PageSourceSearch

https://www.ajayjain.net/

html ajayjain.net collected 2026-09-25 21:28:12 UTC 32,432 bytes, 507 lines download raw bytes

1<!DOCTYPE html>
2<html>
3<head>
4    <!-- Global site tag (gtag.js) - Google Analytics -->
vendor: 69 bytes, lines 4-5
4
5    <script async src="https://www.googletagmanager.com/gtag/js?id=
5UA-25091213-1
vendor: 17 bytes, lines 5-6
5"></script>
6    
6<script>
7    
vendor: 144 bytes, lines 7-11
7window.dataLayer = window.dataLayer || [];
8    function gtag(){dataLayer.push(arguments);}
9    gtag('js', new Date());
10
11    gtag('config', '
11UA-25091213-1
vendor: 9 bytes, lines 11-12
11');
12    
12</script>
12
13
14    <meta charset="utf-8">
15    <meta content="width=device-width, initial-scale=1" name="viewport">
16    <link rel="shortcut icon" href="/favicon.ico" type="image/x-icon">
17    <link rel="icon" href="/favicon.ico" type="image/x-icon">
18    <link rel="stylesheet" href="/styles.css" type="text/css">
19    <title>Ajay Jain</title>
20<!-- Global site tag (gtag.js) - Google Analytics -->
vendor: 64 bytes, lines 20-21
20
21<script async src="https://www.googletagmanager.com/gtag/js?id=
21UA-25091213-1
vendor: 12 bytes, line 21
21"></script>
22<script>
23  
vendor: 134 bytes, lines 23-27
23window.dataLayer = window.dataLayer || [];
24  function gtag(){dataLayer.push(arguments);}
25  gtag('js', new Date());
26
27  gtag('config', '
27UA-25091213-1
vendor: 4 bytes, line 27
27');
28</script>
28</head>
29<body>
30    <header>
31        <h1>
32            Ajay Jain
33        </h1>
34        <hr>
35    </header>
36
37    <ul class="links">
38        <li><a  href="https://twitter.com/ajayj_">Twitter</a></li>
39        <li> | </li>
40        <li><a  href="https://scholar.google.com/citations?user=Ih7iLuUAAAAJ">Google Scholar</a></li>
41        <li> | </li>
42        <li><a  href="https://github.com/ajayjain">GitHub</a></li>
43        <li> | </li>
44        <li><a  href="https://www.linkedin.com/in/ajay-jain">LinkedIn</a></li>
45        <li> | </li>
46        <li><a  href="cv.pdf">CV</a></li>
47        <li> | </li>
48        <li><a  href="http://bit.ly/ajayj-portfolio">Fun things I made</a></li>
49        <li> | </li>
50        <li><a  href="https://old.ajayjain.net">Older projects</a></li>
51    </ul>
52
53
54    <img src="https://d33wubrfki0l68.cloudfront.net/cfe7fff8bd56d1d8a8408a5b505ca13c4fc85a19/db8fd/images/ajay.jpg" class="photo" />
55
56    <h3 class="role">Synthesizing visual worlds</h3>
57    <!-- <h3 class="role">Doctoral student (Ph.D.), UC Berkeley</h3> -->
58    <!-- <h3 class="role">NSF Graduate Research Fellow</h3> -->
59    <!-- <h3 class="role">Previous: NVIDIA Research, Google Brain, Uber ATG, Meta AI, MIT</h3> -->
60
61    <!-- p class="institute">
62        Berkeley Artificial Intelligence Research (BAIR)<br>
63        Department of Electrical Engineering and Computer Science (EECS)<br>
64        UC Berkeley
65    </p -->
66    <!--p class="institute">
67        Computer Science and Artificial Intelligence Laboratory (CSAIL)<br>
68        Department of Electrical Engineering and Computer Science (EECS)<br>
69        Massachusetts Institute of Technology
70    </p-->
71
72    <p class="bio">I'm an AI researcher at UC Berkeley, where I work on generative models. My work includes diffusion models, text-to-3D with NeRFs, and scalable ML systems.
