Jonathan Spencer
j dot spencer at princeton dot edu

I have a deep love for all human beings. As such, most of my research interests involve either modeling or interacting with people. Most recently I've been working on projects in inverse reinforcement learning and reinforcement learning for modeling interactions between humans and pedestrians. I'm currently visiting Sidd Srinivasa at University of Washington, spending time working on multi-agent reinforcement learning for autonomous driving. I'm actively seeking an internship for Summer 2019 to work on challenging problems in modeling, prediction and control in autonomous driving.

I am a third year Ph.D. candidate at Princeton University as part of the Edge Lab, co-advised by Mung Chiang and Peter Ramadge. Prior to Princeton I did my bachelors and masters degrees in electrical engineering at Brigham Young University working with Karl Warnick and the MAGICC Lab on the RF circuit design and signal processing of radar systems for drone collision avoidance. I've also spent time designing analog circuits at ON Semiconductor, and I love running, cycling, and learning new languages (currently working on Mandarin).

Resume/CV  /  Google Scholar  /  LinkedIn

Research

My current research thrust is in reinforcement learning and inverse reinforcement learning, and work in that area will be posted on arXiv shortly. The bulk of my digitally available work is from the cool work I did during my masters degree on radar signal processing for drone collision avoidance and some recent work in discussion forum modeling.

MOOCforums

Personalized Thread Recommendation for MOOC Discussion Forums
Andrew Lan, Jonathan C. Spencer, Ziqi Chen, Chris Brinton, Mung Chiang
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) , 2018
arxiv / bibtex

We introduce a temporal point-process based technique for making timely topical thread recommendations in MOOC discussion forums that significantly outperforms baselines.

groundbased

Ground-Based Sense-and-Avoid System for Small Unmanned Aircraft.
Laith Sahawneh, Jared Wikle, Kaleo Roberts, Jonathan C. Spencer, Tim McLain, Karl Warnick, Randy Beard
Journal of Aerospace Information Systems (JAIS) Vol. 15 Iss. 8 Pg. 501-517 , 2018

This work demonstrates a complete end-to-end collision avoidance system where the ego drone communicates in real time with a ground-based radar sensor that detects intruders and computes safe avoidance trajectories. This work was the cumulative effort of many years of work by many, many people.

thesisradar

A Compact Phased Array Radar for UAS Sense and Avoid
Jonathan C. Spencer
Brigham Young University Master's Thesis , 2015

This thesis details the full-stack system design and signal processing of a four channel phased array FMCW radar system. The bulk of the supporting RF circuitry for LO, FMCW modulator, demodulation, and baseband filtering was done by myself, as well as the algorithms for processing and filtering the angular signal on the integrated DSP board.

2016infotec

Minimum Required Sensing Range for UAS Sense and Avoid Systems
Laith Sahawneh, Jonathan C. Spencer, Randy Beard, Karl Warnick
AIAA Infotech @ Aerospace , 2016

Based on realistic sensor models, maximum flight velocities of several different aircraft and a minimum safe distance of 500 ft, we determine that the minimum sensing distance for a UAV to be able to successfully detect, compute and execute a maneuver is 1.8km.

collisionrisk

Airborne Radar-Based Collision Detection and Risk Estimation for Small Unmanned Aircraft Systems
Laith Sahawneh, James Mackie, Jonathan C. Spencer, Randy Beard, Karl Warnick
Journal of Aerospace Information Systems (JAIS) Vol. 12 Iss. 12 Pg. 756-766 , 2015

This work estimates probability of collision risk for a pair of aircraft at the same altitude using state estimates from a radar sensor and a reachable sets framework.

mackieradar

Compact FMCW Radar for a UAS Sense and Avoid System
James Mackie, Jonathan C. Spencer, Karl Warnick
IEEE Antennas and Propagation Society International Symposium (APSURSI) , 2014

This radar demonstrates the effectiveness of an ultra-low cost radar system (>$100) using a 24 GHz radar-on-a-chip system.

Patents
patent

Phased Array Radar Systems for Small Unmanned Aerial Vehicles
US Patent 20180011180A1

Karl Warnick, Jonathan C. Spencer, 2018


This patent covers the use of one-board phased array radar systems as opposed to gimballed single beam radar systems for detecting small unmanned aerial vehicles and covers the majority of the work presented in my BYU masters thesis.

Teaching
teaching

ELE381 - Networks: Friends, Money, Bytes - Fall 2017 (TA)

ECEn549 - VLSI Communication Circuits - Fall 2013 (TA)

ECEn380 - Signals and Systems - Winter 2013 (TA)

ECEn220 - Analog Circuits I - Fall 2012 (TA)


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