Ms. Farzaneh Rastegari | Bioinformatics | Best Researcher Award

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Ms. Farzaneh Rastegari | Bioinformatics | Best Researcher Award

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:

  • A Comprehensive Simulation of Electric Vehicle Energy Consumption: Incorporating Route Planning and Machine Learning-Based Predictions 🚗🔋📊
  • Predictive Modeling of Chronic Kidney Disease Progression Using Longitudinal Clinical Data and Deep Learning Techniques  🩺🧠🤖
  • Exploring the Relationship Between Air Pollution and CNS Disease Mortality in Italy: A Forecasting Study with ARIMA and XGBoost 🌫️🧠📈

 

 

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Mr. Salman Khan | Bioinformatics Awards | Young Scientist Award

Mr. Salman Khan, University of Swat, Pakistan

Salman Khan is a passionate biotechnology graduate from the University of Swat, Pakistan, where he earned a Gold Medal for his academic excellence. Specializing in computational vaccine design and bioinformatics, Salman has shown remarkable proficiency in molecular biology and laboratory techniques. He is driven by a desire to contribute to health biotechnology through research and innovation. With a keen interest in drug and vaccine development, Salman aims to leverage his skills to advance public health solutions. His education, research, and internships have equipped him with the expertise to make meaningful contributions to the field of biotechnology. Salman is dedicated to applying his knowledge and skills to further scientific understanding in the medical and biotechnological sectors.

Professional Profile:

Orcid

Suitability For Young Scientist Award:

Salman Khan’s academic excellence, cutting-edge research in the fields of biotechnology and bioinformatics, and hands-on experience in clinical settings make him an exceptional candidate for the Research for Young Scientist Award. His passion for advancing health technologies and solving pressing global health issues aligns with the core values of the award, positioning him as a promising future leader in the scientific community.

🎓Education

Salman Khan completed his BS in Biotechnology from the University of Swat, securing a Gold Medal with a CGPA of 3.76/4.00. His thesis focused on the “Novel Computational-Based Design of a Multi-Epitope Vaccine Against Bundibugyo Ebolavirus,” showcasing his deep involvement in computational vaccine design. He also holds a Higher Secondary School Certificate (Pre-Medical) from Hira School & College Shah Dherai and diplomas in Health Technology and Information Technology from the National Institute of Medical Sciences and Islamia College of Technology, respectively. These educational achievements reflect his dedication to expanding his expertise in both biotechnology and technological advancements relevant to health.

🏢Experience

Salman Khan’s professional experience includes multiple internships, highlighting his practical skills in biotechnology and healthcare. As an intern at the Public Health Reference Laboratory (PHRL), he contributed to DNA/RNA extraction, PCR, microbial cultures, and antibiotic sensitivity tests. His role at Saidu Groups of Teaching Hospital allowed him to gain hands-on experience in health tech, COVID-19 patient management, and biosafety protocols. Additionally, his internship at Hayatabad Medical Complex’s Medical “A” Unit focused on clinical skills like IV line management, blood sampling, vital signs monitoring, and catheterization. These experiences have sharpened his practical expertise and prepared him for advanced roles in biotechnology and medical research.

🏅Awards and Honors

Salman Khan has earned several prestigious honors for his academic excellence and contributions to biotechnology. As a Gold Medalist from the University of Swat, his academic achievements stand out, particularly in his research in computational vaccine design. He was also a recipient of the Prime Minister Youth Laptop Scheme 2023, recognizing his potential in science and innovation. These awards, coupled with his extensive academic and research background, highlight his dedication to advancing the field of biotechnology. His accomplishments underscore his commitment to making valuable contributions to public health and scientific progress.

🔬Research Focus

Salman Khan’s research focus is in the fields of computational vaccine design, bioinformatics, and molecular biology. His thesis, which explored a computational approach for designing a multi-epitope vaccine against Bundibugyo Ebolavirus, illustrates his commitment to developing innovative solutions in disease prevention. He has also worked on studies involving the binding affinity of stevioside with proteins related to diabetes and developed strategies for multi-epitope vaccination against Guanarito Virus. His work aims to contribute to more effective, computationally-driven approaches for tackling viral diseases and other health-related challenges.

Publication Top Notes:

  • Title: Exploring stevioside binding affinity with various proteins and receptors actively involved in the signaling pathway and a future candidate for diabetic patients
  • Title: Occurrence of tinea infection with comparative study of commercial antifungal and traditional herbs in District Swat, Khyber Pakhtunkhwa, Pakistan
  • Title: Frequency of Helicobacter pylori amongst patients with gastrointestinal tract symptoms
  • Title: Developing A Novel Computational Strategy For A Multi-Epitope Vaccination Against The Guanarito Virus To Eliminate A Deadly Danger To Health