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About

Arnab Kar is a multifaceted data scientist and machine learning expert with a rich blend of academic, entrepreneurial, and corporate experience. Currently pursuing a Ph.D. in Computer Science at Duke University, Arnab specializes in cutting-edge AI and ML technologies, with a strong focus on applications in fintech, real estate, and misinformation detection. His academic journey is backed by a solid foundation in electrical engineering and computer science, and he has consistently applied this knowledge to innovative projects that intersect technology and business strategy. Arnab's entrepreneurial spirit has led him to co-found startups such as Mitify+ and EnergyComply Tech, where he developed intellectual property and technology stacks for misinformation detection and real estate compliance tools, respectively. At Mitify+, he pioneered solutions for misinformation monitoring, while at Exchange Robotics, he contributed to the development of unique technological solutions for the illiquid credit market, engaging with major stakeholders like Moody’s and S&P to enhance platform capabilities. Arnab's corporate experience is equally impressive, having held key roles at Saks Fifth Avenue, where he implemented advanced machine learning models for demand forecasting and improved inventory management processes. His time at E Ink Corporation saw him lead cross-functional teams in developing simulations for ink display behavior, contributing to the company’s patent portfolio and significantly reducing product development timelines. Arnab’s passion for innovation extends beyond his professional roles—he is an active contributor to sustainability initiatives and mentorship programs, aiming to inspire the next generation of tech leaders. His diverse skill set encompasses computational finance, natural language processing (NLP), operations research, and advanced analytical modeling. Throughout his career, Arnab has demonstrated an exceptional ability to align technological advancements with strategic business goals, making him a sought-after leader and innovator in the AI and data science community.

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Skills

Experience

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Lead Data Scientist

MITIFY+

Apr-2023 to Present
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Co-founder, ML and Data Engineering AI

Exchange Robotics (ExR)

May-2023 to Nov-2023
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Deep Learning Engineer II

E Ink Corporation (Eink)

Feb-2021 to May-2022
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Machine Learning Scientist

Saks Fifth Avenue, US

Jun-2022 to Feb-2023
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Senior Data Scientist

Sirion

May-2020 to Feb-2021
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Student Research Intern

Indian Statistical Institute, Kolkata

May-2015 to Dec-2015
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Research Intern

IIT Kharagpur (Indian Institute of Technology)

May-2014 to Dec-2014
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Student Research Associate

IIT Kanpur (Indian Institute of Technology, Kanpur)

May-2017 to Aug-2018
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Pre Doctoral Intern

Institute of Science and Technology Austria (ISTA)

Jan-2018 to Aug-2018

Education

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Indian Institute of Information Technology Allahabad (IIIT-A)

B.Tech in Information Technology(IT)

Passout Year: 2025
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Duke University (DU)

Ph.D. in Computer Science

Passout Year: 2018

Projects

May-2017 to Oct-2018

Multi-agent Generative modeling

Generative Modeling to be able to *understand-ably* generate images (or scenarios). It could reason about the entities (people, things, objects, background) in generated image (and between images). Also applicable to other use-cases such as: ☆ Financial use-case: macro-economic scenario modeling to understand market dynamics with different *risk-leveled* agents/participants (macro-economic scenarios being one of the agent behavior). ☆ Business scenario modeling: impact on different lines of business, and bottom-line under different competitive dynamics, macro-economic conditions, and regulatory environments. ☆ Insurance use-cases: modeling the impact of different market conditions on different industry sectors/sub-sectors (and corresponding lines of underwriting applications).
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Aug-2019 to May-2020

Time (processor) and Space (memory) optimized Machine Learning hardware-accelerator scheduler

Designed, implemented, and tested scheduling techniques to optimally use hardware resources for Machine Learning payloads on tabular-databases (SQL queries, for example), while being under time-limits. Also applicable for following kinds of use-cases: ☆ Project Management: Time and resource optimization for complex engineering/supply-chain/industrial projects, with embedded risk management and contingency planning. ☆ Fleet management: using optimal resources (human resources, and capital), while respecting client requirements, and tolerance limits.
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Sep-2015 to Jun-2016

Provably-verified Data-security when performing data analysis on permission-ed data

Used mathematical techniques to ensure data analysis on dis-aggregated data respects ownership and access-control rules set on data sources. Other use-cases could be:: ☆ Ensuring AI generated content, or AI-enabled knowledge discovery respects ownership rules of underlying data-source; not revealing confidential data. ☆ Ensure financial or corporate reporting does not leak business logic or confidential information: revealing information at the right level of granularity between organizational silo-s, up the organizational pyramid, and/or in public disclosures.
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Aug-2014 to Jun-2015

Knowledge-graph based fact-checks and relationship-discovery in Language-Model generated content

Using graph queries (on knowledge graph) to fact-check content, and verify logical claims (which might not immediately be in the knowledge graph). Other applicable use-cases could be:: ☆ Legal-tech use-case: Enable deductions and implications in corporate and legal communications, enabling better dispute handling. ☆ Insurance verification: claim checks, fact verification, policy-alignment checks. Tool would enable improved cash-flow, reduced administrative burden, improved transparency.
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Jan-2018 to Aug-2018

Distributed Data-Influence detection for (any) Machine Learning model (Interpretability, explainabil...

Developed methods that could explain model behavior, at scale, as it relates to underlying training data. Other similar use-cases could be: ☆ ML Trust and Safety use-cases: ensuring trust in models by attributing model behavior to data; de-biasing models from gendered/societal/cultural artifacts. ☆ De-biasing model-enabled decision-making on protected attributes (gender, race, etc).
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Certificates

Issued : Feb 2025
  • dott image By : Duke University
  • dott image Event : Computational M...
Computational Microeconomics

Scholar9 Profile ID

S9-112024-1206394

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Article Reviewed

(16)

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Academic Identity

SSRN ID
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Questions I Asked

What is the importance of peer review in the research publication process? What is the importance of an ISSN in research publications, and how can I verify or obtain one for my journal? What is a DOI, and why is it important for academic publications? What are the common challenges researchers face during the peer review process? How can I ensure that my journal is compliant with international standards and indexing services using OJS? How will the removal of the UGC CARE List impact academic journal credibility in India? How to start a new Journal & Journal Supporting Software & DOI? How to choose the best journal management software for academic publishing? How to index a new journal in databases like Google Scholar, Scopus, and DOAJ? What are the best hosting options for an open-access journal?