Dr. Ahmad Pouramini

Assistant Professor at Sirjan University of Technology | Ph.D. in AI from Tehran University | Focus: Artificial Intelligence, Machine Learning & NLP

Dr. Ahmad Pouramini
Current Affiliation:
Assistant Professor
Dept. of Computer Engineering
Sirjan University of Technology
Education:
Ph.D. in Artificial Intelligence
University of Tehran

Biography

Dr. Ahmad Pouramini is an Assistant Professor at Sirjan University of Technology with a Ph.D. in AI from the University of Tehran. His research focuses on Machine Learning and Natural Language Processing (NLP).

During my undergraduate studies, I had the privilege of working closely with him from February 2017 to September 2020. He observed my growth across multiple roles, including being his student in Technical English and Algorithm Design, and subsequently serving as his Teaching Assistant (TA) for two consecutive years.

Dr. Pouramini's mentorship was pivotal in developing my algorithmic thinking. Under his guidance, I mastered the art of translating complex theoretical concepts into optimized code, particularly in the realm of competitive programming and resource-efficient software design.

"Ms. Amini established herself as a diligent, inquisitive, and highly active student who possesses a profound aptitude for algorithmic thinking... Her proactive approach not only elevated the quality of her own contributions but also set a high benchmark among her peers."

Key Collaborations & Technical Achievements

  • Teaching Assistant (Algorithm Design):
    • Led mentoring sessions for two years (2018–2020) on Dynamic Programming and Graph Theory.
    • Conducted oral exams and evaluated projects based on Big-O complexity analysis and Clean Code principles.
    • Adapted teaching to online platforms during the 2020 transition to hybrid learning.
  • Advanced Algorithm Implementation:
    • TSP (Traveling Salesperson Problem): Implemented a Branch and Bound method with custom heuristics to optimize search space.
    • Strassen’s Algorithm: Developed a divide-and-conquer approach for matrix multiplication that outperformed classical O(n³) methods.
    • Memory Optimization: Redesigned the Levenshtein Edit Distance algorithm by identifying row dependencies to minimize memory footprint.
  • Technical English & Academic Research:
    • Recognized for an extensive command of specialized vocabulary and active engagement with international academic journals to master technical terminology.

Official Recommendation Letter

Document Status: Signed & Verified
Institution: Sirjan University of Technology
Key Highlight: Exceptional capacity to translate theoretical NP-Hard problems (like TSP) into efficient, working code.