About

I am an Assistant Professor in the Department of electrical and computer engineering at the University of Virginia (UVa). My interests are broadly in the fields of image reconstruction and electrical engineering education, and I am a member of IEEE and ASEE. Before entering graduate school, I worked in government contracting and have done multiple internships. My primary focus as a professor is teaching, with a focus on engineering education and occasionally working with undergraduate students on research.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Mathematics education
• Psychology
• Medicine
• Medical education
• Mathematics
• Management
• Pedagogy
• Algorithm
• Engineering
• Mathematical optimization

Selected publications

• BYOE: Teaching and Assessing Troubleshooting Strategies in Circuits Courses

  2025-08-21

  article1st authorCorresponding
  PublisherDOI

• Developing an introductory machine learning course

  2025-08-21

  article1st authorCorresponding
  PublisherDOI

• Incorporating Giving Voice to Values (GVV) into an Engineering Ethics Course

  2024-02-07 · 1 citations

  articleOpen accessSenior author

  Abstract The Department of Engineering and Society instructors at the University of Virginia recently developed a new course on Engineering Ethics aimed at second- and third-year students. Unlike previous courses in the department, the mid-level course emphasizes micro-ethics and employs the Giving Voice to Values (GVV) framework. The emphasis on micro-ethics is timely and appropriate given the polarization and plurality of views and beliefs in our nation and world and the increasingly higher stakes of engineering practice. To help students understand how they can act on their personal ethics, the course also incorporates the GVV material, originally developed for application in business settings. The GVV modules in this course were adapted specifically for use in engineering education, in collaboration with the GVV founder and the Online Ethics Center (OEC) director and are now available through the OEC for anyone to use. This paper provides an overview of the GVV portion of the new course design and discusses initial impressions from piloting the course over three semesters.

  PublisherOA PDFDOI

• Experimental Self-Efficacy and Troubleshooting Ability in a Chemical Engineering Laboratory

  2024-02-07 · 2 citations

  articleOpen access1st authorCorresponding

  Abstract This research serves as a first step toward investigating how educators might evaluate (and eventually improve) students' self-efficacy and troubleshooting ability in an engineering laboratory. This study uses an established survey to assess the experimental self-efficacy (ESE) of students enrolled in a fourth-year chemical engineering laboratory course at the University of Virginia. The survey measures ESE using four factors: conceptual understanding, procedural complexity, laboratory hazards, and lack of sufficient resources. Results from the ESE survey suggest that students had higher confidence in their conceptual understanding and their ability to avoid laboratory hazards. This study also analyzes students' troubleshooting abilities using an existing chemical reactor system (a water gas shift reaction). Students were asked to use the experimental equipment to perform an activity. To succeed, students needed to identify and correct a series of challenges (e.g., closed gas valves, empty reactant reservoirs). Researchers recorded their observations about students' technical knowledge, processes, and troubleshooting strategies. Analysis of these observations suggests that students are more likely to read and follow directions or "spitball" ideas without strong use of troubleshooting strategies, though some participants successfully referenced conceptual understanding or used backtracking as troubleshooting strategies.

  PublisherOA PDFDOI

• Circuit Troubleshooting Techniques in an Electrical and Computer Engineering Laboratory

  2024-08-04 · 1 citations

  articleOpen access

  Professional Skills and Safety are my main pedagogical

  PublisherOA PDFDOI

• Building Better Engineers: Teaching Chemical Engineers to Troubleshoot in the Laboratory

  2024-08-04

  articleOpen access

  The Chemical Engineering Laboratory is a crucial training ground for students to acquire fundamental professional skills.Among these skills, troubleshooting is exceptionally valuable and significant, yet it is often underemphasized in the engineering curriculum.This study examines the efficacy of structured troubleshooting training modules in enhancing students' troubleshooting skills.Modules were integrated into laboratory lectures to introduce troubleshooting concepts, followed by a hands-on exercise to evaluate proficiency.Teaching assistants assessed student performance and recorded observations on troubleshooting approaches and strategies.Results suggest that structured training modules improve troubleshooting skills.Our findings highlight the importance of dedicated pedagogy in enhancing student troubleshooting performance.

  PublisherOA PDFDOI

• Factors Influencing Conceptual Understanding in a Signals and Systems Course

  2021 ASEE Virtual Annual Conference Content Access Proceedings · 2024-02-20 · 1 citations

  articleOpen access1st authorCorresponding

  Dr. Finelli's current research interests include student resistance to active learning, faculty adoption of evidence-based teaching practices, and the use of technology and innovative pedagogies on student learning and success

  PublisherOA PDFDOI

• Bilevel Methods for Image Reconstruction

  2022-01-01 · 2 citations

  book1st author

  Methods for image recovery and reconstruction aim to estimate a good-quality image from noisy, incomplete, or indirect measurements. Such methods are also known as computational imaging. New methods for image reconstruction attempt to lower complexity, decrease data requirements, or improve image quality for a given input data quality. Image reconstruction typically involves optimizing a cost function to recover a vector of unknown variables that agrees with collected measurements and prior assumptions. State-of-the-art image reconstruction methods learn these prior assumptions from training data using various machine learning techniques, such as bilevel methods. This review discusses methods for learning parameters for image reconstruction problems using bilevel formulations, and it lies at the intersection of a specific machine learning method, bilevel, and a specific application, filter learning for image reconstruction. The review discusses multiple perspectives to motivate the use of bilevel methods and to make them more easily accessible to different audiences. Various ways to optimize the bilevel problem are covered, providing pros and cons of the variety of proposed approaches. Finally, an overview of bilevel applications in image reconstruction is provided.

