Naif Alkhunaizi Email: naif.alkhunaizi@mbzuai.ac.

ae Mobile 1: +971-545-343-088
Mobile 2: +966-545-821-122
LinkedIn: https://linkedin.com/in/naif-alkhunaizi-334009149
Personal Website: https://naiftt.github.io
Address 1: Masdar City, Abu Dhabi, UAE
Address 2: P.O.Box 9712, Dammam, Saudi Arabia
Education

•Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, UAE Master of Science - Machine Learning; GPA:
3.92 Jan 2021 - Dec 2022 Courses: Machine Learning, Advanced Machine Learning, Probabilistic and Statistical Inference, Mathematical
Foundations for AI, Optimization, Research Communication and Dissemination

•Arizona State University AZ, USA Bachelor of Science - Electrical Engineering; GPA: 3.42 Aug 2015 - Dec 2019
Courses: Analog Circuits, Random Signal Analysis, Engineering Electromagnetics, Energy Sys/Power Electronics, Digital Design, Applied
Statistics

Skills Summary
• Languages: Python, C, C++, MATLAB, Bash, Swift, SQL, JavaScript, HTML, CSS, Verilog • Frameworks: Scikit, NLTK,
Pytorch, TensorFlow, Keras, Numpy, Pandas, SciPy, Sklearn, Seaborn, Matplot, Django • Tools: Git, SQLite, LaTeX, Conda,
Docker, Nginx, Supervisor, Amazon SageMaker
• Platforms: Linux, AWS, Windows, Arduino, Lab-view, Cadence Circuit Simulation, LTspice Circuit Simulation • Soft Skills:
Leadership, Event Management, Writing, Public Speaking, Time Management Experience

•Researcher/AI Engineer
MBZUAI Jan 2023 - Present ◦ ML experiments: Perform different Machine Learning experiments, and publish findings in top
conferences. ◦ Data Cleaning and Visualization: Work on cleaning and visualizing data to support ML experiments in the lab. ◦

Application: Deploy Machine Learning systems using a combination of MLOps and DevOps practices. •Co-founder/AI Engineer
WaZii Website Jan 2023 - Present ◦ Time Series Forecasting: Implemented a variety of algorithmic trading strategies to
enhance the prediction accuracy of U.S. stock market prices.
◦ Full Stack Developer: Managed website maintenance, consistently incorporating novel features using a range of
cutting-edge CI/CD tools.
◦ Model Deployment: Implemented deployed forecasting models and utilized the MLOps cycle to facilitate daily
retraining on incoming data.

•Graduate Teaching Assistant for Advanced Machine Learning

MBZUAI Jan 2022 - May 2022 ◦ Run Lab sessions : Run and facilitate lab sessions to deepen students understanding of
the materials. ◦ Host office hours: Answering students questions during office hours.

•AI Engineer Intern

Lockheed Martin Jun 2021 - Aug 2021 ◦ Project Course - Develop Computer Vision model for enhanced aircraft inspection:
We utilized lifelong learning methods to develop a computer vision model to detect aircraft paint by implementing Episodic
Memory to prevent catastrophic forgetting when new distribution of the data is introduced.
◦ Agile methodology - Daily Standout: We had daily standouts showing the progress of the project ◦ Impact:
The project was completed and tested by the engineers at Lockheed Martin.

•Data Analyst
Rand International School Website Feb 2020 - Oct 2020 ◦ Data Quality Enhancement: Conducted comprehensive data
quality assessments, identifying and rectifying anomalies, inaccuracies, and missing information within datasets. Employed
data cleansing techniques to enhance data integrity, resulting in improved accuracy and reliability for analysis.
◦ Market Trends Analysis: Analyzed market trends and customer behavior by employing advanced statistical
techniques on large datasets. Utilized tools such as Python and SQL to extract meaningful insights, facilitating
data-driven decision-making for marketing strategies and product development.

•Undergraduate Teaching Assistant for Random Signals Analysis

ASU Aug 2019 - Dec 2019 ◦ Host office hours: Answering student questions during office hours.
◦ Write quizzes: Mark quizzes and mark student assignments.
Projects
• Vison - AnimeGAN: Worked on developing a GAN model that generates animated images and used it to cartoonize movies then
compared it with State Of the Art methods Youtube Link
• ICL Learning: Deployed Falcon 40 B and leveraged ICL for fine-tuning. The system now summarizes daily financial news and
 delivers daily recommendations by receiving daily data from the News API.
• Causal Disovery and Inference: Used Causal Discovery and Causal Inference methods to construct causal graphs for two
real-world datasets to prove that smoking is one of the leading causes of lung cancer
• Federated Learning Settings: Developed simulated federated learning settings using PyTorch, and evaluated the
performance of state-of-the-art methods on diverse real-world datasets in the fields of natural and medical domains
• E-commerce Website: Created E-commerce website using Django, Javascript, HTML and CSS
• New aggregation rule in Federated Setting: Created a new algorithm that can detect against attacks in federated learning
settings, and it outperformed state of the art methods
• TF-IDF algorithm: Implemented TF-IDF algorithm to extract information from meidcal reports •
Reinforcement Learning: Used Reinforcement Learning to program an agent to play Nim
• Traffic Detection: Designed a model using SVR that detects number of accidents in a specific area •
Tic-Tac-Toe: Used Minimax algorithm to teach a computer to play Tic-Tac-Toe
• PageRank: Used Markov chains to implement PageRank algorithm.
• Surfing Behavior: Trained simple Neural Networks to predict customer ‘buying or surfing’ by collecting the data through web
scraping
• Classification Tasks: Trained various Convolutional Neural Networks to classify images using Transfer Learning and Data
Augmentation both in the natural and medical domain

