Felix Mathew

• GPA improved from 3.33 to 3.56 during the program, with 4.00 term GPAs in Summer 2025 and Fall 2025.
• A grades in Parallel Processing, Survey of Software Engineering, Software Quality, Advanced Software Engineering Project, and Formal Methods in Software Engineering.

• Developed and evaluated a research prototype that detects animals on selected frames, tracks their locations with optical flow, removes redundant frames, and compresses the animal region and background at different quality levels.
• Reconstructed video on the receiving server using AMT frame interpolation, compositing, and temporal stabilization; evaluated the architecture on NVIDIA Jetson Orin Nano hardware.
• Across 20 held-out wildlife clips, the selected configuration achieved a 97.40% reduction in transmitted archive size, with mean animal-region PSNR of 36.12 dB and MS-SSIM of 0.9758.
• The detection-and-tracking approach reduced detector calls by 93.28% compared with detection on every frame.
• Extended into “FaunaCodec: ROI-Aware Video Compression and Reconstruction for Wildlife Monitoring,” accepted as a short paper at IEEE ICTAI 2026 (co-author). Implemented the deterministic DCVC-INT16 runtime for the conference work; a collaborating PhD student contributed the generative upscaling component.

• Implemented an INT16-oriented encoding and decoding runtime around Microsoft’s DCVC/DCVC-RT model family with PyTorch and CUDA/C++ integration, rANS entropy coding, and FFmpeg-based video preprocessing and reconstruction.
• Built SHA-256 hash comparisons, byte-level bitstream validation, runtime metadata, automated tests, and setup tooling.
• On a 2,593-frame, 1280×720, 30 FPS video: 4.82 MB bitstream, mean RGB PSNR of 28.90 dB, MS-SSIM of 0.9336; 35 automated tests passed.
• A repeated 32-frame determinism test in the same environment produced identical SHA-256 hashes and byte-for-byte identical bitstreams; cross-device reproducibility requires additional testing. Requires an NVIDIA CUDA GPU.

Core technologies: PyTorch, CUDA/C++, rANS entropy coding, FFmpeg, SHA-256 validation, automated testing

• Led CS 1428 laboratory sessions covering C++ programming, pointers, dynamic memory, object-oriented programming, debugging, and introductory data structures.
• Supported grading coordination and student instruction for an introductory programming course serving more than 100 students.

• Developed backend and full-stack components for production applications using AWS, relational databases, REST APIs, and event-driven architectures.
• Built AWS Lambda-based services and tenant-isolated workflows with MySQL.
• Implemented idempotent processing and developed transaction-safe data recovery procedures.

Core technologies: AWS, AWS Lambda, MySQL, REST APIs, event-driven architecture

• Fixed exception mapping so authentication failures, timeouts, and connection failures retain their error classifications instead of becoming generic adapter errors.
• Consolidated the mapping into a shared helper used by the signals and creative agent registries and added regression tests.

• Proposed a fix for incompatible tool-call thought signatures when the router falls back between Vertex AI and Google AI Studio Gemini endpoints, removing foreign signatures while keeping tool-call and tool-response IDs consistent.
• Added unit and regression tests and addressed automated review feedback; validated at the unit-test level.