73    <p class="bio">Previously, I was at Google Brain, NVIDIA Research, Uber ATG, and Facebook AI. I graduated from MIT with an S.B. in Computer Science and was a director of the nonprofit Machine Intelligence Community. At MIT, I did research at CSAIL and the Media Lab. My research is supported by the NSF Graduate Research Fellowship.</p>
74    <p class="email">Email: ajayj at berkeley dot edu</p>
75
76    <h3>News</h3>
77    <section class="news">
78        <h5><span class="new">New!</span> <b>July 2022:</b> Dream Fields wins the Best Poster award at <a href="https://ai4cc.net/">AI4CC</a></h5>
79        <h5><span class="new">New!</span> <b>May 2022:</b> AdaCat accepted to UAI 2022</h5>
80        <h5><b>March 2022:</b> Dream Fields accepted to CVPR 2022</h5>
81        <h5><b>Aug 2021:</b> ContraCode accepted to EMNLP 2021</h5>
82        <h5><b>July 2021:</b> DietNeRF accepted to ICCV 2021</h5>
83        <!-- h5><span class="new">New!</span> <b>May 2021:</b> Will be interning at Google Brain</h5 -->
84        <!-- h5><b>September 2020:</b> Two papers on <a href="#DDPM">generative models</a> and <a href="#SGM">RL</a> accepted to NeurIPS 2020</h5 -->
85        <!-- <h5><b>May 2020:</b> <a href="#LMConv">LMConv paper</a> on image generation accepted to UAI 2020</h5> -->
86        <!-- <h5><b>January 2020:</b> <a href="#Checkmate">Checkmate paper</a> on memory-efficient DL accepted to MLSys 2020</h5> -->
87        <!-- <h5><b>September 2019:</b> <a href="#DRF-Net">DRF-Net paper</a> on behavior forecasting accepted to CoRL 2019</h5> -->
88        <!-- <h5><b>June 2019:</b> Presented a talk, <a href="#POCSNet">paper</a> at ISCA 2019 Workshop on <a href="https://mlforsystems.org">Machine Learning for Systems</a></h5> -->
89        <!-- <h5><b>June 2019:</b> Poster at ICML 2019 Workshop on <a href="https://deep-phenomena.org">Phenomena of Deep Learning</a></h5> -->
90        <!-- h5><b>June 2019:</b> Started my PhD at UC Berkeley</h5-->
91        <!-- <h5><b>Dec 2018:</b> <a href="#Revec">Revec</a> accepted to Compiler Construction 2019</h5> -->
92        <!-- h5><b>June 2018:</b> Headed to Toronto for a research internship at Uber Advanced Technologies Group</h5-->
93        <!-- h5><b>June 2017:</b> Joining Facebook's Applied Machine Learning team for the summer</h5-->
94        <!-- <h5><b>August 2016:</b> Started my undergrad at MIT</h5> -->
95        <!-- <h5><b>June 2016:</b> Interning at Juniper Networks this summer</h5> -->
96    </section>
97
98    <!--h3>Pre-prints</h3>
99    <section>
100    </section-->
101
102    <h3>Featured Publications <span class="note">* indicates equal contribution</span></h3>
103    <section>
104        <section class="citation featured">
105            <div class="description">
106                <h4>VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models</h4>
107                <h5><b>Ajay Jain</b>*, Amber Xie*, Pieter Abbeel</h5>
108                <h6><span class="conference arxiv">arXiv 2022</span></h6>
109                <p>Generate vector graphics (SVGs), pixel art and sketches from text using the pretrained Stable Diffusion model.</p>
110                <ul>
111                    <li><a href="/vectorfusion/">[Website]</a></li>
112                    <li><a href="/vectorfusion/gallery.html">[Gallery]</a></li>
113                    <li>
113<a href="https://arxiv.org/abs/2211.11319">[Paper]</a></li>
114                </ul>
115            </div>
116            <a href="/vectorfusion/" class="citation-media">
117                <video height="110" muted="" autoplay="" loop="" poster="https://pub-751dccf31fca4af7b5a452d19d49cf43.r2.dev/optimization/submarine.jpeg" playsinline="">
118                    <source src="https://pub-751dccf31fca4af7b5a452d19d49cf43.r2.dev/optimization/submarine_encode.mp4" type="video/mp4">
