A comprehensive showcase of my journey in machine learning, from Android malware detection to national programming competitions
Chronological journey of my technical achievements and learning
Participated in the national programming competition, showcasing advanced algorithmic thinking and problem-solving skills under competitive constraints.
Recognized for significant contributions and dedicated effort in the Software Engineering Club, demonstrating leadership and collaborative skills.
Published comprehensive malware detection system on GitHub, achieving exceptional accuracy rates through advanced machine learning techniques.
Collaborated with cross-functional teams to implement IT solutions, optimize workflows, and achieve organizational goals in a professional environment.
Advanced machine learning system for detecting and mitigating malware threats on Android devices
Developed innovative methodologies combining static and dynamic analysis techniques for comprehensive malware detection.
# Malware Detection Model Training
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
class MalwareDetector:
def __init__(self):
self.model = RandomForestClassifier(n_estimators=100, random_state=42)
self.accuracy = None
def train(self, features, labels):
X_train, X_test, y_train, y_test = train_test_split(
features, labels, test_size=0.2, random_state=42
)
self.model.fit(X_train, y_train)
predictions = self.model.predict(X_test)
self.accuracy = accuracy_score(y_test, predictions)
return self.accuracy
def predict(self, features):
return self.model.predict(features)National-level achievements in competitive programming
National Competition, Saudi Arabia
Among top national participants
Secured 5th place among top national participants, showcasing exceptional programming skills and problem-solving abilities. Delivered optimized solutions to real-world problems using Java, earning recognition for creativity and efficiency.
National Programming Competition
2022 Edition
Active participation in the national programming competition, demonstrating advanced algorithmic thinking and collaborative problem-solving skills in a competitive environment.
Active participation in data science competitions and community
Research impact and recognition
Community engagement
Active participation
A Cross-Platform Mobile Solution powered by Kotlin Multiplatform
Real-time text overlay on dynamic image templates fetched via API.
Localized database caching using SQLDelight for seamless offline browsing.
Single codebase for UI components using Compose Multiplatform.
Implemented a robust networking layer to handle asynchronous data fetching from meme APIs with custom serialization.
Utilized SQLDelight to generate type-safe APIs from SQL statements, enabling a reliable local cache for downloaded memes.
class MemeRemoteDataSource(private val client: HttpClient) {
suspend fun fetchMemes(): List<Meme> {
return client.get("https://api.imgflip.com/get_memes")
.body<MemeResponse>()
.data.memes
}
// Shared business logic for both Android/iOS
fun filterPopular(memes: List<Meme>): List<Meme> {
return memes.filter { it.box_count <= 2 }
}
}Key competencies developed during the KMP lifecycle
Mastered the 'expect/actual' mechanism to implement platform-specific APIs while maintaining a shared core logic.
Advanced use of Kotlin Coroutines and Flows for non-blocking UI updates and efficient background data processing.
Built highly responsive, state-driven interfaces using Compose Multiplatform, ensuring UI consistency across iOS and Android.
Leveraged SQLDelight to bridge SQL and Kotlin, ensuring compile-time safety for all local database interactions.