Data Science Conference 2025

Presented by Theresa McKim, Ph.D., teaching assistant professor, biology, University of Nevada, Reno in collaboration with the Nevada Bioinformatics Center

Python is a high-level, general-purpose, and open-source programming language. It is emerging as one of the most popular programming languages, both for scientific computing and general use. In this Python crash course, we will cover an overview of Python and programming fundamentals and then describe how to use Python for data processing, analysis and visualization. At the end of this module, you will be able to:

• Understand the basic components of a Jupyter notebook
• Write and debug basic Python code
• Use functions, loops, conditional statements, and apply programming fundamentals applicable across computing languages
• Analyze a dataset using Python libraries built for data science
• Learn and discuss best practices for data visualization to communicate your results

Presented by Hans Vasquez-Gross, Ph.D., Bioinformatician, Nevada Bioinformatics Center

This hands-on workshop is designed to help participants master the basics of the command line and UNIX fundamentals. Through interactive sessions, you’ll learn essential skills to navigate, manage files, and perform basic program installations.

• Get started with the command line interface
• Understand and explain the directory structure of computers
• Navigate across and within files and directories
• Create, copy and delete files efficiently
• Install programs using Conda

Whether you're new to the command line, looking to build your computational skills, or hoping to meet like-minded individuals, this workshop is an excellent place to start!

Presented by Keynote Speaker Martin Krzywinski

This workshop will distill the core concepts of information design into practical guidelines for creating visual explanations of science: figures, posters and graphical abstracts. The focus will be on clarity and concision and on the idea that form follows function. You'll learn visual strategies for organization, emphasis and theme. To illustrate these guidelines concretely, Martin will walk you through redesigns of scientific visualizations from the wild.

Data speaks for itself, but what is it saying? Crafting visual explanations of complex ideas.

Moderators

Karla Hernandez, director, Biotechnology Program, and teaching assistant professor, Biochemistry and Molecular Biology, University of Nevada Reno

Katia Albright, director, Nevada Career Studio, University of Nevada Reno

Panelists

Tatiana Burkham

Education: B.S. Economics & Finance
Current Position: Cybersecurity Data Science Manager, Sierra Nevada Corporation; WiDS Ambassador

Tatiana is a data professional with 10 years of experience in analytics, specializing in forecasting, budgeting, research and compliance. She has a proven track record of productivity and quality in fast-paced environments. With strong analytical skills and attention to detail, she excels in troubleshooting data and technical issues, generating reports, training individuals of all levels, and optimizing business processes for efficiency. Tatiana is experienced in leadership, teamwork, and communication, making her a valuable asset in data-driven decision-making.

Alex Knudson

Education: B.S. Mathematics and Statistics; M.S. Statistics and Data Science
Current Position: Software Engineer, Light & Wonder

Alex is a data scientist with a passion for programming. A University of Nevada, Reno alumnus, he earned his M.S. in Statistics and Data Science and has been engaged in bioinformatics and data science since joining the Nevada Bioinformatics Center in 2020. With expertise in software engineering and data-driven problem-solving, Alex continues to contribute to innovative projects in analytics and computation.

Lissa Callahan

Education: A.S. Mathematics; B.S. Mathematics
Current Position: Statistician and Data Scientist, Northern Nevada Public Health

Lissa is a data analyst and statistician specializing in respiratory case data analysis at Northern Nevada Public Health. She develops R-based tools that require no coding experience, making data insights more accessible. Previously, she conducted energy research utilizing regression models. A lifelong Renoite and University of Nevada, Reno graduate, Lissa is passionate about data science, analytics, and public health.

Jackie Zuker

Education: A.S. Natural Sciences; B.S. Biology; M.S. Bioinformatics (Computer Science + Big Analytics)
Current Position: VP of Data and AI Governance, Upland Specialty Insurance

Jackie is a strategic leader in data and AI governance, leveraging technology and innovation to drive growth in highly competitive markets. She specializes in identifying and implementing cutting-edge AI, engineering and IT advancements to revolutionize workflows and expand business opportunities. Jackie has a diverse background spanning bioinformatics, wildlife biology and data science, with extensive experience in the insurance sector. She is also the organizer of the Big Data and Brews meetup, a key networking hub for local data scientists.

Chris Golias

Education: B.S. Biology (Anatomy and Physiology); B.A. Anthropology; Ph.D. Anthropology
Current Position: Senior User Experience Researcher, Google DeepMind

Chris is a technology anthropologist with expertise in applied research across retail, healthcare, indigenous rights, substance use, e-commerce, ecological design, machine learning, and information technology. Currently at Google DeepMind, he applies anthropological insights to UX research, helping shape user-centered AI innovations. He holds a Ph.D. in Anthropology from the University of Pennsylvania.