  PublisherDOI

• Instructional factors influencing conceptual understanding of signals and systems

  European Journal of Engineering Education · 2022-09-07 · 2 citations

  article1st authorCorresponding

  This paper investigates what instructional factors influence conceptual understanding (CU) of signals and systems for senior undergraduate engineering students. Previous results show students in signals and systems courses typically gain little CU, though evidence-based instructional practices, such as active learning, can increase gains in CU. However, few studies consider CU of senior students or other instructional practices that increase CU. To explore possible factors, we interviewed two faculty members, eight undergraduate seniors, five graduate students, and four practicing engineers then analyzed the transcribed interviews using a constant comparative method. Participants identified lectures presenting CU along-side mathematical expressions; lectures emphasising purpose and connections; hands-on activities where students have control, receive immediate feedback, or where they have to apply and synthesise concepts; and repetition of concepts across multiple courses as factors that helped build CU. Grades that emphasise procedural knowledge over CU and heavy workloads were noted as hindrances to CU. This paper relates these findings to theories on conceptual understanding and previous results on factors that influence student learning.

  PublisherDOI

• How Students and Algorithms Learn to Filter: Investigating Students' Understanding of Signal Processing Concepts and Bilevel Methods for Learning Filters for Image Reconstruction

  Deep Blue (University of Michigan) · 2022-01-01

  articleOpen access1st authorCorresponding

  Signals and systems (S&S) concepts are the theoretical foundation of machine learning and signal processing, cutting-edge fields with real-world applications in many domains. This dissertation combines two projects on S&S in the fields of engineering education research and image reconstruction. Within the field of engineering education research, this dissertation discusses which S&S concepts students understand and what factors—such as motivation, choice of upper-level electives, or use of evidence-based instructional practices like active learning—influence their understanding. This research project involved three phases. The first phase used quantitative methods to measure CU and investigated factors that predict CU of students at the end of their S&S course. This phase found that measures of ability and motivation are significantly predictive of CU. Phase one also served as a pilot project for the following two phases that concentrate on CU of senior undergraduate students. The second phase used think-aloud interviews and a concept inventory to measure CU of S&S. The results show that many seniors understand some topics, such as filtering and time invariance, but struggle with other S&S concepts, such as linearity and convolution. The third phase used interviews and qualitative data analysis methods to investigate what factors impact CU over the course of an undergraduate degree. The results provide recommendations for how instructors and curriculum designers can improve students’ CU of S&S, such as emphasizing the purpose of concepts, using contrasting examples in lectures, translating mathematics, and repeating concepts across multiple courses. The second part of this dissertation applies concepts from S&S to image reconstruction. Image reconstruction is the process of taking input data from one signal space and producing an interpretable image. In medical image reconstruction, state-of-the-art methods use advances in machine learning and training datasets to learn parameters that can be used to reconstruct high-quality images with fewer measurements, thus decreasing radiation exposure for patients while providing doctors with high-quality images to properly diagnose and treat many diseases. The image reconstruction project in this dissertation motivates and reviews bilevel methods for learning image reconstruction parameters. Bilevel methods are task-based, so that learned parameters are expected to perform best at reconstructing; are explainable and interpretable, thus improving the likelihood that doctors will trust and adopt them; and allow for different measures of image quality, including traditional mean square error metrics that are easy to use and metrics that more accurately capture human perception. The results demonstrate that parameters learned in a common non-bilevel formulation under-perform handcrafted parameters due to the structure of the learning problem and that bilevel methods help to address this gap.

  PublisherDOI

Frequent coauthors

• Cynthia Finelli

  University of Michigan–Ann Arbor

  44 shared

• Maura Borrego

  28 shared

• Prateek Shekhar

  University of Southern California

  28 shared

• Kevin Nguyen

  28 shared

• Sneha Tharayil

  The University of Texas at Austin

  27 shared

• Robert DeMonbrun

  Southern Methodist University

  25 shared

• Cindy Waters

  25 shared

• Robyn Rosenberg

  University of Michigan–Ann Arbor

  25 shared

Education

• PhD, Electrical Engineering and Computer Science

  University of Michigan

  2022

• Bachelor of Science, Electrical and Computer Engineering

  University of Virginia

  2015