Publications
• FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling: Published in Medical Image Computing and
Computer-Assisted Intervention conference (MICCAI 2023) [Link]
• FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face
Presentation Attack Detection: Published in IEEE International Joint Conference on Biometrics (IJCB 2023) [Link]
• Suppressing Poisoning Attacks on Federated Learning for Medical Imaging: Published in Medical Image Computing
and Computer-Assisted Intervention conference (MICCAI 2022) [Link]

Certificates
• Machine Learning Data Lifecycle in Production: DeepLearning.AI [Link]
• Introduction to Machine Learning in Production: DeepLearning.AI [Link]
• Leading Teams: Developing as a Leader: University of Illinois at Urbana-Champaign [Link] • Neural
Networks and Deep Learning: DeepLearning.AI [Link]

Honors and Awards

• Awarded dean’s list for 4 semesters during undergraduate studies.
• Runner’s Up at GITEX 2021.
• Second place in table tennis tournament among 30 students.

Volunteer Experience

•Volunteered at the World Future Energy Summit

Greeted visitors to our booth, and explained projects that work on sustainability and clean energy. 2022

•Dedicated Member, Sun Devils Are Better Together - ASU

Effectively enhanced cross-cultural understanding, and discussing solutions to improve campus life. 2015 - 2019

•Dedicated Member, Theta Tau - ASU

Built relationships with fellow engineers, and conducted workshops on campus in different subjects. 2018 - 2019

•Theatre Director - Dar Al-Hikma High School

Conducted weekly rehearsal sessions for three months to prepare our play for the graduation ceremony. 2014
References

•Dr. Karthik Nandakumar Associate Professor, MBZUAI karthik.nandakumar@mbzuai.ac.ae 2020-2022 •Dr. Martin
Tak´aˇc Associate Professor, MBZUAI martin.takac@mbzuai.ac.ae 2020-2022

•Dr. Lalitha Sankar Associate Professor, ASU lsankar@asu.edu 2018-2019

Kaggle, amazon, deployment, DLP, Data loss prevetntion, excellent presentation, system updating, 10 years of experience, data flow analysis, kaggle competition, statstical models, new algorithms, machine learning pipelines,
documentation, scientific methods, EM algorithm, Gaussian Mixture, Analysis, evaluating changes, system component, machine control algorithm, risk analysis, identify missing data, data visualizion, hard working, written or
communication skills, Visualization BI tool such as power BI, relation SQL and NOSQL, mongo, data warehouse modeling, SSIS, ETL, SQL DW, marketing, mobile app, react, flask, SAP, microsoft office, big data, Hadoop, Hive, Spark,
Kafka, HBase, Flume, Pig, KUDU, Java, SCALA, Storm, Spark Streaming, Insurance, health, medical imaging, web analytics, programmatic, DMP, Datalake, dataviz, lifelong learning, passionate, passion, excitement, love, forecasting,
mathematics, statstics modeling, Strong Programming Skills Data Structures,Data Modeling,Predictive Modeling,Regression, Classification Clustering Models,Keras ,Unit Testing and CI/CD,Machine Learning
technology,MATLAB,Explanatory Analysis,Natural Language Processing,C++(STL) ,PySpark.ML,Python, Tableau, Hadoop, R, DATABASE,Supervised Learning,Unsupervised Learning, Decision Trees, Generative Model, Discriminative
Model,Probability, Software Design, Reading, Hard worker, Proficiency in Unix, networking, and cloud computing. Expertise in Python frameworks/tools: pip, pytest, boto3, pyspark, pandas. Familiarity with Agile/Lean methodologies and
workflow tools like Apache Airflow. Knowledge of columnar and big data databases: Athena, Redshift, Hive. Experience with version control (git), AWS services, and container management (Docker). Familiarity with JVM languages: Kotlin,
Java, Scala. Understanding of RDBMS, NoSQL databases, and BI tools like Tableau. Knowledge of data science environments and log monitoring (ELK stack). Proficiency in metadata catalog and security tools: Amundsen, Apache
Ranger. Familiarity with messaging systems: Kafka, RabbitMQ. Understanding of streaming frameworks: Spark Streaming, Apache Beam.