Hi! I'm Rochie.

I am a food scientist who has entered the wonderful world of data science. I enjoy finding insights from data and communicating these insights with non-technical audiences. I use Python and R to wrangle and to analyse data; and a mix of Matplotlib, HTML, CSS, and JavaScript libraries to visualise the data and breathe life into a database of numbers.

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About Papers Projects Contact

Projects

Click on a project's name to go to its page.

Project Summary
Text Analyses of Scientific Abstracts Authored by Staff at the International Rice Research Institute (1964–June 2019) Textual data derived from over 4000 scientific abstracts underwent Natural Language Processing (NLP). Data visualisations developed using JavaScript lead to insights about IRRI's body of scientific work.
Keyword Extraction from the Poetry of Frost, Kipling, and Yeats Term Frequency-Inverse Document Frequency (TF-IDF) was used to identify keywords from poems of three famous poets. Sentiment analysis was conducted using TextBlob. A network graph, based on keyword co-occurrence, was generated using Gephi. These are all presented in a dashboard that also showcases the poets' works.
Citibike Ridership in New York City A dashboard, with visualisations built using Tableau Public, provides a bird's eye view of Citibike ridership patterns in New York in 2018.
Belly Button Diversity: Microbes Calling the Navel Home Data about the microflora of the belly buttons of 153 volunteers, collected and characterised by North Carolina State University's Rob Dunn Laboratory, is presented in dynamic graphs generated using JavaScript.
Aliens Among Us: Cataloguing UFO Sightings A dataset of January 2010 sightings of mysterious and extra-terrestrial objects in the USA skies is presented in a table and in static bar graphs built using JavaScript.
WeatherPy: Weather Indicators on October 14, 2018

Weather information, obtained through OpenWeatherMap for over 500 randomly chosen cities, is plotted in graphs using Matplotlib.

Curriculum Vitae

This section can be downloaded as a PDF.


Technical Skills

Languages: R, Python, SQL, HTML, CSS, JavaScript

Data Visualisation: pandas, matplotlib, ggplot2, circlize, gridExtra, corrplot

Statistical Analysis: correlation analyses, cluster analyses, ANOVA, t-test, multinomial logistic regression, random forest

Laboratory: HPLC, capillary electrophoresis, differential scanning calorimetry, sensory evaluation, rheometry, texture profile analysis, SDS-PAGE, PCR

Communications: scientific writing, technical presentation, communicating science to non-technical audiences

Education

Certificate, Data Analytics and Visualisation. University of California, Berkeley Extension.

Ph.D., Agricultural Science. University of Queensland.

  • Doctoral Thesis: Starch microstructure and functional properties in waxy rice (Oryza sativa L.)
  • Fields: Starch chemistry, Rice science

B.Sc., Biology. University of the Philippines Los Baños.

  • Honours: Magna cum laude
  • Awards: Bank of the Philippine Islands Science Award, UPLB College of Arts and Sciences Outstanding Student Award
  • Thesis: Production and utilisation of crude tylosin from high-yielding Streptomyces fradiae NRRL 2702 Mutant No. 93 as therapeutic agent in broilers
  • Major: Microbiology

Professional Experience

Consultant, Data Analytics (2019–present)

  • Develops SQL databases containing survey and expert elicitation data gathered by market researchers, consumer specialists, and anthropologists.
  • Employs machine learning techniques, natural language processing, and statistical analyses (in Python) to draw insights from surveys and expert elicitation data.
  • Interprets results of expert elicitations and consumer surveys in the context of impactful decisions towards nutrition and "planetary health diet" food choice interventions for low- to middle-income rice consumers in eastern India.

International Rice Research Institute

Scientist (2015–2018)

  • Applied R machine learning packages to model a novel rice classification scheme based on high-dimension sensory and instrumental data and to develop insights on consumer food choice in the Philippines based on consumer survey and expert elicitation data.
  • Collaborated with economists in conducting a hedonic pricing analysis for rice grain quality.
  • Led internal sensory panels developed based on client objectives.
  • Informed breeders and geneticists on grain quality considerations that led to crucial breeding pipeline decisions.
  • Generated funding for a project on understanding food choice behaviours in India and in the Philippines.

Consultant (2014–2015)

  • Developed and maintained an internal sensory evaluation panel and a basic sensory evaluation laboratory.
  • Developed and adapted instrumental and sensory methodologies for analyses of important sensory attributes.
  • Designed and initialised the execution of sensory evaluation and instrumental characterisation surveys to develop an understanding of complex sensory attributes of rice.
  • Interpreted sensory evaluation and instrumental characterisation results to inform business decisions of various actors in the restaurant industry (e.g., chefs, restaurateurs).

Post-doctoral Fellow (2010–2014)

  • Used statistical techniques to identify and analyse starch chemistry-rice quality associations.
  • Designed streamlined screening tools for defined rice quality targets with plant breeders.

