Higher accuracy, lower compute
Multi-task perceptionDeveloped multi-task architectures for 2D/3D pose estimation and object detection, improving accuracy by 30% while reducing computational cost by 40%.
8+ years of experience across computer vision, multimodal AI, agentic workflows, real-time edge inference, and large-scale image pipelines.
Representative results from work on multi-task learning, real-time edge inference, and large-scale vision datasets.
View selected workDeveloped multi-task architectures for 2D/3D pose estimation and object detection, improving accuracy by 30% while reducing computational cost by 40%.
Optimized machine-learning inference on edge devices to 30 FPS, achieving a threefold performance improvement.
Designed and operated machine-learning data pipelines handling more than one billion image records.
Technical projects exploring efficient language-model inference and the delivery of complex native applications through modern web runtimes.
A browser-native playground for quantized Bonsai language models. It runs ONNX inference with WebGPU, streams generated tokens from a Web Worker, and caches model assets locally without a server-side inference backend.
Designed and implemented the client-side inference flow, model lifecycle, streaming chat interface, and WebGPU capability checks.
A browser port of OpenTTD compiled with Emscripten and WebAssembly, bringing the C++ simulation engine to a full-screen web canvas without a native installation.
Ported and packaged OpenTTD for browser execution, integrating the WebAssembly runtime, asset bundle, loading states, and canvas-based application shell.
Experience across the machine-learning lifecycle, from model development and optimization to system integration and production delivery.
Development of image-processing, object-detection, and 2D/3D pose-estimation systems for real-time and research applications.
Design of systems that combine image and language models for context-aware generation, analysis, and product workflows.
Architecture of tool-using agents that execute multi-step workflows through external services and APIs.
Containerized services, reproducible model delivery, and engineering practices that support reliable iteration and deployment.
Model and inference optimization for constrained devices, real-time video pipelines, and IR-assisted perception systems.
Compact indexing and multimodal retrieval across image, audio, and natural-language queries.
An academic foundation in computer engineering, supported by machine-learning research, a conference publication, and academic distinctions.
Graduate studies in Computer Engineering
B.Sc. in Computer Engineering
A.T. Albayrak, İ. Atıl, “Hyper Parameter Optimization for Deep Learning using Volunteer Computing,” 6th High Performance Computing Conference, 2020.
For roles and collaborations involving production computer vision, multimodal AI, agentic systems, and machine learning infrastructure.
Ankara · Remote · International