About me

Hello! I'm a senior undergraduate student at Middle East Technical University, studying statistics and mathematics. I am interested in fairness and uncertainty in machine learning, particularly in their applications to cognitive science. Currently, as a part of METU ImageLab and Cambridge AFAR, I work on methods of fairness and bias mitigation for multimodal models of depression detection. Previously, I worked Professor Barbaros Yet on multi-agent Bayesian models of human-AI interactionn in medical decision-making. Before that, I was a participant in Teknofest's 2024 international AI competition under the supervision of Professor Ilkay Ulusoy.

Research Interests

  • design icon

    CogSci

    How does our brain work?

  • Web development icon

    ProbProg

    Exploring machines that learn and infer probabilistically.

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    AI Fairness

    Designing models of unbiased decisions for everyone.

  • mobile app icon

    AI Uncertainty

    Handling uncertainty for transparent AI decisions.

Daniel lewis
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Notes and Slides

Hosted on GitHub Pages. Modified from the
original theme by codewithsadee.

Resume

Education

  1. Middle East Technical University

    2020 — 2025 - [Transcript]

  2. Major in Statistics: CGPA 3.91/4.00 -- First Rank in a Class of 113
  3. Minor in Mathematics: CGPA 4.00/4.00
  4. Graduate Coursework: Probability Theory, Probabilistic Programming, Probabilistic Models of Cognition, Deep Learning (year long), Computational Semantics and Syntax (year long)
  5. For a PDF version of my CV, click here.

Research (Undergraduate Researcher)

  1. Multimodal Fairness in Depression (Advisor: Sinan Kalkan, Jiaee Cheong)

    Feb 2024 - Present

    Joint work between Cambridge Affective Intelligence and Robotics Lab (AFAR) and METU ImageLab.
    In this project, we worked on deep learning-based depression detection utilizing audio and neurological EEG data. We analyzed the effects of unsupervised learning multimodal methods using variance and covariance on fairness. ▶ Keywords: Multimodal Fusion Models, Unsupervised Learning, Deep Learning-based Classification, Depression (Affective Disorder)

  2. Human-AI Interaction in Medical Shared Decision-Making (Advisor: Barbaros Yet)

    Feb 2024 - Present - [GitHub]

    In this project, we worked on a model of Medical Shared Decision Making based on Bayesian decision theory. The focus was to build a comprehensive model of two-way doctor-patient conversations in a case study on antibiotic prescriptions. We modeled the language strategies employed by patients and the doctors as a language game, consisting of various utilities, costs, and rewards. ▶ Keywords: Bayesian Decision-Theory, Shared Decision Making, Human-AI interaction

  3. Alzheimer's Diagnosis System (Advisor: İlkay Ulusoy)

    July 2023 - Jan 2024

    ○ As the data analysis team leader, I designed and oversaw the preprocessing and segmentation process of magnetic resonance neurological datasets. ○ In addition, I utilized Dynamic Bayesian Networks developed from fMRI data to to compare Alzheimer's vs Normal Brain region connectivity. ○ I also worked on Gaussian mixture models, to cluster the Inter-Subject Correlation features in fMRI and extract the brain’s activity regions. ○ The project's goal was to design a multimodal deep learning based Alzheimer's diagnosis system, using various features extracted from the data. This project was a participator in Teknofest's 2024 international AI competition.
    ▶ Keywords: Neurological Data Analysis, Bayesian networks, Brain Image Segmentation, Autoencoders

  4. Common Carotid Artery Segmentation

    Dec 2022 - Oct 2023 - [GitHub]

    In this project I contributed to the development of a transformers-based deep learning model for Carotid Artery Ultrasound segmentation and contributed to surpassing previous benchmark results. ▶ Keywords: Medical Segmentation, Transformers, Deep Larning-based Segmentation Models

  5. Satisfaction Survey (Instructor/Advisor: Fulya Gökalp Yavuz)

    Nov 2022 - Jan 2023 - [GitHub]

    We conducted a survey on international students at 17 Turkish universities about their motivations, expectations, and satisfaction using the poststratification sampling method. Then, we analyzed the results using statistial modeling and machine learning. ▶ Keywords: Survey and Sampling, Design of Experiment, Psychology, Machine Learning

Teaching

  1. Student Teaching Assistant (Course Instructor: Zeynep Işıl Kalaylıoğlu)

    2022-2023 academic year

    I was the student assistant for upper devision theoretical courses, Mathematical Statistics I (STAT 303) and Mathematical Statistics II (STAT 304). I led office hours for students and provided weekly individualized support and guidance on course material and problem-solving. ▶ Keywords: Theory of Statistical Inference, Theory of Estimation, Bayesian Inference

My skills

  • Computational Cognitive Science
  • Deep Learning
    80%
  • Statistical Data Analysis
    90%
  • Structure and Interpretation of Computer Programs
    50%

Projects

Projects

  1. First-Order PPL [Ongoing] [GitHub]

    Implemented of a first-order Bayesian probabilistic programming language in Julia, supporting various inference algorithms such as Importance Sampling, Metropolis Hastings, Gibbs Sampling, and Variational Inference. ▶ Skills: Bayesian Statistics, Structure and Interpretation of Computer Programs, Julia

  2. Make Me a BNN [GitHub]

    Implemented and reproduced the results of the paper "Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models" (CVPR 2024) from scratch, contributing to the field as no implementation was available for this paper. ▶ Skills: Uncertainty-based Deep Learning, Segmentation, Classification, Python (pytorch)

  3. CCG Semantic Parser[GitHub]

    Implemented a simple CCG-based semantic parser in Python. For the syntactic rules , Combinatory Categorical Grammar (CCG) alongside internal merge grammar was used. For logic and semantics, Lambda Calculus and Modality Logic were implemented. ▶ Skills: Combinatory Categorical Grammar, Syntax and Semantics, Data Structures, Python

  4. Scheme Interpreter [GitHub]

    Implemented a Scheme interpreter in Julia. For a large part, I followed the content of SICP Textbook and Peter Noverg's Tutorials. ▶ Skills: Structure and Interpretation of Computer Programs, Julia

  5. Politeness Rational Model [GitHub]

    Implemented and reproduced the full results of the rational speech acts model of politeness in Gen probabilistic programming language. ▶ Skills: Rational Speech Acts, Data Structures, Gen, Julia

  6. BART's Bayesian Analysis [GitHub]

    Applied Bayesian methods in analysis of the BART (Balloon Analog Risk Task) cognitive test. ▶ Skills: Bayesian Linear and Non-linear models, Bayesian Multi-level Models, Bayesian Gaussian processes, Python (PyMC)

  7. Human Activity Recognition [GitHub]

    Used machine learning models for human activity recognition Using Smartphones dataset as a competitor in a Kaggle competition. ▶ Skills: Machine Learning, Advanced Preprocessing Methods, Python

  8. Developed an R Shiny app [GitHub]

    Designed an interactive R Shiny app using asylum seekers' data. ▶ Skills: Machine Learning, Statistical Methods, R (Shiny)

Contact

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