Professional Service Staff (2008–2010)

  • Strengthened the data-driven basis of the GQNC-Quality Evaluation Services’ full-cost recovery program through the development of cost databases (MS Access).

Researcher (2004–2005)

  • Collected data about rice starch properties using differential scanning calorimetry, rheometry, size-exclusion chromatography, and fluorophore-assisted capillary electrophoresis.

Researcher (2002–2003)

  • Identified the putative location of the low-tillering gene in two japonica rice mapping populations through molecular marker-based data collection and analyses.

Antonina Industrial Corporation (2003–2004)

Quality Assurance Supervisor

  • Participated in sensory evaluation of reconstituted powdered beverage products.
  • Decreased incidences of environmental and finished-goods microbial contamination and monthly consumer product complaints by ~70% through data-driven changes in business processes.
  • Traced potential product losses amounting to approximately ~USD 158,400 through in-depth analyses of data generated by production and logistics departments, which eventually led to the implementation of stricter tolerances to finished-good product weights.

Publications List

The chronological list can be downloaded as a PDF.


  • Scientific papers and reports
    • Graham-Acquaah, S., A. Mauromoustakos, R. P. Cuevas, J. T. Manful. 2019. Difference in physicochemical properties of commercial rice from urban markets in West Africa. Journal of Food Science and Technology 57: 1505–1516.

    • Demont, M., M. C. Custodio, J. Ynion, A. Samaddar, R. P. Cuevas, A. Ray (Chakravarti), S. K. Mohanty. 2019. What affects households’ food choice in West Bengal? Geography and You 19(24): 26–30.

    • Custodio, M. C., R. P. Cuevas, J. Ynion, A. G. Laborte, M. L. Velasco, M. Demont. 2019. Rice quality: How is it defined by consumers, industry, food scientists, and geneticists? Trends in Food Science and Technology 92: 122–137.

    • Anacleto, R., S. Badoni, S. Parween, V. M. Butardo, Jr., G. Misra, R. P. Cuevas, M. Kuhlmann, T. P. Trinidad, A. C. Mallillin, C. Acuin, A. R. Bird, M. K. Morell, N. Sreenivasulu. 2018. Integrating a genome‐wide association study with a large‐scale transcriptome analysis to predict genetic regions influencing the glycaemic index and texture in rice. Plant Biotechnology Journal 17: 1261–1275.

    • Cuevas, R. P., C. J. Domingo, N. Sreenivasulu. 2018. Multivariate-based classification of predicting cooking quality ideotypes in indica germplasm. Rice 11: 56.

    • Misra, G., S. Badoni, C. J. Domingo, R. P. Cuevas, C. Llorente, E. G. N. Mbanjo, N. Sreenivasulu. 2018. Deciphering the genetic architecture of cooked rice texture. Frontiers in Plant Science 9: 1405.

    • Cuevas, R. P., A. de Guia, M. Demont. 2017. Developing a framework of gastronomic systems research to unravel drivers of food choice. International Journal of Gastronomy and Food Science 9: 88–99.

    • Cuevas, R. P., V. O. Pede, J. McKinley, O. Velarde, M. Demont. 2016. Rice grain quality and consumer preferences: A case study of two rural towns in the Philippines. PLOS One 11(3): e0150345.

    • Custodio, M. C., M. Demont, A. G. Laborte, C. Diaz, J. Ynion, R. Islam, R. P. Cuevas, N. C. Paguirigan. 2016. Rapid Value Chain Assessment and Rice Preferences of Consumers, Farmers, and Other Rice Value Chain Actors in Bangladesh. TRB Report. Los Baños, Philippines: International Rice Research Institute.

    • Anacleto, R., R. P. Cuevas, R. Jimenez, C. Llorente, E. Nissila, N. Sreenivasulu. 2015. Prospects of breeding high-quality rice using post-genomic tools. Theoretical and Applied Genetics 128 (8): 1449–1466.

    • Sreenivasulu, N., V. M. Butardo, G. Misra, R. P. Cuevas, R. Anacleto, P. B. Kavi Kishor. 2015. Designing climate-resilient rice with ideal grain quality suited for high-temperature stress. Journal of Experimental Botany. 66 (7): 1737–1748.

    • Cuevas, R. P., M. Demont. 2015. Rice: An international staple. SansRival 5 (3): 12–13.

    • Butardo, V. M., V. D. Daygon, M. L. Colgrave, P. M. Campbell, A. P. Resurreccion, R. P. Cuevas, S. A. Jobling, I. Tetlow, S. Rahman, M. K. Morell, M. A. Fitzgerald. 2012. Biomolecular analysis of starch and starch granule proteins in the high-amylose rice mutant Goami 2. Journal of Agricultural and Food Chemistry. 60 (46): 11576–11585.

