Computational systems engineer and security researcher building high fidelity labs to study how complex systems fail. Specializing in computational pathology, machine learning pipelines, defensive security operations, and infrastructure automation.
Contact: matthew.vaishnav@gmail.com
Portfolio: matthewvaishnav.github.io/portfolio
Location: Kitchener-Waterloo, ON
A comprehensive portfolio showcasing the intersection of programming and security skills. Built with Next.js, React, and Three.js for interactive 3D experiences. Features both computational pathology research and defensive security projects, all tested on an 18-node home lab.
Computational Pathology Research
Machine learning pipelines for analyzing histopathology images. Implements deep learning models for cancer detection and tissue classification. Includes data preprocessing, model training, and inference pipelines with MLflow tracking and experiment management.
drift
Real-time data processing system for computational pathology workflows. Handles high-throughput image analysis with distributed computing. Features automatic scaling, fault tolerance, and comprehensive monitoring with Prometheus and Grafana.
SENTINEL
Infrastructure monitoring and alerting system. Tracks system health, performance metrics, and failure patterns across distributed environments. Provides predictive analytics for system reliability and automated incident response.
Log Correlation Engine
Python tool that parses auth.log and web access logs to detect attack patterns. Identifies brute-force attempts, reconnaissance, and credential stuffing. Maps findings to MITRE ATT&CK techniques with 0 false positives across 14,822 log entries.
Sigma Detection Rules
15+ detection rules covering T1110.001, T1003.006, T1548.003. Each rule written after executing attacks in the lab environment. Process: run attack, capture logs, write rule, validate triggers, document false positive rate.
DevSecOps Pipeline
GitHub Actions workflow with ESLint → Bandit SAST → Trivy container scan → Terraform validate → deploy. Pipeline fails on any vulnerability with CVSS ≥ 7. Caught 6 critical CVEs before production.
AWS Infrastructure
Terraform code deploying hardened VPC with NAT gateway, bastion host, least-privilege security groups, VPC Flow Logs, and encrypted S3 buckets. Deployed in ca-central-1 with zero security findings.
18-Node Home Lab
- pfSense routing traffic between 6 isolated VLANs
- Security Onion monitoring everything on SPAN port
- 24/7 monitoring with Prometheus and Grafana
- Version-controlled with Ansible and Terraform
- Rebuilds in minutes if host fails
Security Testing Environment
Every security project tested against running systems with real attack scenarios. Lab simulates enterprise infrastructure for realistic threat detection and response testing.
Languages: Python, JavaScript, TypeScript, Bash
Frameworks: Next.js, React, Three.js, FastAPI, Flask
ML/AI: PyTorch, TensorFlow, scikit-learn, OpenCV, MLflow
Security: SIEM, Sigma Rules, MITRE ATT&CK, Security Onion, pfSense
Infrastructure: Docker, Kubernetes, Terraform, Ansible
Cloud: AWS, Azure, Google Cloud Platform
Databases: PostgreSQL, MongoDB, Redis
Monitoring: Prometheus, Grafana, ELK Stack
Computer Systems Technician, Conestoga College (2025-2027)
Focus: Systems programming, network infrastructure, computational methods, and security operations
Available for co-op starting Summer/Fall 2026.