Dinesh Manocha is currently Phi Delta Theta/Matthew Mason Distinguished Professor of Computer Science in the College of Arts and Sciences at the University of North Carolina at Chapel Hill. He received his B.Tech degree in Computer Science and Engineering from the Indian Institute of Technology, Delhi in 1987; Ph.D. in Computer Science at the University of California at Berkeley in 1992. He has coauthored more than 420 papers in the leading conferences and journals on computer graphics, robotics and scientific computing.
Manocha has received awards including an IBM Fellowship, Alfred P. Sloan Fellowship, NSF Career Award, Office of Naval Research Young Investigator Award, SIGMOD IndySort Winer, Honda Research Award, Hettleman Award at UNC-Chapel Hill and 14 best paper awards at leading conferences. He is a Fellow of ACM, AAAS and IEEE and received a Distinguished Alumni Award from Indian Institute of Technology, Delhi.
He has served on the editorial board of 10 leading journals and program committees of 100-plus conferences in computer graphics, robotics, high performance computing, geometric computing and symbolic computation. He has been the program chair and general chair of more than 13 conferences/workshops in these areas. He also served as Director-at-Large of ACM SIGGRAPH from 2011-2014.
Manocha has supervised 65-plus M.S. and Ph.D. students over the last 23 years at UNC-Chapel Hill. His research group has developed many well-known software packages for collision detection, triangulation, GPU-based algorithms, solid modeling and solving algebraic systems. These packages have been downloaded by more than 150,000 users worldwide and licensed to more than 55 industrial organizations including Intel, Microsoft, Disney, Ford, Kawasaki, Siemens, Phillips Labs, MSC Software, Lockheed Martin, Raytheon etc.
Manocha's research has been supported by ARO, NSF, DARPA, RDECOM, ONR, NIH and many industrial organizations (e.g. Intel, Samsung, Google, Microsoft, Honda, Ford, NVidia, AMD, Disney, Willow Garage). He has served as a Principal Investigator or Co-Principal Investigator on more than 75 grants.
The website for his research and outreach is: http://gamma.web.unc.edu/.
Areas of Expertise (8)
Google Research Award (2015) (professional)
Google Research Awards are one-year awards structured as unrestricted gifts to universities to support the work of world-class permanent faculty members at top universities around the world.
1000Talent Scholar (2014) (professional)
Dr. Manocha is the 2014 1000Talent Scholar, Zhejiang University, China.
Fellow of American Association for the Advancement of Science (AAAS) (professional)
Fellow of Institute of Electrical and Electronics Engineers (IEEE) (professional)
Fellow of Association for Computing Machinery (ACM) (professional)
National Science Foundation CAREER Award (professional)
University of California at Berkeley: Ph.D., Computer Science 1992
University of California at Berkeley: M.S., Computer Science 1990
Indian Institute of Technology, Delhi, India: B.E., Computer Science and Engineering 1987
- Institute of Electrical and Electronics Engineers (IEEE) : Fellow
- American Association for the Advancement of Science (AAAS) : Fellow
- Association for Computing Machinery (ACM) : Fellow
Media Appearances (8)
More media appearances
by Dinesh Manocha online
View more media appearances here
Politics Aside, Counting Crowds Is Tricky
There has been a lot of arguing about the size of crowds in the past few days. Estimates for President Trump's inauguration and the Women's March a day later vary widely.
But even computers have limits, says Dinesh Manocha of the University of North Carolina at Chapel Hill. They have no problem sorting a few people who aren't packed together. But when you have big crowds, like those seen across the country in the past few days, it gets tricky.
"When it's more than 100,000, we just can't estimate right. We don't have an answer today," he says.
Sounds Good: Valve Acquires 3D Audio Company Impulsonic
Note: News of the UNC-based spinoff company, Impulsonic, acquired by Valve, was actively covered in the technology media, including UPLOADVR, Tom's Hardware, REDDIT, NeoGAF and GGNews.
The company was founded by professors Dinesh Manocha and Ming Lin with two former PhD students, Anish Chandak and Lakulish Antani.
DARPA seeks "non-traditional" robotics innovators
Nov. 18, 2015
One of those pioneering researchers is Dinesh Manocha, Distinguished Professor of Computer Science at the University of North Carolina, Chapel Hill. Dr. Manocha and his team are developing advanced motion and trajectory planning systems for high degree of freedom robots.
UCF Researchers Perform World’s First Automated Mass-Crowd Count
Space Coast Daily online
"Computers have scanned aerial photographs and conducted the first automated mass-crowd count in the world, thanks to the work of researchers at the University of Central Florida."
This article features Dr. Manocha.
Making movies, video games sound as good as they look
The Boston Globe online
Dr. Manocha is featured in this article for the Boston Globe.
