Resume

• 2007

  Georgia Institute of Technology

  Leads the Socially Intelligent Machines Lab in the School of Interactive Computing.

  Assistant Professor

  Greater Atlanta Area

  Georgia Institute of Technology

  Leads the Socially Intelligent Machines Lab in the School of Interactive Computing.

  Associate Professor

  Greater Atlanta Area

  Georgia Institute of Technology

  Leads a robotics lab in the Electrical and Computer Engineering department.

  The University of Texas at Austin

  Scientist/Engineer

  In the RS/6000 Division

  at the Austin TX campus

  I worked with a group of 12 engineers charged with simulating (in C++) the memory subsystems of a RISC symmetric multiprocessor

  for the purpose of circuit design verification. In this year I wrote a cache preloader to save millions of cycles needed to achieve specific cache states in processor simulation. I interned with this same group for several semesters during my undergraduate studies.

  International Business Machines

  Diligent Robotics

  Inc.

  Austin

  TX

  Diligent Robotics is a human-centered robotics company. Our mission is to make technical advances towards robots and humans working together side by side

  with an emphasis on human-centric design. Diligent Droids is developing a suite of artificial intelligence that enables robots to collaborate with and adapt to humans in everyday environments. Our service robots are designed to participate and work together with teams of humans.

  co-Founder

  CEO

  Interned with the Communities Technologies Group (now Social Computing)

  lead by Marc Smith. Expanded the group's Usenet technologies for mining social statistics to the realm of email.

  Microsoft Corporation

• 2002

  Ph.D.

  I completed my Ph.D. thesis under the advice of Cynthia Breazeal

  in the Robotic Life Group at the MIT Media Lab. My thesis topic is Socially Guided Machine Learning

  detailing ways in which machine learning algorithms can be better suited for real-time interactive learning with everyday people.

  Media Arts & Sciences

  B.S.

  Electrical and Computer Engineering

  University of Texas at Austin

  MIT

  S.M.

  Media Arts & Sciences

• Human Computer Interaction

  Research

  LaTeX

  Simulations

  Machine Learning

  Computer Vision

  Algorithms

  Software Engineering

  Computer Science

  Robotics

  Science

  Matlab

  Artificial Intelligence

  Programming

  C++

  Controlling social dynamics with a parametrized model of floor regulation

  Turn-taking is ubiquitous in human communication

  yet turn-taking between humans and robots\ncontinues to be stilted and awkward for human users. The goal of our work is to build autonomous\nrobot controllers for successfully engaging in human-like turn-taking interactions. Towards this end

  \nwe present CADENCE

  a novel computational model and architecture that explicitly reasons about\nthe four components of floor regulation: seizing the floor

  yielding the floor

  holding the floor

  and\nauditing the owner of the floor. The model is parametrized to enable the robot to achieve a range\nof social dynamics for the human-robot dyad. In a between-groups experiment with 30 participants

  \nour humanoid robot uses this turn-taking system at two contrasting parametrizations to engage users\nin autonomous object play interactions. Our results from the study show that: (1) manipulating\nthese turn-taking parameters results in significantly different robot behavior; (2) people perceive\nthe robot’s behavioral differences and consequently attribute different personalities to the robot;\nand (3) changing the robot’s personality results in different behavior from the human

  manipulating\nthe social dynamics of the dyad. We discuss the implications of this work for various contextual applications as well as the key limitations of the system to be addressed in future work.

  Controlling social dynamics with a parametrized model of floor regulation

  Turn-taking interactions with humans are multimodal and reciprocal in nature. In addition

  the timing of actions is of great importance

  as it influences both social and task strategies. To enable the precise control and analysis of timed discrete events for a robot

  we develop a system for multimodal collaboration based on a timed Petri net (TPN) representation. We also argue for action interruptions in reciprocal interaction and describe its implementation within our system. Using the system

  our autonomously operating humanoid robot Simon collaborates with humans through both speech and physical action to solve the Towers of Hanoi

  during which the human and the robot take turns manipulating objects in a shared physical workspace. We hypothesize that action interruptions have a positive impact on turn-taking and evaluate this in the Towers of Hanoi domain through two experimental methods. One is a between-groups user study with 16 participants. The other is a simulation experiment using 200 simulated users of varying speed

  initiative

  compliance

  and correctness. In these experiments

  action interruptions are either present or absent in the system. Our collective results show that action interruptions lead to increased task efficiency through increased user initiative

  improved interaction balance

  and higher sense of fluency. In arriving at these results

  we demonstrate how these evaluation methods can be highly complementary in the analysis of interaction dynamics.

  Timing in multimodal turn-taking interactions: Control and analysis using timed Petri nets

  Andrea L.

  Thomaz

  International Business Machines

  The University of Texas at Austin

  Microsoft Corporation

  Diligent Robotics

  Inc.