Arbaaz Qureshi
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Hey!

I'm an Applied Scientist at Lowe's, broadly working on search, recommendation, and personalization problems. Previously, I was a graduate student at the Robert and Donna Manning College of Information & Computer Sciences, University of Massachusetts Amherst.

I interned at Google in the Fall of 2022. Here, I worked with the Pixel Watch Ambient Compute team on the low latency off-body detection feature of the Google Pixel Watch. I built various convolutional neural network based detection algorithms to realize the said feature. During the summer of 2022, I interned at Lowe's, where I worked with Dr. Surya Kallumadi on extreme multi-label classification for semantic product search and query auto-completion.

Before moving to the US for graduate studies, I was a Research Fellow at Microsoft Research, where I worked with Sonu Mehta, Dr. Ranjita Bhagwan, and Dr. Rahul Kumar of team Sankie. I worked on source code processing to automatically classify and describe code edits in natural language, and on machine learning techniques which help prevent bugs and misconfiguration in large services.

Before joining Microsoft Research, I was a summer research intern at the GREYC lab, University of Caen Normandy, France. I worked with Prof. Gaël Dias on analyzing the effects of gender information on the estimation of depression severity using multimodal and multitask deep learning techniques.

I earned a Bachelor's degree in Computer Science and Engineering in 2019, from the Indian Institute of Technology Patna. I defended my Bachelor's thesis under the supervision of Dr. Sriparna Saha and Dr. Mohammed Hasanuzzaman, at the AI-NLP-ML lab, where I explored various multimodal and multi-task deep learning techniques to concurrently estimate depression severity and emotion intensity.

Experience

Lowe's
July 2023 - Present

Applied Scientist | Manager: Surya Kallumadi

  • Built a multi-modal chatbot for product discovery/purchase/comparison and home improvement question answering for the shopping website. Currently building an evaluation system for the chatbot.
  • Worked with different LLMs (different GPT models, Gemini, LLaMA etc), and with different paradigms of in-context learning (few shot learning, chain-of-thought reasoning, retrieval augmented generation for home improvement project question answering) to realize an efficient chatbot.
  • Built a query auto-complete system that suggests auto-completions based on semantic similarity with the user session's previous queries. This system is deployed on Lowe's website.
  • The developed framework shows improved recall and a reduced average number of characters typed for the ground truth to appear in the auto-complete suggestions. A/B test reveals a 1% increase in product conversion rate and about 2% increase in user engagement with the auto-complete suggestions.
  • Currently working on capturing product associations (example, hammer → nails) based on historical query logs. Trying out embedding based and association rule mining based approaches.

    • IBM
    February 2023 - June 2023

    Graduate Student Researcher | Managers: Dr. Taesung Lee and Dr. Youngja Park

  • Devised a method to obtain text descriptions per neuron, of concepts that activate those neurons in a BERT model. Our method alleviates the requirement of Human in the loop, to generate the text descriptions.
  • Created a dataset of 72K Amazon reviews and their annotated concepts, using various open-source LLMs like Flan-T5 XL and Pythia 12B. Analyzed which annotated concepts trigger neurons in the BERT language model using this dataset.
  • Work published in AAAI-24 (to be released on February 20. 2024).

    • Google
    September 2022 - December 2022

    Engineering Intern | Manager: Cac Nguyen

  • The goal of this internship was to develop machine learning models which can detect whether a Google Pixel Watch is on-wrist, or whether it is off-wrist (low latency off-body detection).
  • Created an end-to-end framework which loads and processes wear data from the cloud, trains any generic neural network on the wear data to detect whether the watch is on-wrist or off-wrist, and evaluates how the network performs in a real-world setting.
  • Performed extensive experimentation with different convolutional neural networks and different off-body detection algorithms, and achieved a performance which competes with the deployed heuristic algorithm.

    • Lowe's
    June 2022 - August 2022

    Graduate Applied Science Intern | Manager: Surya Kallumadi

  • Trained extreme multi-label classification (XMC) models for semantic product search and query auto-completion, using the PECOS XMC framework.
  • The trained XMC semantic product search and query auto-completion models show recall values competitive with the deployed search and type-ahead prediction systems respectively, and (most importantly!) improve the per-query serving latency by at least 20 times.
  • Both the developed XMC models have been moved to production, and are serving approximately 20 million customer transactions every week.

