Can Bakiskan

Work Experience

AI Software Engineer/Researcher

Santa Clara, CA

Intel Corporation

Oct 2022 - present

Architected and implemented an internal web application for clustering and visualizing tens of millions of integrated circuit layout clips for comparing devices. Responsibilities include developing data generation algorithms, massively parallel data processing and automation, UI/UX design, backend and frontend (full-stack) development, benchmarking and performance improvements.
Spearheaded and managed developer productivity and code quality improvements for the whole project team, including adoption of modern tools and techniques such as REST APIs, Vite, SWC, TypeScript, History API, ES6 modules, and ESLint.
Co-maintained the nightly builds and deployment infrastructure.

Graduate Student Researcher - Teaching Assistant (TA)

Santa Barbara, CA

University of California, Santa Barbara

Sept 2017 - Aug 2022

Improved the robustness of deep neural networks against adversarial attacks and noise using sparse encoding, dictionary learning, quantization, population coding, and Hebbian learning.
TA for 6 courses in a wide range of fields. As the head TA, designed and implemented the course project for the Data Science Capstone course.

Machine Learning Software Engineer Intern

Santa Barbara, CA

Intel Corporation

July 2021 - Sept 2021

Grouped and reduced 3D XPoint die-level failure maps to fundamental failure modes using dimensionality reduction, clustering and sparse dictionary learning.
Visualized the results using a Dash/Plotly based prototype UI.

Visiting Research Student, Software Tools

Durham, NC

Duke University Nicolelis Lab

June 2016 - Sept 2016

Built signal processing tools for the analysis of vocalization, video and neural activation recordings of mice and rats from experiments, in MATLAB and Python.

Embedded Software Developer Intern

Istanbul, Turkey

Epsilon Elektronik

Sept 2015 - June 2016

Converted Visual Basic driver code to C using libusb-hidapi for a modernization of medical gamma spectrum analyzer project.
Built a local web server running on a single board computer using LAMP stack and jQuery to serve data coming from the spectrum analyzer in real time.

Programming Experience

Expert:

Python, PyTorch, JavaScript, TypeScript, Node.js, React

Intermediate:

C, C++, Cython, Linux/Bash

Worked with:

CUDA, Java, MATLAB, Mathematica, PHP, Rust

Expert: Python, PyTorch, JavaScript, TypeScript, Node.js, React

Intermediate: C, C++, Cython, Linux/Bash

Worked with: CUDA, Java, MATLAB, Mathematica, PHP

Awards and Honors

Intel CMT Department One Intel Award – for developer experience and code quality improvements

Apr 2024

Intel CMT Department Recognition Award – for pattern clustering work

Nov 2023

Outstanding Teaching Assistant Award

Dec 2020

Fulbright Scholarship Selected Candidate

Mar 2017

Top Ranking Double Major Student in Electrical Engineering

June 2017

Best Research Oriented Senior Design Project Award

June 2016

Dean's High Honor List

2013 - 2017

Publications

Can Bakiskan, Metehan Cekic, Upamanyu Madhow, "Early Layers Are More Important For Adversarial Robustness," in International Conference on Machine Learning (ICML) Workshop - New Frontiers in Adversarial Machine Learning, 2022

Metehan Cekic, Can Bakiskan, Upamanyu Madhow, "Layerwise Hebbian/anti-Hebbian (HaH) Learning In Deep Networks: A Neuro-inspired Approach To Robustness," in International Conference on Machine Learning (ICML) Workshop - New Frontiers in Adversarial Machine Learning, 2022

Can Bakiskan*, Metehan Cekic*, Upamanyu Madhow, "Neuro-Inspired Deep Neural Networks with Sparse, Strong Activations," in IEEE International Conference on Image Processing (ICIP), 2022

Metehan Cekic, Can Bakiskan, Upamanyu Madhow, "Towards Robust, Interpretable Neural Networks via Hebbian/anti-Hebbian Learning: A Software Framework for Training with Feature-Based Costs," in Software Impacts Journal, 2022

Can Bakiskan, Metehan Cekic, Ahmet Dundar Sezer, Upamanyu Madhow, "Sparse Coding Frontend for Robust Neural Networks," in International Conference on Learning Representations (ICLR) Workshop on Security and Safety in Machine Learning Systems, 2021

Can Bakiskan, Metehan Cekic, Ahmet Dundar Sezer, Upamanyu Madhow, "A Neuro-Inspired Autoencoding Defense Against Adversarial Attacks," in IEEE International Conference on Image Processing (ICIP), 2021

Can Bakiskan, Soorya Gopalakrishnan, Metehan Cekic, Upamanyu Madhow, Ramtin Pedarsani, "Polarizing Front Ends for Robust CNNs," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

*: Equal contribution

Teaching Experience

Data Science Capstone

Communication Systems Design

Digital Communication Fundamentals

Machine Learning from Signal Processing Perspective

Signal Analysis

Introduction to Fields and Waves

Work Experience

Education

Programming Experience

Awards and Honors

Publications

Teaching Experience