1<!DOCTYPE html> 2<!-- 3 flatironinstitute.org 4 --> 5<html lang="en"> 6 <head> 7 <title>Ruben Ohana, Ph.D. | Generative AI Research Scientist</title> 8 <meta charset="utf-8" /> 9 <meta 10 name="viewport" 11 content="width=device-width, initial-scale=1" 12 /> 13 <meta 14 name="description" 15 content="Personal website of Ruben Ohana, Ph.D., Senior Research Scientist in Generative AI at NVIDIA." 16 /> 17 <link rel="canonical" href="https://rubenohana.github.io/" /> 18 <meta property="og:title" content="Ruben Ohana, Ph.D." /> 19 <meta 20 property="og:description" 21 content="Senior Research Scientist in Generative AI at NVIDIA." 22 /> 23 <meta property="og:type" content="website" /> 24 <meta property="og:url" content="https://rubenohana.github.io/" /> 25 <!-- TODO: If you wish to use your center color scheme, please replace main.css with the center acronymn ex: 26 cca.css, ccb.css, etc 27 --> 28 <link rel="stylesheet" href="assets/css/ccm.css" /> 29 <link rel="icon" type="image/svg+xml" href="images/favicon.svg" /> 30 </head> 31 <body class="is-preload"> 32 <!-- Header --> 33 <header id="header"> 34 <div class="inner"> 35 <!-- TODO: Add a personal photo in /images folder or switch src link to avatar.jpg for a gray circle --> 36 <a href="#" class="image avatar" 37 ><img src="images/Headshot.jpeg" alt="Portrait of Ruben Ohana" 38 /></a> 39 </div> 40 <nav id="nav"> 41 <ul> 42 <li> 43 <a 44 href="#about" 45 style="-webkit-tap-highlight-color: rgba(0, 0, 0, 0)" 46 >About</a 47 > 48 </li> 49 <li> 50 <a 51 href="#research" 52 style="-webkit-tap-highlight-color: rgba(0, 0, 0, 0)" 53 >Research</a 54 > 55 </li> 56 <li> 57 <a 58 href="#publications" 59 style="-webkit-tap-highlight-color: rgba(0, 0, 0, 0)" 60 >Publications</a 61 > 62 </li> 63 <li> 64 <a 65 href="#talks" 66 style="-webkit-tap-highlight-color: rgba(0, 0, 0, 0)" 67 >Talks</a 68 > 69 </li> 70 <!-- <li> 71 <a 72 href="#software" 73 style="-webkit-tap-highlight-color: rgba(0, 0, 0, 0)" 74 >Software</a 75 > 76 </li> --> 77 <li> 78 <a 79 href="#contact" 80 style="-webkit-tap-highlight-color: rgba(0, 0, 0, 0)" 81 >Contact</a 82 > 83 </li> 84 <!-- <ul class="icons"> --> 85 <!-- TODO: Add your social media handles here --> 86 <!-- Other icons are available here: https://fontawesome.com/v4.7/icons/ just change the fa-tag --> 87 <!-- <li> 88 <a href="#" class="icon brands fa-twitter" 89 ><span class="label">Twitter</span></a 90 > 91 <a href="#" class="icon brands fa-google-scholar" 92 ><span class="label">Instagram</span></a 93 > 94 </li> --> 95 <!-- </ul> --> 96 97 </ul> 98 99 </nav> 100 </header> 101 <!-- Main --> 102 <div id="main"> 103 <!-- About --> 104 <section id="about"> 105 <header class="major"> 106 <!-- TODO: Add your name --> 107 <h1>Ruben Ohana, Ph.D.</h1> 108 </header> 109 <!-- TODO: Add your own bio --> 110 <p> 111 I am a Senior Research Scientist in Generative AI at <a href="https://www.nvidia.com/en-us" rel="noreferrer noopener" target="_blank">NVIDIA</a> in Santa Clara, CA. Previously, I was a Research Fellow at the <a href="https://www.simonsfoundation.org/flatiron/center-for-computational-mathematics/" rel="noreferrer noopener" target="_blank">Center for Computational Mathematics</a> of the <a href="https://www.simonsfoundation.org/flatiron/" rel="noreferrer noopener" target="_blank">Flatiron Institute</a> in New York, and a member of the <a href="https://www.polymathic-ai.org" rel="noreferrer noopener" target="_blank">Polymathic AI</a> collaboration. I received my Ph.D. in Computer Science from the Physics Lab of <a href="https://www.ens.psl.eu">Ecole Normale Supérieure</a>
