Shruti Singh
PhD Candidate in Computer Science | Machine Learning & Reinforcement Learning | Seeking Summer 2026 Internship
About Me
Background
Hi, I’m Shruti Singh — a Ph.D. candidate in Computer Science at the University of Dayton, where my research centers on reinforcement learning, adversarial resilience, and applied machine learning. During my time at Walmart, I focused on designing and implementing scalable data-driven solutions — from replicating barcode logic in Python and validating GS1 standards across complex edge cases, to optimizing scanning processes with SQL-driven analysis. On the academic side, my projects range from entropy-driven defenses in RL systems to building practical AI applications, including a facial recognition attendance system developed for my professor’s online classes. Outside of research, I make it a priority to stay engaged and well-rounded — I volunteer with service dog training programs at my college, am an active member of the Society of Women Engineers (SWE), and fuel my curiosity through travel, with adventures from Paris to Niagara Falls (and hopefully the northern lights next!).
🎯 At a Glance
- 🔐 Reinforcement Learning (RL): Built adversarial defense frameworks (Gym: LunarLander, BipedalWalker) to improve model robustness.
- 📈 Deep Learning: Applied LSTM models for time-series prediction, outperforming traditional baselines.
- 🎥 Computer Vision: Developed a facial recognition attendance system (Python, OpenCV, dlib) with 99.39% accuracy.
- 🎬 Recommender Systems: Engineered MovieLens-based KNN & SVD recommenders with real-time Streamlit deployment.
- 🎓 Education:
Ph.D., Computer Science – University of Dayton (GPA: 3.95)
M.S., Computer Science – University of Dayton (GPA: 4.00)
Skills & Technologies
Experience
University of Dayton
- •Developed an entropy-driven adversarial defense framework in RL environments (Gym Lunar Lander, Bipedal Walker), achieving 95% accuracy and outperforming KL divergence and joint entropy baselines.
- •Simulated adversarial attacks in RL pipelines to evaluate robustness and improve policy behavior.
- •Facial-recognition attendance system in Python/OpenCV/dlib (~99.39% accuracy) for online sessions.
Walmart
- •Researched and implemented GS1 barcode standards to address 10+ case exceptions (oversized, perishable, hazardous items, etc.) at distribution centers.
- •Developed a Python three-phase function for structure validation, checksum generation, and barcode encoding, optimizing scanning processes.
- •Compiled and executed BigQuery SQL searches to collect 100+ diverse test inputs covering barcode types, product categories, and exceptions.
- •Queried and analyzed 100+ test cases in SQL, achieving 100% accuracy compared to legacy logic
Projects
Entropy-driven feature selection for adversarial robustness. 94-95% accuracy across Gym (LunarLander/BipedalWalker). Outperformed KL Divergence and Joint Entropy at detecting adversarial noise.
Deep learning for time-series prediction. Advanced preprocessing, tuning, and regularization (LR scheduling, early stopping). Benchmarked vs MLP/CNN/CNN-LSTM; LSTM captured long-range temporal patterns.
Full-stack recommender (MovieLens 1M). KNN & SVD with Surprise; Streamlit UI with real-time recs. RMSE ~0.87, Precision@10 ~0.81; persistent pipelines & evaluation dashboards.
Classical search & RL policies in a custom environment. DFS, BFS, UCS, and A* (Manhattan). Value iteration, policy iteration, and Q-learning (ε-greedy) with trajectory visualizations.
Publications
Imposter Injection: Learning to Select Features in Reinforcement Learning
Mouse Brain Cell Segmentation in Fluorescence Microscopy Images
Virtual Yoga Instructor with Real-Time Feedback
Predicting an Optimal Medication/Prescription Regimen for Patient Discordant Chronic Comorbidities Using Multi-Output Models