119                    Your browser does not support the video tag.
120                </video>
121            </a>
122        </section>
123
124        <section class="citation featured">
125            <div class="description">
126                <h4>DreamFusion: Text-to-3D using 2D Diffusion</h4>
127                <h5>Ben Poole, <b>Ajay Jain</b>, Jonathan T. Barron, Ben Mildenhall</h5>
128                <h6><span class="conference arxiv">arXiv 2022</span></h6>
129                <p>We optimize a NeRF from scratch using a pretrained text-to-image diffusion model to do text-to-3D generative modeling.</p>
130                <ul>
131                    <li><a href="https://dreamfusion3d.github.io/">[Website]</a></li>
132                    <li><a href="https://dreamfusion3d.github.io/gallery.html">[Gallery]</a></li>
133                    <li><a href="https://arxiv.org/abs/2209.14988">[Paper]</a></li>
134                </ul>
135            </div>
136            <a href="https://dreamfusion3d.github.io/" class="citation-media">
137                <video height="110" muted="" autoplay="" loop="" poster="https://dreamfusion-cdn.ajayj.com/dreamfusion_square_teaser.jpg" playsinline="">
138                    <source src="https://dreamfusion-cdn.ajayj.com/dreamfusion_square_teaser.mp4" type="video/mp4">
139                    Your browser does not support the video tag.
140                </video>
141            </a>
142        </section>
143
144        <section class="citation featured">
145            <div class="description">
146            <h4>Zero-Shot Text-Guided Object Generation with Dream Fields</h4>
147            <h5><b>Ajay Jain</b>, Ben Mildenhall, Jon Barron, Pieter Abbeel, Ben Poole</h5>
148            <h6><span class="conference cvpr">CVPR 2022</span> Conference on Computer Vision and Pattern Recognition</h6>
149            <p>We combine neural rendering with multi-modal image and text representations to synthesize diverse 3D objects solely from natural language descriptions.</p>
150            <ul>
151                <li><a href="/dreamfields/">[Website]</a></li>
152                <li><a href="https://arxiv.org/abs/2112.01455">[arXiv]</a></li>
153                <li><a href="https://www.youtube.com/watch?v=1Fke6w46tv4">[Overview video]</a></li>
154                <li><a href="https://github.com/google-research/google-research/tree/master/dreamfields">[Code]</a></li>
155                <li><a href="https://colab.research.google.com/drive/17GtPqdUCbG5CsmTnQFecPpoq_zpNKX7A?usp=sharing">[Colab demo]</a></li>
156                <li><a href="/dreamfields/assets/dreamfields_cvpr_poster.pdf">[Poster]</a></li>
157            </ul>
158            </div>
159            <a href="/dreamfields/" class="citation-media">
160                <video height=110 muted="" autoplay="" loop="" class="bordered" alt="a sculpture of a rooster" poster="https://dreamfusion-cdn.ajayj.com/dreamfield_after.jpg">
161                    <source src="https://dreamfusion-cdn.ajayj.com/dreamfield_after.mp4" type="video/mp4">
162                    Your browser does not support the video tag.
163                </video>
164            </a>
165            <!-- <p><em>"a sculpture of a rooster"</em></p> -->
166        </section>
167
168        <section class="citation featured" id="DDPM">
169            <div class="description">
170                <h4>Denoising Diffusion Probabilistic Models</h4>
171                <h5>Jonathan Ho, <b>Ajay Jain</b>, Pieter Abbeel</h5>
172                <h6><span class="conference neurips">NeurIPS 2020</span> 34th Conference on Neural Information Processing Systems</h6>
173                <p>High-quality likelihood-based image generation; connect diffusion models to denoising score matching and Langevin dynamics; compression, reconstruction and interpolation</p>
174                <ul>
175                    <li><a href="https://hojonathanho.github.io/diffusion/">[Website]</a></li>
176                    <li><a href="https://arxiv.org/abs/2006.11239">[arXiv]</a></li>
177                    <li><a href="https://slideslive.com/38936172">[Talk]</a></li>
178                    <li><a href="images/diffusion_face_and_bedroom_video.mp4">[Sample video]</a></li>
179                    <li><a href="https://github.com/hojonathanho/diffusion">
179[Code]</a></li>
180                </ul>
181            </div>
182            <a class="citation-media" href="images/diffusion_face_and_bedroom_video.mp4">
183                <img src="https://d33wubrfki0l68.cloudfront.net/52717fb07257d1729a14abaeb44505067bab084a/cce70/images/diffusion_face_video_small_square.gif" alt="Sampling four 256x256 px photos of faces from a Denoising Diffusion Probabilistic Model." height=110>
184            </a>
185        </section>
186
187    </section>
188
189    <h3>More publications</h3>
190    <section>
191        <section class="citation">
192            <div class="description">
193                <h4>Journey to the BAOAB-limit: finding effective MCMC samplers for score-based models</h4>
194                <h5><b>Ajay Jain</b>*, Ben Poole*</h5>
195                <h6><span class="conference workshop">Score-based Methods @ NeurIPS 2022</span></h6>
196                <p>Sample diffusion models with a single noise level + high diversity.</p>
197                <ul>
198                    <li><a href="/journey/">[Website]</a></li>
199                    <li><a href="https://colab.research.google.com/drive/17kesyBVqubV_Zzchf2XoR-7MHk5jxTuo?usp=sharing">[Colab]</a></li>
200                    <li><a href="https://github.com/ajayjain/journey-diffusion-samplers">[Code]</a></li>
201                    <li><a href="/journey/Journey to the BAOAB-limit finding effective MCMC samplers for score-based models.pdf">[Paper]</a></li>
202                </ul>
203            </div>
204            <a href="/journey/" class="citation-media">
205                <video height="110" muted="" autoplay="" loop="" poster="https://pub-14c9ec3d906f4413affd3ae6bba970a7.r2.dev/macarons.jpeg" playsinline="">
206                    <source src="https://pub-14c9ec3d906f4413affd3ae6bba970a7.r2.dev/macarons gs 3, 5k steps, baoab-limit, t300 encoded.mp4" type="video/mp4">
