and publicity chair</li>
<li>HPTS’22 PC Member</li>
<li>SIGMOD’22 PC Member and publicity chair</li>
<li>SIGMOD’22 publicity chair</li>
<li>VLDB’22 PC Member</li>
<li>VLDB’22 Workshop Co-Chair</li>
<li>KDD’21 PC Member</li>
<li>SIGMOD’21 PC Member (Demo track)</li>
<li>VLDB’21 PC Member (Distinguished Reviewer Award)</li>
<li>ICDE’21 PC Member</li>
<li>VLDB’20 PC Member</li>
<li>SoCC’20 PC Member</li>
<li>SIGMOD’19 PC Member (Distinguished Reviewer Award)</li>
<li>VLDBJ Reviewer</li>
<li>TKDE Reviewer</li>
<li>TODS Reviewer</li>
<li>SIGMOD Record</li>
</ul>
</section>
</div>
</body>
</html>