I am currently an Applied Scientist @ Amazon. My email is .
I defended my Ph.D thesis in 2019 summer and obtained my Second PhD degree in the Computer Science Department at The University of California, Los Angeles, under the supervision of Distinguished Prof. Demetri Terzopoulos. My research interests are focused on Computer Graphics and Computer Vision.
Prior to joining UCLA, I obtained my First PhD at The Pennsylvania State University under the supervision of Distinguished Prof. Mark Latash. During my time at Penn State, I studied behavioral neuroscience in the Kinesiology Department with a research focus on human motor control and biomechanics. I obtained my Master degree in Robotics and Bachelor degree in Automation from Tongji University in China.
My research goal is to close the gap between artificial and real human. My results lie in the sensorimotor control of a biomechanical human musculoskeletal model and its application on medicine using deep learning among other techniques. To that end, I am broadly interested in virtual human control, neuroscience, robotics, VR/AR, human-robot interaction and robot-aided rehabilitation.
I have taught 2 different classes at UCLA, "Introduction to Computer Science" and "Data Structure", and 2 different classes at Penn State, "Biomechanics" and "The Neurobiology of Motor Control and Development".
Papers published in recent 3 years
Nakada M, Arjun L, Chen H, Ling N, Zhou T, Terzopoulos D,
Biomimetic Eye Modeling & Deep Neuromuscular Oculomotor Control,
1. Zhou T, Falaki A, Latash ML. Unintentional movements induced by sequential transient perturbations in a multi-joint positional task. Human movement science vol. 46 p. 1-9, 2016. [pdf]
2. Qiao M, Zhou T, Latash ML. Positional errors introduced by transient perturbations applied to a multi-joint limb. Neuroscience letters, 595: 104-7, 2015. [pdf]
3. Zhou T, Latash ML. Unintentional changes in the apparent stiffness of the endpoint of a multi-joint limb. Experimental brain research, 233(10): 2989-3004, 2015. [pdf]
4. Ambike S, Zhou T, Latash ML. Moving a hand-held object: Reconstruction of referent coordinate and apparent stiffness trajectories. Neuroscience, 298: 336-56, 2015. [pdf]
5. Zhou T, Zhang L, and Latash ML. Intentional and unintentional multi-joint movements: their nature and structure of variance. Neuroscience, 289: 181-93, 2015. [pdf]
6. Zhou T, Zhang L, Latash ML. Characteristics of Unintentional Movements by a Multijoint Effector. J Mot Behav:1-10, 2015 [pdf]
7. Zhou T, Solnik S, Wu YH, and Latash ML. Unintentional movements produced by back-coupling between the actual and referent body configurations: violations of equifinality in multi-joint positional tasks. Experimental brain research, 232: 3847-3859, 2014. [pdf]
8. Falaki A, Zhou T, Towhidkhah F, and Latash ML. Task-specific stability in muscle activation space during unintentional movements. Experimental brain research, 232: 3645-3658, 2014. [pdf]
9. Zhou T, Solnik S, Wu YH, Latash ML. Equifinality and its violations in a redundant system: control with referent configurations in a multi-joint positional task. Motor Control 18:405- 424, 2014. [pdf]
10. Zhou T, Wu YH, Bartsch A, Cuadra C, Zatsiorsky VM, and Latash ML. Anticipatory synergy adjustments: preparing a quick action in an unknown direction. Experimental brain research, 226: 565-573, 2013. [pdf]
11. Wang ZP, Zhou T, Mao Y, and Chen QJ. Adaptive recurrent neural network control of uncertain constrained nonholonomic mobile manipulators. International Journal of Systems Science, 45: 133-144, 2012. [pdf]
12. Wang ZP, Zhou T, and Chen QJ. Control of uncertain constrained nonholonomic mobile manipulator based on recurrent neural network. In: Intelligent Control and Automation (WCICA), 2010 8th World Congress, p. 532-536. [pdf]
13. Wang ZP, and Zhou T. Control of an uncertain nonholonomic mobile manipulator based on the Diagonal Recurrent Neural Network. In: Control and Decision Conference (CCDC), 2011 Chinese , p. 4044-4047.
@ Penn State
Equifinality and its violations in multi-joint positional tasks [pdf] [slides]
Core training: Learning deep neuromuscular control of the torso for anthropomimetic animation [pdf] [slides]