Product Management

Date/Time: Wednesday, May 13 2:00 p.m - 3:00 p.m.
Location: Webex
Speaker: Ambika Murali

Abstract:This session will cover the complexities of launching a new product or scaling a product by discussing the role of a Product Manager (PM) within technology. We'll discuss what is product management, how ideas and concepts become reality, and how a PM works cross-functionally to launch and scale products to serve users' pain points.  Takeaways for attendees include

  • An understanding of product management & its collaboration with engineering
  • How to pitch ideas and prototype
  • How to optimize the backend processes for speed
 

Biosketch:Born and raised in India, Ambika's passion for global education and culture has led her to live and work in 5 countries. Academically trained in data science, Ambika currently works as a Technical Group Product Manager in the Bay Area for US Bank.  She is responsible for launching their new digital experience for checking accounts (mobile and web), with a keen focus to achieve the fastest time-to-fund a digital checking account in the US. Previously, Ambika worked at SoFi and launched multi-billion dollar new product lines and helped deploy machine learning models which attained SoFi the fastest time-to-fund a personal loan in the US. Prior to tech, Ambika worked in the financial industry for Goldman Sachs, Deutsche Bank, Central Bank of Chile, and the International Monetary Fund. Ambika is committed to the causes of education and theadvancement of women.  Ambika is an Advisory Board Member of the School of Computing at Montana State University and Head of Programming for a nonprofit called Advancing Women in Product (AWIP). 

Award Seminar

Date/Time: Monday, April 22 4:10 p.m - 5:00 p.m.
Location: Webex
Facilitator: John Paxton

Abstract: At the end of every academic year, we celebrate the accomplishments of members of the Gianforte School of Computing.   Join us for this year's celebration where we will reflect on our accomplishments and present awards.

Continuum and Stochastic Models for Transcription on a Crowded Gene

Date/Time:  Monday, March 9 4:10 p.m - 5:00 p.m.
Location: Barnard Hall 108
Speaker:  Lisa Davis

Abstract: In fast-transcribing prokaryotic genes, such as an rrn gene, many RNA polymerases (RNAPs) transcribe the DNA simultaneously.  Active elongation of RNAPs involves periods of fast forward motion that are often interrupted by pauses.  In some literature, this has been observed to cause RNAP traffic jams.  However, other studies indicate that elongation is faster in the presence of multiple RNAPs than elongation by a single polymerase.  Over the course of this research project, we have considered several mathematical models to capture the essential behaviors known to this phenomena.  I will give a brief overview of the essential biological quantities of interest, and the remainder of the talk will focus on an overview of two mathematical models that have been proposed.  The first is a continuum model taking the form of a nonlinear conservation law PDE where transcriptional pausing is incorporated into the flux term with a piecewise continuous density-velocity relationship.  The velocity relation is parametrized according to the user-specified (or randomly generated) spatial locations and time duration of the pauses.  The second model is a stochastic one that is based on the classical TASEP model but with added complexity to account for the interactions among neighboring RNAPs that can influence local elongation velocities.  I'll mention the algorithms that were used for model simulation for a series of parameter studies. If there's time, I'll discuss future directions to combine the lessons learned from previous models into the development of a specific second order PDE formulation which allows for an adaptive density-velocity relationship. 

Biosketch: Dr. Lisa Davis is a Professor of Mathematics at Montana State University.  She received her B.S. in Mathematics from the University of Virginia’s College at Wise and her M.S. and Ph.D. in Mathematics from Virginia Tech.  Her research combines areas of computational mathematics, sensitivity analysis and mathematical modeling of biological processes.  Her most recent work is in the area of model construction and numerical simulation for bio-polymerization models.

Strategies for Resisting the Racial Injustice of Predictive Policing

Date/Time:  Monday, March 2 4:10 p.m - 5:00 p.m.
Location: Barnard Hall 108
Speaker:  Bonnie Sheehey

Abstract: The recent use of predictive technologies in fields of criminal justice like policing has become a growing site of concern for activists, theorists, and technicians. Prevalent among these concerns is the way in which these technologies reinforce, and potentially even exacerbate, conditions of racial injustice that have long plagued the criminal justice system in the U.S. One common response to this problem is to call for more transparency in the deployment of predictive policing technologies. An ethics of transparency, however, is insufficient to address the racial harms of predictive policing. What is needed, I argue, is a set of ethical strategies that aim at resisting the racial injustice of predictive policing. By thinking of ethics in terms of resistant practices, we can begin to consider a notion of responsibility that holds us and the technologies we bind ourselves to accountable for the harms created by this bond. 

