Bio

Hulya Torun is a postdoctoral fellow at Stanford Neurology and Neurological Sciences, continuing her specialization in Biomedical Sciences and Engineering. Her focus involves brain aging & neurodegeneration and Raman spectroscopy-based diagnostic technologies for the precise detection of brain tumors. Hulya is dedicated to making significant contributions to medicine through translational research. She has published in esteemed journals, including 'Advanced Devices & Instrumentation' and 'Small.' Notably, she has been selected as Stanford Representative for a Pediatric Brain Tumor Fellowship Application, a finalist for the Stanford Biodesign MEDTech Spectrum Grant, and the recipient of the OPTICA Zuegel Scholarship, ISEV 2024 International Researcher Award, 1st place in KUIMPACT 2023 Patent Competition, SNO 2023 International Scholar Award, 3rd place in KUIMPACT 2021 Patent Competition, and SPIE Student Travel Grant, underscoring her commitment to impactful research.

Beyond her academic pursuits, Hulya actively engages in mentorship programs, notably serving as a mentor in the Stanford Canary CREST Program, supported by the National Cancer Institute (NCI), where she guides undergraduate students from diverse backgrounds. Additionally, she holds the esteemed position of President of Stanford Optical Society after her former position as the Co-Chair of the Stanford University Photonics Retreat (SUPR 2024), showcasing her leadership capabilities within the academic community. Her multidimensional engagement, strong research acumen, and dedication to advancing healthcare technology underscore her potential as a future leader in the realm of neuroengineering. Outside of her research endeavors, Hulya is an avid participant in professional extracurricular activities such as dancing and volleyball, reflecting her well-rounded approach to personal and professional development.

Honors & Awards

  • Pathways to Neurosciences, Stanford Wu Tsai Neurosciences Institute (September 2024)
  • 2224 - Travel Grant Program for Participation of Scientific Meetings Abroad, TUBITAK (The Scientific and Technological Research Council of Turkiye) (2024)
  • Biosciences Student Travel Grant, Stanford Office of Graduate Education (OGE) (2024)
  • International Researcher Scholar Award (Research Excellence), International Society of Extracellular Vesicles (ISEV 2024 Annual Meeting) (2024)
  • KUIMPACT 2023 Patent Competition Award, 1st Place, Koç University (2024)
  • Mikitani Cancer Research Grant, Co-PI, Stanford Cancer Institute (2024)
  • Student Leadership Award, Frontiers in Optics, OPTICA (2024)
  • Travel Grant, Koç University Graduate School of Health Sciences (2024)
  • Zuegel Scholarship, Siegman School of Lasers, OPTICA (2024)
  • International Outreach Student Scholarship Award, Society for NeuroOncology (SNO) (2023)
  • Stanford Biodesign MEDTech Spectrum Grant, Finalist, Stanford Biodesign, Stanford University (2023)
  • KUIMPACT 2021 Patent Competition Award, 3rd Place, Koç University (2022)
  • SPIE Student Travel Grant, Photonics West BIOS, SPIE (2022)
  • Graduate Student Fellowship, Koç University Graduate School of Health Sciences (2021)
  • Best Poster Presentation Award, Research Days, Koç University School of Medicine (2019)
  • Erasmus+ Traineeship Award (Charité – Universitätsmedizin Berlin, European Commission of Education, Audiovisual, and Culture Executive Agency (EACEA) (2018)
  • Erasmus+ Exchange Study Scholarship Award (Technische Universitaet Berlin), European Commission of Education, Audiovisual, and Culture Executive Agency (EACEA) (2016)

Boards, Advisory Committees, Professional Organizations

  • President, Stanford Optical Society (2024 - Present)
  • Co-Chair, Stanford University Photonics Retreat (SUPR) 2024 Committee, Stanford Optical Society (2023 - 2024)
  • Board Member, Koç University Quality Board (2021 - 2022)
  • Executive Board Committee Member, Koç University Student Council (2020 - 2022)
  • President/Graduate Student Representative, Koç University Graduate Student Council (2020 - 2022)
  • Student Representative, Koç University Graduate School of Sciences & Engineering (2020 - 2022)
  • Student Representative, Koç University Biomedical Sciences & Engineering Graduate Program (2020 - 2022)
  • Student Representative, Yildiz Technical University, Department of Bioengineering (2013 - 2017)

Professional Education

  • Postdoctoral Fellow, Stanford Wu Tsai Neuroscience Institute - Knight Initiative for Brain Resilience, Brain Resilience
  • Postdoctoral Fellow, Stanford Neurology and Neurological Sciences (Tony Wyss-Coray Lab), Brain Aging and Neurodegeneration
  • PhD, VSR, Stanford University, Radiology (2024)
  • PhD, Koç University, Biomedical Sciences & Engineering (2024)
  • MS, Koç University, Biomedical Sciences & Engineering (2021)
  • MS, VSR, UCSF, Neuropathology (2019)
  • BS, Yildiz Technical University, Bioengineering (2017)
  • Erasmus, Technical University of Berlin, Biotechnology (2016)

