Hongxu (Danny) Yin: Senior Research Scientist at NVIDIA. Hongxu (Danny) Yin is a senior research scientist at Learning and Perception Research (LPR) at NVIDIA. He obtained his Ph.D. at Princeton University, New Jersey, USA, and B. Eng. from Nanyang Technological University, Singpore. He is a recipient of Princeton Yan Huo 94* Graduate Fellowship, Princeton Best Dissertation Finalist within Department, Princeton Natural Sciences and Engineering Fellowship, Defense Science & Technology Agency gold medal, and Thomson Asia Pacific Holdings gold medal. His research interests mainly include 246 data-/execution-efficient and secure deep learning overseeing CNNs and transformers. He has been the organizer of several tutorial/workshop at CVPR and ICCV. He has been featured as Global Outstanding Chinese Power 100 Award by 36Kr and Top 60 Elite Chinese in North America by Forbes. (primary contact: dannyy at nvidia.com)
Sifei Lie: Senior Research Scientist at NVIDIA. Sifei Liu is a senior research scientist at Nvidia Research in Santa Clara, US. She obtained her Ph.D. at the University of California Merced, Department of EECS, advised by Professor Ming-Hsuan Yang. Her research interests lie in computer vision and deep learning. She worked as an intern student at Baidu IDL from 2013 to 2014, at the multimedia lab at CUHK in 2015, and at NVIDIA research in 2017. She was the program committee member for many international conferences like IEEE CVPR, IEEE ICCV, ECCV, ACCV, and NeurIPS. She also serves as an area chair for ICCV, AAAI, and WACV. She was the recipient of the Baidu Graduate Fellowship in 2013, the NVIDIA Pioneering Research Award in 2017, Rising Star EECS in 2019, and nominated for VentureBeat Women in AI Award in 2020. (primary contact: sifeil at nvidia.com)
Ji Lin: Researcher at OpenAI. Ji Lin is currently a Member of Technical Staff at OpenAI. He obtained his Ph.D. from MIT and B.Eng from Tsinghua University. His research focuses on efficient deep learning computing for ML and recently, accelerating large language models (LLMs). His work has been widely integrated by industry solutions (NVIDIA FasterTranformer/TensorRT-LLM, Intel Neural Compressor/Q8Chat, FastChat, vLLM, HuggingFace Transformers/TGI, LMDeploy, etc.). His work has been covered by MIT Tech Review, MIT News (homepage), WIRED, Engadget, VentureBeat, etc. Ji is an NVIDIA Graduate Fellowship Finalist in 2020, and Qualcomm Innovation Fellowship recipient in 2022.
Maying Shen: Senior Research Engineer at NVIDIA. Maying Shen is currently a senior autonomous driving research engineer at NVIDIA. Prior to joining NVIDIA, she graduated from CMU majoring in computer vision, where she developed her interest in seeing the world through the computer’s eyes. Her interests include deep learning efficiency from both, training and inference side, working on aspects such as neural network pruning, distillation, or quantization among others. She has co-organized the Full-Stack, GPU-based Acceleration of Deep Learning tutorial and workshop on Autonomous Driving in conjunction with CVPR 2023/2024.
Jason Clemons: Senior Research Scientist at NVIDIA. Jason Clemons received his Ph.D. in computer science and engineering from the University of Michigan, Ann Arbor, MI, USA where he researched computer architectures for mobile computer vision. In his senior research scientist role at NVIDIA his current research focuses on domain-specific computing, in particular the intersection of machine learning, computer vision, and computer architecture. He has worked on machine learning accelerators, computer vision accelerators, accelerating DNN training on GPUs, and accelerating RL using GPUs. He is an IEEE senior member and serves on IEEE International Symposium on Performance Analysis of Systems and Software steering committee.
Xin Wang: Senior Researcher at Microsoft Research. Xin Wang is a Senior Researcher in the Physics of AGI group led by Dr. S ́ebastien Bubeck at Microsft Research Redmond. Before that she was a member of the Computer Vision Group at MSR, which she’s still associated with. She used to work on core vision problems and design generalizable, robust and efficient learning systems. Prior to MSR, she was a Ph.D. student at UC Berkeley, working with Prof. Trevor Darrell and Prof. Joseph E. Gonzalez in the BAIR Lab and RISE Lab.
