Amy Mueller, Ph.D.
Assistant Professor (Jointly Appointed) - Civil & Environmental Engineering and Marine & Environmental Sciences, Northeastern University | Faculty Affiliate, Global Resilience Institute
Boston, MA, UNITED STATES
Professor Mueller focuses on sensors and in-situ systems, anthropogenic process optimization, and coastal environments.
Biography
Professor Mueller is interested in utilizing toos from electrical engineering/computer science to solve challenges faced in the natural and built environments. Her research focuses on improving our ability to characterize natural systems (and their response to changing climate or ecosystem conditions) and control municipal and industrial systems to reduce environmental footprint. For her work, she uses embedded systems, electronic and electrochemical sensors, wireless networking, signal processing, and closed-loop controls to tackle important challenges in science, remediation, energy, and manufacturing processes.
You can read more about her research here (northeastern.edu/envsensorslab).
Areas of Expertise (3)
Sensors
Coastal Environments
Embedded Systems
Accomplishments (4)
National Science Foundation, Ocean Sciences Postdoctoral Research Fellowship
2015 - 2016
National Science Foundation, Graduate Research Fellowship
2004 - 2007
MIT-Xerox Fellowship
2009
Geological Society of America, Graduate Research Grant
2009
Education (2)
MIT: Ph. D., Environmental Chemistry 2012
Awarded from "Department of Civil and Environmental Engineering"
MIT: M.Eng, Electrical Engineering and Computer Science 2003
Affiliations (5)
- American Geophysical Union
- Geological Society of America
- Institute of Electrical and Electronics Engineers
- AEESP
- ASCE
Links (1)
Articles (2)
Statistical generation of training sets for measuring NO3 , NH4+ and major ions in natural waters using an ion selective electrode array
Environmental Sciences. Process & Impacts
Amy V. Mueller and Harold F. Hemond
2016 Knowledge of ionic concentrations in natural waters is essential to understand watershed processes. Inorganic nitrogen, in the form of nitrate and ammonium ions, is a key nutrient as well as a participant in redox, acid-base, and photochemical processes of natural waters, leading to spatiotemporal patterns of ion concentrations at scales as small as meters or hours. Current options for measurement in situ are costly, relying primarily on instruments adapted from laboratory methods (e.g., colorimetric, UV absorption); free-standing and inexpensive ISE sensors for NO3(-) and NH4(+) could be attractive alternatives if interferences from other constituents were overcome. Multi-sensor arrays, coupled with appropriate non-linear signal processing, offer promise in this capacity but have not yet successfully achieved signal separation for NO3(-) and NH4(+)in situ at naturally occurring levels in unprocessed water samples
Extended artificial neural networks: Incorporation of a priori chemical knowledge enables use of ion selective electrodes for in-situ measurement of ions at environmentally relevant levels
Science Direct
Amy V. Mueller, Harold F. Hemond
2013 A novel artificial neural network (ANN) architecture is proposed which explicitly incorporates a priori system knowledge, i.e., relationships between output signals, while preserving the unconstrained non- linear function estimator characteristics of the traditional ANN. A method is provided for architecture layout, disabling training on a subset of neurons, and encoding system knowledge into the neuron structure. The novel architecture is applied to raw readings from a chemical sensor multi-probe (electric tongue), comprised of off-the-shelf ion selective electrodes (ISEs), to estimate individual ion concentrations in solutions at environmentally relevant concentrations and containing environmentally representative ion mixtures.
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