Bio

Oncel Tuzel is a Distinguished Scientist at the Machine Learning Research Team at Apple. Previously, he served as a Senior Principal Research Scientist at Mitsubishi Electric Research Laboratories (MERL) in Cambridge. He received his B.S. and M.S. degrees in Computer Engineering from Middle East Technical University, Ankara, Turkey, in 1999 and 2002, respectively, and his Ph.D. in Computer Science from Rutgers University in 2008. Prior to pursuing his Ph.D., he worked as a Lead Software Engineer for four years in Ankara, Turkey, developing 3D games and simulations.

His research focuses on machine learning, with an emphasis on efficient ML algorithms, controllable generative AI, and learning with synthetic data. His work has contributed to numerous Apple features and products, including Siri Invocation, Siri ASR, Scribble, Keyboard Intelligence, Face Recognition, VisionKit, Apple Maps, Apple Photos, and Vision Pro. He has co-authored over 80 peer-reviewed publications and holds more than 60 U.S. and international patents. His research has garnered several awards, including the Best Paper Award at CVPR 2017, the Best Paper Runner-Up Award at CVPR 2007, a Best Paper nomination at RSS 2015, Top Paper Recognition at ICASSP 2022, and the 2014 R&D 100 Award, which recognizes the 100 most innovative technologies introduced in 2013.

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News

Awards
  • Best paper award in leading machine learning and computer vision conference, IEEE Conf. on Comp. Vis. and Pattern Rec. (CVPR), out of 2620 papers, 2017
  • Best paper runner-up award in leading machine learning and computer vision conference, IEEE Conf. on Comp. Vis. and Pattern Rec. (CVPR), out of 1300 papers, 2007
  • Best paper honorable mention in leading robotics conference, Robotics: Science and Systems (RSS), 2015
  • R&D 100 award for MELFA 3D Vision System (Awarded to 100 most innovative technology introduced in 2013), 2014
  • MERL research excellence award for contributions to business of intelligent transportation systems, 2013
  • Mitsubishi Electric Corporation Information Technology R&D Center (ITC) research excellence award for contributions to product development of vehicle recognition systems, 2012
  • MERL directors’ award for contributions to sequential data analysis methods for visual surveillance systems, 2010
  • MERL directors’ award for contributions to product development of robotic assembly systems, 2009

