Travis Desell Headshot

Travis Desell

Associate Professor
Department of Software Engineering
Golisano College of Computing and Information Sciences
Graduate Program Director, Data Science

585-475-2991
Office Location

Travis Desell

Associate Professor
Department of Software Engineering
Golisano College of Computing and Information Sciences
Graduate Program Director, Data Science

Bio

I am an Associate Professor specializing in Data Science, housed in the Department of Software Engineering in the B. Thomas Golisano College of Computing and Information Sciences (GCCIS). My research focuses on the application of machine learning to large-scale, real world data sets using high performance and distributed computing, with an emphasis on developing systems for practical scientific use. I'm interested in the intersection of evolutionary algorithms and neural networks, or 'neuro-evolution', where evolutionary algorithms are used to automate and optimize the design of neural network architectures. I am actively developing the Evolutionary eXploration of Augmenting Convolutional Toplogies (EXACT) and Evolutionary eXploration of Augmenting Memory Models (EXAMM, formerly known as EXALT) algorithms, which are hosted on GitHub.

I am also active in the area of volunteer computing and citizen science, where I did the initial development of MilkyWay@Home, and more recently the Citizen Science Grid and NSF funded Wildlife@Home which has volunteer citizen scientists annotate hundreds of thousands of hours of video and millions of images to help in the development of computer vision algorithms. Recent work on Wildlife@Home has focused on the development of convolutional neural networks to detect various wildlife species in imagery collected from unmanned aerial systems.

My currently funded research projects include the National General Aviation Flight Information Database (NGAFID), used by general aviation institutions across the country to monitor and predict potential flight safety issues. We are actively developing an interface and methods to detect potential flight issues, trends and mine this massive database of over 800,000 hours of flight data. I am also working on Department of Energy Award #FE0031547, Improving Coal Fired Plant Performance through Integrated Predictive and Condition-Based Monitoring Tools, where we are developing neuro-evolution algorithms to evolve recurrent neural networks to predict coal fired power plant data.

I have also been a main contributor in the development of both the compiler and runtime of SALSA and SALSA Lite, a programming language based on the actor model of computation. SALSA enables easy development of concurrent and transparently distributed applications by following actor semantics.

Currently Teaching

SWEN-561
3 Credits
The first course in a two-course, senior-level, capstone project experience. Students work as part of a team to develop solutions to problems posed by either internal or external customers. Problems may require considerable software development or evolution and maintenance of existing software products. Culminates with the completion and presentation of the first major increment of the project solution. Students must have co-op completed to enroll.
SWEN-261
3 Credits
An introductory course in software engineering, emphasizing the organizational aspects of software development and software design and implementation by individuals and small teams within a process/product framework. Topics include the software lifecycle, software design, user interface issues, specification and implementation of components, assessing design quality, design reviews and code inspections, software testing, basic support tools, technical communications and system documentation, team-based development. A term-long, team-based project done in a studio format is used to reinforce concepts presented in class.
DSCI-799
3 - 6 Credits
This non-class-based experience provides the student with an individual opportunity to explore a project-based or a research-based project that advances knowledge in an area of data science. The student selects a problem, conducts background research, develops the system or devises a research approach, analyses the results, and builds a professional document and presentation that disseminates the project. The report must include a literature review. The final report structure is to be determined by the capstone advisor.
SWEN-562
3 Credits
This is the second course in a two-course, senior-level capstone project experience. Students submit one or more additional increments that build upon the solution submitted at the end of the first course. Students make major presentations for both customers as well as technical-oriented audiences, turn over a complete portfolio of project-related artifacts and offer an evaluation of the project and team experience.
DSCI-650
3 Credits
This course will cover concurrent, parallel and distributed programming paradigms and methodologies with a focus on implementing them for use in applied data science or scientific computing tasks. In particular, the course will focus on developing software using graphical processing units (GPUs) and the message passing interface (MPI); with an emphasis on properly handling large-scale, real-world data as part of these applications. The course will also teach scalability and load balancing techniques for developing efficient distributed systems. Programming assignments are required.
SWEN-250
3 Credits
This is a project-based course to enhance individual, technical engineering knowledge and skills as preparation for upper-division team-based coursework. Topics include adapting to new languages, tools and technologies; developing and analyzing models as a prelude to implementation; software construction concepts (proper documentation, implementing to standards etc.); unit and integration testing; component-level estimation; and software engineering professionalism.
DSCI-681
1 Credits
This course provides an opportunity for a student to perform a research and/or development of an applied data science project under the supervision of a data science advisor and project sponsor, which will have been proposed and selected during the Applied Data Science I course. Students will have regular meetings with the project advisor and sponsors who will guide the students initial project design and development.
DSCI-602
1 Credits
This is the second of a three course applied data science seminar series. Students will design an implementation plan and preliminary documentation for their selected applied data science project, along with an in class presentation of this work. At the end of the semester students will present preliminary demos of their project and write a preliminary project report. Writing and presentations will be peer reviewed to further enhance data science communication skills. This course will keep students up to date with the broad range of data science applications.
DSCI-601
1 Credits
This is the first of a three course applied data science seminar series. Students will be introduced to the data science masters program along with potential projects which they will develop over the course of this series in conjunction with the applied data science directed studies. Students will select a project along with an advisor and sponsor, develop a written proposal for their work, and investigate and write a related work survey to refine this proposal with their findings. Work will be presented in class for peer review with an emphasis on developing data science communication skills. This course will keep students up to date with the broad range of data science applications.