Biosketch: Dr. Bonnie Sheehey is an Assistant Professor in the Department of History and Philosophy at Montana State University. She received her B.A. in Philosophy from the University of Hawaii at Manoa and her M.A. and Ph.D. in Philosophy from the University of Oregon. Her current research critically examines the ethical, social, and political implications of contemporary technologies, especially as these are used in fields of criminal justice.

Analyzing time series data as graphs

Date/Time:  Monday, February 24 4:10 p.m - 5:00 p.m.
Location: Barnard Hall 108
Speaker:  Bree Cummins

Abstract: The timing of the extrema of an experimental time series (or its derivative) contains information about the underlying dynamical system. Time series often contain significant measurement errors that interfere with the ability to extract information from the data. I will discuss a method for characterizing a time series for any assumed level of measurement error ε by a sequence of intervals, each of which is guaranteed to contain an extremum for any function that ε-approximates the time series. I will show that there is a well-defined partial order on these intervals across a collection of time series comprising a dataset. These partial orders can be thought of as graphical representations of the time series data. My collaborators and I have used these graphs to assess similarity between experimental replicates and between gene expression in different malaria strains. To do this, we defined a metric on graphs based on the maximum common edge-induced subgraph problem for directed graphs (DMCES). I will discuss that the definition fulfills the criteria for a metric and introduce a newly defined structure called the labeled line graph to show that DMCES can be reduced to the maximum clique-finding problem.

Biosketch: Dr. Bree Cummins is an Assistant Research Professor in the Department of Mathematical Sciences at Montana State University. She received her B.S. in Mathematics and Biology at Boise State University and her M.S. and Ph.D. at Montana State University in the field of very low Reynolds number fluid dynamics. She then went to New Orleans for a postdoc at Tulane, where she continued work on her thesis subject and did a project on an agent-based model of mosquito host-seeking strategies. Her current research focuses on dynamics on networks and graphs in the context of biological systems, with a focus on genetic regulatory networks.

Hyperspectral Image Classification with Low-Cost 3D-2D Convolutional Neural Networks

Date/Time:  Monday, February 10 4:10 p.m - 5:00 p.m.
Location: Barnard Hall 108
Speaker:  Giorgio Morales

Abstract: Hyperspectral images provide us a useful tool for extracting complex information when visual spectral bands are not enough to solve certain tasks. However, usually, processing them is computationally expensive due to the great amount of both spatial and spectral data they incorporate. In this seminar, I will present a low-cost convolutional neural network designed for hyperspectral image classification. Its architecture consists of two parts: a series of densely connected 3-D convolutions used as a feature extractor, and a series of 2-D separable convolutions used as a spatial encoder. We show that this design involves fewer trainable parameters compared to other approaches, yet without detriment to its performance. What is more, we achieve comparable state-of-the-art results testing our architecture on four public remote sensing datasets: Indian Pines, Pavia University, Salinas, and EuroSAT; and a dataset of kochia leaves (Bassia scoparia) with three different levels of herbicide-resistance.

Biosketch: Giorgio Morales is a first-year PhD student in computer science at Montana State University and a current member of the Numerical Intelligent Systems Laboratory (NISL). He received his BSc degree in mechatronic engineering from National University of Engineering, Lima, Peru. His research interests include image and video processing, computer vision, and machine learning algorithms with a focus on remote sensing and precision agriculture applications. 

