Hi, I’m
Dr. Tina Ghafari
Frankfurt Am Main
Summary
Accomplished data scientist with a strong track record in leveraging advanced analytics, machine learning, and deep neural networks to derive actionable insights from complex datasets. Proficient in handling massive data volumes, utilizing Hadoop, cloud-based solutions, and SQL for efficient management and analysis. Experienced in developing predictive models and deploying sophisticated algorithms to drive impactful business decisions. Adept at merging technical expertise with strategic thinking to innovate and solve complex data-driven challenges. Proven ability to optimize data workflows and contribute to scientific advancements through innovative solutions in a dynamic, results-oriented environment.
Overview
10
years of professional experience
Work History
Frankfurt School Of Finance & Management
Data Scientist
08.2021 - Current
Job overview
Advanced Machine Learning Proficiency: Developed and deployed sophisticated machine learning models in finance projects, resulting in a 15% increase in accuracy and 40 % higher speed in computation.
- Created and implemented new forecasting models to increase company productivity
- Coached and mentored junior data scientists on SAS and data mining techniques
Bethe Center For Theoretical Physics (BCTP)
Data Scientist/Researcher, Particle Physics
11.2018 - 08.2021
Job overview
- Advanced-Data Generation Techniques: Expertise in generating high-quality image datasets using variational Autoencoders (VAE) and generative adversarial networks (GAN), showcasing creativity and technical skills.
- Analytical Process Design and Implementation: Successfully designed and implemented analytical processes, particularly in XENON1T Dark Matter Project, showcasing proficiency in project management and execution.
Center Of Advanced European Studies And Research (CAESAR), Max Planck Institute
Data Scientist/ Researcher, Neuroscience
05.2019 - 05.2020
Job overview
- Neuroscience Data Modeling with CNNs: Applied Convolutional Neural Networks (CNN) to model complex neuroscience data, resulting in a 25% improvement in identifying neural patterns related to memory encoding.
- Computer Vision Expertise: Successfully extracted significant insights from diverse visual datasets using advanced computer vision techniques, leading to a 20% enhancement in understanding neural connectivity in neuroscientific research.
Institute For Studies In Theoretical Physics (IPM)
Big Data Scientist/Junior Data Analyst
09.2014 - 12.2018
Job overview
- Cloud-based Machine Learning Solutions: Deployed and scaled machine learning models on cloud platforms, enabling a 50% reduction in computational costs for advanced analysis of LHC data in distributed environments.
- Predictive Modeling and Optimization: Developed predictive models from LHC data using machine learning techniques, leading to a 30% increase in experimental process optimization and accuracy in predictive analysis.
Education
Bonn & Sharif UniversityGerman & Iran
Ph.D. from Engineering Physics
08.2021
Skills
- Data Mining
- Machine Learning
- Database Management
- Big Data Analytics
- Data Visualization
- Python
- R Programming
- Deep Learning
Availability
See my work availability
Not Available
Available
monday
tuesday
wednesday
thursday
friday
saturday
sunday
morning
afternoon
evening
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Languages
German
Upper intermediate (B2)
English
Advanced (C1)
Arabic
Intermediate (B1)
Persian
Bilingual or Proficient (C2)
Timeline
Data Scientist
Frankfurt School Of Finance & Management
08.2021 - Current
Data Scientist/ Researcher, Neuroscience
Center Of Advanced European Studies And Research (CAESAR), Max Planck Institute
05.2019 - 05.2020
Data Scientist/Researcher, Particle Physics
Bethe Center For Theoretical Physics (BCTP)
11.2018 - 08.2021
Big Data Scientist/Junior Data Analyst
Institute For Studies In Theoretical Physics (IPM)
09.2014 - 12.2018
Bonn & Sharif University
Ph.D. from Engineering Physics
Awards
- 2019: Winner of a fully funded participation in summer school at ICTP, Italy
- 2019: Winner of a fully funded participation in winter school at Galileo Galilei Institute, Florence, Italy
- 2019: Winner of a research scholarship from DAAD (”Deutscher Akademischer Austauschdienst”)
- 2018: Winner of abroad research scholarship from Ministry of Science, Iran
- 2017: Winner of fully funded participation in summer school at ICTP, Italy
- 2014: Ranked 25th in the university Ph.D. entrance exam in physics with more than 4.000 participants in Iran.
- 2012: Distinguished M.Sc. graduated student at the department of physics, Tafresh University, Iran
- 2009: Ranked 2nd among all physics B.Sc. graduates, physics department Arak University, Iran
Publications and Invited Talks
• 2022: Impact of the bounds on the direct search of bino-like Dark Matter on naturalness . (PHYSICAL REVIEW D, accepted 9 September 2021) • 2022: Phenomenology of the Stückelberg U(1) Extended MSSM (Stückelberg MSSM Revisited) (ISI) (acceptance pending).
• 2020: Invited talk at the “Deutsches Elektronen-Synchrotron” (DESY, German Electron Synchrotron) Virtual Theory Forum 2020, Hamburg, Germany. Topic of the talk: Impact of the bounds on the direct search of bino-like Dark Matter on naturalness, September 2020.
• 2019: Ghaffari, Ghazaal and Mahdi Torabian. "Split supersymmetry breaking from Stückelberg mixing of multiple U (1)'s", Physics Letters B 796 (2019): 32-37.
• 2018: Invited talk at the Sharif University, Tehran, Iran. Topic of the talk: Supersymmetry (SUSY) Breaking and Mediation through the Stückelberg Mixing, November 2018.
• 2017: Invited talk at the Sharif University, Tehran, Iran. Topic of the talk: Massive U(1) mixing with Hypercharge in Supersymmetric Model and its Phenomenology, December 2017.
• 2017: Farahani, S. V., Ghanbari, E., Ghaffari, G., & Safari, H. (2017). Torsional wave propagation in solar tornadoes. Astronomy & Astrophysics, 599, A19.
• 2014: Ghaffari, G., & Tazimi, N. (2014). Longitudinal Proton Structure Function at LO and NLO Approximations. International Journal of Theoretical Physics, 53(6), 1832-1839.
• 2014: Torsional oscillations in jets (conference), Kerman Astrophysics Conference.
• 2011: Proton Longitudinal Structure Function (conference), Yazd Particle Physics Conference.
• 2011: Proton Longitudinal Structure Function at LO Approximation (conference), Uromie Physics Conference, https://www.psi.ir/farsi.asp?page=physics90
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