About me

I am a fifth year Computer Science PhD candidate at Boston University in the Image and Video Computing Group and a member of the Kolachalama Laboratory at the BU School of Medicine. I am advised by Professors Sarah Adel Bargal, Bryan A. Plummer, and Vijaya B. Kolachalama. Before joining BU, I received a Bachelor's degree in Computer Science at the American University of Beirut, Lebanon. My research interests fall under the umbrella of explainable AI, efficient machine learning and computer vision. I am interested in improving applications at the intersection of AI and health care through interpretable, efficient and trustworthy machine learning models. My thesis revolves around learning robust and generalizable representations of neuro-imaging data for the assessment of Alzheimer's disease and related neurodegnerative disorders.

Graduate Coursework

  • CS542 Machine Learning
  • CS640 Artificial Intelligence
  • CS585 Image and Video Computing
  • CS535 Complexity Theory
  • CS530 Advanced Algorithms
  • CS552 Introduction to Operating Systems
  • CS511 Formal Methods I

Interests

  • Computer Vision
  • Efficient Machine Learning
  • Deep Learning
  • Explainable AI

Education

  • PhD in Computer Science, Sept 2019 - Present

    Boston University

  • BSc in Computer Science, 2016 - 2019

    American University of Beirut

News

  • June 2024: Our paper on using AI to identify etiologies contributing to dementia in individuals was accepted at Nature Medicine!
  • April 2024: Paper was accepted at Human Brain Mapping.
  • September 2022: Completed an Applied AI Scientist Summer internship at Inari Medical, Inc.
  • January 2022: Joined The Kolachalama Laboratory at the BU School of Medicine.
  • August 2021: Gave a guest lecture at AI4ALL about an introduction to Deep Learning and Explainable AI.

Papers

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for …
Chonghua Xue*, Sahana S. Kowshik*, Diala Lteif, Shreyas Puducheri, Varuna H. Jasodanand, Olivia T. Zhou, Anika S. Walia, Osman B. Guney, J. Diana Zhang, Serena T. Pham, Artem Kaliaev, V. Carlota Andreu-Arasa, Brigid C. Dwyer, Chad W. Farris, Honglin Hao, Sachin Kedar, Asim Z. Mian, Daniel L. Murman, Sarah A. O’Shea, Aaron B. Paul, Saurabh Rohatgi, Marie-Helene Saint-Hilaire, Emmett A. Sartor, Bindu N. Setty, Juan E. Small, Arun Swaminathan, Olga Taraschenko, Jing Yuan, Yan Zhou, Shuhan Zhu, Cody Karjadi, Ting Fang Alvin Ang, Sarah A. Bargal, Bryan A. Plummer, Kathleen L. Poston, Meysam Ahangaran, Rhoda Au, Vijaya B. Kolachalama
PDF

Disease-driven domain generalization for neuroimaging-based assessment of Alzheimer’s disease.

Development of deep learning models to assess the degree of cognitive impairment on magnetic resonance imaging (MRI) scans has high …
Diala Lteif, Sandeep Sreerama, Sarah Adel Bargal, Bryan A. Plummer, Rhoda Au, Vijaya B. Kolachalama
PDF

Ani-GIFs: A benchmark dataset for domain generalization of action recognition from GIFs.

Deep learning models perform remarkably well for the same task under the assumption that data is always coming from the same …
Shoumik Sovan Majumdar*, Shubhangi Jain*, Isidora Chara Tourni*, Arsenii Mustafin, Diala Lteif, Stan Sclaroff, Kate Saenko, Sarah Adel Bargal
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Experience

Research

  • Research Fellow, Summer 2021 - Present

    Boston University, Department of Computer Science & School of Medicine

    Working with Profs. Sarah Adel Bargal, Bryan Plummer and Vijaya B. Kolachalama on explainability for tackling bias in medical imaging.

  • Research Fellow, 2020 - Present

    Boston University, Department of Computer Science

    Working with Prof. Bryan Plummer on cross-layer parameter sharing for knowledge transfer and resource-efficient on-device machine learning.

  • Research Fellow, 2020 - Present

    Boston University, Department of Computer Science

    Working with Prof. Sarah Adel Bargal on spatiotemporal visualizations for explaining deep video classification models.

Industry

  • Applied AI/ML Scientist Intern, June 2022 - Sept 2022

    Inari Medical Inc., Irvine, California, United States

    Worked on developing novel AI solutions for minimally-invasive endovascular procedures, with the specific application of intravenous ultrasound medical imaging.

  • Intern, Aug 2018 - Sept 2018

    PinPay s.a.l., Beirut Digital District, Lebanon

    Worked on Amazon Alexa, Google Assistant services and front-end web development.

Teaching

  • Teaching Fellow, Sept 2019 - Present

    Boston University, Department of Computer Science

    • CS542: Machine Learning (Fall 2021)
    • CS330: Introduction to Algorithms (Spring 2020)
    • CS103: Introduction to Internet Technologies and Web Programming (Fall 2019)

  • Instructor, July 2018

    Future Developer Summer Camp, American University of Beirut, Lebanon

    Taught Unity and iOS development to students between the ages 12 and 18.

  • Instructor, Feb 2017 - May 2017

    S.A.S. Tutoring Center, Beirut, Lebanon

    Taught Biology, Chemistry and Physics to high school students.

Contact

  • dlteif@bu.edu
  • Center of Computing and Data Sciences, 665 Commonwealth Avenue, Boston, MA 02215