Aditya Rajeev

I'm a fourth-year Computer Science (Specialist) undergraduate at the University of Toronto, focused on the intersection of computer systems and machine learning. My research interests center on the interaction between machine learning models and their underlying systems and hardware, working to improve the models and the kernels and systems infrastructure that run them as a single co-design problem.

Currently, I'm a Research Assistant at the embARC Lab with Prof. Nandita Vijaykumar, funded by the Department of Computer Science, developing optimization frameworks for GPU kernels for cryptographic workloads.

Previously, I worked as a Software Engineering Intern at Mezzi and Tigera, where I built scalable backend systems and AI-powered tooling. My research has spanned GPU security, neuro-inspired memory models, and medical imaging ML.

I hold a 3.99/4.0 GPA (Dean's List).

Academia

2026–present

embARC Lab

With Prof. Nandita Vijaykumar. Building a zkML Transformer Hardware Optimization Framework — writing GPU kernels for finite field arithmetic and hardware-software codesign infrastructure for cryptographic workloads.

2026

SITH Lab

With Prof. Gururaj Shaileshwar. GPU/security research on Rowhammer on AMD GPUs. Under review at ACM CCS 2026.

2025–26

Computational Neuroscience Lab

With Prof. Brokoslaw Laschowski. Building neuro-inspired memory models. Findings under review at NeurIPS; reviewer at NeurIPS 2026.

Winter 2026

Teaching Assistant, Systems Programming (CSC209)

2024–25

Vector Institute / UHN

With Prof. Michael Brudno. Building ML models for pneumothorax detection.

Awards:

• Department of Computer Science Award ($12,000, 2026)
• University of Toronto Excellence Award ($7,500, 2024)
• Dean's List

Industry

Coursework

• Neural Networks & Deep Learning CSC413
• Introduction to Machine Learning CSC311
• Operating Systems Design CSC469
• Operating Systems CSC369
• Compilers CSC488
• Computer Architecture CSC368
• Algorithm Design CSC373
• Data Structures & Algorithms CSC263
• Introduction to Databases CSC343
• Computer Organization CSC258
• Systems Programming CSC209
• Linear Algebra MAT223

Projects

• Train in a Snap - AR emergency training with Snap Spectacles, integrating Snap3D and Vapi Voice AI over WebSockets (Hack the North semifinalist, top 12% of 256 teams)
• Mini-Dynamo - Fault-tolerant key-value store in C with consistent hashing, vector clocks, and a POSIX multi-threaded networking engine
• mAIstro - Full-stack RAG engine with cross-document indexing across PDF/DOCX and a spaced-repetition scheduler (GenAI Genesis)