111 in 2022, and was at the same time a Research Scientist for the startup <a href="https://lighton.ai/">LightOn</a>, where I worked on developing new machine learning algorithms and applications (adversarial robustness, differential privacy, time-series prediction...) for Optical Processing Units. During my PhD, I interned at the <a href="https://ailab.criteo.com/">Criteo AI Lab</a>. 112 </p> 113 <p> 114 My academic research focused on building foundation models for scientific data (mostly numerical simulations of partial differential equations and astrophysics data), as well as dataset building and on training large deep learning models, as part of the Polymathic AI collaboration. 115 </p> 116 <p> 117 Before my Ph.D., I graduated with an engineering degree from <a href="https://www.espci.psl.eu/en/">ESPCI Paris</a>, an MSc in Condensed Matter from <a href="https://www.ens.psl.eu">Ecole Normale Supérieure</a>, and an MSc in Statistics from <a href="https://www.sorbonne-universite.fr/en/university">Sorbonne University</a>. During these undergraduate studies, I interned at <a href="https://www.rd.ntt/e/brl/">NTT Basic Research Laboratory</a> in Japan, the <a href="https://www.ligo.caltech.edu/">LIGO</a> at <a href="https://www.mit.edu/">M.I.T.</a> and the <a href="https://www.lip6.fr/recherche/team.php?LANG=en&acronyme=QI">Quantum Information Group</a> at the <a href="https://www.lip6.fr/?LANG=en">LIP6</a> at Sorbonne University. 118 </p> 119 <p> 120 You can find more information about me here: <a href="pdfs/CV.pdf" rel="noreferrer noopener" target="_blank">CV</a> - 121 <a href="https://scholar.google.fr/citations?user=F9qNg2wAAAAJ&hl=en" rel="noreferrer noopener" target="_blank">Google Scholar</a> - 122 <a href="https://twitter.com/oharub" rel="noreferrer noopener" target="_blank"> 123 <i class="fab fa-twitter"></i> 124 </a> - 125 <a href="https://www.linkedin.com/in/rubenohana/" rel="noreferrer noopener" target="_blank"> 126 <i class="fab fa-linkedin"></i> 127 </a> - 128 <a href="https://github.com/rubenohana" rel="noreferrer noopener" target="_blank"> 129 <i class="fab fa-github"></i> 130 </a> 131 </p> 132 </section> 133 <!-- Research --> 134 <section id="research"> 135 <h2>Spotlight Research</h2> 136 <!-- TODO: Replace with project info and choose image type --> 137 <div class="row"> 138 <article class="col-6 col-12-xsmall work-item"> 139 <h3>The Well: a large-scale collection of physics numerical simulations for machine learning</h3> 140 <p> 141 We released the most extensive collection of physics numerical simulations datasets, which comprises numerical simulations of diverse physics phenomena ranging from biology to astrophysics. We hope it will advance both the developement of ML surrogate models, accelerate scientific discovery, and allow the training of foundation models. The code can be found <a href="https://github.com/PolymathicAI/the_well" rel="noreferrer noopener" target="_blank">here</a> and you can read more about it in our <a href="https://arxiv.org/abs/2412.00568" rel="noreferrer noopener" target="_blank"> NeurIPS 2024</a> paper. 142 </p> 143 </article> 144 <article class="col-6 col-12-xsmall work-item"> 145 <img class="image fit" src="images/biggif.gif" alt="Visualization for The Well project" /> 146 </article> 147 <article class="col-6 col-12-xsmall work-item"> 148 <h3>Blind denoising using Gibbs-Diffusion.</h3> 149 <p> 150 We developed a very cool blind denoising algorithm for natural images, with an application in astrophysics. It can at the same time do parameter inference with uncertainties. We believe it has the potential to be applied in many different scientific fields. The project was accepted at <a href="https://arxiv.org/abs/2402.19455" rel="noreferrer noopener" target="_blank">ICML 2024</a> and the code can be found 151 <a 152 rel="noreferrer noopener" 153 target="_blank" 154 href="https://github.com/rubenohana/Gibbs-Diffusion" 155 >here.