Derek Ross

Education: A.A. Business Administration and Management, B.B.A. Management Information Systems
Current Position: Field Chief Technology Officer, TrueMark

Derek has over 10 years of experience leading enterprise delivery teams, specializing in enterprise architecture, CI/CD pipeline orchestration, and DevOps. Previously, he was a Senior Solutions Architect at AWS, focusing on code quality and delivery velocity. His background spans mobile technology, aviation engineering, logistics, and operations. A former Special Forces Medical Sergeant, small business owner and sales associate, Derek brings diverse expertise in leadership, technology, and collaboration, with a strong commitment to quality and innovation.

Sonali Dey

Education: B.S. Mathematics and Statistics; M.S. Statistics and Data Science
Current Position: AI Engineer, IBM

Sonali is an AI Engineer with IBM’s Client Engineering team, specializing in developing data-driven software solutions across diverse industries. She holds a degree in Engineering Science from the University of Toronto, where she majored in Machine Intelligence, and is currently pursuing a master’s in data science from the University of Texas at Austin. A strong programmer, communicator and critical thinker, Sonali is passionate about advancing machine learning and looks forward to sharing her insights on the panel.

Presenters

Michael Martin, computer science undergraduate student, College of Engineering, University of Nevada, Reno

Harnessing Machine Learning and NASA Satellite Big Data for Enhanced Wildfire Prediction and Air Quality Forecasting

Advancements in Machine Learning (ML) have transformed time series prediction, particularly in projection of extreme weather phenomena such as wildfires. This research aims to improve wildfire prediction using NASA satellite remotely sensed big data. We analyzed 25 years of NASA high-resolution environmental variables over the Western U.S. to develop ML-based models for enhancing accuracy of fire-induced air pollutants. We developed deep learning architectures to enhance the precision of wildfire-related emission predictions. Feature importance analysis using SHAP improves model interpretability, ensuring transparency in AI-driven forecasts. This approach is crucial for developing efficient, explainable data-driven models to support informed decision-making in environmental scenarios.

Arielle Pastore, anthropology graduate student, College of Liberal Arts, University of Nevada, Reno

Re-thinking Ordinal Data in Dental Anthropology: Insights from Multiple Correspondence Analysis

This study re-evaluates the utilization of ordinal data in dental anthropology by employing multiple correspondence analysis (MCA) across four samples, including the Subadult Virtual Anthropology Database, Texas State University Donated Skeletal Collection, University of Tennessee, Knoxville Donated Skeletal Collection, and a European sample from Christy G. Turner. Eigenvalue analysis across these datasets reveals that the first principal dimension consistently accounts for 11.4% to 12.7% of total variance, indicating a recurrent pattern of dimensional importance. Explorations of inter-trait relationships among diverse datasets expose their critical role in morphological differentiation and highlight their utility as robust markers in population-level analyses.

Eriko Sakamura, M.S., M.A., instructor, Computer Science and Technology, Truckee Meadows Community College

Undergraduate projects at TMCC: What community college students can do with data science?

At TMCC, we have been active in data science-related undergraduate research, providing students with opportunities to apply analytical skills to real-world problems. Our projects span various disciplines, including biology, public health, and environmental science, allowing students to work with real datasets and develop critical thinking skills. In this talk, I will highlight ongoing undergraduate projects, including an INBRE-funded study on biodiversity and climate, as well as coursework-driven projects from DATA101 and DATA220. By showcasing these initiatives, I aim to demonstrate the potential of community college students in data science and how these experiences prepare them for future academic and professional success.

Timothy Malacarne, Ph.D., assistant professor of Data Science, Nevada State University

Tipping Hats, Riding Coattails: Reference Networks in Country Music

This project examines reference networks in American country music lyrics. References to icons, heroes, and ancestors provide rich material for narrative construction of self and one’s imagined community. This project uses a dataset of 74,000 American country music songs from the past 60 years to investigate artists’ use of lyrical citation of peers and predecessors, as part of a complex process of meaning-making at the individual and cultural levels. It has also served as a teaching resource for my courses in Nevada State University's Data Science program.

Drinks and appetizers will be served. Please visit our sponsor booths!

