Deniz Ayma K.

Key Skills

Software

Scale AI

Argilla

Axiom AI

Clickworker

CloudFactory

CrowdFlower

CrowdSource

CVAT

Data Annotation Tech

Dataloop

Datatroniq

Datumbox

Datasaur

Datature

Dataturk

Deep Systems

Diffgram

Doccano

Encord

Figure Eight

Google Cloud Vertex AI

Hasty

HiveMind

Humanatic

iMerit

Img Lab

Kili Technology

Labelbox

LabelImg

Label Studio

LightTag

Mercor

Lionbridge

Micro1

Mighty AI

Mindrift

OneForma

OpenCV AI Kit (OAK)

Playment

Prodigy

Redbrick AI

Remotasks

Roboflow

Sama

Sloth

Snorkel AI

SuperAnnotate

Supervisely

Surge AI

Tagtog

Toloka

Telus

Trilldata Technologies

VoTT

V7 Labs

Other

Internal/Proprietary Tooling

Top Subject Matter

Molecular Cellular Biology

AI Model Evaluation & Scientific Reasoning

Scientific Analysis

Top Data Types

Computer Code Programming

Document

Image

Top Task Types

Classification

Entity (NER) Classification

Data Collection

Transcription

Evaluation/Rating

Question Answering

Point/Key Point

Segmentation

Prompt + Response Writing (SFT)

Function Calling

Freelancer Overview

MSC Researcher / Project & Data Support — University of Sussex (SRSF4 localization imaging and quantitative image analys. Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include ImageJ (JaCoP), NCBI GEO, and GEODatasets. Education includes Master of Science, University of Sussex (2025) and Preparatory Program Class, Study Group ISC (2024). AI-training focus includes data types such as Images and Text and labeling workflows including Classification and Entity (NER) Classification.

Labeling Experience

MSC Researcher / Project & Data Support — University of Sussex (SRSF4 localization imaging and quantitative image analysis)

Classification

You performed quantitative analysis of immunofluorescence microscopy data to quantify subcellular localization signals under ER stress. You used nuclear speckle vs cytoplasmic distribution metrics to classify and interpret localization changes from experimental images. This work required careful per-sample image processing and consistent labeling of regions of interest across conditions. • Used confocal microscopy channels (SC35/Alexa Fluor 488, SRSF4/Cy3, DAPI) for image-based evidence. • Computed Pearson correlation and Manders coefficient for localization quantification. • Applied ImageJ with JaCoP tools to support the image analysis workflow. • Interpreted results focused on nuclear speckles vs cytoplasmic localization shifts.

2024 - 2025

Project Researcher — HPV16/HPV18 Gene Expression Analysis — Study Group ISC

TextEntity (NER) Classification

You applied bioinformatics analyses to label and interpret gene expression and protein structure outputs for HPV16/HPV18-related datasets. You used curated biological databases and analysis tools to identify key overexpressed genes and tissue-specific expression patterns from computational results. The outputs were structured into candidate biological entities and proposed downstream interpretations for therapeutic strategy development. • Analyzed gene expression patterns using GEO datasets and differential expression approaches. • Identified key overexpressed genes (e.g., S100A7, PAK3, PI3, KRT13) from dataset results. • Used tools to support protein structure/function interpretation (ModBase, KEGG, AlphaFold). • Produced interpretations linking tissue-specific expression to proposed therapeutic strategies.

2024 - 2024

Education

Work History