</a 156 > 157 </p> 158 </article> 159 <article class="col-6 col-12-xsmall work-item"> 160 <img 161 class="image fit" 162 src="images/denoising_effect2.gif" 163 alt="Example of Gibbs-Diffusion image denoising" 164 /> 165 </article> 166 167 </div> 168 </section> 169 <!-- Papers --> 170 <section id="publications"> 171 <h2>Publications</h2> 172 <div class="row"> 173 <div class="col-12 col-12-small"> 174 <!-- TODO: Add your citations --> 175 <ol> 176 177 <li><i>Streamlined Optical Training of Large-Scale Modern Deep Learning Architectures with Direct Feedback Alignment.</i> Z. Wang, K. Müller, M. Filipovich, J. Launay, <b>R. Ohana</b>, G. Pariente, S. Mokaadi, C. Brossollet, F. Moreau, A. Cappelli, I. Poli, I. Carron, L. Daudet, F. Krzakala, S. Gigan. <a href="https://doi.org/10.1073/pnas.2532022123" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">PNAS 2026</a></li> 178 179 <li><i>On the Value of Tokeniser Pretraining in Physics Foundation Models.</i> H. Sotoudeh, P. Mukhopadhyay, <b>R. Ohana</b>, M. McCabe, N. D. Lawrence, S. Ho, M. Cranmer. <a href="https://arxiv.org/abs/2603.05598" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">ICLR 2026, AI & PDE Workshop</a></li> 180 181 <li><i>Overtone: Cyclic Patch Modulation for Clean, Efficient, and Flexible Physics Emulators.</i> P. Mukhopadhyay, M. McCabe, <b>R. Ohana</b>, M. Cranmer. <a href="https://arxiv.org/abs/2507.09264" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">ICLR 2026</a></li> 182 183 <li><i>Walrus: A Cross-Domain Foundation Model for Continuum Dynamics.</i> <b>Polymathic AI.</b> <a href="https://arxiv.org/abs/2511.15684" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">ICML 2026 (Spotlight)</a></li> 184 185 <li><i>AION-1: Omnimodal Foundation Model for Astronomical Sciences.</i> <b>Polymathic AI.</b> <a href="https://arxiv.org/abs/2510.17960" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">NeurIPS 2025</a></li> 186 187 <li><i>TC-LoRA: Temporally Modulated Conditional LoRA for Adaptive Diffusion Control.</i> M. Cho, <b>R. Ohana</b>, C. Jacobsen, A. Jothi, M.-H. Chen, Z. Morley Mao, E. Can. <a href="https://arxiv.org/abs/2510.09561" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">NeurIPS 2025, Space in VLEAI Workshop</a></li> 188 189 <li><i>Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation.</i> F. Rozet, <b>R. Ohana</b>, M. McCabe, G. Louppe, F. Lanusse, S. Ho. <a href="https://arxiv.org/abs/2507.02608" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">NeurIPS 2025</a></li> 190 191 <li><i>
191Predicting Partially Observable Dynamical Systems via Diffusion Models with a Multiscale Inference Scheme.</i> R. Morel, F. P. Ramunno, J. Shen, A. Bietti, K. Cho, M. Cranmer, S. Golkar, O. Gugnin, G. Krawezik, T. Marwah, M. McCabe, L. Meyer, P. Mukhopadhyay, <b>R. Ohana</b>, L. Parker, H. Qu, F. Rozet, K. D. Leka, F. Lanusse, D. Fouhey, S. Ho. <a href="https://arxiv.org/abs/2511.19390" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">NeurIPS 2025</a></li> 192 193 <li> 194 <i>The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning. </i> <b>R. Ohana and M.McCabe, Polymathic AI. </b> 195 <!-- TODO: Add external links --> 196 <a 197 href="https://arxiv.org/abs/2412.00568" 198 rel="noreferrer noopener" 199 target="_blank" 200 class="button primary icon xsmall solid fa-external-link-alt" 201 >NeurIPS 2024 D&B track.</a 202 > 203 </li> 204 205 <li> 206 <i>Multiple Physics Pretraining for Spatiotemporal Surrogate Models. </i> <b>Polymathic AI.</b> 207 <!-- TODO: Add external links --> 208 <a 209 href="https://arxiv.org/abs/2310.02994" 210 rel="noreferrer noopener" 211 target="_blank" 212 class="button primary icon xsmall solid fa-external-link-alt" 213 >NeurIPS 2024.