Travis Desell Headshot

Travis Desell

Associate Professor

Department of Software Engineering
Golisano College of Computing and Information Sciences
Graduate Program Director, Data Science
tjdvse@rit.edu
585-475-2991
Office Location
GOL-1559

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.

tjdvse@rit.edu
585-475-2991
Personal Links
Personal Website
Google Scholar
Areas of Expertise
Artificial Intelligence
Data Science

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Published Conference Proceedings
ElSaid, AbdElRahman, Alexander Ororbia, and Travis Desell. "Ant-based Neural Topology Search (ANTS) for Optimizing Recurrent Networks." Proceedings of the 23nd International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2020). Ed. Unknown. N/A, N/A: n.p., Print.
ElSaid, AbdElRahman, et al. "Neuro-Evolutionary Transfer Learning through Structural Adaptation." Proceedings of the 23nd International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2020). Ed. Unknown. N/A, N/A: n.p., Print.
Desell, Travis, AbdElRahman ElSaid, and Alexander Ororbia. "An Empirical Exploration of Deep Recurrent Connections Using Neuro-Evolution." Proceedings of the 23nd International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2020. Ed. Unknown. n.p., Print.
Ororbia, Alexander, AbdElRahman ElSaid, and Travis Desell. "Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution." Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2019). Ed. Unknown. n.p., Print.
ElSaid, AbdElRahman, et al. "Evolving Recurrent Neural Networks for Time Series Data Prediction of Coal Plant Parameters." Proceedings of the 22nd International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2019). Ed. Unknown. n.p., Print.
Journal Paper
Bowley, Connor, et al. "An Analysis of Altitude, Citizen Science and a Convolutional Neural Network Feedback Loop on Object Detection in Unmanned Aerial Systems." Journal of Computational Science 34. 19 (2019): 102-116. Print.

Currently Teaching

DSCI-601
Applied Data Science I
3 Credits
This is the first of a two 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 con-junction 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. Students will begin preliminary design and implementation of their project. 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.
DSCI-602
Applied Data Science II
3 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-603
Applied Data Science III
1 Credits
This is the final course in the three course applied data science seminar series. Students will complete the implementation of their projects under guidance of their advisor and sponsor. Students will present a mid-term and final demo, and participate in a project poster session. Students will complete their final project report or thesis in the case of thesis track students. Peer reviews will be made of presentations, posters and final reports/theses for mastery of data science communication skills. This course will keep students up to date with the broad range of data science applications.
DSCI-633
Foundations of Data Science and Analytics
3 Credits
A foundations course in data science, emphasizing both concepts and techniques. The course provides an overview of data analysis tasks and the associated challenges, spanning data preprocessing, model building, model evaluation, and visualization. The major areas of machine learning, such as unsupervised, semi-supervised and supervised learning are covered by data analysis techniques including classification, clustering, association analysis, anomaly detection, and statistical testing. The course includes a series of assignments utilizing practical datasets from diverse application domains, which are designed to reinforce the concepts and techniques covered in lectures. A substantial project related to one or more data sets culminates the course.
DSCI-640
Neural Networks
3 Credits
This course will cover modern and deep neural networks with a focus on how they can be correctly implemented and applied to a wide range of data types. It will cover the backpropagation algorithm and how it is used and extended for deep feedforward, recurrent and convolutional neural networks. An emphasis will be placed on the implementation, design, testing and training of neural networks. The course will also include an introduction to using a modern neural network framework.
DSCI-644
Software Engineering for Data Science
3 Credits
This course focuses on the software engineering challenges of building scalable and highly available big data software systems. Software design and development methodologies and available technologies addressing the major software aspects of a big data system including software architectures, application design patterns, different types of data models and data management, and deployment architectures will be covered in this course.
DSCI-682
Applied Data Science Directed Study II
2 Credits
This course provides will have a student complete a research and/or development of an applied data science project under the supervision of an data science advisor and project sponsor, which will have been begun during the Applied Data Science II and Applied Data Science Directed Study I courses. Students will have regular meetings with the project advisor sponsors who will guide the students final project design, development and provide feedback on the student’s final report or thesis.
DSCI-781
Continuation of Capstone
0 - 1 Credits
This course provides the student with an opportunity to complete their capstone project, if extra time is needed after enrollment in the on campus capstone courses DSCI-601 and DSCI-602 (Applied Data Science I and II) or the online capstone course DSCI-799 (Graduate Capstone). The student continues to work closely with his/her advisor to complete their project.
DSCI-789
Advanced Topics In Data Science
1 - 3 Credits
This course will cover advanced specialized topics data science. Such topics are may be emerging and advanced. Specific prerequisites will be noted for each specific special topic.
DSCI-790
Independent Study
1 - 3 Credits
This course provides the graduate student an opportunity to explore an aspect of data science independently and in depth, under the direction of an advisor. The student selects a topic and then works with a faculty member to describe the value of the work and the deliverables.
DSCI-799
Graduate Capstone
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-561
Software Engineering Project I
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-562
Software Engineering Project II
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.
SWEN-599
Independent Study
1 - 3 Credits
The student will work independently under the supervision of a faculty adviser on a topic not covered in other courses (proposal signed by a faculty member)
SWEN-780
Capstone Research Project
3 - 6 Credits
This course provides the student with an opportunity to explore a project-based research experience that advances knowledge in that area. The student selects a research problem, conducts background research, develops the system, analyses the results, and builds a professional document and presentation that disseminates the project. The report must include an in-depth research report on a topic selected by the student and in agreement with the student's adviser. The report must be structured as a conference paper, and must be submitted to a conference selected by the student and his/her adviser.
SWEN-781
Continuation of Capstone
0 Credits
This course provides the student with an opportunity to complete their capstone project, if extra time if needed after enrollment in SWEN-790. The student continues to work closely with his/her adviser.
SWEN-790
Thesis
6 Credits
This course provides the student with an opportunity to execute a thesis project, analyze and document the project in thesis document form. An in-depth study of a software engineering topic will be research focused, having built upon the thesis proposal developed prior to this course. The student is advised by their primary faculty adviser and committee. The thesis and thesis defense is presented for approval by the thesis adviser and committee.

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