Eniola A.

Key Skills

Software

AWS SageMaker

CVAT

Dataloop

Datasaur

Diffgram

Doccano

Encord

Google Cloud Vertex AI

Hasty

Kili Technology

Labelbox

LabelImg

Prodigy

Roboflow

Scale AI

SuperAnnotate

Supervisely

Tagtog

V7 Labs

Top Subject Matter

Cybersecurity intrusion detection (IDS) and malicious traffic classification

Cybersecurity IDS model development for zero-day and social engineering threats

Machine Learning Fundamentals (teaching labs and recitations)

Top Data Types

Document

Computer Code Programming

Text

Top Task Types

Classification

Fine-tuning

Transcription

Data Collection

Computer Programming/Coding

Text Summarization

Question Answering

Text Generation

Object Detection

Prompt + Response Writing (SFT)

Function Calling

Evaluation/Rating

Red Teaming

RLHF

Cuboid

Polyline

Entity (NER) Classification

Segmentation

Polygon

Bounding Box

Freelancer Overview

2021–2023 IDS Framework: Intelligent-based System for Classification (Research/Project). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include scikit-learn, Python (PyTorch, and TensorFlow). Education includes Doctor of Philosophy, Carnegie Mellon University (2024) and Master of Science, Georgia Institute of Technology (2020). AI-training focus includes data types such as Computer Code, Programming, and Document and labeling workflows including Classification, Evaluation, and Rating.

Labeling Experience

2021–2024 Research Assistant, CyLab Security and Privacy Institute (CMU)

Document

Conducted research analyses focused on zero-day evasion techniques and social engineering threats to improve intrusion detection models. This work supported the development of lightweight IDS models for resource-constrained IoT devices, which typically requires constructing datasets and evaluating model outputs against known threat behaviors. The role emphasized iterative model refinement based on deep-dive security analysis and collaborative engineering. • Analyzed evasion and threat techniques to inform IDS model improvements • Developed lightweight IDS models suitable for IoT/resource-constrained settings • Collaborated with a cross-functional team on security model development • Iteratively evaluated and refined approaches based on security research findings

2021 - 2024

2021–2023 IDS Framework: Intelligent-based System for Classification (Research/Project)

Classification

Designed and built an intrusion-detection classification model using decision trees to identify malicious traffic patterns. Work involved preprocessing and analyzing the NSL-KDD dataset to support model training and evaluation, including efforts to reduce false positives in real-time contexts. The model development included selecting and applying decision tree variants such as C4.5 and CART to learn from labeled network traffic features. • Used NSL-KDD dataset preprocessing to prepare inputs for training and evaluation • Applied decision tree algorithms (C4.5 and CART) for intrusion classification • Tuned the approach to improve detection accuracy (reported >95%) and reduce false positives • Validated model performance for real-time-like environments

2021 - 2023

2020–2022 Graduate Teaching Assistant, College of Computing (Georgia Tech)

Document

Supported undergraduate instruction in cryptography and machine learning fundamentals by designing lab assignments and running weekly recitations for a large student cohort. The role focused on helping students practice core ML concepts through structured lab activities. This functionally supports AI training/learning through guided exercises and feedback. • Designed lab assignments for ML fundamentals and related course content • Led weekly recitation sessions for over 100 students • Facilitated hands-on practice aligned with machine learning coursework • Provided instructional support for understanding ML concepts and methods

2020 - 2022

Education

Work History