</a 214 > 215 </li> 216 217 <li> 218 <i>Listening to the Noise: Blind Denoising with Gibbs Diffusion </i> D. Heurtel-Depeiges, C. Margossian, R. Ohana, B. Régaldo. 219 <!-- TODO: Add external links --> 220 <a 221 href="https://arxiv.org/abs/2402.19455" 222 rel="noreferrer noopener" 223 target="_blank" 224 class="button primary icon xsmall solid fa-external-link-alt" 225 >ICML 2024</a 226 > 227 </li> 228 229 <li> <i>MoMo: Momentum Models for Adaptive Learning Rates.</i> F. Schaipp, <b>R. Ohana</b>, M. Eickenberg, A. Defazio, R. M. Gower. <a href="https://arxiv.org/abs/2305.07583" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">ICML 2024</a> </li> 230 231 232 <li> 233 <i>xVal: A Continuous Number Encoding for Large Language Models. </i> <b>Polymathic AI.</b> 234 <!-- TODO: Add external links --> 235 <a 236 href="https://arxiv.org/abs/2310.02989" 237 rel="noreferrer noopener" 238 target="_blank" 239 class="button primary icon xsmall solid fa-external-link-alt" 240 >NeurIPS, AI4Science Workshop 2023.</a 241 > 242 </li> 243 244 <li> <i>Removing Dust from CMB Observations with Diffusion Models.</i> D. Heurtel-Depeiges, B. Burkhart, R. Ohana, B. Régaldo-Saint Blancard. <a href="https://arxiv.org/abs/2310.16285" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt"><u>Oral</u> @ NeurIPS, MLPS Workshop 2023.</a> </li> 245 246 247 <li> <i>AstroCLIP: a cross-modal foundation model for galaxies.</i> <b>Polymathic AI.</b> <a href="https://academic.oup.com/mnras/article/531/4/4990/7697182" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">MNRAS 2024</a> </li> 248 249 250 <li> <i>Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances.</i> <b>R. Ohana<sup>*</sup></b>, K. Nadjahi<sup>*</sup>, A. Rakotomamonjy, L. Ralaivola. <a href="https://arxiv.org/abs/2206.03230" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">ICML 2023</a> </li> 251 252 <li> <i>Linear Optical Random Projections Without Holography.</i> <b>R. Ohana</b>
252, D. Hesslow, D. Brunner, S. Gigan, K. Müller. <a href="https://arxiv.org/abs/2305.12988" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">Optics Express</a> </li> 253 254 <li> <i>Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel.</i> J. Wacker, <b>R. Ohana</b>, M. Filippone. <a href="https://proceedings.mlr.press/v206/wacker23a/wacker23a.pdf" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">AISTATS 2023</a> </li> 255 <li> <i>Adversarial Robustness by Design through Analog Computing and Synthetic Gradients.</i> <b>R. Ohana<sup>*</sup></b>, A. Cappelli<sup>*</sup>, J. Launay, L. Meunier, I. Poli, F. Krzakala. <a href="https://arxiv.org/abs/2101.02115" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">ICASSP 2022</a> </li> 256 257 <li> <i>Photonic Differential Privacy with Direct Feedback Alignment.</i> <b>R. Ohana<sup>*</sup></b>, H.J. Ruiz<sup>*</sup>, J. Launay<sup>*</sup>, A. Cappelli, I. Poli, L. Ralaivola, A. Rakotomamonjy. <a href="https://proceedings.neurips.cc/paper/2021/file/b8c4c8b2271787e2f78b5fe2ce193caa-Paper.pdf" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">NeurIPS 2021</a> </li> 258 259 <li><i>LightOn Optical Processing Unit: Scaling-up AI and HPC with a Non von Neumann Co-processor.</i> C. Brossollet, A. Cappelli, I. Carron, C. Chaintoutis, A. Chatelain, L. Daudet, S. Gigan, D. He