    • Boualaphanh, C., M. Calingacion, R. P. Cuevas, D. Jothityangkoon, J. Sanitchon, M. A. Fitzgerald. 2011. Yield and quality of traditional and improved Lao varieties of rice. ScienceAsia 37: 89–97.

    • Tran, N. A., V. D. Daygon, A. P. Resurreccion, R. P. Cuevas, H. M. Corpuz, M. A. Fitzgerald. 2011. A single nucleotide polymorphism in the Waxy gene explains a significant component of gel consistency. Theoretical and Applied Genetics 123(4): 519–525.

    • Cuevas, R. P., V. D. Daygon, M. K. Morell, R. G. Gilbert, M. A. Fitzgerald. 2010. Using chain-length distributions to diagnose genetic diversity in starch biosynthesis. Carbohydrate Polymers 81(1): 120–127.

    • Cuevas, R. P., J. Peate, M. A. Fitzgerald, R. G. Gilbert. 2010. Structural differences between hot-water-soluble and hot-water-insoluble fractions of starch in waxy rice (Oryza sativa L.). Carbohydrate Polymers 81: 524–532.

    • Cuevas, R. P., V. D. Daygon, H. M. Corpuz, R. Reinke, D. L. E. Waters, M. A. Fitzgerald. 2010. Melting the secrets of gelatinization temperature. Functional Plant Biology 37(5): 439–447.

    • Cuevas, R. P., M. A. Fitzgerald. 2007. Linking starch structure to rice cooking quality. IREC Farmers’ Newsletter 177: 16–17.

    • Fukuta, Y., E. Araki, L. Ebron, R. P. Cuevas, D. Mercado-Escueta, G. S. Khush, J. E. Sheehy, H. Tsunematsu, H. Kato. 2006. Identification of low tiller gene in two rice varieties, Aikawa 1 and Shuho of rice (Oryza sativa L.). JIRCAS Working Rep. 46: 86– 92.

  • Book chapters
    • Cuevas, R.P., M.C. Custodio, J. Ynion, A. Samaddar, M. Demont. A toolkit for gastronomic systems research to capture diversity and drivers of food choice and identify entry points for novel food products and nutritional interventions. In: Gastronomy and Food Science. Amsterdam, The Netherlands: Elsevier. In press.
    • Lapis, J. R., R. P. Cuevas, N. Sreenivasulu, L. Molina. 2018. Measurement of head rice recovery in rice. In: Rice Grain Quality: Methods and Protocols. Ed: N. Sreenivasulu. New York: Springer. pp. 89–98.

    • Santos, M. V., R. P. Cuevas, N. Sreenivasulu, L. Molina. 2018. Measurement of rice grain dimensions and chalkiness, and rice grain elongation using image analysis. In: Rice Grain Quality: Methods and Protocols. Ed: N. Sreenivasulu. New York: Springer. pp. 99–108.

    • Jimenez, R., L. Molina, I. Zarei, J. R. Lapis, R. Chavez, R. P. Cuevas, N. Sreenivasulu. 2018. Method development of near-infrared spectroscopy approaches for nondestructive and rapid estimation of total protein in brown rice flour. In: Rice Grain Quality: Methods and Protocols. Ed: N. Sreenivasulu. New York: Springer. pp. 109–136.

    • Molina, L., R. Jimenez, N. Sreenivasulu, R. P. Cuevas. 2018. Multi-dimensional cooking quality classification using routine quality evaluation methods. In: Rice Grain Quality: Methods and Protocols. Ed: N. Sreenivasulu. New York: Springer. pp. 137–150.

    • Cuevas, R. P., P. S. Takhar, N. Sreenivasulu. 2018. Characterization of mechanical texture attributes of cooked milled rice by Texture Profile Analyses and unraveling viscoelastic properties through rheometry. In: Rice Grain Quality: Methods and Protocols. Ed: N. Sreenivasulu. New York: Springer. pp. 151–168.

    • Molina, L., J. R. Lapis, N. Sreenivasulu, R. P. Cuevas. 2018. Determination of macronutrient and micronutrient content in rice grains using Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES). In: Rice Grain Quality: Methods and Protocols. Ed: N. Sreenivasulu. New York: Springer. pp. 253–264.

    • Molina, L., J. R. Lapis, N. Sreenivasulu, R. P. Cuevas. 2018. Determination of cadmium concentration in milled and brown rice grains using graphite furnace atomic absorption spectrometry. In: Rice Grain Quality: Methods and Protocols. Ed: N. Sreenivasulu. New York: Springer. pp. 265–276.

    • Cuevas, R. P., M. A. Fitzgerald. 2012. Genetic Diversity of Rice Grain Quality. In: Genetic Diversity in Plants. Rijeka: InTech. pp. 285–310.