CS at University of Maryland || Ex-Atlassian || DTU'21
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Assessing your cultural and operational fit
VDGD
June 21, 2024 – May 7, 2025
Code for ICLR 2025 Paper: Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs
View ProjectABEX
May 17, 2024 – July 8, 2024
Code for ACL 2024 paper -- ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions
View ProjectCompA
January 18, 2024 – July 10, 2024
Code for ICLR 2024 Paper: CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models
View ProjectACLM
May 17, 2023 – July 19, 2023
Code for ACL 2023 Paper: ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER
View ProjectBioAug
April 10, 2023 – November 15, 2023
Code for SIGIR 2023 paper: BioAug: Conditional Generation based Data Augmentation for Low-Resource Biomedical NER
View ProjectMMER
March 29, 2022 – March 12, 2024
Code for the InterSpeech 2023 paper: MMER: Multimodal Multi-task learning for Speech Emotion Recognition
View ProjectAutonomous-Tagging-Of-Stack-Overflow-Questions
July 18, 2019 – May 25, 2020
Auto tagging of stack overflow questions. Used dataset: https://www.kaggle.com/stackoverflow/stacksample
View ProjectCar-Numberplate-Recognition
February 2, 2019 – April 19, 2019
Using Machine learning to locate the number-plate and identify the car number
View ProjectGesture-Recognition-ML
December 10, 2018 – June 20, 2019
CNN and OpenCV are used to predict real time gestures.
View ProjectCultural Fit Analysis
The candidate's projects are heavily research-oriented and academic, focusing on publishing papers. While this demonstrates strong technical depth, there is limited evidence of experience in collaborative, product-driven environments or understanding of business impact, which are crucial for cultural fit in many Data Scientist roles. The lack of professional experience or team projects makes it difficult to assess alignment with a typical industry culture.
Soft Skills & Operational Fit
The candidate's profile primarily showcases technical research capabilities. There is insufficient data to assess soft skills such as teamwork, communication, or leadership, or operational fit within a typical corporate environment.