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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.
Skills & Expertise
C++
Java
Financial Analysis
Deep Learning
Data Visualization
Statistical Data Analysis
Computational Finance
Curve Fitting
Agile Methodologies
R (Programming Language)
Machine Learning Algorithms
Multivariate Statistics
Business Analysis
SQL
Social Network Analysis
Economic Data Analysis
Exploratory Data Analysis
Parallel Computing
High Performance Computing (HPC)
Semiconductor Fabrication
Formal Verification
Time Series Analysis
Financial Modeling
Analytical Modelling
Natural Language Processing (NLP)
Object-Relational Mapping (ORM)
Computer Vision
Neural Networks
Computational Fluid Dynamics (CFD)
Electrostatics
Mathematical Modeling
Statistical Modeling
Partial Differential Equations
Multi-channel Retail
Data Structures
Database Management System (DBMS)
Research Interests
Data Scientist
Machine Learning Engineer
Curve Fitting
Quantitative Research
Network Science
Electrostatics
Mathematical Modeling
Operations Management
Connect With Me
Experience
Lead Data Scientist
- Founding Engineer; Lead Data Scientist; cybersecurity, social-media misinformation monitoring ● 0-to-MVP in 4 months. IP generation (3 patents filed) and implementation: 1) PR management, 2) Brand impact estimation, 3) propaganda/risk ratings. ● NLP, Q/A ML models, Hierarchical Clustering, Event/Entity detection & disambiguition, Emotion-cause-pair NLP models, Vision-to-text models, Vertex-AI, Metabase. ● Investor, clients, partners relations. Scoped requirements for antivirus companies, media houses, and government (B2B, B2G). Promised €5.7M over 3 years by govt. Developing IP and technology stack for language misinformation (NLP) detection suite. ☆ Intellectual Property: Spearheaded the development of core IPs crucial for advanced data analysis and misinformation detection. ☆ Technology Development: Led creation of technology stacks that form the backbone of the platform’s offerings. Created all Intellectual Property. ☆ Stakeholder Management: Managed requirements across diverse sectors (internal and external).
Co-founder, ML and Data Engineering AI
- Gen AI for Illiquid credits. ☆ Intellectual Property: Developed technological solutions and IPs for the firm, catering to secondary credit market. ☆ Technology Development: Oversaw the creation of technological solutions enhancing platform capabilities for financial markets. ☆ Stakeholder Management: Engaged major stakeholders such as Moody’s and S&P, contributing significantly to partnership development and fundraising.
Machine Learning Scientist
- Demand forecasting, operations research, financial budgeting. ☆ Technology Development: Enhanced machine learning models for demand forecasting, increasing accuracy significantly. ☆ Business Implementation: Implemented multi-modal models that improved inventory management, directly affecting financial strategies and purchase decisions.
Deep Learning Engineer II
- ☆ Intellectual Property: Engineered simulations for ink display behaviors that served as foundational technologies for product development. (lead to 5 patents/trade-secrets) ☆ Technology Development: Accelerated the digital emulation of physical displays, reducing product delivery timelines. ☆ Stakeholder Management: Collaborated with a cross-functional team including executives and production managers to align technological developments with strategic business goals.
Senior Data Scientist
- ☆ Technology Development: Utilized advanced NLP techniques for analyzing and managing legal contracts. ☆ Business Implementation: Improved data accessibility and operational efficiency by automating the extraction and mapping of contract details.
Pre Doctoral Intern
- Distributed Computing, Distributed Systems, Distributed Machine Learning.
Student Research Associate
- Generative Adversarial Networks for image understanding, and representation learning. Multi-agent game theoretical modeling of neural models.
Student Research Intern
- Modeling chip performance, and security analysis. Analyzed attack vectors with fabrication simulators. Modeling chip performance, and security analysis. Analyzed attack vectors with fabrication simulators. Skills: Formal Verification · C++ · Java · Data Science · Semiconductor Fabrication
Research Intern
- Economic Data Analysis · Java · Network Science · Machine Learning · High Performance Computing (HPC) · SQL · Social Network Analysis · Exploratory Data Analysis · Data Science · Parallel Computing
Education
Indian Institute of Information Technology Allahabad (IIIT-A)
Duke University (DU)
Projects
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.
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.
Distributed Data-Influence detection for (any) Machine Learning model (Interpretability, explainability)
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).
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).
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.
Certificates & Licenses (1)
Computational Microeconomics
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