- Georgia Institute of Technology | Master of Science in Computer Science
- Specialization in Machine Learning. Focus on algorithm theory and development, in particular related to game theory, propositional logic, supervised, unsupervised, and reinforcement learning models.
- University of Maryland, College Park | Bachelor of Science in Computer Science
- Minor in Astronomy. Mathematical foundations in CS, with additional focus in bioinformatics, machine learning, and databases.
Biophysical variable estimation
Researching and applying machine learning models to remotely sensed imagery in order to accurately estimate water quality parameters (e.g. Chl-a, TSS, etc.), absorption and scattering spectra, and other inherent optical properties. These imagery are sourced from multispectral satellite missions such as Landsat and Sentinel, as well as from a number of hyperspectral sources in preparation for PACE. The goal is to provide consistent global products across any period of time with satellite coverage, primarily targeting ecosystems in near-shore / inland bodies of water with highly turbid and eutrophic compositions.
Atmospheric Correction Intercomparison Exercise
ACIX is an international collaborative initiative to inter-compare a set of atmospheric correction (AC) algorithms for moderate-resolution optical sensors. The exercises will focus on Sentinel-2 and Landsat-8 data over a set of test areas. The end goal is to identify strengths and weaknesses of various atmopsheric correction methods and propose a hybrid approach to generate operational Level-2 reflectance products.
Visualization of products to support country reporting of Sustainable Development Goals (SDG) 6.3.2 and 6.6.1 related to water quality
Development of tools which aid in monitoring and reporting of water quality across a number of sites in Africa. Water authorities and Ministry of Environment in these countries are working with us to evaluate satellite-derived products at the pilot sites.
SSAI, NASA GSFC - Greenbelt, MD
September 2017 - Present
Research into models applied to satellite imagery and in situ measurements, producing accurate retrieval of biophysical variables in optically complex waters.
Validation of models which offer insight into environmental policy for developing countries in coordination with the United Nations Environment Programme (UNEP).
NASA GSFC - Greenbelt, MD
July 2017 - September 2017
Development and publication of neural network algorithm to perform spectral band adjustments for instruments aboard NASA Earth Science satellite missions.
Georgia Institute of Technology - Atlanta, GA
October 2016 - 2017
Knowledge-Based Artificial Intelligence (CS7637). Development of ML-based essay grading and rationale generation; mentoring graduate students through their development of conversational agents modeled on Jill Watson, via state-of-the-art cognitive frameworks (IBM Bluemix, API.ai, WIT.ai).
Artificial Intelligence (CS6601). Designed and implemented foundation for Jack Watson plagiarism detection framework; focus on teaching Hidden Markov Models via projects and exams.
O'Shea, R. E., N. Pahlevan, B. Smith, et al. M. Bresciani, T. Egerton, C. Giardino, L. Li, T. Moore, A. Ruiz-Verdu, S. Ruberg, S. G. Simis, R. Stumpf, and D. Vaičiūtė. 2021. Advancing cyanobacteria biomass estimation from hyperspectral observations: Demonstrations with HICO and PRISMA imagery Remote Sensing of Environment 266 112693 [10.1016/j.rse.2021.112693]
Pahlevan, N., A. Mangin, S. V. Balasubramanian, et al. B. Smith, K. Alikas, K. Arai, C. Barbosa, S. Bélanger, C. Binding, M. Bresciani, C. Giardino, D. Gurlin, Y. Fan, T. Harmel, P. Hunter, J. Ishikaza, S. Kratzer, M. K. Lehmann, M. Ligi, R. Ma, F.-R. Martin-Lauzer, L. Olmanson, N. Oppelt, Y. Pan, S. Peters, N. Reynaud, L. A. Sander de Carvalho, S. Simis, E. Spyrakos, F. Steinmetz, K. Stelzer, S. Sterckx, T. Tormos, A. Tyler, Q. Vanhellemont, and M. Warren. 2021. ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters Remote Sensing of Environment 258 112366 [10.1016/j.rse.2021.112366]
Smith, B., N. Pahlevan, J. Schalles, et al. S. Ruberg, R. Errera, R. Ma, C. Giardino, M. Bresciani, C. Barbosa, T. Moore, V. Fernandez, K. Alikas, and K. Kangaro. 2021. A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks Frontiers in Remote Sensing 1 (5): [10.3389/frsen.2020.623678]
Pahlevan, N., B. Smith, C. Binding, et al. D. Gurlin, L. Li, M. Bresciani, and C. Giardino. 2021. Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters Remote Sensing of Environment 253 112200 [https://doi.org/10.1016/j.rse.2020.112200]
Balasubramanian, S. V., N. Pahlevan, B. Smith, et al. C. Binding, J. Schalles, H. Loisel, D. Gurlin, S. Greb, K. Alikas, M. Randla, M. Bunkei, W. Moses, H. Nguyễn, M. K. Lehmann, D. O'Donnell, M. Ondrusek, T.-H. Han, C. G. Fichot, T. Moore, and E. Boss. 2020. Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters Remote Sensing of Environment 246 111768 [10.1016/j.rse.2020.111768]
Hakimdavar, R., A. Hubbard, F. Policelli, et al. A. Pickens, M. Hansen, T. Fatoyinbo, D. Lagomasino, N. Pahlevan, S. Unninayar, A. Kavvada, M. Carroll, B. Smith, M. Hurwitz, D. Wood, and S. Schollaert Uz. 2020. Monitoring Water-Related Ecosystems with Earth Observation Data in Support of Sustainable Development Goal (SDG) 6 Reporting Remote Sensing 12 (10): 1634 [10.3390/rs12101634]
Pahlevan, N., B. Smith, J. Schalles, et al. C. Binding, Z. Cao, R. Ma, K. Alikas, K. Kangro, D. Gurlin, N. Hà, B. Matsushita, W. Moses, S. Greb, M. K. Lehmann, M. Ondrusek, N. Oppelt, and R. Stumpf. 2020. Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach Remote Sensing of Environment 111604 [10.1016/j.rse.2019.111604]