Jose M. Alvarez: Senior Research Manager at NVIDIA. Jose M. Alvarez leads the perception for autonomous driving research team at NVIDIA. The main focus of the team and his interests are the scalability of deep learning and its applicability to autonomous driving. Prior to NVIDIA, he was a research scientist at TRI and NICTA, and worked as a postdoc researcher at NYU under Yann LeCunn. Jose Alvarez got his PhD in computer vision in Barcelona, working on road scene understanding for autonomous driving with research stays at Volkswagen and the University of Amsterdam. Dr. Alvarez is on the editorial board of IEEE Trans. Intelligent Transportations Systems and served as area chair for ICRA 2015, WACV-2016/2018, MM 2016 (Autonomous driving track) IV-2018/2019 and ITSC-2019. Dr. Alvarez has led the organization of several workshop series including DeepVision, Workshop on Computer Vision in Vehicular Technology, Workshop on Autonomous Driving, Embodied AI workshop, Color in Computer Vision (CPCV) or Road Scene Understanding in the main computer vision conferences (ICCV, ECCV, CVPR). In short, Jose M. Alvarez has been the organizer of more than 20 workshops including the largest workshops in related venues (Deep Vision and workshop on Autonomous Driving at CVPR and ICCV) for several consecutive years. Dr. Alvarez also leads the tutorial on Perception for Autonomus driving at Scale at IEEE-IV in 2019.
Pavlo Molchanov: Distinguished Scientist at NVIDIA. Pavlo Molchanov is a distinguished research scientist and research lead with NVIDIA Research since 2015. His research is focused on efficient deep learning and human-centric computer vision in LPR team lead by Jan Kautz. In the area of network efficiency he is working on methods for model acceleration, inversion, novel architectures and adaptive/conditional inference. In the area of human-centric vision he is working on face/body/hand landmarks and pose estimation, action/gesture recognition and designing novel human-computer interaction systems. He holds a degree in signal processing obtained in Tampere University of Technology, Finland in 2014. He served as a program committee member of IEEE AAAI. He has co-organized the Accelerating Computer Vision with Mixed Precision tutorial in conjunction with ICCV 2019.
Xueyan Zou: Posdoc at UCSD. Xueyan Zou is a PhD Candidate at UW-Madison supervised by Prof. Yong Jae Lee and will join UCSD as a Postdoctoral Scholar. Her research interests lies on vision-language understanding, especially powered by foundation model and LLMs. She contributes and develop a series of works including X-Decoder, SEEM, OpenSEED, SemanticSAM, and FIND toward a generalized approach for visual understanding cross modality and granularity. In addition, she also contributes to power pixel level understanding for visual chat such as LLaVA-Plus, LLaVA-Grounding. Her long term goal is contributing to develop generalized models toward visual assist understanding, reasoning, and creating content in the real world.
Xiaolong Wang: Assistant Professor at UCSD. Xiaolong Wang is an Assistant Professor of Electrical and Computer Engineering at the University of California, San Diego. He is affiliated with the Center for Visual Computing and the Contextual Robotics Institute. He received his Ph.D. in Robotics at Carnegie Mellon University, where his study is supported by the Facebook Fellowship and the NVIDIA Fellowship. His postdoctoral training was at the University of California, Berkeley. His research focuses on the intersection between computer vision and robotics. He is particularly interested in learning visual representation from videos in a self-supervised manner and use this rep- resentation to guide robots to learn. Xiaolong is the Area Chair of CVPR 2021, AAAI 2021, ICCV 2021. He has organized and served as keynote speakers at workshops and tutorials at CVPR, ECCV, ICCV. He is the recipient of the NSF CAREER Award, Sony Research Award, and Amazon Research Award.
Song Han: Associate Professor at MIT / Distinguished Research Scientist at NVIDIA. Song Han is an associate professor at MIT and a distinguished research scientist and NVIDIA. He received PhD degree from Stanford University. He proposed the “Deep Compression” technique including pruning/quantization that is widely adopted, and “Efficient Inference Engine” that first brought weight sparsity to modern AI chips. He pioneered TinyML research that brings deep learning to IoT devices, enabling learning on the edge. His team’s work on hardware-aware neural architecture search enables users to design, optimize, shrink and deploy AI models to resource-constrained hardware devices. Song received best paper awards at ICLR and FPGA, faculty awards from Amazon, Facebook, NVIDIA, Samsung and SONY. Song was named “35 Innovators Under 35” by MIT Technology Review, and received the NSF CAREER Award, IEEE “AIs 10 to Watch: The Future of AI” award, and Sloan Research Fellowship.
Jan Kautz: Vice President of Research at NVIDIA. Jan Kautz is the Vice President of Learning and Perception Research at NVIDIA. He and his team pursue fundamental research in the areas of computer vision and deep learning, including visual perception, geometric vision, generative models, and efficient deep learning. Their work has been given various awards and has been regularly featured in the media. Before joining NVIDIA in 2013, Jan was a tenured faculty member at University College London. He holds a degree in Computer Science from the University of Erlangen-Nürnberg (1999), an MMath from the University of Waterloo (1999), received his PhD from the Max-Planck-Institut für Informatik (2003), and worked as a post-doctoral researcher at the Massachusetts Institute of Technology (2003-2006). Jan has chaired numerous conferences and has served on several editorial boards.