Here's a copy of my CV

About Me

I am an applied scientist at Microsoft working on multimodal information retrieval, search, and recommendation problems. Before my current position, I was a PhD student in the Data and Information Systems (DAIS) Group, Computer Science Department, University of Illinois at Urbana-Champaign (UIUC), advised by Hari Sundaram.

The goal of my PhD research is to develop user-centric deep-learning models for Recommendation Systems, Graph Mining, Information Retrieval, Behavior Modeling and NLP. I aim to identify real-world constraints in training models, and overcome the implicit assumptions that underlie commonly employed training algorithms, and model architectures.

Prior to joining UIUC, I obtained my Bachelors and Masters in Computer Science at IIT Madras, supervised by Sayan Ranu. My undergraduate thesis was centered on problems surrounding Information Retrieval and Statistical Data Modeling.

Publications [Google Scholar]

Peer-Reviewed Conference/Journal Papers

arXiv pre-prints

  • [PP3] Beyond Localized Graph Neural Networks: An Attributed Motif Regularization Framework

    arXiv preprint arXiv:2009.05197
    Aravind Sankar, Junting Wang, Adit Krishnan, Hari Sundaram
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                   title={Beyond localized graph neural networks: An attributed motif regularization framework},
                   author={Sankar, Aravind and Wang, Junting and Krishnan, Adit and Sundaram, Hari},
                   booktitle={2020 IEEE International Conference on Data Mining (ICDM)},

  • [PP2] An Induced Multi-Relational Framework for Answer Selection in Community Question Answer Platforms

    arXiv preprint arXiv:1911.06957
    Kanika Narang, Chaoqi Yang, Adit Krishnan, Junting Wang, Hari Sundaram, Carolyn Sutter
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      title={An Induced Multi-Relational Framework for Answer Selection in Community Question Answer Platforms},
      author={Narang, Kanika and Yang, Chaoqi and Krishnan, Adit and Wang, Junting and Sundaram, Hari and Sutter, Carolyn},
      journal={arXiv preprint arXiv:1911.06957},

  • [PP1] Improving Latent Generative Models in Social Media Applications

    arXiv preprint arXiv:1711.11124
    Adit Krishnan, Ashish Sharma, Hari Sundaram
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      title={Improving Latent User Models in Online Social Media},
      author={Krishnan, Adit and Sharma, Ashish and Sundaram, Hari},
      journal={arXiv preprint arXiv:1711.11124},

Patent Submissions

  • [PS9] Leveraging Large Pretrained Neural Models for Real-Time Language and Visual Content Analysis via Scalable Knowledge Distillation (Filing Underway)
    Adit Krishnan, Ji Li, Yixuan Wei, Xiaozhi Yu, Qi Dai, Han Hu

  • [PS8] Personalized Enterprise and Consumer Content Retrieval (Filing Underway)
    Ji Li, Dachuan Zhang, Amit Srivastava, Adit Krishnan

  • [PS7] Zero-Shot Design Template Indexing and Retrieval via Multimodal Tensor-to-Tensor Ranking (Filing Underway)
    Adit Krishnan, Ji Li, Amit Srivastava

  • [PS6] Scalable Retrieval System for Suggesting Textual Content (Filing Underway)
    Ji Li, Adit Krishnan, Aman Malik, Amit Srivastava

  • [PS5] Unified Zero-Shot Indexing and Search over Heterogenous Visual Assets (Filing Underway)
    Ji Li, Adit Krishnan, Amit Srivastava, Han Hu, Qi Dai, Yixuan Wei, Yue Cao

  • [PS4] Method, System, and Computer Program Product for Knowledge Graph Based Embedding, Explainability, and/or Multi-Task Learning (US20220114456A1)
    Adit Krishnan, Mangesh Bendre, Mahashweta Das, Fei Wang, Hao Yang, Azita Nouri

  • [PS3] Meta-Transfer Learning via Contextual Invariants for Cross-Domain Recommendation (US20210110306A1)
    Adit Krishnan, Mahashweta Das, Mangesh Bendre, Hao Yang

  • [PS2] Personalized Marketing based on Sequence Mining (US20160148271A1)
    Ritwik Sinha, Sanket Mehta, Tapan Bohra, Adit Krishnan [Equal Contribution]

  • [PS1] Multi Channel Marketing Campaigns (US20160148248A1)
    Ritwik Sinha, Sanket Mehta, Tapan Bohra, Adit Krishnan [Equal Contribution]

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