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Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
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About me
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
My work on fully autonomous aerial systems including key systems outputs like SoftwarePilot and My work on fully autonomous aerial systems including key systems outputs like SoftwarePilot and the Fleet Computer. 
Current testbed infrastructure lacks the programmability, close hardware approximation, and sensor availability needed for constrained edge workloads like those required for autonomous UAV. PROWESS solves this problem by positioning edge hardware near sensor endpoints, connecting these edge systems to a centralized experiment scheduling server, and leveraging institutional networks like those at universities and corporations to build an end to end experimentation platform for edge workloads. 
In today’s contested radio-frequency environment, high-fidelity wireless communication is both more important than ever, and more difficult to maintain. Furthermore, bad actors can jam crucial signals sent to self-driving cars, autonomous UAV and manned aircraft. AutoWav, an AFRL-sponsored research project, seeks to mitigate RF interference by avoiding it entirely by using neural networks to redesign waveforms in real time. AutoWav has resulted in a number of waveform design techniques, culminating in >1000X speedup over conventional convex optimization for waveform design. 
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Undergraduate course, Binghamton University, 2022
Introduction to operating system concepts: process, CPU scheduling, memory management, file system and storage, and multi-threaded programming.