Mete Ismayilzada
Research Intern at USI Universit�� della Svizzera italiana
Lausanne, Vaud, Switzerland
Summary
👤
Senior🎓
Top SchoolMete Ismayilzada is a PhD candidate in Computer Science at EPFL specializing in natural language processing and computational creativity, with additional research interests in neuro-symbolic AI, common-sense reasoning, and compositional generalization. He combines 11 years of industry experience—from embedded systems at Freescale and network automation at Cisco to ML engineering and federated learning at integrate.ai—with entrepreneurial leadership as co-founder and CTO of Destin AI. His practical contributions include implementing a differentially private Random Forest for IBM’s Diffprivlib and building dialog systems that mimic fictional characters during an NLP internship at Sony. Currently researching how large language models can accelerate scientific discovery at Microsoft while visiting USI, he also teaches graduate NLP courses at EPFL. Comfortable moving between low-level engineering and cutting-edge research, he brings a rare blend of production-grade systems experience and deep academic focus.
11 years of coding experience
7 years of employment as a software developer
Computer Science, Computer Science at Baku State UniversityDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at EPFLBachelor of Computer Science (BCS), Computer Science, Bachelor of Computer Science (BCS), Computer Science at University of Waterloo
English, Russian, Azerbaijani, Turkish, French
Github Skills (12)
scikit-learn10
machine-learning10
differential-privacy10
random-forest10
python10
scikit10
data-privacy9
numpy8
data-structure7
data-structures7
algorithm7
algorithms7
Programming languages (4)
Github contributions (5)
IBM/differential-privacy-library
Apr 2021 - Sep 2021
Diffprivlib: The IBM Differential Privacy Library
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 8 commits, 1 PR in 4 months
Contributions summary:Mete primarily contributed to the implementation of a differentially private Random Forest Classifier algorithm within the IBM Differential Privacy Library. This involved adding the core functionality of the classifier, including its fit and predict methods, along with associated helper functions and data structures. Subsequent commits refactored the code to align with the sklearn library's structure, demonstrating a focus on integrating the new algorithm within a broader machine learning framework. The user also updated the code to be compatible with sklearn v1.0.
pythondata-privacyprivacydifferential-privacymachine-learning
mismayil/ideapool
May 2019 - Oct 2019
Record your ideas before you forget them.
Contributions:17 commits, 1 push in 4 months
record
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