• Designed a whole brain model to quantify intersubject variation in functional connectivity architecture of attention and working memory.
• Analyzed single nucleotide polymorphisms (SNP), structural variants (SV), and gene expression patterns to determine the genetic origins of intersubject variation in these cognitive functions.

• Designed a generative model for the enhanced reconstruction of perceived images from human brain activity.
• Worked on the FPGA acceleration of this generative model.

• Designed a semi-supervised continual learning algorithm that analyzes amino acid sequences to infer protein functions (>98% accuracy) while reducing the computational cost to update big biological databases (1.65 times less memory usage)
• Taught "Intro to Bash/Unix" at 2021 Biological Data Science Summer Workshop organized by our lab.

• Worked on using machine learning models (VGG19, GAN, DNN) to analyze fMRI scans of human visual cortex with the purpose of reconstructing visual experiences.

• Worked on the development of a low-cost brain-computer interface (BCI) system for people with Amyotrophic Lateral Sclerosis (ALS).
• Designed the P300 brain wave-based control system that turns brain signals into computer commands for home environment control—turning on/off lights, TV, and A/C.
• Designed the user interface for the BCI.

• Designed novel computational models for non-canonical viral vector-based programmable medicines.

• Designed computational models for RNA structure analysis, metagenomics, and viral vector based cargo delivery for plant therapeutics.

• Project lead for computational virus discovery.
• Designed metagenomics pipelines for Illumina MiSeq, Nanopore MinION, PacBio, BGISEQ, and Sanger sequencing data.
• Automated quality control for NGS data.
• Designed a taxonomic classifier sensitive at the genus level for RNA viruses.
• Designed a probabilistic model for viral host prediction.
• Automated peak alignment for nucleic acid capillary electrophoresis analysis via dynamic time warping. A critical step for high-throughput RNA structure analysis.
• Automated data transfer from Illumina MiSeq to AWS S3.
• Performed gel electrophoresis and microinjections.

• Designed programming exercises and projects for introductory Python programming.
• Supervised students during the lab sessions.

Shell scripting, Matlab, Python, R, Julia, C++, Nextflow, VHDL, HTML, CSS

CNN, GAN, LSTM, PCA, Diffusion Models, Tensorflow

AWS, Docker, Slurm, NI Multisim, PathWave ADS, Linux, Code Ocean, MS Office, Google Colab

EEG, fNIRS, MRI, PET, SPM 12, FSL, OpenVibe

Illumina MiSeq, Nanopore MinION, PacBio, BGISEQ, Sanger, RACE

RF Circuit Design, Digital Logic Design, Digital Signal Processing, Oscilloscope, High Performance Computing

English (fluent), French (fluent), Turkish (Fluent), German (Intermediate)