Sean Grimes

Developing and teaching lecture material across a wide variety of classes within the Computer Science Department

Develop new lecture material, create exam material, assist with course management, teach lectures, hold regular office hours in the Department of Computer Science help center, and assist with grading assignments, exams, and course projects.

Individual and collaborative research efforts in the fields of biologically inspired A.I. and swarm A.I. through the Geometric Biomedical Computing Group. Previous work includes developing a behaviorally accurate representation of the worm C. elegans and developing self-organizing swarms based on simulated chemical diffusion. Current work includes predicting specific future events through a combination of classification, multi-agent interactions, wisdom of crowds, and swarm intelligence.

Assisting the Computer Science department within the College of Computing and Informatics where I hold regular office hours, assist with grading, and provide general support to all Drexel University Computer Science students.

Ecodren Research, founded by three Computer Science Ph.D. students, is a custom computing company designed to bring A.I. solutions to every-day problems. Currently Ecodren is working with area Universities to provide students with an iOS and Android app that allows them, through the use of Natural Language Processing, to provide feedback to specific departments / people without needing to identify the proper recipient of their feedback.

Irregex was a computer and network technology startup focused on data mining, data-analysis, web / database design, development, implementation and management. At Irregex I was partially responsible for setting up a network of 5 CentOS / Ubuntu servers, providing XMPP, E-mail, VPN, SSH services, and worked on developing / maintaining websites for Irregex and it's main project, Corticus.

Drexel University, College of Computing and Informatics

Ph.D., Computer Science

Drexel University, College of Computing and Informatics

MS, Computer Science

Ursinus College

BS, Neuroscience

WoC-Bots: Swarms of Biologically Inspired Prediction Agents

Dissertation

Author: Sean Grimes

Available Here

April 2023

This dissertation presents Wisdom-of-Crowds-Bots (WoC-Bots), biologically-inspired, simple, and modular agents which work together in a multi-agent environment to collectively make binary predictions. Building on the theoretical underpinnings of Wisdom of Crowds, WoC-Bots represent a knowledge-diverse crowd where each agent is trained on a subset of available information. A honeybee-derived swarm aggregation mechanism was developed to elicit a collective prediction with an associated confidence score. Due to the multi-agent architecture, WoC-Bots can be distributed across multiple compute nodes, reducing training and inference time. Importantly, this architecture demonstrates significant key advantages over traditional classification methods while maintaining comparable predictive performance. Specifically, new and previously unknown input features can be included in an existing classification problem without retraining existing agents. New input features, combined with existing features, are encapsulated into newly generated agents before agents are injected into an existing classification task. Further development led to a ``meta-swarm'', where an external prediction is used as the core belief of an agent, replacing a simple multi-layer perceptron network. The external prediction requires zero knowledge of source data, maintaining the localization and privacy of the data used to generate the prediction, enabling collaboration between institutions unable to share their data externally.

A Multi-Agent Approach to Binary Classification Using Swarm Intelligence

Future Internet, Special Issue on Modern Trends in Multi-agent Systems

Authors: Sean Grimes and David E. Breen

doi: 0.3390/fi15010036

Available Here

January 2023

Wisdom-of-Crowds-Bots (WoC-Bots) are simple, modular agents working together in a multi-agent environment to collectively make binary predictions. The agents represent a knowledge-diverse crowd, with each agent trained on a subset of available information. A honey-bee-derived swarm aggregation mechanism is used to elicit a collective prediction with an associated confidence value from the agents. Due to their multi-agent design, WoC-Bots can be distributed across multiple hardware nodes, include new features without re-training existing agents, and the aggregation mechanism can be used to incorporate predictions from other sources, thus improving overall predictive accuracy of the system. In addition to these advantages, we demonstrate that WoC-Bots are competitive with other top classification methods on three datasets and apply our system to a real-world sports betting problem, producing a consistent return on investment from 1 January 2021 through 15 November 2022 on most major sports.

An agent-based approach to predicting lymph node metastasis status in breast cancer

IEEE International Conference on Bioinformatics and Biomedicine

Authors: Sean Grimes, Mark D. Zarella, Fernando U. Garcia, David E. Breen

doi: 10.1109/BIBM52615.2021.9669624

Available Here

December 2021

We present a flexible, multi-agent approach to predictive classification problems which uses simple, modular agents that interact and share information socially in an arena with a variable number of participants. Opinion aggregation is accomplished using a honey-bee-derived optimization algorithm that improves accuracy and reduces variance compared with existing weighted and unweighted voter mechanisms. Confidence metrics may be derived from the agent interactions. We apply our system to a data set of 483 de-identified breast cancer patients to predict node-positive or node-negative disease with over 78.5% accuracy in general. When eliminating low-confidence predictions, which leaves 79.5% of patients, classification accuracy improves to 84.5%.

WoC-Bots: An Agent-Based Approach to Decision-Making

Applied Sciences

Authors: Sean Grimes and David E. Breen

doi: 10.3390/app9214653

Available Here

October 2019

We present a flexible, robust approach to predictive decision-making using simple, modular agents (WoC-Bots) that interact with each other socially and share information about the features they are trained on. Our agents form a knowledge-diverse crowd, allowing us to use Wisdom of the Crowd (WoC) theories to aggregate their opinions and come to a collective conclusion. Compared to traditional multi-layer perceptron (MLP) networks, WoC-Bots can be trained more quickly, more easily incorporate new features, and make it easier to determine why the network gives the prediction that it does. We compare our predictive accuracy with MLP networks to show that WoC-Bots can attain similar results when predicting the box office success of Hollywood movies, while requiring significantly less training time.

Directing chemotaxis-based spatial self-organisation via biased, random initial conditions

International Journal of Parallel, Emergent and Distributed Systems

Available Here

June 2018

Authors: Sean Grimes, Linge Bai, Andrew W.E. McDonald, David E. Breen

Inspired by the chemotaxis interaction of living cells, we have developed an agent-based approach for self-organising shape formation. Since all our simulations begin with a different uniform random configuration and our agents move stochastically, it has been observed that the self-organisation process may form two or more stable final configurations. These differing configurations may be characterised via statistical moments of the agents' locations. In order to direct the agents to robustly form one specific configuration, we generate biased initial conditions whose statistical moments are related to moments of the desired configuration. With this approach, we are able to successfully direct the aggregating swarms to produce a desired macroscopic shape, starting from randomised initial conditions with controlled statistical properties.

Ortus: an Emotion-Driven Approach to (artificial) Biological Intelligence

The European Conference on Artificial Life

Available Here

September 2017

Authors: Andrew W.E. McDonald, Sean Grimes, David E. Breen

Ortus is a simple virtual organism that also serves as an initial framework for investigating and developing biologically based artificial intelligence. Born from a goal to create complex virtual intelligence and an initial attempt to model C. elegans, Ortus implements a number of mechanisms observed in organic nervous systems, and attempts to fill in unknowns based upon plausible biological implementations and psychological observations. Implemented mechanisms include excitatory and inhibitory chemical synapses, bidirectional gap junctions, and Hebbian learning with its Stentian extension. We present an initial experiment that showcases Ortus fundamental principles; specifically, a cyclic respiratory circuit, and emotionally driven associative learning with respect to an input stimulus. Finally, we discuss the implications and future directions for Ortus and similar systems.