I teach machinessignal detectedto think.
Bridging academic research and production engineering. A 4× IEEE published author and MS Data Science candidate at UB, turning messy real-world data into ML systems, pipelines, and decisions that hold up in practice.
Core Stack
Research
Awards
By the Numbers
Signature Stack
Tools grouped by how I actually use them — not a skills bar chart.
Who I Am
I'm obsessed with building intelligent systems—whether it's predicting graduate admissions with 85% accuracy or detecting wildlife in complex environments. I specialize in turning messy, real-world data into AI systems that actually hold up in practice.
Currently pursuing my MS in Data Science at University at Buffalo, where I work with Dr. David Doermann on admission prediction systems. I've published 4 IEEE papers on optimization algorithms and explainable AI, and I'm always looking for the next interesting problem to solve.
When I'm not training models...
Work Experience
Where I've applied data science to solve real problems.
Data Scientist / Researcher
University at Buffalo
- //Developed an end-to-end admissions ML pipeline processing 3,000 applications per cycle, reducing approximately 300-400 manual review hours and admissions processing time by around 10% for a pilot CSE department using AHP-based interpretable feature engineering.
- //Standardized and clustered 100+ academic major titles, reducing preprocessing effort by 20% for admissions data preparation workflows still in testing using TF-IDF, K-Means, and fuzzy matching pipelines.
- //Trained Random Forest and GPU-accelerated PyTorch models on approximately 20,000 applications, improving minority-class recall and F1-score by 15% during model validation using Focal Loss to address class imbalance.
- //Achieved 85.1% accuracy, 95.9% recall, and 90.7% F1-score across roughly 20,000 applications, automating approximately 20% of rejection reviews while improving fairness via feature removal using LIME-based model explanations.
Python Developer
Markytics
- //Optimized Django application performance, improving response times by 30% and reducing load times for approximately 1,000 users in data-heavy workflows through query optimization and caching strategies.
- //Integrated REST APIs for internal and external systems, reducing data latency by around 10% and improving load performance for large datasets in production applications using standardized API-based communication mechanisms.
- //Implemented code review checklists, improving review throughput by 50% and reducing post-deployment issues across a five-developer engineering team by enforcing clean-code and review standards.
My Education
Building a strong foundation in data science and computer science.
MS in Data Science
University at Buffalo, SUNY
Buffalo, NY, USA
// Machine Learning · Statistical Learning · Data Mining
Bachelor of Technology in Computer Science
MIT World Peace University
Pune, India
// Algorithms · Data Structures · Software Engineering
Featured Projects
Real-world applications of machine learning, from research to production systems.
Tableau Dashboards
Explore my interactive Tableau dashboards showcasing data analysis and insights.
Publications & Papers
Peer-reviewed research contributions in machine learning and optimization.
Deep Learning for Exoplanet Exploration
ICDAI 2025 - Springer Nature
ANN and Gradient Boosting pipelines achieving 88.3% detection precision and 91.06% habitability prediction on NASA data.
Capacitated VRP using Ant Colony Optimization
IEEE 2025
Capacity-aware logistics routing reducing travel distance by ~2,000 km and cost by ~20,000 units using ACO.
YOLOv5/YOLOv8 for Bird Species Identification
IEEE 2025
Comparative analysis of object detection models for wildlife identification in complex environments.
Fake Profile Detection Using Machine Learning
IEEE 2024
ML-based approach for identifying fake social media profiles with high accuracy classification.
Professional Certifications
Certifications and coursework demonstrating hands-on learning and validated credentials.
What People Say
Verified recommendations from colleagues and mentors on LinkedIn.
View All on LinkedIn“I worked with Deven during his internship at Markytics, where he consistently demonstrated strong data science and analytics capabilities. He was particularly effective in using Python and SQL to analyze data, build models, and support data-driven decision-making across projects.”
“Deven showed a solid ability to translate business requirements into structured analytical solutions and automate repetitive workflows, improving efficiency and reliability. His approach to modeling and analysis was thoughtful and well-executed, with a clear focus on producing actionable results rather than theoretical outcomes.”
“Beyond his technical skills, Deven worked very well within the team. He communicated clearly, collaborated effectively with both technical and non-technical stakeholders, and took ownership of tasks while remaining receptive to feedback.”
“I had the pleasure of supervising Deven Shah during our collaborative research on the Capacitated Vehicle Routing Problem (CVRP) project, which later culminated in a successful IEEE publication. Throughout this period, Deven consistently demonstrated exceptional analytical depth, strong modeling skills, and a remarkable ability to translate theoretical concepts into practical, data-driven solutions.”
“His work on developing and fine-tuning optimization algorithms showcased not only his technical proficiency in Python, machine learning, and heuristic modeling, but also his keen understanding of data integrity and real-world application constraints.”
“Beyond his technical strengths, what truly sets Deven apart is his collaborative approach. He is a thoughtful team player who elevates discussions with critical insights while remaining open to diverse perspectives.”
Let's Connect
Got an interesting problem? Building something cool? Let's chat.
GitHub
github.com/ShahaDevenLocation
Buffalo, NY, USA// I'll get back to you within 24 hours