Organizers
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391 | 392 | Arun Mallya is a Research 393 | Scientist at NVIDIA Research. He obtained his Ph.D. from the University of 394 | Illinois at Urbana-Champaign in 2018, with a focus on performing multiple tasks efficiently with 395 | a single deep network. He holds a B.Tech. in Computer Science and Engineering from the Indian 396 | Institute of Technology - Kharagpur (2012), an MS in Computer Science from the University of 397 | Illinois at Urbana-Champaign (2014). His current interests are on making neural networks faster, 398 | better, and more compact. 399 | | 400 |
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404 | 405 | Paulius Micikevicius works in compute architecture at NVIDIA on accelerating DNN training 406 | and 407 | inference through reduced arithmetic and other optimization techniques. In his previous roles at 408 | NVIDIA Paulius focused on parallelizing various compute workloads for GPUs, including DL, 409 | computer vision, scientific, and engineering applications. He also worked worked on developer 410 | education, connecting chip architecture details to software optimization techniques, resulting 411 | some of the highest rated and attended GPU Technology Conference talks. Paulius has also worked 412 | on perception while at a self-driving car company Zoox and as an assistant professor of Computer 413 | Science at Armstrong Atlantic State University. He holds a PhD in Computer Science from the 414 | University of Central Florida. 415 | | 416 |
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420 | 421 | Carl Case is a senior architect in compute architecture at NVIDIA, where he works on 422 | reduced-precision arithmetic for training deep neural networks. His focus is optimizing the 423 | entire stack of deep learning training - from hardware to high-level software - to accelerate 424 | the pace of AI development. Previously, he worked as a machine learning researcher on Deep 425 | Speech and its successor speech recognition systems at Baidu's Silicon Valley AI Lab. He holds 426 | bachelor's and master's degrees in computer science from Stanford University. 427 | | 428 |
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432 | 433 | Pavlo Molchanov obtained PhD from Tampere University of Technology, Finland in the area of 434 | signal processing in 2014. His dissertation was focused on designing automatic target 435 | recognition systems for radars. Since 2015 he is with the Learning and Perception Research team 436 | at NVIDIA, currently holding a senior research scientist position. His research is focused on 437 | methods for neural network acceleration, and designing novel systems for human-computer 438 | interaction and human understanding. For network acceleration, he is interested in neural 439 | network pruning methods and conditional inference. For human understanding, he is working on 440 | landmark estimation, gesture recognition, hand pose estimation. He received the EuRAD best paper 441 | award in 2011 and EuRAD young engineer award in 2013. 442 | | 443 |
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447 | 448 | Karan Sapra 449 | is a research scientist at NVIDIA in Santa Clara, US. He obtained his PhD. 450 | from Clemson University in 2018. During his PhD studies, he was an intern at Oak Ridge National 451 | Lab in 2015. His research interest lies in using deep learning for computer vision and computer 452 | graphics. During his PhD. he has also worked on various other research areas such as Peer2peer 453 | (P2P) networks, computer networking and security, and high performance computing (HPC) 454 | | 455 |
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459 | 460 | Guilin Liu is a senior 461 | research 462 | scientist at NVIDIA in Santa Clara, US. He obtained 463 | his Ph.D. from George Mason University in 2017. During his P.h.D study, he as an intern at Adobe 464 | Research in 2016. He received his B.E. from Wuhan University in 2012. His research interest lies 465 | in the intersection among deep learning, computer vision and graphics. His recent research 466 | interest focus on using deep learning for image processing and estimating physical properties 467 | from images. His works have been published at ICCV, CVPR, ECCV, NeurIPS, IROS etc and featured 468 | in some mainstream media outlets including Fortune, Yahoo Finance, VentureBeat etc. 469 | | 470 |
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474 | 475 | Ting-Chun Wang is a research 476 | scientist at NVIDIA in Santa Clara, US. He obtained his Ph.D. 477 | from 478 | University of California, Berkeley, department of EECS, advised by Professor Ravi Ramamoorthi 479 | and Alexei A. Efros. He received his B.E from National Taiwan University. He is a recipient of 480 | the Berkeley Fellowship. His research interests include computer vision, machine learning and 481 | computer graphics, particularly the intersections of all three. His recent research focus is on 482 | using generative adversarial models to synthesize realistic images and videos, with applications 483 | to rendering, visual manipulations and beyond. 484 | | 485 |
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489 | 490 | Ming-Yu Liu is a principal research 491 | scientist at NVIDIA Research. Before joining NVIDIA in 492 | 2016, 493 | he 494 | was a principal research scientist at Mitsubishi Electric Research Labs (MERL). He earned his 495 | Ph.D. 496 | from the Department of Electrical and Computer Engineering at the University of Maryland College 497 | Park in 2012. He is a recipient of the R&D 100 Award by R&D Magazine for his robotic bin picking 498 | system in 2014. His semantic image synthesis paper and scene understanding paper are in the best 499 | paper finalist in the 2019 CVPR and 2015 RSS conferences, respectively. In SIGGRAPH 2019, he won 500 | the 501 | Best in Show Award and Audience Choice Award in the Real Time Live show for his image synthesis 502 | work. His research focus is on generative image modeling. His research goal is to enable 503 | machines 504 | human-like imagination capability. 505 | | 506 |