Dheeraj Mekala

Dheeraj Mekala

Ph.D. Student in Computer Science

University of California, San Diego

Biography

I am a Ph.D. student in the Computer Science department at the University of California, San Diego working with Prof. Jingbo Shang. I am broadly interested in Machine Learning and Natural Language Processing.

For summer 2021, I am interning at Amazon Science with Dr. Xin Luna Dong. I completed my Bachelor Of Technology in Computer Science And Engineering from the Indian Institute of Technology, Kanpur in 2017, where I worked with Prof. Harish Karnick and Prof. Purushottam Kar. I worked as a Data Scientist and Product Engineer at Sprinklr for 2 years and I interned at Microsoft India in the summer of 2016.

Current Research

Few Shot & Weakly Supervised Learning I develop high performing deep neural frameworks with minimal human supervision such as just class labels or a few label-indicative seed words. I am interested in leveraging massive amounts of unstructured and unlabeled data available on the internet for supervision and additional contextual information. Further, I am also keen on beneficially leveraging pre-trained language models to reduce the need for annotated data.

Security I study vulnerabilities of current NLP systems such as data poisoning and trigger-based backdoor attacks and work towards developing strong defense methods against such attacks.

Deep Learning A deep neural network is known to learn/overfit any randomly labeled data. I am interested in understanding and unveiling the learning process of deep neural architectures and further use it to analyze the quality of data.

Apart from Academics, I enjoy spending time playing Ukulele, playing Football(soccer) and I rarely write too. Checkout my blog!

Interests

  • Machine Learning
  • Natural Language Processing

Education

  • PhD in Computer Science, 2025 (expected)

    University of California, San Diego

  • MS in Computer Science, 2021

    University of California, San Diego

  • B.Tech. in Computer Science, 2017

    Indian Institute of Technology, Kanpur

Publications

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(2021). BFClass: A Backdoor-free Text Classification Framework. EMNLP Findings 2021.

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(2021). Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data. EMNLP 2021.

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(2021). X-Class: Text Classification with Extremely Weak Supervision. NAACL 2021.

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(2020). META: Metadata-Empowered Weak Supervision for Text Classification. EMNLP 2020.

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(2020). Contextualized Weak Supervision for Text Classification. ACL 2020.

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(2018). User Bias Removal in Review Score Prediction. CODS-COMAD 2018.

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(2017). SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations. EMNLP 2017.

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(2017). Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss. Preprint.

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Experience

 
 
 
 
 

Data Scientist

Sprinklr

Apr 2018 – Jul 2019 Gurgaon, India

Part of Machine Learning team:

  • Architected and built most of Sprinklr AI’s visual insights module that is now used by over 1200 Sprinklr clients.
  • Developed in-house computer vision models for visual sentiment, gender, age, inappropriate content detection in images and videos.
  • Implementation was done using asynchronous programming and as a result, throughput was increased by 65% and total resources cost reduced by 50%.
  • Built in-house computer vision model that identifies the font and suggests similar fonts from an image.
  • Developed a dockerized auto-scaling python-based framework which is deployed in kubernetes for image classification. It works over a stream of data published to Kafka and thus is auto-scaled based on lag in Kafka queue.
  • Developed a scalable system capable of running classification models over 500 million messages per day using the latest technologies like Caffe, Tensorflow, Kafka and Elasticsearch.
  • Deployed a centralized monitoring environment(Grafana, InfluxDB) which gather system metrics as well as docker run-time metrics.
 
 
 
 
 

Product Engineer

Sprinklr

Jul 2017 – Apr 2018 Gurgaon, India

Part of Paid Advertising team:

  • Implemented an end to end pipeline that incorporates DoubleClick tracking in ads for integrated reporting.
  • Expanded the reach of the product by integrating Ads APIs of various social media channels like LinkedIn, Twitter, Google DCM.
  • Researched, Designed and Implemented core functionalities in backend code to improve the feature of importing and exporting ads which is the primary way, the users undergo to create ads.
 
 
 
 
 

Machine Learning Intern

Microsoft India

May 2016 – Jul 2016 Bangalore, India
Developed tree-based models for predicting the ideal assignment candidate for a case in Microsoft Dynamics CRM.
 
 
 
 
 

Software Development Intern

ASnTech & Engineering Services

Dec 2015 – Jan 2015 Hyderabad, India
Designed and implemented an algorithm to speed up search queries related to the location of a vehicle, from 120 seconds to 5 seconds.

Recent Posts

March 2021 NLP Reading:

The blog post summarizing a few papers from EMNLP 2020 and some recent papers that I have enjoyed reading in March 2021.

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