207                    Your browser does not support the video tag.
208                </video>
209            </a>
210        </section>
211
212        <section class="citation">
213            <div class="description">
214            <h4>Adaptive Categorical Discretization for Autoregressive Models</h4>
215            <h5>Colin Li, <b>Ajay Jain</b>, Pieter Abbeel</h5>
216            <h6><span class="conference uai">UAI 2022</span> Conference on Uncertainty in Artificial Intelligence</h6>
217            <p>AdaCat learns expressive autoregressive models by capturing fine-grained variation in continuous distributions with discrete density estimators.</p>
218            <ul>
219                <li><a href="https://openreview.net/forum?id=HMzzPOLs9l5">[Paper]</a></li>
220                <li><a href="https://colinqiyangli.github.io/adacat/">[Website]</a></li>
221                <li><a href="https://github.com/ColinQiyangLi/AdaCat">[Code]</a></li>
222                <li><a href="/adacat/adacat_uai_poster.pdf">[Poster]</a></li>
223            </ul>
224            </div>
225            <a href="https://colinqiyangli.github.io/adacat/" class="citation-media">
226                <img src="https://d33wubrfki0l68.cloudfront.net/9c9b711dd48615853cfc84ab6b399a2a56b7f1b7/b31eb/adacat/demo.gif" alt="Optimizing a flexible 1D AdaCat distribution." width=110>
227            </a>
228        </section>
229
230        <section class="citation">
231            <div class="description">
232            <h4>Contrastive Code Representation Learning</h4>
233            <h5>Paras Jain*, <b>Ajay Jain</b>*, Tianjun Zhang, Pieter Abbeel, Joseph E. Gonzalez, Ion Stoica</h5>
234            <h6><span class="conference emnlp">EMNLP 2021</span> Empirical Methods in Natural Language Processing</h6>
235            <p>Learn to represent software functionality for automated software engineering tasks like type inference, clone detection and summarization. Improving robustness of ML4Code.</p>
236            <ul>
237                <li><a href="https://parasj.github.io/contracode/">[Website]</a></li>
238                <li><a href="https://aclanthology.org/2021.emnlp-main.482/">[ACL]</a></li>
239                <li><a href="https://arxiv.org/abs/2007.04973">[arXiv]</a></li>
240                <li><a href="https://ajayjain.github.io/assets/2021-contracode-poster-emnlp.pdf">[Poster]</a></li>
241                <li><a href="https://ajayjain.github.io/assets/2021.10.11-contracode-slides-emnlp.pdf">[Slides]</a></li>
242                <li><a href="https://www.youtube.com/watch?v=0deUDO6BqYY">[Talk]</a></li>
243                <li><a href="https://github.com/parasj/contracode/">[Code]</a></li>
244            </ul>
245            </div>
246            <a href="https://parasj.github.io/contracode/" class="citation-media">
247                <img src="https://d33wubrfki0l68.cloudfront.net/541535e70398430a3025313fe619fb3e84420422/80d72/images/contracode.png" alt="Conceptual diagram illustrating Contrastive Code Representation Learning." width=110>
248            </a>
249        </section>
250
251        <section class="citation">
252            <div class="description">
253            <h4>Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis</h4>
254            <h5><b>Ajay Jain</b>, Matthew Tancik, Pieter Abbeel</h5>
255            <h6><span class="conference iccv">ICCV 2021</span> International Conference on Computer Vision</h6>
256            <p>CLIP + NeRF: Given only a few images of an object or scene, we reconstruct its 3D structure &amp; render novel views using prior knowledge contained in large image encoders.</p>
257            <ul>
258                <li><a href="/dietnerf/">[Website]</a></li>
259                <li><a href="https://arxiv.org/abs/2104.00677">[arXiv]</a></li>
260                <li><a href="https://github.com/ajayjain/DietNeRF/">[Code]</a></li>
261                <li><a href="https://www.youtube.com/watch?v=RF_3hsNizqw">[Overview video]</a></li>
262            </ul>
263            </div>
264            <a href="/dietnerf/" class="citation-media">