Preliminary Geothermal Play Fairway Workflow for the Great Basin Region, USA

Authors: Nicole Wagoner, Mark Coolbaugh, James Faulds, Elijah Mlawsky, Cary Lindsey, Whitney Trainor-Guitton

Play fairway analysis (PFA) is an exploration tool to assess geothermal resource potential and reduce geothermal resource exploration risk. Geothermal exploration risk is particularly high when searching for hidden geothermal systems (i.e., systems without surface expressions such as hot springs). Many hidden systems exist in the Great Basin region (GBR) of the western United States, a world-class geothermal province with over 1 GWe installed nameplate capacity. The INGENIOUS project aims to build on previous PFAs, as well as recent machine learning-based work to improve methodologies for discovering new, economically viable, hidden systems in the GBR. Here, we present a preliminary GBR play fairway workflow built from the assessment of 14 newly updated regional geological, geophysical, and geochemical datasets. The datasets have been analyzed with weights of evidence, logistic regression, and other tools to identify statistically significant relationships between data layers and known geothermal systems. Additionally, feature engineering has been utilized to extract maximum value from the data by developing hybrid predictive features consistent with previously identified physiographic relationships. The identified key predictive feature layers were then statistically integrated using PFA architecture into a preliminary GBR play fairway model. The results improve our understanding of GBR geothermal resources and facilitate identification of new hidden geothermal systems.

Examining Test Anxiety in Math Using R: Mean Profile Plot in Repeated-Measures ANOVA with Bonferroni Adjustment of Matched Sample T-Tests

Authors: Carson Perkins, Colleen I. Murray

We use R to demonstrate a repeated-measures ANOVA with a mean profile plot and then conduct a post-hoc comparison of means using matched sample t-tests with Bonferroni adjustments. Analyses are based on a sample of heart rates of students at baseline, before a math test and after a math test.

The Practicality of Japanese Tests Created in One Hour Using AI: Teaching Experience or AI Utilization Experience?

Authors: Akio Abe, Soichiro Motohashi, Yoshie Kadowaki

This poster presentation explores the potential of AI integration in the creation of Japanese proficiency tests. A hypothetical scenario was designed in which an unexpected situation required a Japanese language teacher to recreate an assessment test just one week before its administration. Two teachers participated: one with over ten years of Japanese language teaching experience but limited AI experience, and another with only six months of teaching experience but extensive experience in utilizing AI. Both were given a one-hour time limit to create a test based on the same instructional content.

The tests were then evaluated by another Japanese language teacher with extensive experience in both Japanese language education and AI usage, using a rubric-based assessment framework. The results showed that the test created by the teacher with significant AI experience, but limited teaching experience demonstrated greater content diversity and fewer minor errors in question formulation.

These findings suggest that while expertise in language education remains essential, AI utilization skills can also play a significant role in test creation. Moreover, they highlight the importance of further research on AI applications in teacher training and language education.

Weather and Climate in Modelling Energy Usage in Nevada

Authors: Kevin Bumgartner

There is a tight relationship between daily residential energy usage and the average daily temperature, with single-family homes in Las Vegas using up to roughly 4 times as much energy on very hot days as they use on more temperate days. For the past six years, the public utilities commission of Nevada has required energy utilities to incorporate local warming trends into the models used to compensate for temperature in long-term energy forecasting, and in models used for analysis of historical data used in rate-setting. Kevin Bumgartner's poster presents some representations of such models, along with some interesting plots of weather and usage data, the surprising results of distinct-month models of regional climate change in Reno and Las Vegas, and distinct-month models of the energy/temperature response.  All modelling shown is done using linear regression.

Using R for Correspondence Analysis and Visualization of Multivariate Data: Relationships among Categorical Variables

Authors: Alexandria B. Stanton, Colleen I. Murray

Correspondence analysis (CAs) was conducted using R to examine associations between categorical variables without forcing data into a linear relationship. CA analysis tested associations among demographic variables and factors that may be associated with positive perceptions, such as hope.

Leveraging Machine Learning and NASA Satellite Data to Improve Wildfire Prediction and Environmental Insights

Authors: Michael Martin, Lei Yang Farnaz Hosseinpour

The increasing frequency of wildfires in California highlights the need for improved prediction methods to support environmental management. This project aims to improve wildfire prediction using NASA satellite remotely sensed big data. We analyzed 25 years of NASA high-resolution environmental variables over the Western U.S. to develop Machine Learning (ML) models for enhancing accuracy of fire-induced air pollutants. We developed deep learning architectures to enhance the precision of wildfire-related emission predictions. Feature importance analysis using SHAP improves model interpretability, ensuring transparency in AI-driven forecasts. This approach is crucial for developing efficient, explainable data-driven models to support informed decision-making in environmental scenarios.