In crowds, human 'particles' follow laws of movement
The Boston Globe online
Dec. 14, 2014
Research on crowd behavior has a lot of immediate applications. Rules of movement can help architects design buildings to better handle the way people really flow through them. Dinesh Manocha, a computer scientist at the University of North Carolina, is currently working with Boeing to model the movement of passengers onto and off of airplanes; he calls the recent paper “a wonderful explanation of humans in nature.”
Speeding up Science
Scientific Computing online
Dr. Manocha is featured in this article for Scientific Computing.
Event Appearances (6)
View more events, conferences and workshops here
http://gamma.cs.unc.edu/news/events.html by Dinesh Manocha
2015 IEEE International Symposium focused on multimedia
World Robotics Conference, November 2015 Beijing, China
SIAM Conference on Geometric & Physical Modeling
Presentation Salt Lake City, Utah
Interactive Sound Simulation and Rendering
VRIPHYS 10th Workshop on Virtual Reality Interaction and Physical Simulation Lille, France
Interactive Sound Rendering
Distinguished Lecture Series UNC Charlotte
The Gamma Group's research includes design and implementation of algebraic, geometric, and scientific algorithms and their applications to computer graphics, robotics, virtual environments, CAD/CAM, acoustics, pedestrian dynamics, and medical simulation.
We present an algorithm for computing the global penetration depth between an articulated model and an obstacle or between the distinctive links of an articulated model. In so doing, we use a formulation of penetration depth derived in configuration space. We first compute an approximation of the boundary of the obstacle regions using a support vector machine in a learning stage. Then, we employ a nearest neighbor search to perform a runtime query for penetration depth. The computational complexity of the runtime query depends on the number of support vectors, and its computational time varies from 0.03 to 3 milliseconds in our benchmarks. We can guarantee that the configuration realizing the penetration depth is penetration free, and the algorithm can handle general articulated models. We tested our algorithm in robot motion planning and grasping simulations using many high degree of freedom (DOF) articulated models. Our algorithm is the first to efficiently compute global penetration depth for high-DOF articulated models.
Refractive media, in which light and sound propagate along curved paths, are ubiquitous in the natural world. We present algorithms that achieve efficient and scalable propagation computation for fully general media profiles and complex scene configurations.
We present an algorithm to accelerate resolution independent curve rendering on mobile GPUs. The bottleneck for certain platform in- dependent GPU implementations is in generating grayscale textures on the CPU containing the amount that each pixel is covered by the curve. In this paper, we demonstrate that generating a compressed grayscale texture prior to uploading it to the GPU creates faster ren- dering times in addition to the memory savings. We implement a real-time compression technique for coverage masks and compare our results against the GPU-based implementation of the highly op- timized Skia rendering library. We observe up to a 2X speed im- provement over the existing GPU-based methods in addition to up to a 9:1 improvement in GPU memory gains. We demonstrate the performance on multiple mobile platforms.
We present a new collision and proximity library that integrates several techniques for fast and accurate collision checking and proximity computation. Our library is based on hierarchical representations and designed to perform multiple proximity queries on different model representations. The set of queries includes discrete collision detection, continuous collision detection, separation distance computation and penetration depth estimation. The input models may correspond to triangulated rigid or deformable models and articulated models. Moreover, FCL can perform probabilistic collision checking between noisy point clouds that are captured using cameras or LIDAR sensors. The main benefit of FCL lies in the fact that it provides a unified interface that can be used by various applications. Furthermore, its flexible architecture makes it easier to implement new algorithms within this framework. The runtime performance of the library is comparable to state of the art collision and proximity algorithms. We demonstrate its performance on synthetic datasets as well as motion planning and grasping computations performed using a two-armed mobile manipulation robot.
In this paper, we present a formal approach to reciprocal n-body collision avoidance, where multiple mobile robots need to avoid collisions with each other while moving in a common workspace. In our formulation, each robot acts fully independently, and does not communicate with other robots. Based on the definition of velocity obstacles , we derive sufficient conditions for collision-free motion by reducing the problem to solving a low-dimensional linear program. We test our approach on several dense and complex simulation scenarios involving thousands of robots and compute collision-free actions for all of them in only a few milliseconds. To the best of our knowledge, this method is the first that can guarantee local collision-free motion for a large number of robots in a cluttered workspace.
We present a novel approach to direct and control virtual crowds using navigation fields. Our method guides one or more agents toward desired goals based on guidance fields. The system allows the user to specify these fields by either sketching paths directly in the scene via an intuitive authoring interface or by importing motion flow fields extracted from crowd video footage. We propose a novel formulation to blend input guidance fields to create singularity-free, goal-directed navigation fields. Our method can be easily combined with the most current local collision avoidance methods and we use two such methods as examples to highlight the potential of our approach. We illustrate its performance on several simulation scenarios.