265                <img src="https://d33wubrfki0l68.cloudfront.net/8f2a5fbbb0ccbae01a0f5402cef18a682db8905a/1c039/dietnerf/assets/img/dietnerf_method_anim_25p.gif" alt="Overview of DietNeRF's semantic consistency loss." width=110>
266            </a>
267        </section>
268
269        <section class="citation" id="SGM">
270            <div class="description">
271            <h4>Sparse Graphical Memory for Robust Planning</h4>
272            <h5>Scott Emmons*, <b>Ajay Jain</b>*, Michael Laskin*, Thanard Kurutach, Pieter Abbeel, Deepak Pathak</h5>
273            <h6><span class="conference neurips">NeurIPS 2020</span> 34th Conference on Neural Information Processing Systems</h6>
274            <p>Provably robust+efficient long-horizon monocular navigation combining sparse graph planning and RL. Propose two-way consistency to find landmark memories and create a topological map.</p>
275            <ul>
276                <li><a href="https://mishalaskin.github.io/sgm/">[Website]</a></li>
277                <li><a href="https://arxiv.org/abs/2003.06417">[arXiv]</a></li>
278                <li><a href="https://papers.nips.cc/paper/2020/hash/385822e359afa26d52b5b286226f2cea-Abstract.html">[NeurIPS]</a></li>
279                <li><a href="https://slideslive.com/38936447">[Talk]</a></li>
280                <li><a href="https://youtu.be/n3F3i7F3Lcg">[Video]</a></li>
281                <li><a href="https://github.io/ajayjain/assets/2020-sgm-poster-neurips.pdf">[Poster]</a></li>
282                <li><a href="https://github.com/scottemmons/sgm">[Code]</a></li>
283            </ul>
284            </div>
285            <a href="https://mishalaskin.github.io/sgm/" class="citation-media">
286                <img src="https://d33wubrfki0l68.cloudfront.net/b71e320294b25174b29a3d092cd2836848929075/dd313/images/sgm_cleanup.gif" alt="Cleaning up errors in the sparse graphical memory data structure." height=110>
287            </a>
288        </section>
289
290        <section class="citation" id="LMConv">
291            <div class="description">
292                <h4>Locally Masked Convolution for Autoregressive Models</h4>
293                <h5><b>Ajay Jain</b>, Pieter Abbeel, Deepak Pathak</h5>
294                <h6><span class="conference uai">UAI 2020</span> 36th Conference on Uncertainty in Artificial Intelligence</h6>
295                <p>Outpainting with PixelCNNs. Our efficent op allows PixelCNNs to generate images in arbitrary orders.</p>
296                <ul>
297                    <li><a href="https://ajayjain.github.io/lmconv/">[Website]</a></li>
298                    <li><a href="https://arxiv.org/abs/2006.12486">[arXiv]</a></li>
299                    <li><a href="https://proceedings.mlr.press/v124/jain20b.html">[PMLR]</a></li>
300                    <li><a href="https://www.youtube.com/watch?v=uGRRv3SExSs">[Talk]</a></li>
301                    <li><a href="https://github.com/ajayjain/lmconv">[Code]</a></li>
302                </ul>
303            </div>
304            <a href="https://ajayjain.github.io/lmconv/" class="citation-media">
305                <img src="https://d33wubrfki0l68.cloudfront.net/4061396a135d425092ec9d4f251914f0151bbae9/0e60a/images/lmconv.png" alt="Locally Masked Convolution applies customizable masks to patches of an image" width=110>
306            </a>
307        </section>
308
309        <section class="citation" id="Checkmate">
310            <div class="description">
311                <h4>Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization</h4>
312                <h5>Paras Jain*, <b>Ajay Jain</b>*, Aniruddha Nrusimha, Amir Gholami, Pieter Abbeel, Kurt Keutzer, Ion Stoica, Joseph E. Gonzalez</h5>
313                <h6><span class="conference mlsys">MLSys 2020</span> 3rd Conference on Machine Learning and Systems</h6>
314                <p>Use up to 5x less memory when training DNNs by recomputing activations</p>
315                <ul>
316                    <li><a href="https://checkmateai.github.io/">[Website]</a></li>