Big Data in Environmental Science: Insights into Wildfire Smoke Emission

Authors: Rahele B. Vaezi, Farnaz Hosseinpour

Over the past few decades, there has been a significant rise in extreme fires, which have an adverse effect on air quality and the environment. Wildfire smoke can be transported over large distances, acting as air pollutants that affect adjacent and distant downwind communities. This study explored the possible relationships between wildfire smoke emissions and climate indices in the western U.S. Climatology helps identify long-term trends that rely on large-scale datasets. We applied an ensemble of remotely sensed observations and the historical Modern-Era Retrospective Analysis for Research and Applications, the second version (MERRA-2) reanalysis, and the Gridded Surface Meteorological (gridMET) data. These datasets were used due to their high spatial and temporal resolution, which provided insight into wildfire smoke transport and its effects on a regional scale. Given the global hourly MERRA-2 data since 1980, even a single variable can yield several terabytes of data over prolonged temporal scales, necessitating advanced computational methods, requiring specialized expertise for efficient storage, processing, and analysis.

Chess's Impact on Memory - A Study on the Predictive Power of Chess in Memory

Authors: Alec Brooks

This study explores the relationship between playing chess and memory improvement using data from a simulated population on The Islands website platform. Over a simulated fall semester, students were divided into control and participant groups, with the participants playing daily chess games while both groups completed daily memory tests. Although the participant group showed greater improvement in memory scores compared to the control group, the difference was low statistical significance, likely due to the small sample size. However, linear regression revealed that study duration and chess win/loss records were meaningful predictors of memory scores, accounting for a substantial portion of the observed variation. These findings suggest a potential link between playing chess and memory enhancement, but further research with larger, real-world populations is necessary to confirm these results.

Phylogenomic analysis sheds light on the origin of Burkholderia gladioli in Bangladesh

Authors: Ismam Ahmed Protic, Md. Nasir Uddin, Andrew Gorzalski, Md. Rashidul Islam, David Alvarez-Ponce

Bacterial Panicle Blight (BPB), caused by Burkholderia gladioli, has recently been found in multiple rice-growing regions in Bangladesh, raising questions about how the pathogen was introduced in the country. To investigate the introduction and origin of B. gladioli, we sequenced the complete genomes of 19 strains isolated from symptomatic rice panicles from four major rice-growing areas in Bangladesh. Construction of a phylogenetic tree using all 320 publicly available genomes along with the 19 newly sequenced genomes revealed 5 phylogenomically distinct clans. Our results suggest that B. gladioli was introduced in Bangladesh at least 5 times. The tree topology suggests that four of the introductions may have originated from the United States of America (USA), and these four clans are sister groups with several clinical strains of B. gladioli from the USA.

Wildlife and Weather Patterns: A Data-Driven Exploration

Authors: Seth Miller, Alec Brooks, Minsung Jung, Megan Lahti, Eriko Sakamura, Cecilia Vigil

From agriculture and infrastructure to energy and water supplies, climate plays a significant role in societal functionality and health. As weather and climate change grow increasingly volatile, it's essential to examine their impact on biodiversity. This project utilizes data gathered locally by faculty and students from the Biology Department at Truckee Meadows Community College (TMCC), analyzed in conjunction with weather, temperature, and snow accumulation data collected by the California Data Exchange Center (CDEC). This study aims to review the correlation between flowering data and seasonal weather conditions in the Great Basic region. This is a part of TMCC Undergraduate research project mentored by four TMCC faculties and supported by INBRE and NASA grants.

Gym Member Activity

Authors: Ty Martin

Many gym-goers either lack structured workout knowledge or are experienced individuals seeking optimization. This study analyzes the relationship between workout types, water intake, and exercise duration using the Gym Members Exercise Dataset from Kaggle. Key research questions focus on whether workout type affects BMI, if water intake influences session duration, and whether workout frequency correlates with body weight. Data was categorized by workout type, frequency, session duration, and gender. Findings indicate no significant difference in workout preference between genders. Additionally, increased water consumption led to shorter workout durations, while frequent exercise correlated with lower or recommended body weight. However, workout type did not significantly impact BMI. These insights contribute to a better understanding of exercise habits and their effects on fitness outcomes.

The Bridge to AI and Neurocomputing Core of the UNR Center for Integrative Neuroscience

Authors: Michael Rudd, Alireza Tavakkoli, Eelke Folmer, Michael A. Webster

The new Bridge to AI and Neurocomputing (BAIN) Core of the UNR Center for Integrative Neuroscience provides expert consulting in the areas of machine learning and AI, advanced statistical methods and computational modeling, and AR/VR and visualization to help investigators apply the latest computational modeling methods in their research. Our machine learning experts can help you pose your research hypotheses in the form of testable algorithmic models and run these models on our high-speed online pipeline. Our statistics consultant can help you prepare methods sections for grant submissions or publications, revise manuscripts, and advise you on experimental design, experimental design, and power analysis. We also offer expertise and equipment for applying virtual and augmented reality in your research. Come to this poster to find out how these methods can be used to enhance your research, and join the computational modeling and AI revolution today!

