point belowcard piece, 关note “CVer” public No.
AI/CV heavy-duty information, delivered as soon as possible
Click to enter—>[Visual and Transformer] WeChat communication group
Scan the QR code to join CVer Knowledge Planet,You can learn the latest paper ideas in the latest top conferences and journals as quickly as possible and CV from entry-level to proficient materials, as well as cutting-edge projects and applications! Publish a paper, highly recommended!
Reprinted from: The Heart of the Machine | Editor: Zenan
"They are the most talented PhD students in the world."
This Friday, the recipients of the highly anticipated NVIDIA Scholarship were announced.
For more than two decades, the NVIDIA Graduate Fellowship Program has supported graduate students doing outstanding work related to NVIDIA technology. NVIDIA has awarded $6 million in grants to nearly 200 students to date, advancing research in areas such as machine learning, computer vision, robotics and systems programming.
This year’s fellowship program provides scholarships of up to $60,000 each to 10 doctoral students involved in research in all areas of computational innovation.
The NVIDIA Scholarship is open to scholars from all over the world, and there is fierce competition every year. This year, there are more than 500 candidates, and five of the ten finalists are Chinese.
According to reports, the winners will enter NVIDIA for summer internships before the scholarship year, and the work they participate in is at the forefront of accelerated computing - including projects such as deep learning, robotics, computer vision, computer graphics circuits, autonomous driving and programming systems.
Bill Dally, chief scientist at NVIDIA, said that recipients of the NVIDIA Scholarship are the most talented researchers in the world and the scientific problems they are studying are of vital importance.
Ten nominees
Recipients of the 2024-2025 scholarships include:
Bailey Miller
from Carnegie Mellon University (CMU).
Research directions: Develop practical Monte Carlo methods for physical simulation to match the scalability and robustness of Monte Carlo rendering algorithms, focus on designing accelerated random walk methods that are easy to differentiate, and use volumetric models to deal with intractable problems complex geometric shapes.
Nicklas Hansen
from the University of California, San Diego (UCSD), mentored by Xiaolong Wang and Hao Su.
Research direction: Develop data-driven world models to enable robots to understand and interact with the real world.
To him Behnam
from Georgia Institute of Technology, mentored by Alexey Tumanov.
Research Interests: High-performance, low-latency, and energy-efficient design at the intersection of machine learning and systems.
Reinhard Wiesmayr
from ETH Zurich.
Research direction: Machine learning-assisted signal processing methods for wireless communication systems.
Songwei Ge
from the University of Maryland, College Park, mentored by Jia-Bin Huang and David Jacobs.
Research interests: Generative models applied to images and videos. He works on developing synthetic methods for content generation, controllable creation processes where humans provide guidance, and easy-to-interact interfaces that promote human participation.
Toluwanimi Odemuyiwa
from the University of California, Davis (UC Davis), mentored by John Owens.
Research interests: Designing end-to-end abstractions and frameworks for graph algorithms using tensor algebraic languages, from platform-independent declarative descriptions of computations to platform-specific implementations.
Yiming Li
from New York University, mentored by Chen Feng.
Research interests: Develop robust, efficient, and scalable artificial intelligence algorithms for 3D scene parsing and decision-making based on high-dimensional sensory input, and organize large-scale data sets to efficiently train and validate these algorithms for autonomous robots.
Yue Zhao
from the University of Texas, Austin, mentored by Philipp Krähenbühl.
Research direction: Train machine learning algorithms on workstation-class hardware and deploy them on everyday devices such as laptops and mobile devices to enable widespread use, training, and collaborative sharing.
Zhiqi Li
from Nanjing University, mentored by Tong Lu.
Research direction: Development of vision-centered perception methods for autonomous driving.
Zihao Ye
from the University of Washington, mentored by Luis Ceze.
Research directions: machine learning compilation, basic model serving system and sparse computing.
Other finalists
NVIDIA also announced the five finalists for the 2024-2025 scholarship. They are:
Andrew Szot, from Georgia Institute of Technology.
Bobbi Winema Yogatama, University of Wisconsin Madison.
Guanzhi Wang, from Caltech, mentored by Georgia Gkioxari and Yisong Yue.
Sehoon Kim from UC Berkeley.
Xi Deng, from Cornell University, mentored by Steve Marschner.
We look forward to the contributions these researchers will make in various fields.
At the same time, Nvidia is recruiting interns as it approaches the top artificial intelligence conferences EMNLP and NeurIPS. Jim Fan, senior scientist at Nvidia and head of the AI Agent Group, said that this winter, Lao Huang has prepared the newly released GPU for everyone.
Reference content:
https://blogs.nvidia.com/blog/23rd-graduate-fellowship-awards-applications-open/
https://twitter.com/yukez/status/1733223032678674771
CVPR/ICCV 2023 Papers and Code Downloads
Backend reply:CVPR2023,You can download the collection of CVPR 2023 papers and code open source papers
后台回复：ICCV2023，即可下载ICCV 2023论文和代码开源的论文合集计算机视觉和Transformer交流群成立
扫描下方二维码，或者添加微信：CVer444，即可添加CVer小助手微信，便可申请加入CVer-计算机视觉或者Transformer 微信交流群。另外其他垂直方向已涵盖：目标检测、图像分割、目标跟踪、人脸检测&识别、OCR、姿态估计、超分辨率、SLAM、医疗影像、Re-ID、GAN、NAS、深度估计、自动驾驶、强化学习、车道线检测、模型剪枝&压缩、去噪、去雾、去雨、风格迁移、遥感图像、行为识别、视频理解、图像融合、图像检索、论文投稿&交流、PyTorch、TensorFlow和Transformer、NeRF等。
一定要备注：研究方向+地点+学校/公司+昵称（如目标检测或者Transformer+上海+上交+卡卡），根据格式备注，可更快被通过且邀请进群▲扫码或加微信号: CVer444，进交流群
CVer计算机视觉（知识星球）来了！想要了解最新最快最好的CV/DL/AI论文速递、优质实战项目、AI行业前沿、从入门到精通学习教程等资料，欢迎扫描下方二维码，加入CVer计算机视觉（知识星球），已汇集近万人！
▲扫码加入星球学习▲点击上方卡片，关注CVer公众号