Stanford Advisors

Contact

  • Academic
    University - Scholar Department: Adult Neurology Position: Postdoctoral Scholar

Additional Info

All Publications

  • A liquid biopsy approach: Neural networks-based identification of brain tumor exosomes via their SERS signatures ISEV 2024 Annual Meeting (International Society of Extracellular Vesicles) Torun, H. 2024: 284/451

    View details for DOI 10.1002/jev2.12444

  • Label-Free Identification of Exosomes using Raman Spectroscopy and Machine Learning. Small (Weinheim an der Bergstrasse, Germany) Parlatan, U., Ozen, M. O., Kecoglu, I., Koyuncu, B., Torun, H., Khalafkhany, D., Loc, I., Ogut, M. G., Inci, F., Akin, D., Solaroglu, I., Ozoren, N., Unlu, M. B., Demirci, U. 2023: e2205519

    Abstract

    Exosomes, nano-sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label-free Raman spectroscopy method's prediction ratio correlates with the ratio of HT-1080 exosomes in the mixture. This machine learning-assisted SERS method enables a new direction through label-free investigation of EV preparations by differentiating cancer cell-derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.

    View details for DOI 10.1002/smll.202205519

    View details for PubMedID 36642804

  • Rapid Nanoplasmonic-Enhanced Detection of SARS-CoV-2 and Variants on DNA Aptamer Metasurfaces Advanced Devices & Instrumentation Torun, H., et al 2023; 4

    View details for DOI 10.34133/adi.0008

  • Microfluidic contact lenses for ocular diagnostics and drug delivery NANO SELECT Torun, H., Fazla, B., Arman, S., Ozdalgic, B., Yetisen, A. K., Tasoglu, S. 2023; 4 (1): 79-89

    View details for DOI 10.1002/nano.202200202

    View details for Web of Science ID 001176557000005

  • Machine Learning-Based Approach to Identify Formalin-Fixed Paraffin-Embedded Glioblastoma and Healthy Brain Tissues Torun, H., Batur, N., Bilgin, B., Esengur, O., Baysal, K., Kulac, I., Solaroglu, I., Onbasli, M., Campagnola, P. J., Maitland, K. C., Roblyer, D. M. SPIE-INT SOC OPTICAL ENGINEERING. 2022

    View details for DOI 10.1117/12.2608957

    View details for Web of Science ID 000812269100005

  • Clinical Validation of SERS Metasurface SARS-CoV-2 Biosensor Bilgin, B., Torun, H., Ilgu, M., Yanik, C., Batur, N., Celik, S., Ozturk, M., Dogan, O., Ergonul, O., Solaroglu, I., Can, F., Onbasli, M., Huang, Z. SPIE-INT SOC OPTICAL ENGINEERING. 2022

    View details for DOI 10.1117/12.2607929

    View details for Web of Science ID 000825421900007

  • Genetic Algorithm-Driven Surface-Enhanced Raman Spectroscopy Substrate Optimization. Nanomaterials (Basel, Switzerland) Bilgin, B., Yanik, C., Torun, H., Onbasli, M. C. 2021; 11 (11)

    Abstract

    Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive and molecule-specific detection technique that uses surface plasmon resonances to enhance Raman scattering from analytes. In SERS system design, the substrates must have minimal or no background at the incident laser wavelength and large Raman signal enhancement via plasmonic confinement and grating modes over large areas (i.e., squared millimeters). These requirements impose many competing design constraints that make exhaustive parametric computational optimization of SERS substrates prohibitively time consuming. Here, we demonstrate a genetic-algorithm (GA)-based optimization method for SERS substrates to achieve strong electric field localization over wide areas for reconfigurable and programmable photonic SERS sensors. We analyzed the GA parameters and tuned them for SERS substrate optimization in detail. We experimentally validated the model results by fabricating the predicted nanostructures using electron beam lithography. The experimental Raman spectrum signal enhancements of the optimized SERS substrates validated the model predictions and enabled the generation of a detailed Raman profile of methylene blue fluorescence dye. The GA and its optimization shown here could pave the way for photonic chips and components with arbitrary design constraints, wavelength bands, and performance targets.

    View details for DOI 10.3390/nano11112905

    View details for PubMedID 34835670

    View details for PubMedCentralID PMC8618775

  • Machine learning detects SARS-CoV-2 and variants rapidly on DNA aptamer metasurfaces medRxiv Torun, H., et al 2021

    View details for DOI 10.1101/2021.08.07.21261749

  • Raman Spectroscopic and Microscopic Analysis of Tissue Type, Molecular Composition, and Glioblastoma Identification in Brain Tissue Sections Torun, H. Koc University Graduate School of Science & Engineering. 2021

    View details for DOI 10.48288/KUREPOSITORY

  • Optimization of brain tissue section preparation and raman spectroscopy measurement protocols Torun, H. 2020

    View details for DOI 10.1117/12.2567118

https://orcid.org/0000-0001-5939-4006