Presented by the Nevada Bioinformatics Center and Pronghorn

Morning session: Introduction to Linux/Unix and HPC

In the first session of our comprehensive workshop, we will guide participants through the essential aspects of Linux/Unix and high-performance computing (HPC). We will cover fundamental concepts and commands that are crucial for participants to understand and navigate effectively within a Linux/Unix environment.

Session highlights:

• Directory structure of a Linux/Unix system
• Anatomy of a Linux command
• Understanding the concept of a shell
• Command line options and TAB completion
• Navigation techniques: Relative vs absolute pathing
• CONDA installation: A tool for user-level program installations
• Working with files using fundamental Linux commands
• Introduction to piping commands
• Covered commands: ls, cd, mkdir, mv, touch, tree, htop, nano, cat, less, wc, grep, rm, rmdir, history

Afternoon session: Utilizing Pronghorn and Interacting with SLURM Job Scheduler

The second half of the day will focus on practical applications, delving into the effective use of Pronghorn and interaction with the SLURM job scheduler while running automated analysis execution.

Session Highlights:

• Working with compressed data
• Utilizing screen commands
• Initiating your first SLURM job
• SLURM interactive session for testing
• Scripting for automated analysis execution
• Converting scripts into SBATCH scripts for Pronghorn
• Transferring data files to/from Pronghorn
• Singularity containers: Rstudio server use case
• Processing multiple samples in parallel with array SBATCH scripts

Presented by Professor Kyra Stull and the Nevada Bioinformatics Center

The R language, along with its amazing collection of libraries, is one of the most powerful tools you can use to tackle data analysis and to generate informative visualizations.

Morning session: Getting started with R programming

Discover the fundamentals of R programming and RStudio in this hands-on workshop, designed for beginners with no prior knowledge. Dive into essential data manipulation, organization, and exploration using base R and tidy format with provided example datasets. Covering R basics, including command line calculations, variable usage, and data structures, the workshop guides participants in loading, accessing, and summarizing data through statistics and plots. This session provides a practical introduction to programming with R, emphasizing its application in data science and research.

Afternoon session: Data visualization with R

This session will teach participants how to display their data appropriately using ggplot2, which is an R package dedicated to data visualization. We will cover visualizations for univariate, bivariate, and multivariate relationships and for both categorical and continuous data. This includes bar plots, histograms, boxplots, line plots, lollipops, scatterplots, bubble plots, correlations, and more.

Presented by Research Data Services (part of University Libraries)

Learn how to easily tackle grant funder requirements for a data sharing and management plan (NIH, NSF, and others by request). This workshop, aimed at faculty and researchers, will help you enhance your data handling skills and ensure your research meets the highest standards. Engage in a step-by-step process to create your DMS through hands-on exercises and gain a set of resources and tools for continuous support in data management planning. Feel free to bring your existing DMS for the peer-review session, offering constructive discussions and refinements.

Presented by the School of Business and Teaching & Learning Technologies

This roundtable session invites undergraduate students to engage in an interactive exploration of the exciting possibilities and challenges presented by artificial intelligence in higher education. Through open discussions and collaborative brainstorming, participants will have the opportunity to share their perspectives on how AI can enrich their learning experiences, address academic needs, and shape the future of undergraduate education. Together, we will delve into real-world examples and potential applications of AI, fostering a student-centered conversation that empowers undergraduates to become active contributors in the advancement of AI technology in higher education. 

First hour: Tips and tricks for success

• Facilitated discussion by University faculty and staff on AI applications and advice for practical use for students
• Demonstrations showcasing successful strategies, tools, and prompts
• Sharing of resources, case studies, and best practices for integrating AI in undergraduate learning.
• Q&A sessions for participants

Second hour: Open discussion with students

• A student-led conversation where participants can openly share their experiences with AI
• Opportunities for students to discuss challenges, innovative uses, and personal stories related to AI in education
• Peer-to-peer knowledge exchange and brainstorming sessions
• Encouragement for students to propose new ideas or initiatives involving AI in their educational journey at the University

Presented by IBM

Learn how IBM watsonx unlocks the new potential of AI Assistants powered with generative AI and automation to drive productive employee and customer experiences.