Publications

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005 & earlier


Patents
  • Image Upsampling using Global and Local Constraints, Oncel Tuzel, Yuichi Taguchi, pending 2016
  • System and Method for Semantic Segmentation using Gaussian Random Field Network, Raviteja Vemulapalli, Oncel Tuzel, Ming-Yu Liu, pending 2016
  • Method and System for Detecting Actions in Videos, Mike Jones, Tim Marks, Oncel Tuzel, Singh, B., pending 2016
  • Method and System for Denoising Images Using Deep Gaussian Conditional Random Field Network, Oncel Tuzel, Ming-Yu Liu, Raviteja Vemulapalli, pending 2015
  • System and Method for Testing and Evaluating Vehicle Components, Zafer Sahinoglu, Oncel Tuzel, pending 2015
  • Method for Training Classifiers to Detect Objects Represented in Images of Target Environments, Oncel Tuzel, Jay Thornton, pending 2015
  • Method for Labeling Images of Street Scenes, Ming-Yu Liu, Ramalingam S., Oncel Tuzel, pending 2015
  • Method for Estimating Locations of Facial Landmarks in an Image of a Face using Globally Aligned Regression, Oncel Tuzel, Salil Tambe Tim Marks, pending 2015
  • Method for Semantically Labeling an Image of a Scene using Recursive Context Propagation, Oncel Tuzel, Ming-Yu Liu, Abhishek Sharma, pending 2014
  • Method for detecting geometric edges of a scene under multiple illumination conditions, Tim Marks, Oncel Tuzel, Fatih Porikli, Jie Ni, 2016, 9,418,434
  • Method for factorizing lighting of a scene into basis images, Oncel Tuzel , Tim Marks, Fatih Porikli, Jie Ni, 2016, 9,384,553
  • Image Denoising Using a Library of Functions, Oncel Tuzel, Jay Thornton, Van Baar, J., 2016, 9,262,810
  • Method for Determining Object Poses Using Weighted Features, Oncel Tuzel, Ming-Yu Liu, Yuichi Taguchi, Arvind Raghunathan, 2016, 9,280,827
  • Specular Edge Extraction Using Multi-Flash Imaging, Yuichi Taguchi, Oncel Tuzel, Srikumar Ramalingam, 2016, 8,913,825
  • Method for Detecting Objects in Stereo Images, Ming-Yu Liu, Oncel Tuzel, 2015, 9,195,904
  • Method for Increasing Resolutions of Depth Images, Oncel Tuzel, Ming-Yu Liu, Yuichi Taguchi, 2015, 8,983,177
  • Method and System for Generating Structured Light with Spatio-Temporal Patterns for 3D Scene Reconstruction, Yuichi Taguchi, Amit Agrawal, Oncel Tuzel, 2014, 8,805,057
  • Voting-based Pose Estimation for 3D Sensors, Yuichi Taguchi, Oncel Tuzel, Srikumar Ramalingam, Changhyun Choi, Ming-Yu Liu, 2014, 8,908,913
  • Upsampling of natural images, Oncel Tuzel, Fatih Porikli, Chinmay Hegde, 2013, 8,620,073
  • Appearance and Context Based Object Classification in Images, Oncel Tuzel, Pillai, J., 2014, 8,718,362
  • Method and System for Generating Structured Light with Spatio-Temporal Patterns for 3D Scene Reconstruction, Yuichi Taguchi, Amit Agrawal, Oncel Tuzel, 2014, 8,805,057
  • Object detecting with 1D range sensors, Oncel Tuzel, Gungor Polatkan, 2014, 8,824,548
  • Method for Segmenting Images Using Superpixels and Entropy Rate Clustering, Oncel Tuzel, Srikumar Ramalingam, Ming-Yu Liu, 2013, 8,428,363
  • Object detection in depth images, Mike Jones, Oncel Tuzel, Si, W., 2013, 8,406,470
  • Method for Determining Compressed State Sequences, Oncel Tuzel, Gungor Polatkan, 2013, 8,405,531
  • Camera and Method for Focus Based Depth Reconstruction of Dynamic Scenes, Ashok Veeraraghavan, Nitesh Shroff, Yuichi Taguchi, Oncel Tuzel, 2013, 8,432,434
  • Method for Reconstructing Surfaces of Specular Object from Sparse Reflection Correspondence, Ashok Veeraraghavan, Oncel Tuzel, Amit Agrawal, Aswin Sankaranarayanan, 2012, 8,229,242
  • Determining Points of Parabolic Curvature on Surfaces of Specular Objects, Ashok Veeraraghavan, Oncel Tuzel, Amit Agrawal, Aswin Sankaranarayanan, 2012, 8,155,447
  • Method and System for Determining Poses of Objects, Oncel Tuzel, Ashok Veeraraghavan, 2012, 8,306,314
  • Method and system for determining poses of specular objects, Srikumar Ramalingam, Ashok Veeraraghavan, Yuichi Taguchi Oncel Tuzel, Nitesh Shroff, 2012, 8,165,403
  • Method for Clustering Samples with Weakly Supervised Kernel Mean Shift Matrices, Oncel Tuzel, Fatih Porikli, 2012, 8,296,248
  • Method and System for Detecting and Tracking Objects in Images, Oncel Tuzel, Fatih Porikli, 2011, 7,961,952
  • Detecting Moving Objects in Video by Classifying on Riemannian Manifolds, Fatih Porikli, Oncel Tuzel, 2011, 7,899,253
  • Method for Classifying Data Using an Analytic Manifold, Fatih Porikli, Oncel Tuzel, 2010, 7,724,961
  • Method for Constructing Covariance Matrices from Data Features, Fatih Porikli, Oncel Tuzel, 2010, 7,720,289
  • Method for Tracking Objects in Videos Using Covariance Matrices, Fatih Porikli, Oncel Tuzel, 2009, 7,620,204
  • Modeling Low Frame Rate Videos with Bayesian Estimation, Fatih Porikli, Oncel Tuzel, 2008, 7,466,842
  • Tracking Objects in Low Frame Rate Videos, Fatih Porikli, Oncel Tuzel, 2008, 7,418,113
  • Adaptive Background Image Updating, Dirk Brinkman, Fatih Porikli, Oncel Tuzel, 2007, 7,224,735