Curriculum Vitae (CV)
Harvard University (2023-Present) — Cambridge, MA
- Ph.D. Bioengineering
- Biohybrid Organs & Neuroprosthetics (Bionics) Lab
- Advisor: Dr. Shriya Srinivasan
Drexel University (2019-2023) — Philadelphia, PA
- B.S. Electrical Engineering
- Concentrations: Wireless Electronics, Digital Signal Processing, Control Theory
- Organizations:
- • President ('22-'23), IEEE Eta Kappa Nu Honor Society
- • Member, Drexel Society of Artificial Intelligence
- • Member, Drexel Neuroscience Collaborative
Galatasaray High School (2014-2019) — Istanbul, Turkey
Senior Thesis Research — Sep. 2022 – Present
@Ecological and Evolutionary Signal Processing & Informatics (EESI) Lab, Drexel UniversityResearch Assistant, Machine Learning & Neuromorphic Computing — Sep. 2021 – Mar. 2022
@Distributed Intelligent Scalable Computing (DISCO) Lab, Drexel UniversityComputational Biology Research Co-op — Mar. 2021 – Sep. 2022
@Ecological and Evolutionary Signal Processing & Informatics (EESI) Lab, Drexel UniversityVIP Research, Machine Learning & Neuromorphic Computing — Sep. 2020 – Mar. 2021
@Distributed Intelligent Scalable Computing (DISCO) Lab, Drexel UniversityResearch Assistant, Neuroengineering — Sep. 2020 – Mar. 2021
@Neuroengineering & Neuroergonomics Lab, Drexel UniversityConsultant, Computational Biology — Oct. 2022 – Present
@Flagship Labs 95, Inc.Consultant, Computational Biology — Oct. 2022 – Present
@Flagship Labs 70, Inc.Computational Biologist (Co-op) — Mar. 2022 – Sept. 2022
@Flagship Labs 70, Inc.Teaching Assistant, "ENGR 131: Introductory Programming for Engineers" — Sept. 2021 – Mar. 2022
@Drexel UniversityProgramming
Machine Learning
Software
Neuroengineering
Sequencing Analysis
Engineering Expertise
Languages
- M. Halac, M. Isik, H. Ayaz and A. Das, "Multiscale Voxel Based Decoding For Enhanced Natural Image Reconstruction From Brain Activity," 2022 International Joint Conference on Neural Networks (IJCNN), 2022, pp. 1-7, doi: 10.1109/IJCNN55064.2022.9892430.
- M. Halac, B. Sokhansanj, W. Trimble, T. Coard, N. Sabin, E. Ozdogan, R. Polikar, G. Rosen, "Incremental & Semi-Supervised Learning for Functional Analysis of Protein Sequences," 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 01-08, doi: 10.1109/SSCI50451.2021.9659958.
- E. Ozdogan, N. Sabin, T. Gracie, S. Portley, M. Halac, T. Coard, W. Trimble, B. Sokhansanj, G. Rosen, R. Polikar, "Incremental and Semi-Supervised Learning of 16S-rRNA Genes For Taxonomic Classification," 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1-7, doi: 10.1109/SSCI50451.2021.9660093.
- M. Sahal, E. Dryden, M. Halac, S. Feldmen, T. Heiman-Patterson, H. Ayaz, "Augmented Reality Integrated Brain Computer Interface for Smart Home Control," Advances in Neuroergonomics and Cognitive Engineering (pp. 89--97), 2021, Springer International Publishing.
- M. De Lorenzo, M. Halac, S. Sivakumar, H. Ayaz, "Deep Neural Network Modeling Using CNN and BiLSTM for Online Passive EEG Brain-Computer Interface to Classify Mental Workload," 2021 Neuroergonomics Conference, 2021.
- E. Moyer, J. Winchell, I. Isozaki, Y. Alparslan, M. Halac, E. Kim, "Functional Protein Structure Annotation Using a Deep Convolutional Generative Adversarial Network," 2021, arXiv: 2104.08969.
- Polynucleotides for Modifying Organisms, Provisional filed 11 Oct 2022, International (PCT) and AR, UY, PY applications filed 31 Oct 2022
- Polynucleotides for Modifying Organisms, Provisional filed 11 Oct 2022
- Outstanding Undergraduate Student Award — Jan. 2023
- Undergraduate Research Mini Grants — Nov. 2022
- Dean's List — 2019-2023
- Nomination, BCI Award — 2021
- Dean's Scholarship — 2019
- RNA Society, Member — 2024 - Present
- Biomedical Engineering Society (BMES), Member — 2024 - Present
- American Academy of Pain Medicine (AAPM), Member — 2024 - Present
- American Society of Pain & Neuroscience (ASPN), Member — 2024 - Present
- IEEE Computational Intelligence Society (CIS), Member — 2022 - Present
- IEEE, Member — 2021 - Present
Education
Research Experience
• 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.
Work Experience
• Built a novel computational discovery and development platform for noncanonical 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 Python with Dr. Andrew Cohen for the winter term of the 2021-2022 academic year..
• Supervised students with their programming assignments.
Skills
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)