Presented by Professor Theresa McKim in collaboration with the Nevada Bioinformatics Center

Python is a high-level, general-purpose, and open-source programming language. It is emerging as one of the most popular programming languages, both for scientific computing and general use. In this Python crash course, we will cover an overview of Python and programming fundamentals, and then describe how to use Python for data processing, analysis and visualization. At the end of this module, you will be able to:

• Understand the basic components of a Jupyter notebook
• Write and debug basic Python code
• Use functions, loops, conditional statements, and apply programming fundamentals applicable across computing languages
• Analyze a dataset using Python libraries built for data science
• Learn and discuss best practices for data visualization to communicate your results

Are you interested in government service? Federal executive branch roles? Top national positions? Security clearances? Join us for a round table discussion of questions such as:

• How could interested students take the first steps toward government service?
• What is the application process for internships? Full-time positions?
• What should students focus on in their studies to be competitive for these positions?
• What is the polygraph test like? Do I need to take it?
• What is a security clearance?

Curious about AWS cloud computing services? Interested in exploring additional training opportunities or overcoming challenges from past projects? Or if you're unsure where to start your cloud computing journey, join us for an engaging meet and greet with the AWS team!

How data & AI are transforming cancer detection, prevention, and treatment.

James Ragsdale, doctoral student and instructor, Social Psychology Interdisciplinary Graduate Program, Graduate School, University of Nevada, Reno

We will explore the ethical dilemmas that shape scientific research, from historical controversies to contemporary challenges in data science. Biases, social pressures, and small but consequential decisions all influence the integrity of scientific findings. Through case studies, ranging from Semmelweis to climate change, and real-world examples like vaccine misinformation and the replication crisis, we will examine how cognitive biases contribute to ethical lapses and public distrust in science. Finally, we will discuss strategies to enhance transparency and reduce bias in data-driven fields, ensuring a more trustworthy foundation for scientific inquiry.

Moderator

Albert Gonzales, Ph.D., assistant professor, Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine

Panelists

Karla Hernandez, director, Biotechnology Program and Teaching Assistant Professor, Biochemistry and Molecular Biology, University of Nevada Reno

AI: Use responsibly

Ed Huffman, Executive Director, Teaching & Learning Technologies, University of Nevada Reno

AI is here... Now what?

James Ragsdale, Doctoral Student and Instructor, Social Psychology Interdisciplinary Graduate Program, Graduate School, University of Nevada, Reno

Trust the data? Bias and the fragility of scientific integrity

Ankita Shukla, assistant professor, Computer Science & Engineering, University of Nevada, Reno

AI: Social good

Nicholas Seltzer, Ph.D., Associate Professor, Department of Political Science at University of Nevada, Reno

Are we cyborgs yet?

Aditya Sriwasth, business technology leader, IBM

AI for applications in Eco-conservation and Science.

Details pending.

M. Griswold, Ph.D., program director, Health Workforce Research, School of Medicine, University of Nevada, Reno

Nevada Instant Atlas website

A presentation of county level data presented on the website with an emphasis on social determinants of health and population health data. The project management plan which involves training undergraduate and graduate students in data base collection and maintenance with the resulting publication of the biennial Nevada Rural and Frontier Health Data Book for the legislature and health policy makers.

Lukasz Sznajder, Ph.D., assistant professor, Department of Chemistry and Biochemistry, University of Nevada, Las Vegas

RNA toxicity in neuropsychiatric disorders

Tong Zhou, Ph.D., assistant professor, Department of Physiology and Cell Biology, School of Medicine, University of Nevada, Reno

The expanding territory of small noncoding RNAs

Emerging evidence indicates that the territory of small noncoding RNAs keeps expanding. In comparison with the well-known microRNAs, noncanonical small noncoding RNAs, such as, tRNA derived small RNAs (tsRNAs) and rRNA derived small RNAs (rsRNAs), have be discovered in both prokaryotes and eukaryotes and found to play various roles in biological processes (e.g., epigenetic regulation, translational inhibition, immune response, and cell differentiation). Digging into small RNA sequencing data, with a focus on these noncanonical small RNAs will bring us novel insights into RNA biology research. The superior information capacity within these small RNAs potentially contributes to future molecular medicine.

Anna Panorska, Ph.D., professor, Department of Mathematics & Statistics, College of Science, University of Nevada, Reno

Machine learning for the classification of bacterial virulence via their nanomotion

The World Health Organization highlights the urgent need to address the global threat posed by antibiotic-resistant bacteria. Efficient and rapid detection of bacterial response to antibiotics and their virulence state is crucial for the effective treatment of bacterial infections. However, current methods for investigating bacterial antibiotic response and metabolic state are time-consuming and lack accuracy. To address these limitations, we propose a novel method for classifying bacterial virulence based on statistical analysis of nanomotion recordings. We demonstrated the method by classifying living Bordetella pertussis bacteria in the virulent or avirulence phase, and dead bacteria, based on their cellular nanomotion signal. Our method offers significant advantages over current approaches, as it is faster and more accurate. Additionally, its versatility allows for the analysis of cellular nanomotion in various applications beyond bacterial virulence classification.

