CSE Speaker Series – Milagre Coates

On Friday, November 30 at 3:00 pm in Jones Annex 101, Milagre Coates will give a talk on “Database Design in the Real World: my summer job with the New Mexico Higher Ed. Department”.


This past summer I was given the unique opportunity to work with the New Mexico Higher Education Department to help them build a database for the new Common Course Numbering System (CCNS). The CCNS aims to standardize course numbering for secondary education institutions across New Mexico, so Calculus 2 or Chemistry 1 will have the same course number everywhere from Tech to Western to UNM.  I was involved with the project for about 2 1/2 months during which time I gained experience not only in relational database theory and design, but also working with clients on a small-scale project. One of the most challenging aspects of the project was the amount of effort I spent trying to clean up and organize the provided data so it could be input into the database. This was made especially challenging because I was usually working with an incomplete data set and needed to match up any of my design decisions to the client’s needs and expectations. Toward the end of the project I passed along my designs and code to an Oracle DBA (Database Administrator) who focused more on the front-end/user interface portion of the database. The completed project can be found at http://ccns.hed.state.nm.us/.

Independent Study Defense – Kallol Das

On Thursday, November 29 at 2:00 in the Center for Graduate Studies conference room, Kallol Das will defend his independent study “Aggregating Ensemble Weather Predictions for Rainfall Predictions”.

Atmospheric science extensively uses conceptual models that simulating weather. Traditionally, conceptual models are dynamical systems represented as differential equations involving `important’ variables. A meteorologist faces the challenge of choosing from the (often conflicting) predictions of different conceptual models. In this paper, we explore applying Machine Learning techniques to aggregate conceptual weather models of rainfall. Specifically, we explore the question `is it possible to form an aggregate model of existing conceptual models for predicting rainfall based only on the outputs of these models (that is more accurate at predicting rainfall)?’ Our secondary goal is to advance atmospheric science. We explore the correlation of predictive power of different conceptual models. This is a first step in categorizing the predictive power of the components (variables and parameters) of various models as a function of geographical location, time, etc. This information, in turn, helps in developing better conceptual models as well as making better aggregated predictions. We aimed to build an aggregate model for each geographical grid point, and at more than half the geographical locations, we were able to build ones that outperform existing conceptual models. We found that locations where the aggregate model was better than the existing ones were such that the best existing model had low errors, i.e., where the existing models are good, the aggregate model is better. We also discovered that locations that are geographically near each other tend to use the same group of conceptual models to build the aggregate one. We plan to analyze this to discover the commonalities in the conceptual models used with an aim to determine which model parameters are best suited for which area.

Independent Study Defense – Peshal Pokhrel

On November 27 at 11:45 am in Cramer 221, Peshal Pokhrel will defend his independent study “Advancing Early Forest Fire Detection UtilizingSmart Wireless Sensor Networks”.

Forests are an important part of the ecosystem and play a crucial role in preserving and maintaining the global environment. Forest fires around the world cost billions of dollars and priceless human lives. Earlier detection of forest fires might mitigate their threat. In this paper, a smart forest fire detection system which combines Wireless Sensor network (WSN) and Artificial Neural Network (ANN) technologies(henceforth SWSN) is proposed. A small-scale experimental emulation of controlled fire is carried out with a deployed SWSN collecting data for many simulated scenarios, including the crucial  fire-about-to-start scenario. The sensed data are used to train different models of ANNs while measuring scenario detection success rates. Obtained experimental results are very promising and set the model in a competitive placement among peer-related work, which encourages the utilization of a SWSNapproach in a range of other civil and military applications

Thesis Defense – Omid Hosseini

On Thursday 11/29/2018, 2:00 – 4:00 PM in the Center for Graduate Studies conference room, Omid Hosseini will defend his thesis “Nutritional phenotyping based on the relationship between body weight, diet, and demographics from epidemiological data using machine learning”

Nutritional phenotyping, a way to classify individuals based on their nutritional and health status is now universally accepted as one of the primary ways to personalize nutrition recommendations. Several approaches to phenotyping using metabolomics, nutrigenomics, metabonomics, epigenomics, and bioinformatics have come into existence in nutrition. However, there is yet to be a consensus as to how to bridge these phenotyping tools to personalize nutrition.

Within the framework of these phenotyping tools, statistical analyses have come to play a significant role. Statistical prediction models have been used in nutrition as early as the 1900s, the earliest, simplest examples such as Harris-Benedict equation, or the Weir equation being well acknowledged and used tools in energy metabolism. Classical linear regression analyses were used to derive these prediction models. As the dimensionality of our data increased, more complex tools became necessary to build predictive models.

Traditional multivariate tools such as principal components analyses or hierarchical cluster analyses enable grouping people into categories and are ideal to identify metabolic or nutritional phenotypes. However, they do not simultaneously provide validated predictive modalities, which is crucial to bridge the gap between phenotyping and customizing health solutions. Linear and other discriminant analyses exist that do provide the predictive capability, however, machine learning tools appear to offer promising resources to simultaneously phenotype and predict within the models that are built.

It is possible to train a machine learning based model on a wide multi-omic data-set to predict outcome measures and this has been done to address crucial changes in postprandial glycemic response (PPGR). A different approach, that hasbeen undertaken in this research, is to train a machine learning algorithm on how an individual eats, to predict current body weight, however, with emphasis on understanding how the algorithm groups individuals, and why. Using that information provides the research with insight into latent sub-populations, within the larger whole group. These sub populations can then be categorized or scrutinized and may need a separate model built to represent them. This is phenotyping the population, into different groups, based on the predictive ability of the model. Hence, the model needs to be tested and validated internally, and externally. This research presents such a model that predicts, body weight and BMI categories, based on very few, easy to obtain, and inexpensive input variables, and validated internally. The overall objective was to determine the feasibility of using machine learning tools to predict body weight using diet intake and demographics. A secondary aim was to identify the proportion of individuals these tools aren’t applicable to and understand why.

CSE Speaker Series – LANL Server and Storage Support Team

On Friday, November 16, 2018, at 3:00 pm in Jones Annex 101 the Los Alamos National Labs Server and Storage Support Team will be here to discuss their implementation of a private cloud. From the early stages of trying to better use hardware to what has grown to a virtualize first mentality for much of LANL business computing.  We will also be discussing the hardware needed to meet the ever-increasing needs of our customers.

Other topics will include languages used in addressing custom build issues, types of workloads we have found virtualize well (and some that don’t) and an option we are looking at for a hybrid cloud approach.

The speakers will be three people from the Server and Storage Support Team.

Keith Morgan Team Leader with 23 years of IT experience.

Ryan Iverson Server and Storage Support Team member with 4 years on the team.

Kory Wegmeyer Server and Storage Support Team member with 1.5 years on the team.