317                    <li><a href="https://arxiv.org/abs/1910.02653">[arXiv]</a></li>
318                    <li><a href="https://ajayjain.github.io/assets/2020-checkmate-slides-mlsys.pdf">[Slides]</a></li>
319                    <li><a href="slides/checkmate_slides.pptx">[PPTX]</a></li>
320                    <li><a href="https://github.com/parasj/checkmate">[Code]</a></li>
321                </ul>
322            </div>
323            <a href="https://checkmateai.github.io/" class="citation-media">
324                <img src="https://d33wubrfki0l68.cloudfront.net/54d5e3a3619df537c9095ad229acfe1e06e2721e/50ed7/images/2_illustrated_example.svg" alt="" width=110>
325            </a>
326        </section>
327
328        <section class="citation" id="DRF-Net">
329            <div class="description">
330            <h4>Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction</h4>
331            <h5><b>Ajay Jain</b>*, Sergio Casas Romero*, Renjie Liao*, Yuwen Xiong*, Song Feng, Sean Segal, Raquel Urtasun</h5>
332            <h6><span class="conference corl">CoRL 2019</span> 3rd Conference on Robot Learning, <span style="color: red">Spotlight talk</span></h6>
333            <p>Multimodal, long-range behavior forecasts by predicting state marginals</p>
334            <ul>
335                <li><a href="https://arxiv.org/abs/1910.08041">[arXiv]</a></li>
336                <li><a href="posters/DRFNet_CoRL2019_poster.pdf">[Poster]</a></li>
337                <li><a href="https://youtu.be/n1ehwLhKOLo">[Talk]</a></li>
338                <li><a href="https://ajayjain.github.io/assets/2019-drfnet-slides-corl.pdf">[Slides]</a></li>
339            </ul>
340            </div>
341            <img class="citation-media" src="https://d33wubrfki0l68.cloudfront.net/d032ae9595340c8e0f67c26c1dec27b29cfda82e/f5971/images/drfnet_square.png" alt="DRF-Net 10 second forecast" width=110>
342        </section>
343
344        <section class="citation" id="Revec">
345            <div class="description">
346            <h4>Revec: Program Rejuvenation through Revectorization</h4>
347            <h5>Charith Mendis*, <b>Ajay Jain</b>*, Paras Jain, Saman Amarasinghe</h5>
348            <h6><span class="conference cc">CC 2019</span> 28th International Conference on Compiler Construction</h6>
349            <p>Achieve performance portability for hand-vectorized programs, with up to 1.88x speedup</p>
350            <ul>
351                <li><a href="https://www.nextgenvec.org">[Website]</a></li>
352                <li><a href="https://dl.acm.org/citation.cfm?id=3307357">[Paper]</a></li>
353                <li><a href="https://arxiv.org/abs/1902.02816">[arXiv]</a></li>
354                <li><a href="https://www.youtube.com/watch?v=EoVozNOnr4k">[Talk]</a></li>
355                <li><a href="slides/revec_cc19_slides.pdf">[Slides]</a></li>
356                <li><a href="https://github.com/revec">[Code]</a></li>
357            </ul>
358            </div>
359            <a href="https://www.nextgenvec.org" class="citation-media"><img src="https://d33wubrfki0l68.cloudfront.net/2b2c967fd48df681f264ed1cd971eaacad637c7b/9de39/images/revec.png" alt="" height=110></a>
360        </section>
361
362        <section class="citation">
363            <div class="description">
364            <h4>Autonomy for Surface Ship Interception</h4>
365            <h5>C. Mirabito, D.N. Subramani, T. Lolla, J. P.J. Haley, <b>A. Jain</b>, P.F.J. Lermusiaux, C. Li, D. Yue, Y. Liu, F. Hover, N. Pulsone, J. Edwards, K. Railey, and G. Shaw</h5>
366            <h6><span class="conference oceans">OCEANS 2017</span> 60th OCEANS Conference, MTS/IEEE Aberdeen</h6>
367            <p>Time-optimal path planning for underwater robots</p>
368            <ul>
369                <li><a href="https://ieeexplore.ieee.org/document/8084817">[Paper]</a></li>
370                <li><a href="http://mseas.mit.edu/publications/PDF/mirabito_etal_autonomy_intercept_Oceans2017.pdf">[Open access]</a></li>
371            </ul>
372            </div>
