Hello! I'm Seyhmus.
Senior Data Scientist with 10+ years of experience applying advanced statistical and machine learning models to large-scale datasets. I am an expert in developing computational pipelines for building large-scale multi-omics datasets for early drug discovery, functional neuroimaging technologies, and optimizing non-invasive brain stimulation. I am a detail-oriented problem solver recognized for delivering innovative, data-driven solutions in cross-functional environments. My experience includes modernizing data processing pipelines and infrastructures, shipping new data products, and decommissioning older systems that touch multiple upstream and downstream processes and stakeholders. Having taught multiple courses and mentored peers and interns during my professional career, I have developed strong communication and leadership skills and a passion for sharing knowledge.
I care deeply about advancing medical research and utilizing data science to improve quality of life, and feel lucky to have a career where I can contribute to this goal. During my PhD, I worked on how to more effectively stimulate the human brain and brain computer interfaces. In my postdoctoral studies, I worked on various projects around functional MRI (fMRI) and non-invasive brain stimulation: I developed tailored pipelines for motion correction in fMRI, explored imaging biomarkers of pain, developed protocols for real-time fMRI-neurofeedback, and investigated the effect of non-invasive brain stimulation coupled with acupuncture on chronic low back pain. In my current role at Recursion, I develop computational pipelines and machine learning models to align, process, and analyze large-scale multi-omics datasets. This includes building and maintaining maps of biology that are crucial to Recursion's mission to decode biology to radically improve lives.
Skills and Interests
Data Science & Programming
Python
- Pandas, Polars, NumPy, SciPy
- Scikit-learn, PyTorch, Pandera, Pydantic
- Plotly, Seaborn, Matplotlib
- Requests, Django, Pytest, FastAPI
- uv, venv, production code, mypy
Other Languages & Tools
- Bash, SQL, R, BigQuery, Matlab
- LaTeX, Microsoft Office, Mendeley
MLOps, DevOps & Cloud Technologies
- MLOps: Experience with machine learning model deployment, monitoring, and lifecycle management, W&B, and MLflow.
- CI/CD Pipelines: Expertise in creating and managing continuous integration and deployment pipelines.
- Containerization: Proficient with Docker for building and managing containers for testing and deployment.
- Google Cloud Platform (GCP): Skilled with the gcloud suite, including BigQuery, and Google Cloud Storage, familiar with IAM, Code as Infrastructure (Terraform), and Kubernetes basics.
- Version Control: Git, GitHub, SVN, Bitbucket, Production code workflows, reviewing and merging pull requests.
Neuroimaging Data Analysis
FSL, Nipype, Freesurfer, fMRIPrep, SPM, CONN, BIDS, ANTs, Nilearn, GIFT
Hands-on Experience
Linux workstations, HPC, Operating an MRI scanner, tDCS setup and application, EEG data acquisition and analysis
Languages
Kurdish (Native), Turkish (Native), English (Fluent), Spanish (Beginner)
Hobbies
Dancing, Racquetball, Snowboarding, Canyoneering, Jump rope, Hiking, MTB, Soccer, Chess
Download my one-page CV.
Experience
| June 2024 – Present | Senior data scientist, Recursion Pharmaceuticals, Salt Lake City, UT |
| March 2022 – May 2024 | Data scientist, Recursion Pharmaceuticals, Salt Lake City, UT |
| March 2020 – Feb 2022 | Research fellow, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA |
| Nov. 2016 – Feb. 2020 | Research fellow, Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA |
| June 2016 – Aug. 2019 | Lecturer (part-time), Department of Electrical and Computer Engineering, Northeastern University, Boston, MA |
| Sep. 2011 – Oct. 2016 | Research assistant (Ph.D. candidate), Department of Electrical and Computer Engineering, Northeastern University, Boston |
Education
| 2011 – 2016 | Ph.D. in Electrical Engineering, Northeastern University, Boston Thesis title: Brain Stimulus Pattern Optimization Using Scalp and Cortical Electrode Arrays. Advisor: Dana H. Brooks. |
| 2006 – 2011 | B.Sc. in Electrical and Electronics Engineering, , Bilkent University, Ankara, Turkey |
| 2002 - 2006 | High school diploma, Izmir Science High School, Izmir, Turkey |
Awards and scholarships
| 2011–2016 | Graduate research assistantship (GRA), Northeastern University, Boston, MA |
| 2006–2011 | Full scholarship for undergraduate studies, Bilkent University, Ankara, Turkey |
| 2006 | 210th in National University Entrance Exam of Turkey (1.5 million students) |
| 2005 | 5th (Silver medalist) in 13th National Mathematics Olympiad, TUBITAK, Ankara |
| 2002 | 21st (Silver medalist) in 7th National Secondary School Mathematics Olympiad, TUBITAK, Ankara |
Tutorials
Coming soon!
Select Journal and Conference Papers
- Guler, S., Cohen, A. L., Afacan, O., & Warfield, S. K. (2021). Matched feedback during fMRI neurofeedback differentially activates reward-related circuits in active and sham groups. J Neuroimaging, 00, 1-9.
- Tu, Y., Cao, J., Guler, S., Zhang, T., Camprodon, J. A., Vangel, M., ... & Kong, J. (2021). Perturbing fMRI brain dynamics using transcranial direct current stimulation. NeuroImage, 118100.
- Guler, S., Erem, B., Cohen, A. L., Afacan, O., & Warfield, S. K. (2020, April). Dynamic Missing-Data Completion Reduces Leakage of Motion Artifact Caused by Temporal Filtering that Remains After Scrubbing. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (pp. 1-4). IEEE.
- Guler, S., Dannhauer, M., Roig-Solvas, B., Gkogkidis, A., Macleod, R., Ball, T., ... & Brooks, D. H. (2018). Computationally optimized ECoG stimulation with local safety constraints. NeuroImage, 173, 35-48.
- Yokota, T., Erem, B., Guler, S., Warfield, S. K., & Hontani, H. (2018). Missing slice recovery for tensors using a low-rank model in embedded space. In Proceedings of the IEEE conference on computer visionand pattern recognition (CVPR) (pp. 8251-8259).
- Guler, S., Dannhauer, M., Erem, B., Macleod, R., Tucker, D., Turovets, S., ... & Brooks, D. H. (2016,April). Optimizing stimulus patterns for dense array tDCS with fewer sources than electrodes usinga branch and bound algorithm. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) (pp. 229-232). IEEE.
- Guler, S., Dannhauer, M., Erem, B., Macleod, R., Tucker, D., Turovets, S., ... & Brooks, D. H. (2016). Optimization of focality and direction in dense electrode array transcranial direct current stimulation(tDCS). Journal of neural engineering, 13(3), 036020.
