Payam Barnaghi Gül İnan

Room: 424, Department of Mathematics
Faculty of Arts and Sciences
Istanbul Technical University
34469, Maslak, İstanbul, Turkey
Email: inan[at]itu.edu.tr

Payam on Slideshare   Payam on Linkedin   Payam on Twitter



About me

I am an assistant professor in Statistics in the Department of Mathematics at Istanbul Technical University (ITU), Istanbul, Turkey since 2018, where the department offers a mathematical engineering programme. I received my Ph.D., M.Sc., and B.Sc. all in Statistics from Department of Statistics, Middle East Technical University (METU), Ankara, Turkey, in 2014, 2009, and 2006, respectively. I did my post-doctoral studies at School of Statistics, University of Minnesota, USA, and at Department of Biostatistics, University of North Carolina-Chapel Hill, USA, between 2015-2016.

Research interests

In the past, I worked on developing new statistical methodologies for analysis of longitudinal data and zero-inflated data for discrete outcomes motivated by health sciences. Most of the time, I dealt with computationally intensive models where the parameter optimization was a challeging task. This experience motivated me to continue to work on computationally heavy methods. My current research interest is on statistical learning/deep learning algorithms for image classification, high-dimensional sparse data analysis, and topic modeling for digitized historical documents. Apart from these, I am also interested in history of mathematics, statistics, and programming languages.


Fall 2021 Course Webpages


Supervision

  • Master Thesis Project*:
    • Ozan Güldali (August 2021). Deep feature transfer from deep learning models into machine learning algorithms to classify Covid-19 from chest X-ray images. [YOK Tez soon], [Code].
  • Undergraduate Senior Thesis Project:
    • Sercan Yıldız, Paşa Çağrı Köroğlu (August 2021).
    • Nemat J. Jahangirov (January 2021).
    • Sami Aldağ, Doğukan Topçuoğlu, Bertan Taylan, Hakan Turgay (January 2020).
    • Erol Kıbrıs (June 2019).
  • *Qualifications required for potential graduate students: Previous course work on: Statistics, machine learning, deep learning, linear algebra, and optimization, good level of Python knowledge, familiarity with Git/GitHub and LaTeX (and/or Overleaf), and being a research-oriented person, being able to read and critize research papers published in machine learning venues from a mathematical/statistical/computational view point, and strong communication skills (both verbally and orally). A lightened version of these rules applies to undergraduate students as well.


Publications

  Google Scholar

Abstracts in Conferences


Talks

  • "A score test for testing a marginalized zero-inflated Poisson regression model against a marginalized zero-inflated negative-binomial regression model" at Basque Center for Applied Mathematics, Bilbao, Spain, September 2017.
  • "A score test for testing a marginalized zero-inflated Poisson regression model against a marginalized zero-inflated negative-binomial regression model" at School of Science, Royal Melbourne Institute of Technology, Melbourne, Australia, June 2017.
  • "A score test for testing a marginalized zero-inflated Poisson regression model against a marginalized zero-inflated negative-binomial regression model" at Department of Statistics, Middle East Technical University, December 2016.
  • "A marginalized multilevel model for bivariate longitudinal binary data", at Department of Statistics, Hacettepe University, January 2015.
  • "A marginalized multilevel model for bivariate longitudinal binary data", at Department of Statistics, Middle East Technical University, June 2014.

Computer Skills

  • Operating Systems: Microsoft Windows, macOS, Linux.
  • Version Control: Git, GitHub/GitHub classroom.
  • Mark-up languages: LaTeX, Markdown, HTML (along with some CSS), R Markdown.
  • Presentation frameworks: Beamer, R xaringan (along with some Remark.js).
  • Statistical software: R (developed packages, taught courses), SAS, Jags.
  • IDEs: RStudio for R, JupyterLab and Google Colab for Python.
  • HPC clusters: Used HPC clusters of METU and University of Minnesota for conducting statistical simulation studies of aforementioned papers.

Statistical Software Developed


Teaching Experience

  • Instructor at ITU:
    • Mat221E: Probability Theory (Fall21).
    • Mat242E: Statistics (Summer18, Spring21).
    • Mat381E: Introduction to Data Science (Spring21, Fall21). Course GitHub organization is available here.
    • Mat103E: Calculus I (Fall18, Spring19, Fall19, Fall20, Spring20).
    • BTE575E: Statistics for Information Technologies (Fall19, Fall20).
  • Instructor at METU:
    • BA4520: Applied Statistical Computing with R (Fall17).
    • Stat292: Statistical Computing II (with SAS) (Spring17).
    • Stat291: Statistical Computing I (with R) (Fall16).
    • Stat221: Fundemantals of Statistics (Fall14).
  • Teaching assistant at METU:
    • Stat291: Statistical Computing I (Fall 2013), Stat292: Statistical Computing II (Spring 2013), Stat363: Linear Models I (Fall 2006, 2007, Summer 2014), Stat364: Linear Models II (Spring 2007, 2008), Stat365: Survey Sampling Techniques (Fall 2008), Stat457: Statistical Design of Experiments (Fall 2008, 2009, 2010, Spring 2009), Stat462: Biostatistics (Spring 2011, Fall 2011), and Stat472: Statistical Decision Analysis (Spring 2010).

