Chandra Gummaluru
Curriculum Vitae
Experience
Computer Science Lecturer | University of Toronto, DCS, UTIAS
Jan 2021 – Present
- Lectured 1000+ undergraduate students across 7 courses covering Algorithms, Data Science, Supervised/Unsupervised Learning, Reinforcement Learning, Probabilistic Reasoning, and Computer Vision.
- Exceeded departmental performance in course evaluations, achieving 5/5 overall instructional quality.
Computer Vision Research Assistant | Bernhardt-Walther Lab
Apr 2020 – Sep 2021
- Trained 2 CNN models using OpenCV and PyTorch for edge-detection, achieving 1.5× higher F1 scores than gPb and DexiNed.
- Developed a novel inpainting algorithm, published in BMVC 2021:
“Contour-guided Image Completion with Perceptual Grouping.”
Software Engineer | Coursera
Sep 2019 – Sep 2020
- Led a team of 8 engineers to build a robust payment system for Coursera’s enterprise suite, supporting 50+ organizations.
- Engineered and maintained 20+ RESTful APIs in Scala, leveraging Jenkins for CI/CD and SQL for complex database operations.
Projects
Intelligent Ground Vehicle Competition Robot
May 2019 – Dec 2020
- Designed 2 CNN models to identify road obstacles from LIDAR point clouds to augment SLAM algorithms.
Generative Deep Learning Model for Instrumentals
May 2018 – Dec 2019
- Trained an RNN model to generate instrumental tracks of up to 30 seconds for vocal accompaniment.
Education
MSc Computer Engineering | University of Toronto
- Research areas: Game Theory, Control Systems, Reinforcement Learning, Autonomous Vehicles
BSc Computer Engineering | University of Toronto
- Relevant courses: Machine Learning, Artificial Intelligence, Deep Learning
Skills
- Languages: Python (12 yrs), Java (10 yrs), C++ (8 yrs), Scala (5 yrs), SQL (3 yrs)
- Libraries & Tools: PyTorch (5 yrs), OpenCV (3 yrs), Pandas (3 yrs), scikit-learn (2 yrs), Airflow (2 yrs)
Certifications
-
Machine Learning in Production (MLOps) DeepLearning.AI -
Machine Learning Specialization DeepLearning.AI
Publications
- C. Gummaluru. “A Game-Theoretic Approach to Analyzing Transit Systems with Autonomous Vehicles.” Master’s Thesis
- M. Rezejenejad, S. Gupta, C. Gummaluru, R. Marten, J. Wilder, et al. “Contour-Guided Image Completion with Perceptual Grouping,” British Machine Vision Conference, 2021
Teaching
| Department of Computer Science (DCS) | University of Toronto |
- CSC384: Artificial Intelligence (Winter 2022, Summer 2023, Winter 2024)
- CSC311: Machine Learning (Winter 2023)
- CSC420: Image Processing (Winter 2021)
| Institute for Aerospace Studies (UTIAS) | University of Toronto |
- ROB311: Reinforcement Learning (Winter 2023, Winter 2024, Winter 2025)
- ROB310: Mathematics for Robotics (Fall 2024)