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Experience

  • 2026 - present

    Toronto, ON

    Principal ML Engineer, AI Team
    Equinix
  • 2021 - 2026

    New York, NY

    Quantitative Researcher
    Balyasny Asset Management
    • Led AI/ML initiatives in the Applied AI team, building production-grade agentic AI systems and a custom domain-adapted LLM for tool use in financial workflows (2023–2025).
    • Built and deployed large-scale NLP models for financial text analysis with state-of-the-art domain-specific performance; published at EMNLP 2024 [EMNLP], model weights released on Hugging Face.
    • Developed financial text embedding models by distilling Mixtral-8x22B and Llama-3.3-70B; selected a 560M-parameter final model through systematic performance vs. compute trade-off evaluation.
    • Developed and productionized ML models for alpha generation in US equities (2021–2023), applying computational learning theory for model validation and generalization in non-stationary settings.
  • 2019 - 2021

    New York, NY

    Research Scientist
    Lifion by ADP
    • Led an ML team of 3 researchers building recommendation and search systems for an HR tech platform; architected and deployed the recommendation engine and a BERT-based semantic search system.
    • Built real-time inference pipelines with sub-100ms latency and an A/B testing framework to drive data-driven product decisions.
  • 2016 - 2019

    Chicago, IL

    PhD Thesis Research
    University of Illinois at Chicago
    • Designed algorithms for learning graph structure from queries [FSTTCS], graph verification [ALT], and anomaly detection in dynamic networks via non-local curvature [Algorithmica]. Advised by Prof. Bhaskar DasGupta and Prof. Lev Reyzin.
  • 2018

    New York, NY

    Research Intern
    Google Research
    • Developed deep learning architectures for multi-horizon time-series forecasting in the Research and Machine Intelligence team, with large-scale experiments over millions of time series. Advised by Dr. Vitaly Kuznetsov.

Education

  • 2014 - 2019

    Chicago, IL

    PhD
    University of Illinois at Chicago
    Mathematics
    • Advisor: Prof. Lev Reyzin
  • 2009 - 2014

    Thiruvananthapuram, India

    MS
    Indian Institute of Science Education and Research
    Mathematics

Technical Skills

Machine Learning: Large Language Models, NLP, Deep Learning, Time Series Forecasting, Recommender Systems, Knowledge Graphs, Online & Reinforcement Learning
Tools: Python, C++, PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, XGBoost, NumPy, Pandas, AWS, Git, Linux

Talks & Service

Talks: Graph Verification with a Betweenness Oracle: Google Research Seminar, New York (2018); Algorithmic Learning Theory, Kyoto University (2017). Network Construction with Ordered Constraints: FSTTCS, IIT Kanpur (2017).
Reviewer: RANDOM 2018, ALT 2019, COLT 2019

Publications

  1. EMNLP
    Greenback Bears and Fiscal Hawks: Finance is a Jungle and Text Embeddings Must Adapt
    Peter Anderson, Mano Vikash Janardhanan, Jason He, and 2 more authors
    In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, Nov 2024
    arXiv Bib HTML
  2. Allerton
    On Learning a Hidden Directed Graph with Path Queries
    Mano Vikash Janardhanan and Lev Reyzin
    In 58th Annual Allerton Conference on Communication, Control, and Computing, 2022
    arXiv Bib
  3. Algorithmica
    Why Did the Shape of Your Network Change? (On Detecting Network Anomalies via Non-local Curvatures)
    Bhaskar DasGupta, Mano Vikash Janardhanan, and Farzane Yahyanejad
    Algorithmica, 2020
    arXiv Bib
  4. LAA
    On Matchable Subsets in Abelian Groups and Their Linear Analogues
    Mohsen Aliabadi and Mano Vikash Janardhanan
    Linear Algebra and its Applications, 2019
    arXiv Bib
  5. PhD Thesis
    Algorithms for Learning Networks and Learning from Networks
    Mano Vikash Janardhanan
    University of Illinois at Chicago, 2019
    Bib
  6. AJC
    On Local Matching Property in Groups and Vector Spaces
    Mohsen Aliabadi and Mano Vikash Janardhanan
    Australasian Journal of Combinatorics, 2018
    arXiv Bib PDF
  7. ALT
    Graph Verification with a Betweenness Oracle
    Mano Vikash Janardhanan
    In Proceedings of the 28th International Conference on Algorithmic Learning Theory, 2017
    Bib HTML
  8. FSTTCS
    Network Construction with Ordered Constraints
    Yi Huang, Mano Vikash Janardhanan, and Lev Reyzin
    In 37th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS), 2017
    DOI arXiv Bib
  9. Integers
    Ramsey Functions for Generalized Progressions
    Mano Vikash Janardhanan and Sujith Vijay
    Integers, 2015
    arXiv Bib
  10. MS Thesis
    Topics in Ramsey Theory
    Mano Vikash Janardhanan
    Indian Institute of Science Education and Research, Thiruvananthapuram, Apr 2014
    arXiv Bib