    • Microsoft Research
    August 2019 - July 2021

    Research Fellow | Managers: Sonu Mehta, Dr. Ranjita Bhagwan, and Dr. Rahul Kumar

  • Worked on ML for code. Built various AI-powered source code processing tools: automatic code-edit classifier, and automatic code-edit description generator; these are deep graph encoding neural networks built on Code2Seq, that encode the abstract syntax tree of input source code [Paper].
  • Worked on association-rule mining based file recommender system which helps prevent bugs and misconfiguration in large services, where the association rules are mined from GitHub commits.

    • GREYC lab, University of Caen Normandy, France
    May 2019 - July 2019

    Research Intern | Managers: Prof. Gaël Dias

  • Worked on different multi-task deep learning networks and algorithms (shared-private and adversarial shared-private networks) to estimate depression severity separately in men and women.
  • Established that information about gender improves estimation of depression severity, and that adversarially learning to predict the depression score separately for men and women improves the performance of depression severity estimation in both the genders.
  • Works published in IJCNN 2021 and the French Journal of Psychiatry 2019.

    • AI-NLP-ML lab, IIT Patna, India
    August 2018 - May 2019

    Research Assistant | Managers: Dr. Sriparna Saha and Dr. Mohammed Hasanuzzaman

  • Worked on the multimodal fusion of audio, video and text modalities to estimate depression severity. Developed a novel attention fusion mechanism to achieve the SOTA results.
  • Worked on multi-task deep learning to concurrently estimate depression severity and emotion intensity. Established that learning to estimate emotion intensity supplements predicting depression severity.
  • Beat the then SOTA on the DAIC-WOZ depression dataset. Works published in IEEE CIM 2020 and IEEE IS 2019, and hosted on arXiv

    • Quantela
    May 2018 - Jul 2018

    Machine Learning Intern | Manager: Mr. Sanjiv Kumar Jha

  • Explored various sequence-to-sequence models like LSTM and GRU networks, to predict the local weather of Jaipur, India, based on different input parameters.
  • The trained seq-to-seq model exceeded Quantela's deployment performance.
  • Built time and memory profilers for Python functions as a side task.

    • Selected Projects

    clean-usnob Research Paper Tagger (RPT)
    code | pdf
    Advisor: Prof. Mohit Iyyer, UMass Amherst

    Objective: Automatically tagging the research track of an NLP research article, given the title, abstract and the authors.

  • Built a dataset of 1744 research paper-research track pairs from ACL 2021, 2020 and 2019. Fine-tuned a BERT-based classifier on the collected dataset, on various combinations of title, abstract and the authors.
  • Achieved a top-1 accuracy of 70% and a top-3 accuracy of 83% on the test split of the collected dataset. Working on publishing this work and the dataset in a conference/workshop.


    		•
    clean-usnob Cycle Location and Anti Theft System (CLATS)
    code | pdf
    Advisor: Prof. Jimson Mathew, IIT Patna

    Objective: Tracking and preventing the theft of bicycles inside gated regions.

  • Built a working prototype of a lightweight system that detects the current location of the user's bicycle and prevents its usage without the consent of the owner.
  • Used RFID tags and readers (placed at strategically identified locations) to uniquely identify bicycles. A huge incentive for using RFID was the throwaway cost of an RFID tag to the end user (less than $0.15).
  • This project was chosen to be patented by the faculty of Computer Science and Engineering department, IIT Patna.


    		•

    Publications

    Towards generating informative textual description for neurons in language models
    Shrayani Mondal*, Rishabh Garodia*, Arbaaz Qureshi*, Taesung Lee, Youngja Park
    Association for the Advancement of Artificial Intelligence, 2024. To be released on Feb 20, 2024. (AAAI, 2024)

    Gender-aware Estimation of Depression Severity Level in a Multimodal Setting
    Arbaaz Qureshi, Gaël Dias, Sriparna Saha, Mohammed Hasanuzzaman
    International Joint Conference on Neural Networks, 2021 (IJCNN, 2021)
    website | pdf | video | code

    Improving depression level estimation by concurrently learning emotion intensity
    Arbaaz Qureshi, Gaël Dias, Sriparna Saha, Mohammed Hasanuzzaman
    IEEE Computational Intelligence Magazine, 2020 (IEEE CIM, 2020)
    website | code

    Multitask representation learning for multimodal estimation of depression level
    Arbaaz Qureshi, Sriparna Saha, Gaël Dias, Mohammed Hasanuzzaman
    IEEE Intelligent Systems, 2019 (IEEE IS, 2019)
    website | pdf | code

    Automatic Prediction of PHQ-8 Questionnaire Scores using Artificial Intelligence
    Gaël Dias, Arbaaz Qureshi, Sriparna Saha, Mohammed Hasanuzzaman
    French Journal of Psychiatry, 2019
    website