MUFAKIR ANSARI
Ph.D. Candidate · Data Scientist · AI Researcher
28Citations
3h-index
8+Yrs Exp
3.91M.S. GPA
Professional Biography
Mufakir Ansari is a Ph.D. Data Scientist and AI Researcher with more than eight years
of combined research and engineering experience building machine learning and data systems across
biomedical AI, scientific computing, high-performance computing, and applied analytics.
He is currently pursuing his doctorate in Computer Science and Engineering at
Wright State University (Dayton, OH), where his research focuses on scalable AI systems,
intelligent agent design, and scientific workflows.
Mufakir's technical profile is defined by a commitment to rigorous measurement and
production-grade delivery. His clinical cancer detection pipeline achieved AUC-ROC 0.950
processing 277,000+ pathology image patches using domain-specific SimCLR pretraining —
with false negative rates as low as 0.34%. His Ebola outbreak genomics pipeline automated
end-to-end RNA-seq analysis of 356 samples on national HPC infrastructure using SLURM and
a fully checkpoint-resumable workflow. His distributional feature selection framework (DDFF)
addresses biomarker discovery in high-dimensional, low-sample-size biological datasets
where standard methods fail.
Prior to academia, Mufakir served as Technical Lead at Orcinus IT Solutions,
delivering end-to-end ML and ETL systems for seven SaaS clients, reducing pipeline latency
by 35% and improving deployment reliability by 30%. Earlier, as Business Operations Lead at
PanunKart.com, he used data analysis and customer segmentation to support 150% sales growth.
His range spans data science, machine learning engineering, analytics, forecasting,
HPC systems, NLP, and privacy-preserving product development.
Areas of Expertise
Data Science and Analytics · Machine Learning Engineering · Deep Learning (PyTorch, TensorFlow) ·
Natural Language Processing · Large Language Models · Computer Vision · Biomedical AI ·
Scientific Computing · High-Performance Computing · Forecasting and Time Series Analysis ·
Statistical Modeling · A/B Testing and Causal Inference · MLOps and Reproducible Research ·
Privacy-Preserving AI · Research-to-Production Delivery
Career History
01/2026 – Present
Ph.D. Researcher — Computer Science & Engineering
Wright State University · Dayton, OH
Scalable AI systems, scientific workflow automation, intelligent agent design.
06/2024 – Present
Graduate Research Assistant
Transportation Systems Research Lab · University of Toledo
Ensemble ML pipelines over 37,000+ records; AWS and Azure Databricks workflows.
08/2023 – 08/2025
M.S. Graduate Research Assistant — HPC Lab
University of Toledo · GPA 3.91/4.00
Distributed GPU training optimization; energy-aware SLURM scheduling; doubled training efficiency.
05/2018 – 12/2022
Technical Lead
Orcinus IT Solutions
End-to-end ML and ETL delivery for 7 SaaS clients; 35% latency reduction; 30% reliability improvement.
09/2017 – 03/2020
Senior Engineer / Consultant
MyFajir IT Solutions
Production APIs, BI dashboards, cloud ERP systems; 99.9% uptime SLA.
08/2013 – 09/2017
Business Operations Lead
PanunKart.com
Data strategy, segmentation, and experimentation driving 150% sales growth.
Education
01/2026 – Present
Ph.D., Computer Science & Engineering
Wright State University · Dayton, OH
08/2023 – 08/2025
M.S., Computer Science & Engineering — AI Track · GPA 3.91/4.00
University of Toledo · Toledo, OH
07/2009 – 07/2013
B.Tech., Electronics & Communication Engineering
National Institute of Technology · Srinagar, India
Selected Research & Publications
Under Review — Quantum Machine Intelligence
Dy-Part: A Dynamic, Noise-Aware Scheduler for Optimizing Hybrid Quantum-Classical Algorithms
DOI: 10.21203/rs.3.rs-8041248/v1
Under Review — Signal, Image and Video Processing
High-Sensitivity Detection of Invasive Ductal Carcinoma via Domain-Specific SimCLR Pre-Training
DOI: 10.21203/rs.3.rs-8031909/v1
Submitted — Global AI Summit 2025
From Text to Returns: Using Large Language Models for Mutual Fund Portfolio Optimization
arXiv: 2512.05907v1
Published 2025 — IoTBDS
Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning
Proceedings pages 207–214
Published 2025 — American Journal of Computer Science and Technology
Racing to Idle: Energy Efficiency of Matrix Multiplication on Heterogeneous CPU and GPU Architectures