259sslow, F. Krzakala, J. Launay, S. Mokaadi, F. Moreau, K. Müller, <b>R. Ohana</b>, G. Pariente, I. Poli, E. Tommasone. <a href="https://arxiv.org/abs/2107.11814" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">arXiv 2021</a></li> 260 261 <li> <i>ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients.</i> A. Cappelli, J. Launay, L. Meunier, <b>R. Ohana</b>, I. Poli. <a href="https://arxiv.org/abs/2108.04217" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">arXiv</a> </li> 262 263 <li> <i>Photonic co-processors in HPC: using LightOn OPUs for Randomized Numerical Linear Algebra.</i> D. Hesslow, A. Cappelli, I. Carron, L. Daudet, R. Lafargue, K. Müller, <b>R. Ohana</b>, G. Pariente, I. Poli. <a href="https://arxiv.org/abs/2104.14429" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">Hot Chips 2021</a> </li> 264 265 <li> <i>Align, then memorise: the dynamics of learning with feedback alignment.</i> M. Refinetti<sup>*</sup>, S. d'Ascoli<sup>*</sup>, <b>R. Ohana</b>, S. Goldt. <a href="https://arxiv.org/abs/2011.12428" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">ICML 2021</a> </li> 266 267 <li><i>Binarization for Optical Processing Units via REINFORCE.</i> B. Kozyrskiy, I. Poli, <b>R. Ohana</b>, L. Daudet, I. Carron, M. Filippone. <a href="https://www.eurecom.fr/en/publication/6739" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">ASPAI 2021</a></li> 268 269 <li> <i>Reservoir Computing meets Recurrent Kernels and Structured Transforms.</i> <b>R. Ohana<sup>*</sup></b>, J. Dong<sup>*</sup>, M. Rafayelyan, F. Krzakala. <a href="https://papers.nips.cc/paper/2020/file/c348616cd8a86ee661c7c98800678fad-Paper.pdf" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt"><u>Oral</u> @ NeurIPS 2020</a> </li> 270 271 <li> <i>Experimental Approach to Demonstrating Contextuality for Qudits.</i> A. Sohbi, <b>R. Ohana</b>, I. Zaquine, E. Diamanti, D. Markham. <a href="https://arxiv.org/abs/2010.13278" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">Physical Review A</a> </li> 272 273 <li> <i>Kernel Computations from large-scale random features obtained by Optical Processing Units.</i> <b>R. Ohana</b>, J. Wacker, J. Dong, S. Marmin, F. Krzakala, M. Filippone, L. Daudet. <a href="https://arxiv.org/abs/1910.09880" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">ICASSP 2020</a> </li> 274 275 <li> <i>Impact of epitaxial strain on the topological-nontopological phase diagram and semimetallic behavior of InAs/GaSb composite quantum wells.</i> H. Irie, T. Akiho, F. Couedo, <b>R. Ohana</b>, S. Suzuki, H. Onomitsu, K. Muraki. <a href="https://arxiv.org/abs/2002.12503v1" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">Physical Review B</a></li> 276 277 </ol> 278 279 <h2>Patent</h2> <ul> 280 281 <li> <i>Method and System for machine learning using optical data.</i> I. Poli, J. Launay, K. Müller, G. Pariente, I. Carron, L. Daudet, <b>R. Ohana</b>, D. Hesslow. <a href="https://patentimages.storage.googleapis.com/a1/34/7c/3c91a8598ebe1f/US20210287079A1.pdf" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">US Patent</a> 282 </li> </ul> 283 284 <h2>PhD Manuscript</h2> <ul> 285 286 <li> <i>Leveraging (physical) randomness in machine learning algorithms.</i> <b>R. Ohana</b>. <a href="https://theses.hal.science/tel-03828849/document" rel="noreferrer noopener" target="_blank" class="button primary icon xsmall solid fa-external-link-alt">PhD Thesis</a> </li> </ul> 287 288 <h2>Miscellaneous</h2> <ul> 289 <li>Student supervision: 290 <ul> 291 <li><a href="https://david-heurtel-depeiges.github.io/">David Heurtel-Depeiges </a>(PhD student at University of Montreal).</li> 292 <li><a href="https://people.csail.mit.edu/theoxo/">Theo Olausson</a> (PhD student at MIT). 293 </li> 294 <li> <a href="https://francois-rozet.github.io/"> François Rozet</a> (PhD student at University of Liège).</li> 295 <li> <a href="https://www.linkedin.com/in/mhsotoudeh/"> Mohammad-Hadi Sotoudeh</a> (PhD student at Cambridge University).</li> 296 </ul> 297 </li> 298 <li>Reviewer for NeurIPS 2021-2024, ICML 2022-2024, ICLR 2023, ALT 2020, WACV 2022, Nature Communications, Journal of Machine Learning Research (JMLR).</li> 299 </ul> 300 </div> 301 </div> 302 </section> 303 <!-- Talks --> 304 <section id="talks"> 305 <h2>Talks</h2> 306 <div class="row"> 307 <div class="col-12 col-12-small"> 308 <!