Eric Aaron

Eric Aaron

Department of Computer Science
Vassar College

Spring 2018 Office Hours

• T R 4:30-5:30pm (immediately following CMPU 145), and by email appt.

Courses, Spring 2018

• T R 3:10-4:25pm, OH 162; M 3:10-5:10pm, SP 309:
CMPU 145-51: Foundations of Computer Science (Section 51)

• T R 1:30-2:45pm, SP 105:
CMPU 250: Modeling, Simulation and Analysis

Research Interests

Robotics, Computational Intelligence Modeling, Artificial Intelligence, Hybrid Dynamical Systems, System Verification, Cognitive Science, Computational Sciences

CV/List of Selected Publications

My CV is available in PDF format.

Some selected publications, indicative of some primary interests:


Research Focus: Adaptive Intelligence and Hybrid Dynamical Cognitive Agents

My research interests are centered on models and applications of adaptive, dynamically responsive intelligence in dynamic environments. My primary focus is designing and analyzing intelligence models for autonomous agents in complex environments, emphasizing hybrid dynamical system models--models that combine continuous and discrete system dynamics--and applications ranging from workplace courier robots to computer-animated guides through virtual worlds.

This research is motivated by goals for performance, design, and analysis.

My hybrid dynamical cognitive agent research enables dynamically responsive and adaptive intention-guided behavior for goal-based systems, supports efficient collision-free navigation in dynamic environments, and promotes the integration of levels of intelligence that are often modeled separately--e.g., obstacle avoidance and goal-directed action selection--in a formal, unifying hybrid system framework for verifiable dynamic agents. This framework can improve adaptation and overall performance, and it can also support model checking-based system analysis of agent behavior.

Results from this research include several advances in adaptive intelligence and analysis:

The Selected Publications noted above contain more information about these research results and others.

Interdisciplinary Research Areas and Interests

My work also includes cross-disciplinary projects that connect to Cognitive Science or Computational Sciences.

Cognitive Logical Inference, Student Modeling, and Eyetracking

Before turning my attention to intelligent virtual agents and animation systems, I developed formalized mathematical tactics for cognitive inference modeling. I concentrated in particular on developing a model of undergraduate students carrying out logical proofs in a structured framework, but the ideas underlying the work are not student-specific. The many facets of this work are described in my dissertation, Tactic-Based Modeling of Cognitive Inference on Logically Structured Notation. I have also written papers on its components, which range from logic and formalized mathematics (Justifying calculational logic by a conventional metalinguistic semantics) to cognitive science and eyetracking research (Insight into theorem proving via eye movements).

I am currently further investigating the eyetracking component of that research. (See the Cornell University Computer Science 40 Years booklet, pg. 24, for a small sidebar column about my eyetracking research!) There is more information in the data collected during my dissertation research than has previously been analyzed, and my current research with Barbara Juhasz (Wesleyan University Department of Psychology) is a more thorough exploration of the ways in which eye movements can inform our understanding of the cognition and modeling of logical problem solving. More than 668,000 frames of data are encoded for our ongoing data analysis.

Computational Biology and Tumor Modeling

Simulations of tumor development and treatment are often computationally costly. I worked with Ami Radunskaya (Pomona College Department of Mathematics) and others to improve their efficiency by developing modeling and simulation methods that incorporate dynamically sensitive variable scaling, enabling greater detail at critical areas without excessive detail at mundane tissue.

This application is strikingly similar to applications that arise in both the system verification and intelligence modeling aspects of my agent modeling work. In my verification research, efficient yet accurate metrics of relative navigation difficulty may be enabled by state space decompositions with a greater density of states around critical locations (e.g., obstacles, targets) and a sparser distribution in other areas. In my intelligence modeling research, such variable scaling techniques can relate closely to models of perception for animated characters: Because some element of the simulation of an intelligent virtual agent possesses perfect world knowledge, realistic characters must attend to only relevant stimuli in the virtual world around them, requiring efficient focusing of attention on specific points in space. This, in turn, involves intelligently spending less processing time (perhaps none at all) on irrelevant locations while performing detailed analyses on relevant ones, which is the essence of my tumor modeling research.

My collaborators and I are presenting our method for tumor simulation and its behavior in various modeling contexts, and we then intend to extend this work with our variable scaling approach, demonstrating of how biologically inspired notions of practical simulation equivalence can lead to new criteria for evaluating biological simulations and, by extension, faster simulation methods.


Other Noteworthy Publications


Contact Information

Eric Aaron
124 Raymond Avenue
Box 478, Vassar College
Poughkeepsie, NY 12604

Phone: (845) 437-7293
Fax: (845) 437-5995
Office: SP 305

Non-academic stuff

Music is one of my most important non-academic interests. I have written about music (album reviews and features), broadcast as a jazz DJ (a weekly commercial radio show), and played in a few rock bands that successfully made it out of the garage.
Eric and guitar

Eric Aaron,