Machine Learning,C++, Python, R, SQL
Resume:
KOHSHEEN TIKU
ad5ofd@r.postjobfree.com 720-***-**** linkedin.com/in/kohsheentiku/ github.com/kohsheen1234 https://kohsheen1234.github.io/portfolio/ EDUCATION
Brooklyn, NY
Bangalore, IN
PROGRAMMING / TECHNICAL SKILLS / CERTIFICATIONS
• Languages/Technology: Python, Low-latency C++ (shared memory, template metaprogramming, Boost library), OMEX, Java, C, R, JavaScript, SQL, Bash, Linux, PyTorch, Keras, XCode, AWS, Node.js, Kubernetes, Next.js, MongoDB, Git, Docker, React, Redis, Kalfka, Angular, MySQL, Postgres, Distributed Computing, Microservices, Socket Programming, Solidity, Blockchain, HTML, CSS, Vue.js, GoLang
• Quant : Regression, Decision Tress, Probability, Time Series, Modelling, VaR (Value at Risk), Mortgage-backed securities (MBS), Credit Derivatives
• Certifications: Deep Learning specialization, Options 101(Akuna), Bloomberg Market Concepts, GCP, AWS, Oracle Cloud, CFA Level1 Candidate COURSEWORK HIGHLIGHTS
• Machine Learning, Fixed Income Quant Trading, Derivatives pricing, Time-Series Analysis, Numerical Computing, Econometrics, Crypto Derivatives RESEARCH/ GRADUATE EXPERIENCE
WORLDQUANT LLC, Remote 03/23 - Pres.
Quantitative Researcher [Fast expression, BRAIN Platform, Trading strategies, Statistics]
• Deployed trading strategies (400+ alphas with significant weight) through statistical techniques across a variety of assets for the global markets of USA for Mid/Low Frequency Trading while researching financial literature to find signals.
• Gold level holder, offered research consultant position based on performance. Rank top 10 International Quant Championship 2024 (USA) BANK OF AMERICA, New York 05/23 - 08/23
Graduate Student Quantitative Researcher [Capstone/Industry Project, Machine learning/Deep learning, Portfolio optimisation]
• Developed an investment strategy tool using hierarchical reinforcement learning for stock identification and portfolio optimization.
• Implemented a two-tiered decision-making framework: the high-level policy identifies high-potential stocks using GRU-based attention, while the low-level policy with DDPG-driven dynamic portfolio optimization to optimize gains. Ran market simulations to validate the model's performance. Student Research Intern, Voice Intelligence Dept team lead [Python, PyTorch, TensorFlow, GitLab, NLP, Scientific writing, Deep Learning]
• Trained a text-classification CNN (Convolutional Neural Network) using SQuAD dataset to classify prompts, enabling action execution for commands, dynamic responses to queries, and filtering non-actionable input thereby elevating efficiency of Bixby Voice Assistant and reducing computational demands by 20%.
RESEARCH / ACADEMIC PROJECTS / FINANCIAL MODELLING APPLICATION OF TIME VARYING OPTIMAL COPULA IN PAIRS TRADING (IAQF COMPETITION)
• Explored advanced statistical arbitrage technique based on mean reversion to structure pairs trading strategies using Copula Model/LSTM, addressing nonlinear dependencies in volatile markets, selecting pairs through OPTICS density clustering mechanism, aiming to generate alphas, back tested to benchmark against linear regression baselines under guidance of Prof. Ronald Slivka Ph.D. LINK TRADING AND HEDGING LOCAL VOLATILITIES
• Explored concepts of implied and local volatilities, their analogy to forward rates, designed gadgets for interest rates, and developed hedging strategies against changes in forward rates and local volatilities, with practical application using finite volatility gadgets. NEW YORK UNIVERSITY - FINANCE AND RISK ENGINEERING, New York 09/22 - 12/23 Graduate Student Quantitative Researcher and Teaching Assistant
• Fixed Income/ Interest Rate Derivatives: Explored and analyzed the process of developing and applying advanced interest rate models—Vasicek, Cox-Ingersoll-Ross, Ho-Lee, and Hull-White I & II—for precision bond pricing.
• C++ Library: Developed C++ Library for Options Pricing LINK
• Blockchain: Implemented a Decentralized Autonomous Organization (DAO) structure for investment management funds. LINK
• Graduate Teaching assistant for the Course(s): Computational Finance Labratory (Fall'23) with Dr. Edward D. Weinberger; NLP and The Investment Process(Fall'22) with Dr.Daniel Rodriguez; Data Visualisation Lab (Spring'23) with Dr.Francisco Rubio. SAMSUNG RESEARCH AND DEVELOPMENT, India 07/19-12/19 NEW YORK UNIVERSITY, TANDON SCHOOL OF ENGINEERING
Master of Science in Financial Engineering, GPA: 3.6/4.0, Merit Scholarship BMS COLLEGE OF ENGINEERING
Bachelor of Engineering in Computer Science, GPA: 4.0/4.0 PROFESSIONAL WORK EXPERIENCE
MAGNITUDE SOFTWARE, India 01/20 - 07/20
Software Developer [Java, Spring, C++, Gradle, REST, Database connectivity drivers, SQL]
• Enhanced Cisco Control Hub with third-party app integration, developed REST APIs and performed UI (Angular) end-to-end Cypress testing.
• Integrated Beats with the ELK stack to refine log processing, reducing resource consumption and improving data relevancy. Set up advanced monitoring using custom metrics, logs, and tracing to ensure high observability and real-time performance analysis for spring boot apps.
• Implemented PTO (Paid Time Off) Bot using Webex Webhooks for streamlined absence notifications for improving productivity of the team and integrated with Webex chats notification. Recognized with Cisco's 'Innovation Award' for Cisco Internal Hackathon.
• Containerized the Webex B2BUA C/C++ service and segmented monolithic structures into microservices for enhanced scalability.
• Deployed Kubernetes auto-scaling to dynamically manage resource allocation. Utilized gRPC for efficient and robust inter-service communication among distributed microservices, enabling selective component scaling based on real-time demands. CISCO, India 09/20 - 07/22
Software Developer, Webex Cloud [ Python, Postgres, Microservices, Kubernetes, ELK Stack, REST API, Java, SpringbootJavascript]
• Assisting in designing high-performance JDBC and ODBC drivers for RESTful data sources such as Google Analytics and Facebook Ads. Optimized Spark JDBC driver performance through multi-threaded implementations, enhancing transaction processing efficiency and scalability. SELECT PUBLICATIONS
1) K. Tiku, J. Maloo, A. Ramesh and I. R., "Real-time Conversion of Sign Language to Text and Speech," 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2020, pp. 346-351, doi: 10.1109/ICIRCA48905.2020.9182877. 2) Kohsheen Tiku, Jayshree Maloo, R. Indra, "CompNet : A novel Knowledge Graph Embedding Technique for Link Prediction", International Journal of Computer Sciences and Engineering, Vol.8, Issue.8, pp.1-4, 2020.
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