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SHUBHASHIS ROY DIPTA

PhD Researcher ⎟ NLP ⎟ CV ⎟ Multimodal Gen AI ⎟ Data Science

sroydip1@umbc.edu

Baltimore, Maryland-21227

I am a Computer Science PhD Researcher under Dr. Frank Ferarro at the University
of Maryland, Baltimore County (UMBC). My research combines Natural Language
Processing (NLP) and Computer Vision (CV). I have a track record of publishing
in top-tier conferences and journals.

Currently, I’m focused on multimodal counterfactual event reasoning,
understanding, and generation. My previous work, a hierarchical variational
autoencoder for event representation learning, has applications in text
summarization, question answering, and counterfactual reasoning (Published in
*SEM 2023, ACL).

Actively looking for Summer Internship in Data Science, Natural Language
Processing, Computer Vision and/or Machine Learning. Please contact me if you
have any opportunity.

I completed my M.Sc. in CS with Phi Kappa Phi (CGPA 4.0/4.0) in Spring 2023 at
UMBC. Before my M.Sc., I gained over 3 years of industry experience as a
Full-Stack Software Engineer. During that time, I also worked with Dr. Iman
Dehzangi (Rutgers University) and published 4 journal papers.

I have a strong background in ML programming, including PyTorch, 🤗 HuggingFace,
NLTK, Spacy, Matplotlib, Seaborn, Tableau and more. I’ve excelled in machine
learning competitions (Kaggle top-70 🥉) and coding competitions (ACM ICPC 8th
out of 300+ teams) and more. I was the founder of UniShopr, a cross-border
e-commerce for my home country (Bangladesh).


RESEARCH INTEREST

        ✓ Multimodal Generation (Language 📖 + Vision 👀)
        ✓ Natural Language Understanding 📖
        ✓ Computer Vision 👀


I  WRITE ✍️  ON MACHINE LEARNING, NLP, VISION, MULTIMODAL AI

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RECENT NEWS
(SEE ALL)

Jan 31, 2024 Reviewed another paper in Scientific Reports, a reputed journal
from Nature. Jan 4, 2024 One of my small contribution has been merged into the
Pytorch Lightning codebase. See here. Jan 1, 2024 Reviewed 1 paper in W-NUT
workshop of EACL 2024. Dec 31, 2023 Reviewed 1 paper in Plant Methods, a reputed
journal from Springer Nature. Dec 18, 2023 Our paper named “SeeBel: Seeing is
Believing” is now available in arXiv. This is a joint work with Sourajit Saha.


FEATURED PUBLICATIONS
(SHOW MORE)

Check out Google Scholar for a full list of my publications.

 1. ACL
    Semantically-informed Hierarchical Event Modeling
    Shubhashis Roy Dipta, Mehdi Rezaee, and Francis Ferraro
    In Proceedings of the 12th Joint Conference on Lexical and Computational
    Semantics (*SEM 2023), Jul 2023
    
    Abs arXiv Bib HTML PDF Code
    
    Prior work has shown that coupling sequential latent variable models with
    semantic ontological knowledge can improve the representational capabilities
    of event modeling approaches. In this work, we present a novel, doubly
    hierarchical, semi-supervised event modeling framework that provides
    structural hierarchy while also accounting for ontological hierarchy. Our
    approach consistsof multiple layers of structured latent variables, where
    each successive layer compresses and abstracts the previous layers. We guide
    this compression through the injection of structured ontological knowledge
    that is defined at the type level of events: importantly, our model allows
    for partial injection of semantic knowledge and it does not depend on
    observing instances at any particular level of the semantic ontology. Across
    two different datasets and four different evaluation metrics, we demonstrate
    that our approach is able to out-perform the previous state-of-the-art
    approaches by up to 8.5%, demonstrating the benefits of structured and
    semantic hierarchical knowledge for event modeling.
    
    @inproceedings{roy-dipta-etal-2023-semantically,
      title = {Semantically-informed Hierarchical Event Modeling},
      author = {Roy Dipta, Shubhashis and Rezaee, Mehdi and Ferraro, Francis},
      booktitle = {Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)},
      month = jul,
      year = {2023},
      address = {Toronto, Canada},
      publisher = {Association for Computational Linguistics},
      url = {https://aclanthology.org/2023.starsem-1.31},
      doi = {10.18653/v1/2023.starsem-1.31},
      pages = {353--369},
      dimensions = {true}
    }

 2. Elsevier
    SEMal: Accurate protein malonylation site predictor using structural and
    evolutionary information
    Shubhashis Roy Dipta, Ghazaleh Taherzadeh, Md Wakil Ahmad, Md Easin
    Arafat, Swakkhar Shatabda, and Abdollah Dehzangi
    Computers in biology and medicine, Jul 2020
    
    Abs Bib HTML Code
    12
    12 Total citations
    10 Recent citations
    2.2 Field Citation Ratio
    0.95 Relative Citation Ratio
    
    Post Transactional Modification (PTM) is a vital process which plays an
    important role in a wide range of biological interactions. One of the most
    recently identified PTMs is Malonylation. It has been shown that
    Malonylation has an important impact on different biological pathways
    including glucose and fatty acid metabolism. Malonylation can be detected
    experimentally using mass spectrometry. However, this process is both costly
    and time-consuming which has inspired research to find more efficient and
    fast computational methods to solve this problem. This paper proposes a
    novel approach, called SEMal, to identify Malonylation sites in protein
    sequences. It uses both structural and evolutionary-based features to solve
    this problem. It also uses Rotation Forest (RoF) as its classification
    technique to predict Malonylation sites. To the best of our knowledge, our
    extracted features as well as our employed classifier have never been used
    for this problem. Compared to the previously proposed methods, SEMal
    outperforms them in all metrics such as sensitivity (0.94 and 0.89),
    accuracy (0.94 and 0.91), and Matthews correlation coefficient (0.88 and
    0.82), for Homo Sapiens and Mus Musculus species, respectively. SEMal is
    publicly available as an online predictor at: http://brl.uiu.ac.bd/SEMal/.
    
    @article{dipta2020semal,
      title = {SEMal: Accurate protein malonylation site predictor using structural and evolutionary information},
      author = {Dipta, Shubhashis Roy and Taherzadeh, Ghazaleh and Ahmad, Md Wakil and Arafat, Md Easin and Shatabda, Swakkhar and Dehzangi, Abdollah},
      journal = {Computers in biology and medicine},
      volume = {125},
      pages = {104022},
      year = {2020},
      publisher = {Elsevier},
      doi = {10.1016/j.compbiomed.2020.104022},
      dimensions = {true}
    }

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© Copyright 2024 Shubhashis Roy Dipta.