Emre Ayıklar
Research Associate
Türkiye Turkey
Summary
👤
Senior🎓
Top SchoolEmre Ayıklar is a research-focused finance professional with a decade of experience blending M&A execution, valuation, and audit rigor across roles at Hekim Capital Partners, Forvis Mazars Türkiye, and RatioNova. He has practical financial modeling expertise (DCF, comparables, precedents) and has supported live deals, capital raising, and investor relations while strengthening accounting and due diligence skills through audit engagements. Equally comfortable with quantitative research and client-facing deal materials, he produces actionable insights and polished investment memoranda. Unusually for a junior analyst, he also contributed GPU-accelerated linear algebra and CNN utilities to an academic deep-learning framework on GitHub, demonstrating strong technical fluency alongside finance acumen. Seeking analyst roles in investment banking, private equity, or strategy consulting, he brings a mix of analytical depth, technical curiosity, and leadership experience from student governance.
10 years of coding experience
2 years of employment as a software developer
High School Diploma, GPA 3.53 Turkish-Mathematics, High School Diploma, GPA 3.53 Turkish-Mathematics at Kırklareli Atatürk Anatolian High SchoolBachelor's degree, Business Administration, Bachelor's degree, Business Administration at University of Applied Sciences WormsBachelor's degree, Business Administration (%100 English), Bachelor's degree, Business Administration (%100 English) at Ankara Yıldırım Beyazıt Üniversitesi
English, Turkish, German
Github Skills (12)
neural-network10
cuda10
gpu-programming10
machine-learning10
deeplearning-ai10
cublas10
deep-learning10
knn10
knexjs10
linear-algebra10
julia10
deconvolution9
Programming languages (2)
Github contributions (5)
denizyuret/Knet.jl
Oct 2016 - Feb 2017
Koç University deep learning framework.
Role in this project:
ML Engineer
ML EngineerContributions:20 commits, 12 PRs, 7 comments in 4 months
Contributions summary:Emre made significant contributions to the Knet.jl deep learning framework, primarily focusing on GPU acceleration and linear algebra operations. Their work included implementing CUDA kernels for matrix transpose, 3D, 4D, and 5D permutedims operations, and integrating CUBLAS for optimized matrix transposing. Furthermore, the user implemented new distributions, gaussian, xavier, and bilinear for filter initialization. They also added deconv4 and unpool functions, enhancing the framework's capabilities for convolutional neural networks.
data-sciencedeep-learningknetneural-networksmachine-learning
eeyrdkl/CMSE501
Feb 2017 - Aug 2021
Contributions:1 push, 1 branch in 4 years 7 months
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