Nikola Nedovic

Nikola Nedovic joined NVIDIA Research in 2016. He received a Dipl.Ing. degree in electrical engineering from the University of Belgrade, Serbia, in 1998 and the Ph.D. degree from the University of California at Davis, in 2003. Before joining NVIDIA, he was a senior researcher and research manager at Fujitsu Laboratories of America, Inc., Sunnyvale, CA, where he worked on circuits and systems for electrical and optical communications, and energy-efficient VLSI implementations of machine learning computing systems. His research interests include high-speed analog and mixed-signal circuits for wireline communications, design and modeling of large mixed signal systems, adaptation and learning algorithms, and circuit design and clocking strategies for high-performance and low-power digital applications. 

Additional Research Areas: 

Daniel Lustig

Dan Lustig joined NVIDIA Research in December 2015.  He works in the area of computer architecture, with a particular focus on system architecture, memory system design, and memory consistency models.  His PhD thesis focused on specifying and verifying microarchitectural enforcement of memory models.

Dan received his PhD from Princeton in November 2015 under the supervision of Margaret Martonosi.  He received his MA from Princeton in 2011 and his BSE from the University of Pennsylvania in 2009.  He also received an Intel PhD Fellowship in 2013.

Main Field of Interest: 

Jinwei Gu

Jinwei Gu is a Senior Research Scientist in Dr. Jan Kautz's Learning and Perception Research (LPR) group. Before joining NVIDIA, he was a senior researcher in the America Media Lab in Futurewei Technologies. From 2010 to 2013, he was an assistant professor at the Munsell Color Science Laboratory (MCSL) in the Center for Imaging Science at Rochester Institute of Technology (RIT). Jinwei received his PhD degree from Columbia University in May 2010, and his bachelor and master degree from Tsinghua University, China in 2002 and 2005. His research interests are computer vision, computational photography, machine learning and computer graphics. His current research focuses on 3D computer vision, Visual SLAM, and augmented reality, and multi-camera systems for immersive media. More details on his research work can be found on his webpage

Deqing Sun

Deqing Sun is a senior research scientist at the Learning and Perception Research group at NVIDIA.  He was a postdoctoral research fellow and is a visiting researcher at Prof. Hanspeter Pfister’s Visual Computing group at Harvard University. He received his B.Eng. degree in Electronic and Information Engineering from Harbin Institute of Technology, his M.Phil. degree in Electronic Engineering from the Chinese University of Hong Kong, and his M.S. and Ph.D. degrees in Computer Science from Brown University working with Prof. Michael J. Black. He was a research intern at Microsoft Research New England in 2010 working with Dr. Ce Liu. His research interests include computer vision, machine learning, and computational photography, particularly optical flow estimation and the applications to video processing. He regularly serves on program committees and reviews papers for major computer vision, machine learning, and computer graphics conferences. He is/will be serving as an area chair for ECCV 2018 and CVPR 2019 and co-organizing "what is optical flow for?" workshop at ECCV 2018. He and his collaborators won the first place in the optical flow category of the robust vision challenge and received a best paper honorable mention award from CVPR 2018 for their work on sparse lattice networks for point cloud processing.

For my publications before joining NVIDIA, please check here.

Recent Updates
SPLATNet received a best paper honorable mention award from CVPR 2018
PWC-Net won first place in the optical flow track of the robust vision challenge
Press coverage for Super SloMoCNET, ExtremeTech, PCGamesN, The Inquirer, Ubergizmo, Android HeadlinesPetaPixel, Motherboard, and Lowyat.NET etc.
Serving as an area chair at ECCV 2018 and CVPR 2019
Co-organizing "What is optical flow for?" workshop at ECCV 2018

Code and Software
Caffe and PyTorch Code for PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume  CVPR 2018
Matlab Code for  Blind Image Deblurring Using Dark Channel Prior CVPR 2016.
Matlab Code for Optical Flow with Semantic Segmentation and Localized Layers CVPR 2016.
Code for Blind Video Temporal Consistency SIGGRAPH Asia 2015. 
Matlab Code for Layered RGBD Scene Flow Estimation CVPR 2015.
Matlab Code for A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them IJCV 2014.
Matlab Code for Secrets of Optical Flow Estimation and Their Principles CVPR 2010.
Matlab Code for Black and Anandan method
Matlab Code for Horn and Schunck method
Matlab Code for Postprocessing of Low Bit Rate Block DCT Coded Images based on a Fields of Experts Prior TIP 2007.

Brian Zimmer


Brian Zimmer joined the Circuits Research Group in NVIDIA Research in 2015.  His research interests are in energy-efficient digital design, with an emphasis on low-voltage SRAM design and variation tolerance.


He received the B.S. degree in electrical engineering from the University of California at Davis in 2010.  He received the M.S. and Ph.D. degrees in electrical engineering and computer sciences from the University of California at Berkeley in 2012 and 2015, respectively.  During the summer in 2012, he was an intern at Nvidia in the Circuits Research Group.



Main Field of Interest: 
Additional Research Areas: 

Walker Turner

Walker Turner joined the Circuits Research Group at NVIDIA in August 2015. He received the B.S. and M.S. degrees in Electrical and Computer Engineering from the University of Florida in 2009 and 2012, respectively. He received the Ph.D. degree in Electrical and Computer Engineering in 2015 with a focus on power delivery and low-power integrated circuit design. He previously interned at the U.S. Army Research Laboratory where he worked on wireless power transfer and custom integrated circuits for piezoelectric actuators. His current research interests include low-power integrated circuit design for mixed-signal and high-speed signaling applications.

Additional Research Areas: 

Nathaniel Pinckney

Nathaniel Pinckney received his PhD from David Blaauw’s research group at the University of Michigan in 2015. His research focused on near-threshold characterization of planar and FinFET devices, and fast voltage boosting. Prior to UM he worked for two years in Sun Microsystems’ VLSI Research group (presently Oracle Labs). His undergraduate degree is from Harvey Mudd College, where he was advised by David Money Harris.

Additional Research Areas: 

Sanquan Song

Sanquan Song joined NVIDIA Research in 2015. He completed his Ph.D in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. His area of research is high-speed links for high-speed computing and analog/mixed signal circuits. He has previously worked at Samsung Display America Lab from 2013 to 2015, focusing on the SerDes for large display panels; and at Intel Hudson from 2010 to 2013, developing  DDR/VMSE PHY on Intel server chips. 

Additional Research Areas: 

Mike Sullivan

I am from the Washington D.C. area and received a bachelor's and master's degree from George Mason University. Most recently I got a PhD from the University of Texas at Austin under the tutelage of Mattan Erez and Earl E. Swartzlander, Jr.


Subscribe to Research RSS