Honors

  • Ranked 1st among the faculty of Department of Business Administration at METU in student course evaluations in Fall2017.
  • Ranked 2nd among the faculty of Department of Statistics at METU in student course evaluations in Spring2017.
  • Ranked 2nd among the faculty of Department of Statistics at METU in student course evaluations in Fall2016.
  • Ranked 3rd among the faculty of Department of Statistics at METU in student course evaluations in Fall2014.

Travel Grants


Research Fellowships/Scholarships

  • Research fellowship by University of North Carolina-Chapel Hill, USA to visit Department of Biostatistics between April 2016-June 2016.
  • Post-doctoral research fellowship by TUBITAK to visit School of Statistics, University of Minnesota, USA between April 2015-April 2016.
  • National Scholarship for PhD Students by TUBITAK between Fall 2009-Fall 2014.
  • National Scholarship for MSc Students by The Scientific and Technological Research Council of Turkey (TUBITAK) between Fall 2006-Fall 2008.

Research Grants

  • Academic advisor of the project titled "Reading the digitized historical newspapers of İstanbul University Archive via data science tools" with Hüseyin Deniz, Kaan Çorum, Sercan Yıldız, Paşa Çağrı Köroğlu, funded by TUBITAK 2209/A programme between July 2021-January 2022.
  • Principal investigator of the project titled "Bayesian joint modeling of longitudinal data on frequency and duration of migraine", funded by ITU-New faculty start-up programme between January 2019-January 2020.

Reviewer for

  • Communications in Statistics – Theory and Methods (1).
  • Computational Statistics and Data Analysis (2).
  • Hacettepe Journal of Mathematics and Statistics (2).
  • Journal of Applied Statistics (2).
  • Journal of Forecasting (1).
  • Journal of Multivariate Analysis (1).
  • Journal of Statistical Computation and Simulation (5).
  • Journal of Statistics Education (1).
  • Journal of Statistical Software (1).
  • Journal of Statistical Research, ISRT, University of Dhaka (1).
  • Statistical Methods in Medical Research (4).
  • Statistical Modeling (1).
  • Statistics and Probability Letters (1).
  • Statistics in Medicine (5).
  • TUBITAK project grants (3).

Equity and Diversity

In my professional life, I interact with so many scholars and students with diverse backgrounds through exchanging ideas via emails, collobrating on projects, teaching classes, reviewing papers/project grants, chairing conference/workshop sessions and so on. Here, I should say that while I am doing my job, I am committed to cultural and individual differences and diversity as including, but not limited to, age, disability, ethnicity, gender, gender identity, language, national origin, race, religion, culture, and socioeconomic status and I acknowledge the value of differences.


Suggested Readings on Education and Academia

  • On reproducible research: If you are a scholar and interested in how to release your research in different formats (e.g., paper in a pdf file, talk with slides etc) based on a single source file and keep yourself posted with recent relevant technologies, I strongly suggest to you read "You Only Write Thrice: Creating Documents, Computational Notebooks and Presentations From a Single Source" by Kacper Sokol and Peter Flach presented at ICLR 2021 workshop. Here is the paper link.

  • On developing a project based course: Last year, I developed an introductory data science course from scratch and offered it in Spring21. I had to deal with lots of issues along the way. If you are a scholar and interested in how to develop a project-based (machine learning) course, I strongly suggest to you read "Deeper Learning By Doing: Integrating Hands-On Research Projects Into A Machine Learning Course" by Sebastian Raschka presented at ECML 2021. Here is the paper link.

  • On promoting equity and diversion: If you are a scholar and at the stage of developing/revising your syllabus, I strongly suggest to you read "Rethinking the Course Syllabus: Considerations for Promoting Equity, Diversity, and Inclusion" by Milton A. Fuentes, David G. Zelaya, and Joshua W. Madsen workshop. Here is the paper link.

  • On necessity of soft skills: Most of the time students think that succeeding in a class or in a project is all about hard-skills (e.g., knowing the subject, having computing skills etc). However, it is not enough. If you are a student, I strongly suggest to you read "The soft skills college students need to succeed now and in the future" by Drew C. Appleby. Here is the article link.

  • On bullying and mobbing in academia: At the earlier stages of careers, some academics may experience academic bullying and mobbing at workplace due to the hierarchy. Identifying these behaviors may be hard at first sight. If you are a scholar and in a doubt, I strongly suggest you to read "Mind your Head: An introduction to Workplace Bullying in Academia" by Simone Pieber, Elenora van Rijsingen, Derya Gürer and Anouk Beniest. Here is the article link. If you are subject to a constant mobbing, I also strongly encourage to consult the university head. Never be silent!..


Miscellaneous I   

I am a first-generation university graduate and academic in my extended family. Throughout all my education life (starting from primary education), I was able to go to school with the help of academic success scholarships awarded by the government. I do like learning new things a lot. This is the reason why I am pursuing this career. While I listed my achievements (according to me) here, I have a much longer list consisting of failures and rejections. So I keep learning.


Miscellaneous II   

About non-academic me: I do like traveling a lot. Prior to Covid-19, I was very fortunate to travel more than 50 cities in 20 different countries in four different continents. My last international travel was to Portugal in February 2020! (That was the first the time I wore a mask while I was traveling back to Istanbul from Lisbon airport). I am unprofessionally interested in nature (e.g., flowers, migrant birds, waterfalls, geological formations, and meteorological and astronomical events), architecture, and history. Whenever I go to a city for the first time, I prefer to visit national parks, botanical gardens, and art museums. Apart from plants and animals, I do like human beings who talk, write, and behave kindly in their social and professional life.


Last updated: 20-09-2021