Wenrong Cao, Ph.D., assistant professor, Department of Geological Sciences and Engineering, School of Science, University of Nevada, Reno

Continental Magmatism Drives the Global Weathering Budget: New Results from a Spatiotemporally Explicit Perspective

Chemical weathering controls the long-term climate of the Earth. Rapidly eroding continental magmatic belts are the fastest weathering zones on the planet, but their contribution to global weathering and subsequently climate evolution over the Phanerozoic Eon remains elusive. Here, we collate existing datasets into a database of ~54,000 dated isotope samples from magmatic belts. We place these data into their proper paleogeographic and paleoclimatic context to reconstruct the evolution of the global flux and isotope composition from the weathering of continental magmatic belts. Our approach provides an interpretable framework to quantify the independent or coupled roles of tectonics, paleogeography and paleoclimate in shaping the long-term Earth's surface conditions.

Sujung Lim, Ph.D., post-doctoral fellow, School of Life Sciences, University of Nevada, Las Vegas

Using curated HMMs to understand the diversity and distribution of microbial respiratory pathways

While modern high-throughput sequencing technology has provided us with vast quantities of data allowing us to probe the microbial diversity of many discrete environments, more granular observations are necessary to allow study of the evolutionary ecology of core processes such as microbial respiration. We developed curated hidden Markov models (HMMs) from experimentally validated respiratory protein sequences to search for homologs within metagenomic sequence data from diverse environments ranging from the human gut to terrestrial springs. In conjunction with geochemical context, these data give insight into the mechanisms of microbial adaptation to distinct environmental pressures.

Presented by guest speaker Chris Golias, Ph.D., Senior User Experience Researcher, Gemini Internationalization, Google

We examine LLM deployment through African lenses, utilizing and reflecting on ethnographic methods to engage with the continent’s unique technolinguistic landscape. Drawing from primary research in Ethiopia, Ghana, Kenya, Nigeria, and South Africa, we consider how LLMs are integrated and upheld within local contexts. We unearth instances where LLMs could perpetuate digital colonialism or exacerbate existing sociopolitical tensions, as well as how they are contested and adapted. We emphasize the imperative for ethnographic insights in LLM research and provide a detailed framework, both to counteract oversimplification and the misinterpretation of complex interactions and to support culturally informed deployments that enhance local agency.

Chris is also participating in the panel discussion “Career Paths in Data Science – Advice from Industry Experts” on Tuesday, Feb. 18 at 2:30 p.m.\

Golias is a technology anthropologist, currently with Google, who has conducted applied anthropological research across various areas including retail, healthcare, indigenous rights, substance use, ecommerce, governance, machine learning, localization and information technology. He holds a Ph.D. in anthropology from the University of Pennsylvania.

Presented by Carlos Ramirez Reyes, Ph.D., assistant professor, research data services, University Libraries

This workshop will guide participants through practical techniques for handling messy datasets, including managing missing values, removing duplicates, tidying data and reformatting variables using R. It is ideal for researchers and analysts looking to streamline data preparation for analysis. The session will be a demonstration using R and RStudio. While participants of all skill levels are welcome, this is not an introductory R training. Those already familiar with R and RStudio are encouraged to bring their laptops preloaded with the software if they wish to follow along.

Presented by Alex Knudson M.S., software engineer, Light & Wonder in collaboration with the Nevada Bioinformatics Center

This hands-on session will introduce participants to R and RStudio, covering the basics of data import, manipulation and visualization. Ideal for beginners with no prior coding experience.

• Create Basics of R, RStudio and setting up the environment
• Hands-on basics (reading data, simple calculations and navigating R)
• Basic data manipulation and simple visualizations

Curious about AWS cloud computing services or eager to explore their new and exciting tools, like Amazon SageMaker Unified Studio? Join the AWS team for an interactive demo showcasing this powerful tool and its capabilities for accelerating machine learning workflows.

Stay afterward for a relaxed coffee-and-cookie open discussion, where you can ask questions, share challenges from past projects, explore training opportunities, or learn how to begin your cloud computing journey. Everyone is welcome!

Presented by Paul Hurtado, Ph.D., associate professor, mathematics and statistics, University of Nevada, Reno in collaboration with Theresa McKim, Ph.D., teaching assistant professor, biology, University of Nevada, Reno

Learn to turn your data analysis into polished, professional documents! This hands-on workshop covers using R Markdown, Jupyter Notebooks and LaTeX to create dynamic reports, integrate code and visuals, and produce publication-ready outputs.

• Create dynamic reports using R Markdown and Jupyter Notebooks
• Format equations and customize outputs with LaTeX
• Combine code, visualizations, and text for seamless workflows
• Embed R or Python scripts directly into LaTeX documents

Perfect for researchers and students looking to streamline their workflows and enhance the quality of their reports.

