Choras C.

Key Skills

Software

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Top Subject Matter

Psychology Domain Expertise

Mental Health

Financial Compliance & Risk Analysis

Top Data Types

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Image

Medical Dicom

Top Task Types

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Fine-tuning

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Evaluation/Rating

Freelancer Overview

I have an interdisciplinary background combining information management, data analysis, artificial intelligence, and clinical psychology. My undergraduate training provided me with a strong computational foundation in data modeling, machine learning, R, Python, and structured data analysis, while my master’s training in Psychology at Central South University, The Second Xiangya Hospital, strengthened my expertise in psychological assessment, psychometrics, digital mental health, and AI-assisted psychological measurement. My key advantage for AI training and data labeling is that I understand both technical logic and psychological content. I have hands-on experience in data annotation, classification, evaluation, rating, and quality review, including previous data-related work at DataTang/标贝 and confidential psychology-related annotation work in a ByteDance–Central South University collaboration. My psychological counseling experience also helps me interpret clinical language, emotional expression, assessment results, and user intent more accurately. I have received PhD offers from the University of Edinburgh and LMU, reflecting my academic preparation and research potential in AI, psychology, and digital mental health.

Labeling Experience

Psychology Data Annotation Contributor

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I performed psychology-focused data annotation and quality review for AI model training in collaboration between ByteDance and Central South University. This involved interpreting mental health content, evaluating key constructs, and maintaining consistency in annotation decisions. I followed strict project guidelines to protect sensitive information and iteratively improved labeling standards based on reviewer feedback. • Interpreted user-generated or task-specific mental health content for annotation. • Documented ambiguous cases and enhanced guideline clarity for labeling. • Consolidated labeling error feedback to improve annotation quality. • Maintained data privacy and adhered to sensitive information handling protocols.

2025 - Present

Graduate Project Contributor - PsychoGAT

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I contributed to a graduate research project (PsychoGAT) developing an LLM-agent-based psychological measurement paradigm. My role involved refining depression and cognitive distortion scale content for AI-assisted assessment and setting reliability benchmarks. I iteratively reviewed model behavior and adjusted scales using direct performance feedback from model outputs. • Designed interactive narrative tasks for AI-driven psychological assessment. • Enhanced and validated psychological scales for use with LLM agents. • Optimized model feedback loops for empirical validation and reliability testing. • Organized and analyzed narrative-based participant data to refine scales.

2023 - Present

Research Assistant - LLM-based Multi-Agent Interview Framework for Psychiatric Assessment (MAGI)

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I designed and executed the validation data analysis for an LLM-based psychiatric assessment framework in the MAGI project at Emohaa, Tsinghua University. My work compared LLM outputs to clinical expert assessments, defining evaluation criteria and supporting expert-aligned model review. I contributed to refining AI evaluation standards tied directly to psychiatric screening quality and clinical reasoning. • Conducted reliability/validity analyses of LLM psychiatric outputs against human ratings. • Defined and applied evaluation metrics and criteria for psychiatric screening. • Organized and analyzed both simulated and real assessment participant data. • Supported model alignment with human expert outputs in LLM evaluation tasks.

2024 - 2025

Data Analyst (Data Annotation Workflows)

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I participated in data annotation workflows on financial and medical projects for Biaobei Internet Financial Information Services. Duties included interpreting project requirements, executing sample labeling, and organizing quality feedback for structured data. I facilitated annotation-driven model development and ensured delivery alignment with business goals. • Translated business and client requirements into annotation logic. • Cleaned, labeled, and inspected data quality for high-stakes financial/medical datasets. • Coordinated with development teams to meet downstream requirements. • Organized feedback and quality inspection to optimize data annotation deliverables.

2022 - 2023

Education

Work History