Jared Coleman

Assistant Professor of Computer Science Loyola Marymount University

  • Los Angeles CA

Seaver College of Science and Engineering

Contact

Loyola Marymount University

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Media

Biography

Jared Coleman's research interests are in cooperative multi-agent systems, distributed and decentralized computing, online algorithms, distributed ledger technology, the Internet of Things (IoT), artificial intelligence (AI), large language models (LLMs), and language revitalization.

He is a member of the Big Pine Paiute Tribe of the Owens Valley and is involved in efforts to preserve and revitalize the critically endangered Owens Valley Paiute language. Besides computer science, Jared enjoys board games, hiking, and studying the linguistic heritage of his community.

Education

University of Southern California

Ph.D.

Computer Science

2024

California State University, Long Beach

M.S.

Computer Science

2020

California State University, Long Beach

B.S.

Computer Science

2018

Areas of Expertise

Cooperative Mobile Agents
Artifical Intelligence
Internet of Things (IoT)
Online Algorithms
Distributed Computing
Edge Computing
Machine Learning
Blockchain

Languages

  • English
  • Portuguese
  • Owens Valley Paiute

Media Appearances

Imagine Hearing A Distant Relative Telling Stories in a Nearly Forgotten Language. What Would You Do?

University of Southern California  online

2024-06-20

USC Viterbi News article about Jared Coleman's research in using LLMs for endangered language revitalization.

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Esta innovadora herramienta de IA es capaz de rescatar idiomas en peligro de extinción

Wired en Español  online

2024-07-01

Article by Wired en Español about Jared Coleman's research in using LLMs for endangered language revitalization.

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Revitalizing Critically Endangered Languages via Large Language Models

Loyola Marymount University  online

2024-11-14

LMU Newsroom Article describing work on using AI to help revitalize Owens Valley Paiute.

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Courses

Algorithms and Analysis

The study of algorithm paradigms, including divide-and-conquer, greedy methods, dynamic programming, backtracking, and randomization, with an emphasis on combinatorial search. Modern heuristics such as genetic programs and simulated annealing. String processing including matching and longest common subsequence. Advanced sorting. Constraint satisfaction, hill climbing, and optimization. Combinatorial objects such as permutations, combinations, subsets, and partitions. Graph algorithms. Computational geometry. Recurrences and the Master Theorem.

Online and Decentralized Algorithms

In this distributed computing course, we'll explore how knowledge (about the environment, other agents in a multi-agent system, the future, etc.) affects computational strategies. Topics will include: Distributed vs. Decentralized Algorithms, Online vs. Offline Algorithms, Knowledge Models, Competitive Analysis, Cooperative Mobile Agents, Blockchain/Distributed Ledger Technology.