Statistical Signal Processing and Machine Learning
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Nick Roseveare, Phd
Research Scientist
Featured research: High-dimensional Statistical Modeling,
e.g., model-order selection
I focus on development and application of statistical signal processing and
machine learning algorithms for a variety of applications. In particular, I am
interested in theoretical under-pinning and hidden behaviors of ML algorithms
and what can be understood better about them in terms of classical statistics.
Interests and Philosophy of Research
I am always attempting to abide by the following life (and career) principles:
• Learning: I am always trying to become a better engineer and researcher. I
am always absorbing lessons from my own and others’ experience, as well as
reading and keeping up with trends in my field.
• Leadership: I enjoy and work at understanding the big picture in order
to stay on target and to help others do the same. I try to be intentional
and to take ownership of my deliverables. I also recognize that sometimes
others are more suited to a particular task. A good leader creates environments
and teams that don’t need them.
• Teamwork: Excellent research and products require teamwork, collaboration,
and respect for one another. Being right is feels nice, but it always comes in
second-place behind what is good for the project and the team.
• Hard Work: A successful project is always the result of much striving and
frustration. I push hard on a project and catalog all paths until a solution
is found. I find the learning along the way and the hard-won results to be
rewarding twin goals.
• Mentoring: I also very much enjoy the
teaching process, it is gratifying to help other come to an understanding of
a topic and to strengthen and question my own knowledge of the field. A test of
the competence and value of the knowledge you bring is whether you can helpfully
guide and instruct others. I have lead teams of engineers and data scientists and
encouraged their upskilling, technical researches, and personal growth.
My technical strengths include:
• Algorithm development, including initial prototyping from research papers,
progressive improvements and novels approaches, all the way through practical
implementation and infrastructure choices (database, message-queues,
containerization, etc.).
• Statistical signal processing, where I have worked on several challenging
problems in tracking and filtering, time-series smoothing, phase reconstruction,
high-dimensional model-order selection, etc.
• Optimization, where I have worked on a variety of convex-relaxations of
NP-hard problems (TSP, cost assignment, constrained resource allocation, etc.).
• Machine learning, with experience in anomaly detection, natural language
processing, and computer vision utilizing a variety of existing and in-house
developed solutions including ensemble methods, deep-learning, and
high-dimensional correlation analysis.
Philosophy of Teaching
My teaching philosophy is centered on the belief that good education:
• Should be an active, student-centered process that prepares individuals for success in their personal and professional lives.
• Emphasizes an attentiveness to the material and being will to make and learn from mistakes.
• Incorporates individual student projects and practical experiences.
• Should create a dynamic and engaging learning environment that fosters critical thinking and creativity.
• Promotes lifelong love of learning and the ability to engage thoughtfully with any subject.
View summary profile here.
View résumé here.
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