373            <img class="citation-media" src="https://d33wubrfki0l68.cloudfront.net/740f4ab1407eb9d570a34c8d4cd2e7bb3237284e/10523/images/paths.png" alt="" height=110>
374        </section>
375    </section>
376
377    <h3>Short papers</h3>
378    <section>
379        <section class="citation" id="POCSNet">
380            <div class="description">
381            <h4>Learning Automatic Schedulers with Projective Reparameterization</h4>
382            <h5><b>Ajay Jain</b>, Saman Amarasinghe</h5>
383            <h6><span class="conference workshop isca">ISCA 2019</span> 46th International Symposium on Computer Architecture</h6>
384            <h6>Workshop on Machine Learning for Systems, Jun 2019, Talk</h6>
385            <p>Supervised learning of schedulers, with correctness constraints</p>
386            <ul>
387                <li><a href="papers/projective_isca19.pdf">[Paper]</a></li>
388                <li><a href="slides/pocsnet_isca19_slides.pdf">[Slides]</a></li>
389                <li><a href="http://mlforsystems.org/assets/slides/isca2019/MLforSystems2019_Talk_Ajay_Jain.pptx">[PPTX]</a></li>
390            </ul>
391            </div>
392            <img class="citation-media" src="https://d33wubrfki0l68.cloudfront.net/5a60dc1ba36c5b616d64384c234811806ba95ceb/7e6dc/images/projection.png" alt="" height=110>
393        </section>
394
395        <section class="citation">
396            <div class="description">
397            <h4>Using effective dimension to analyze feature transformations in deep neural networks</h4>
398            <h5>Kavya Ravichandran, <b>Ajay Jain</b>, Alexander Rakhlin</h5>
399            <h6><span class="conference workshop icml">ICML 2019</span> 36th International Conference on Machine Learning</h6>
400            <h6>Workshop on Identifying and Understanding Deep Learning Phenomena, Jun 2019</h6>
401            <ul>
402                <li><a href="https://openreview.net/pdf?id=HJGsj13qTE">[Paper]</a></li>
403            </ul>
404            </div>
405        </section>
406
407        <section class="citation">
408            <div class="description">
409            <h4>The Case for GPU Multitenancy</h4>
410            <h5>Paras Jain, Xiangxi Mo, <b>Ajay Jain</b>, Alexey Tumanov, Joseph E. Gonzalez, Ion Stoica</h5>
411            <h6><span class="conference arxiv">arXiv 2019</span> arXiv:1910.02653, Jan 2019</h6>
412            <ul>
413                <li><a href="https://arxiv.org/abs/1901.10008">[arXiv]</a></li>
414            </ul>
415            </div>
416            <img class="citation-media" src="https://d33wubrfki0l68.cloudfront.net/3c8417738473f734cdd1ff8c994da4cf9fc6c9a3/a9951/images/fijit.png" alt="" height=100>
417        </section>
418 
419        <secti
419on class="citation">
420            <div class="description">
421            <h4>Dynamic Space-Time scheduling for GPU inference</h4>
422            <h5>Paras Jain, Xiangxi Mo, <b>Ajay Jain</b>, Harikaran Subbaraj, Rehan Sohail Durrani, Alexey Tumanov, Joseph E. Gonzalez, and Ion Stoica</h5>
423            <h6><span class="conference workshop neurips">NeurIPS 2018</span> 32nd Annual Conference on Neural Information Processing Systems</h6>
424            <h6>Workshop on Systems for Machine Learning, Dec 2018</h6>
425            <p>Demonstrate 2.5x-4.9x speedups for deep learning inference workloads via GPU multitenancy</p>
426            <ul>
427                <li><a href="https://arxiv.org/abs/1901.00041">[arXiv]</a></li>
428                <li><a href="https://github.com/ucbrise/caravel/raw/master/assets/poster.pdf">[Poster]</a></li>
429                <li><a href="https://arxiv.org/abs/1901.10008">[Follow-up paper]</a></li>
430            </ul>
431            </div>
432            <img class="citation-media" src="https://d33wubrfki0l68.cloudfront.net/6e3b675ddf30988a4bbc6f3098122178b35da0e7/33b93/images/gpumulti_learningsys.jpg" alt="" width=110>
433        </section>
434    </section>
435
436    <h3>Invited talks</h3>
437    <section>
438        <ul style="font-size: 16px">
439            <li>Oct 2022: DreamFusion: Text-to-3D using 2D Diffusion, Berkeley Computer Vision</li>
