Jared Coleman
Assistant Professor of Computer Science | Loyola Marymount University
Los Angeles, CA, UNITED STATES
Seaver College of Science and Engineering
Media
Publications:
Documents:
Videos:
Audio/Podcasts:
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 (3)
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 (8)
Cooperative Mobile Agents
Artifical Intelligence
Internet of Things (IoT)
Online Algorithms
Distributed Computing
Edge Computing
Machine Learning
Blockchain
Links (2)
Languages (3)
- English
- Portuguese
- Owens Valley Paiute
Media Appearances (3)
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.
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.
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.
Courses (2)
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.
Social