-- TODO: Add talks --> 309 <ul> 310 <li> 311 Building foundation models for science, Brookhaven National Lab 2024. 312 </li> 313 <li> 314 Building foundation models for science, Sorbonne University 2024. 315 </li> 316 <li> 317 Building foundation models for science, M2Lines annual meeting 2024. 318 </li> 319 <li> 320 <a href="https://www.youtube.com/watch?v=_sKZsx7Iprg" 321 >100 seconds Research Challenge on Gibbs diffusion</a 322 >, ICML 2024 by Neptune AI. 323 </li> 324 <li> 325 <a href="https://simons.berkeley.edu/workshops/aiscience-strengthening-bond-between-sciences-artificial-intelligence">AI for Science Workshop 2024</a>, UC Berkeley. 326 </li> 327 <li>Building Foundation models for science at SuperComputing 2023.</li> 328 329 <li> 330 <a href="https://nips.cc/virtual/2020/public/session_oral_21072.html" 331 >Oral @ NeurIPS 2020 (starting at 46:30).</a 332 > 333 </li> 334 <li> 335 Paris Machine Learning Meetup 2020. 336 </li> 337 <li> 338 Dauphine Journal club 2020. 339 </li> 340 <li> 341 Roscoff meeting 2019. 342 </li> 343 </ul> 344 </div> 345 </div> 346 </section> 347 <!-- Software --> 348 <!-- <section id="software"> 349 <h2>Software</h2> 350 TODO: Add Software --> 351 <!-- <p>I contribute to a number of open source software projects.</p> 352 <div class="table-wrapper"> 353 <table class="alt"> 354 <thead> 355 <tr> 356 <th>
356Name</th> 357 <th>Description</th> 358 </tr> 359 </thead> 360 <tbody> 361 <tr> 362 <td><a href="#">Item One</a></td> 363 <td> 364 Lorem ipsum dolor sit amet, consectetur adipiscing elit. 365 </td> 366 </tr> 367 <tr> 368 <td><a href="#">Item Two</a></td> 369 <td> 370 Vis sunt in culpa qui officia deserunt mollit anim id est 371 laborum. 372 </td> 373 </tr> 374 <tr> 375 <td><a href="#">Item Three</a></td> 376 <td> 377 Ut sed do eiusmod tempor incididunt ut labore et dolore magna 378 aliqua. 379 </td> 380 </tr> 381 <tr> 382 <td><a href="#">Item Four</a></td> 383 <td> 384 Ut enim ad minim veniam, quis nostrud exercitation ullamco 385 laboris. 386 </td> 387 </tr> 388 <tr> 389 <td><a href="#">Item Five</a></td> 390 <td> 391 Duis aute irure dolor in reprehenderit in voluptate velit. 392 </td> 393 </tr> 394 </tbody> 395 </table> 396 </div> 397 </section> --> 398 <!-- Contact --> 399 <section id="contact"> 400 <h2>Contact</h2> 401 <div class="row"> 402 <div class="col-6 col-12-small"> 403 <ul class="icons"> 404 <li> 405 <a href="https://www.nvidia.com/en-us/" class="icon solid fa-home"> 406 <span class="label">Address</span> 407 </a> 408 NVIDIA Corporation<br /> 409 2788 San Tomas Expy<br /> 410 Santa Clara, CA 95051<br /> 411 United States<br /> 412 <a href="mailto:[email protected]" class="icon solid fa-envelope"> 413 <span class="label">Email</span> 414 </a> 415 <a href="https://twitter.com/oharub" class="icon brands fa-twitter"> 416 <span class="label">Twitter</span> 417 </a> 418 <a href="https://www.linkedin.com/in/rubenohana/" class="icon brands fa-linkedin"> 419 <span class="label">LinkedIn</span> 420 </a> 421 </li> 422 </ul> 423 </div> 424 </div> 425 </section> 426 </div> 427 <!-- Footer --> 428 <footer id="footer"> 429 <div class="inner" style="text-align: right; padding-right: 0em;"> 430 <a 431 href="https://www.nvidia.com" 432 class="image logo" 433 target="_blank" 434 id="logo" 435 rel="noreferrer noopener" 436 style="float: right; margin-left: 2em;" 437 > 438 <img src="images/nvidia_logo_white_oldtodelete.svg" alt="NVIDIA logo" id="logo-icon" style="border-radius: 0;" /> 439 </a> 440 </div> 441 </footer> 442 <!-- Scripts --> 443
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448 449
449<script src="assets/js/util.js"></script>
449 450
450<script src="assets/js/main.js"></script>
450 451 </body> 452</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.