440            <li>Feb 2022: 3D content creation with data-efficient consistent neural fields, CSM.ai</li>
441            <li>Jan 2022: Data-Efficient Creative Content Creation, MIT</li>
442            <li>Oct 2021: From Prompts to Pixels: Generative Methods for AI Art, Hitachi</li>
443            <li>Sep 2021: Diffusion Probabilistic Models, BAIR Computer Vision Reading Group</li>
444            <li>Jun 2021: Putting NeRF on a Diet, ML Collective CV Paper Reading Session</li>
445            <li>2021: Putting NeRF on a Diet, MIT MIC Reading Group</li>
446            <li>Jun 2019: Oral presentation, ISCA 2019 ML For Systems</li>
447            <!-- li>NSF Graduate Research Fellowship (2019)</li-->
448        </ul>
449    </section>
450
451    <h3>Service and teaching</h3>
452    <section>
453        <ul style="font-size: 16px">
454            <li>Seminar Coordinator, CS 294-43: Vision and Language AI Seminar, Fall 2022</li>
455            <li>Graduate Student Instructor, CS 184/284a: Computer Graphics and Imaging, Spring 2022</li>
456            <li>UC Berkeley EECS PhD Admissions Committee, 2022</li>
457            <li>Program Committee: ICML 2022, SIGGRAPH 2022, NeurIPS 2021, ICML 2021</li>
458            <li>Discussant, UAI 2020</li>
459            <li>Director, non-profit Machine Intelligence Community</li>
460        </ul>
461    </section>
462
463    <h3>
463Machine Learning Software</h3>
464    <section>
465        <ul style="font-size: 16px">
466            <li><a href="https://github.com/google-research/google-research/tree/master/dreamfields">★ Text-Guided Object Generation (JAX)</a></li>
467            <li><a href="https://github.com/parasj/contracode/">Transformers and LSTMs to represent and summarize code (PyTorch)</a></li>
468            <li><a href="https://github.com/ajayjain/DietNeRF/">★ Data-efficient NeRF for 3D reconstruction and view synthesis (PyTorch)</a></li>
469            <li><a href="https://github.com/ajayjain/VectorAscent/">★ Vector Ascent: Text-Guided Vector Graphics Generation (PyTorch)</a></li>
470            <li><a href="https://github.com/hojonathanho/diffusion">★ Denoising Diffusion Probabilistic Models (Tensorflow)</a></li>
471            <li><a href="https://github.com/scottemmons/sgm">Long-horizon navigation with Sparse Graphical Memory (PyTorch)</a></li>
472            <li><a href="https://github.com/ajayjain/lmconv">Autoregressive image generation and outpainting (PyTorch)</a></li>
473            <li><a href="https://github.com/parasj/checkmate">★ Checkmate: memory-efficient training of deep neural networks (Tensorflow)</a></li>
474        </ul>
475    </section>
476
477    <h3>More Software</h3>
478    <section>
479        <ul style="font-size: 16px">
480            <li><a href="https://github.com/revec">LLVM revectorization</a>: <a href="https://github.com/revec/VectorBench">Benchmark</a>, <a href="https://github.com/revec/llvm-revec">★ Compiler pass</a>, <a href="https://github.com/revec/intransitive">Intrinsic equivalences</a></li>
481            <li><a href="https://github.com/cauldron-desktop/cauldron-desktop">Cauldron Chrome extension for offline browsing</a>
482        </ul>
483    </section>
484
485    <footer>
486        <hr>
487        <p>ajayj at berkeley dot edu</p>
488    </footer>
489
490    
490<script src="/vectorfusion/assets/js/yall.js"></script>
490
491    
491<script src="https://code.jquery.com/jquery-3.5.1.slim.min.js" integrity="sha384-DfXdz2htPH0lsSSs5nCTpuj/zy4C+OGpamoFVy38MVBnE+IbbVYUew+OrCXaRkfj" crossorigin="anonymous"></script>
491
492    
492<script>
493        $(document).ready(() => {
494            document.querySelectorAll('video').forEach((el, _) => {
495                el.onmouseout = el.play;
496                el.onmouseover = el.pause;
497            });
498
499            yall(
500                {
501                    observeChanges: true
502                }
503            );
504        });
505    </script>
505
506</body>
507</html>

Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.