Developing chemistries to produce nonlinear signal gain

Date/Time:  Monday, February 3 4:10 p.m - 5:00 p.m.
Location: Barnard Hall 108
Speaker:  Stephanie McCalla

Abstract: Kinetic measurements to quantify RNA and DNA are easily shifted by reaction conditions such as enzyme efficiency, salt concentrations, or inhibitors that are ubiquitous in biological samples. As a result, it is difficult to determine if a signal is uninhibited and originates from spurious amplification, similar nucleic acid sequences, or from the target of interest. Digital electronics revolutionized computing by creating a robust method to process data: one that relied on digital (on/off) signals that would not be affected by drifting signal amplitude and noise. An analogous method is gaining traction in the field of nucleic acid measurement, where individual molecules are amplified and detected in a multitude of small microfluidic wells. In this seminar, I will discuss our work on designing new reaction chemistries with highly non-linear signal output that are ideal for use in digital systems. These high-gain outputs can be applied to digital detection of specific disease-associated molecules, DNA computing, and DNA-based logic gates.

Biosketch: Stephanie received her BS in Bioengineering from the University of California, San Diego in 2005. After a brief interlude working at Los Alamos National Laboratory, she moved to Rhode Island to attend Brown University and received a PhD in Biomedical Engineering in 2011. Her research focused on novel chemistries and microfluidic reactors for nucleic acid amplification, as well as analyzing the transport of model pharmaceuticals through cellular constructs. She then moved to the California Institute of Technology for a postdoctoral position in the department of Chemistry and Chemical Engineering, where she investigated novel methods for separation and detection of nucleic acids and proteins with a focus on amplification of single molecules. Her research interests include disease diagnostics, separation/detection of nucleic acids and proteins, microfluidics, and mass transport limitations in cells and cellular constructs. 

Designing and Implementing Culturally Responsive Computing Curricula and Pedagogy in the Elementary STEM Teaching Integrating Textiles and Computing Holistically (ESTITCH) Project

Date/Time: Thursday, January 30 11:00 a.m - 11:50 a.m.
Location: Roberts 113
Speaker:  Kristin A. Searle

Abstract:As computer science education becomes more and more prevalent in K-12 classrooms, access and equity remain serious issues. We must design and implement computing curricula that make sense to a wide range of learners from a variety of backgrounds. Through professional development, we must also give teachers the tools to engage their students and the computing content in meaningful ways. One technology that has been shown to be especially promising for broadening participation in computing is electronic textiles (e-textiles). E-textiles bring together familiar aspects of fabric crafts with electronic components that are sewable and programmable, allowing for electronics to be embedded in items like clothing, bags, and decorative pillows. In the Elementary STEM Teaching Integrating Computing and Textiles Holistically (ESTITCH) project, we leverage the affordances of e-textiles for integrated science, social studies, and computing in grades 3-6. Project ESTITCH is a curriculum and professional development project serving elementary teachers and students in two rural school districts in Utah. Drawing on the qualitative and design-based aspects of the work, I discuss the design of curriculum and professional development, paying particular attention to the aspects that are culturally responsive, and share stories from our first year of implementation. Findings highlight both the affordances of culturally responsive computing and also the areas where we have more work to do.

Biosketch: Dr. Kristin A. Searle is an assistant professor of Instructional Technology and Learning Sciences at Utah State University. She received her Ph.D. in education and anthropology from the University of Pennsylvania. Her work focuses on how participating in making activities can broaden students’ sense of what computing is and who can do it, with a focus on the development of culturally responsive computing curricula and pedagogies. Her work has appeared in journals such as Harvard Educational Review and the Journal of Science Education and Technology. 

Representing Shapes Using Topological Descriptors

Date/Time: Monday, January 13 4:10 p.m - 5:00 p.m.
Location: Barnard Hall 108
Speaker: Samuel Micka

Abstract: Techniques developed in the field of topological data analysis have grown increasingly popular in recent years for gathering insight into complex data. One example is the use of topological descriptors, such as the persistence diagram (PD), for representing and discriminating between different shapes. In this talk, we offer insight into the algorithms behind generating a set of descriptors which encode all of the geometric and topological information of a shape. Furthermore, we discuss the benefits of using PDs for representing shapes and describe efficient methods for comparing PDs to one another for the purposes of shape differentiation.

Biosketch: Samuel Micka earned his B.S. in computer science from UW-Oshkosh in 2015 and is scheduled to defend his PhD in the Spring semester of 2020 at Montana State University. His research focuses on shape reconstruction using topological descriptors, efficient searching in the space of persistence diagrams, and, more broadly, lies in the fields of algorithms, topological data analysis, and computational geometry. 

Seminars from 2019.