Ms. Farzaneh Rastegari | University of Connecticut | United States
Farzaneh (Dana) Rastegari is a Ph.D. student in Computer Science and Engineering at the University of Connecticut, specializing in computational genomics and microbiome research. With a strong background in bioinformatics, machine learning, and robotics, she applies statistical and computational techniques to analyze microbiome data. As a Predoctoral Associate at The Jackson Laboratory, she has gained extensive experience in both wet lab methodologies and computational research. Passionate about integrating AI with biological data, she has contributed to multiple high-impact research projects and publications, making strides in genomics, healthcare, and environmental studies. 🌱📊🔬
Professional Profile:
SCOPUS
Suitability for Best Researcher Award:
Farzaneh (Dana) Rastegari is a highly qualified researcher specializing in computational genomics and microbiome research. Her interdisciplinary expertise spans bioinformatics, machine learning, and robotics, making her a strong contender for a Best Researcher Award. Her ability to integrate artificial intelligence with biological data showcases innovation and a forward-thinking approach to scientific discovery.
Education & Experience 🎓🔍
✅ Ph.D. in Computer Science & Engineering – University of Connecticut, USA (Ongoing)
✅ Predoctoral Associate in Genomics Medicine – The Jackson Laboratory 🏥🧬
✅ M.S. in Electrical & Computer Engineering (Communications) – University of New Haven, USA
✅ B.S. in Computer Science (Robotics) – Amirkabir University of Technology, Iran 🤖
📌 Research & Work Experience:
🔹 Microbiome research on mice & human samples 🐭🧪
🔹 Developing computational pipelines for microbiome analysis 💻🔬
🔹 Applying Bayesian ML & AI in genomics 🤖📈
🔹 Signal processing & digital communications 📡🎶
Professional Development 🚀📚
Farzaneh actively enhances her expertise by engaging in diverse research projects, applying machine learning and computational methods in genomics. At The Jackson Laboratory, she mastered microbiome data processing and wet lab techniques, contributing to high-impact publications. Her research at UConn focuses on developing algorithms for phylogenetic networks, Bayesian classification of TCRs, and reinforcement learning. She has also worked on digital signal processing, bioinformatics tools, and robotics. Passionate about interdisciplinary innovation, she continuously integrates AI, statistics, and biology to tackle critical challenges in genomics and healthcare. 🌍🔗
Research Focus 🧪📊
Farzaneh’s research bridges computational biology, AI, and genomics. She specializes in microbiome analysis, applying statistical and machine learning models to understand microbial communities and interactions. Her projects involve phylogenetic inference, Bayesian ML, reinforcement learning, and genomic signal processing. With expertise in bioinformatics tools, she develops pipelines to analyze 16S rDNA and metagenomic data. She also explores disease prediction models, evolutionary genomics, and biomedical applications of AI. Her work contributes to precision medicine, healthcare analytics, and environmental studies, driving breakthroughs in genomics and biotechnology. 🌿🔬🧠
Awards & Honors 🏆🎖️
🏅 Predoctoral Associate Position – The Jackson Laboratory
🏅 Published in “Cancer” Journal for Metagenomics & Chemotherapy Study
🏅 Multiple IEEE Publications in Genomics, AI, & Environmental Studies
🏅 Outstanding Research Contributions in Computational Biology & Bioinformatics
🏅 Certified in Working with Mice for Biomedical Research 🐭🧪
Publication Top notes:
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A Comprehensive Simulation of Electric Vehicle Energy Consumption: Incorporating Route Planning and Machine Learning-Based Predictions 🚗🔋📊
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Predictive Modeling of Chronic Kidney Disease Progression Using Longitudinal Clinical Data and Deep Learning Techniques 🩺🧠🤖
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Exploring the Relationship Between Air Pollution and CNS Disease Mortality in Italy: A Forecasting Study with ARIMA and XGBoost 🌫️🧠📈