Presented by the IBM Team

IBM is hosting an AI Roadmapping and Strategy Workshop to explore the impact of AI on higher education, and provide a forum to explore AI opportunities for the University of Nevada, Reno. Participants will get the opportunity to explore different AI use cases, provide input on existing challenges and pain points, identify how AI can help to resolve these challenges and outline key resources needed for successful implementation and adoption.

Presented by Jeremy Tiedt, assistant dean, undergraduate student success, College of Business, University of Nevada, Reno

Presented by Kanishka Manna, Ph.D., postdoctoral scholar, Nevada Bioinformatics Center

This hands-on workshop introduces the powerful workflow management system Nextflow for creating automated and reproducible computational pipelines. Participants will gain insight into workflow management systems, explore Nextflow’s advantages, and learn pipeline development through a practical example (not domain specific). This workshop is designed to equip researchers with the skills needed to automate and scale their data analyses efficiently.

No prior experience with Nextflow is required, but familiarity with Unix and basic programming concepts are required.

• Understand how automated pipelines increase reproducibility, consistency and scalability in research
• Learn what Workflow Management Systems (WFMS) are, their key features and why they are essential for efficient research workflows
• Understand the core differences between Nextflow and Snakemake to determine which system best suits your needs
• Install and set up Nextflow and the required tools, including WSL (for Windows), Java and VS Code editor
• Walk through a beginner-friendly Nextflow pipeline, including the “Tacos with Nextflow” example, to understand concepts like processes, channels and configuration syntax
• Learn how Nextflow makes it easy to parallelize tasks and scale your workflows for large datasets
• Understand how to generate useful reports and visualize your workflow as a Directed Acyclic Graph (DAG)

This workshop will emphasize practical learning through hands-on exercises, including a creative and accessible example pipeline to demystify pipeline development.

Presented by Elena Azadbakht, associate professor and health sciences librarian, Teresa Schultz, associate professor and scholarly communication and social sciences librarian and Challen Wright, assistant professor, metadata librarian, University of Nevada, Reno.

This workshop will provide hands-on learning for participants through the process of preparing and sharing datasets in data repositories to comply with grant funder mandates. Participants will explore various data repositories, including the University’s data repository Dryad, as well as what they will need to do to prepare their data to ensure the data is reusable and understandable and how they can get credit for sharing their data.

Presented by Cassandra Hui, Ph.D., Bioinformatician, Nevada Bioinformatics Center

Whether you're new to version control or looking to enhance your project management skills, this hands-on workshop will provide the training you need to leverage GitHub effectively for collaboration and personal use.

Join us for an interactive learning experience where you’ll explore:

• Setting up and navigating GitHub
• Creating and managing repositories for academic projects
• Collaborating on shared repositories

Presented by Alex Knudson, software engineer, Light & Wonder in collaboration with the Nevada Bioinformatics Center

Dive deeper into R with this advanced session focusing on data wrangling and visualization using dplyr, tidyr, and ggplot2. Participants will learn practical applications for real-world scenarios.

• Data wrangling with dplyr and tidyr (filtering, grouping, summarizing, reshaping)
• Creating visualizations with ggplot2 (scatterplots, bar charts, customizing themes)
• Applying Tidyverse and ggplot2 in real-world scenarios with guided examples

You’ll leave with practical skills to manage research projects and collaborate seamlessly with others – all while gaining experience with GitHub’s powerful interface. This training will use GitHub online and GitHub Desktop and requires no coding.

Presented by James Ragsdale, doctoral student and instructor, sociology, University of Nevada, Reno

This interactive workshop examines how researcher decisions — known as researcher degrees of freedom — can unintentionally bias results or inflate type I error rates. It covers cognitive heuristics that may unconsciously lead to questionable research practices. Through group discussions, case studies, and data visualization, participants will explore flexibility in study design, analysis, and reporting while learning strategies to reduce bias and strengthen research integrity.

• Identify key stages in the research process where choices influence outcomes
• Understand the impact of p-hacking, selective reporting, motivated reasoning and HARKing
• Learn strategies to avoid questionable research practices
• Enhance research reliability and credibility through methodological rigor

This workshop is essential for anyone aiming to improve the quality and reproducibility of their research.

Presented by IBM Team

Data is at the core of AI but getting access to datasets for Training, Tuning and Inference is complex and costly (or is it?). IBM’s Global Data Platform powers many large-scale AI implementations including IBM’s own Vela and Blue Vela AI supercomputers. Come learn how data Access, Acceleration, Abstraction and Assurance are fundamental to AI and how IBM has designed large AI clusters to train the Granite family of models.