Abhishek Agarwal Email & Phone Number

Santa Clara, California, United States

I am working on the networking fabric to enable scale-out capabilities for Tenstorrent hardware.

San Diego, California, United States

Worked on writing kernel-mode drivers to enable communication between different sub-systems on an SoC. Was also involved in system-wide initiatives to better align the software with organizational goals.● Developed and contributed to IPC drivers for Windows, Qualcomm RTOS, and Zephyr RTOS, for 6 mobile, compute, and automotive chipsets.● Designed and prototyped a Distributed Computing Framework for running services in a distributed fashion on an SoC such as a distributed NoSQL database and a Capability Advertisement service.● Collaborated with cross-functional teams and addressed architectural differences to enable IPC for RISC-V based subsystems.● Led the design and implementation of a highly scalable system control framework compliant with ARM’s SCMI specification.● Implemented a memory allocation scheme to reduce memory corruption; designed experiments to make data-driven decisions.● Streamlined the driver organization on Zephyr RTOS based images and influenced other teams within the department to adopt the same.● Delivered an end-to-end system to monitor key metrics for the API management initiative including the data pipeline and visualization using Power BI.

San Diego, California, United States

Worked with the Core Platform Kernel team on Qualcomm Real-Time Operating System● Analyzed memory and performance trade-offs of deploying new process loading and linking schemes on the hexagon DSP● Implemented a prototype of a run-time loader (rtld) library utilizing GNU hashing, resulting in 50% faster run-time linking● Presented a collection of best practices on creating shared objects for efficient loading and linking of dynamically linked processes● Developed a profiling tool to auto-generate binaries based on user inputs and collect build and run-time performance metrics● Integrated the tool with the existing build system and regression testing framework to conveniently stress test new features

Bengaluru, Karnataka, India

I was responsible for building Fraud Risk management framework for the credit offerings (Buy Now Pay Later and EMIs) of the FinTech division. I also led the development of Credit risk framework (Underwriting and Frauds) for the newly acquired Wholesale business.Consumer Finance Risk Management◦ Collaborated to design and launch the first version of transaction authorization framework for real-time authorization and monitoring of ∼2M monthly Credit transactions◦ Localized 5X risky customers through network graphs and established routine pruning processes◦ Built databases hosting historical credit performance and shopping trends, accelerating routine analyses by >90%◦ Developed scripts to scrape data from the Internet such as device information, IP databases, etc., to augment internal databases for enhanced fraud managementFlipkart Wholesale Easy Credit (Working Capital credit for MSME segment)◦ Led the development of credit underwriting and fraud mitigation strategies for the new wholesale business launch◦ Leveraged non-conventional data sources for risk assessment and ran simulations to maximize approval rates and minimize credit lossesCustomer Affluence Segmentation◦ Modeled customer affluence by utilizing platform engagement and payment instruments◦ Projected additional annual recoveries of $300,000 resulting from focused collection strategies and lower operational costsTech Stack: Python, R, SQL

Mumbai Area, India

I was responsible for building data-driven strategies for insurance products (includes life and general insurance). I worked on launching campaigns and supporting business strategies through statistical analyses.Suraksha 2.0: Pre-Approved Life Insurance Program◦ Developed a logistic regression model to identify customers with the lowest medical risk by predicting the probability of a claim◦ Identified customers eligible for the offer on the basis of propensity, persistency, medical, and financial assessments◦ The program minimized the documentation required for underwriting and reduced the policy issuance time by 90%Profiling of Life Insurance customers:◦ Designed a clustering-based framework to detect outliers in insurance sales using t-SNE and Partitioning Around Medoids◦ Developed an R-shiny app to dynamically cluster and visualize customers’ attributes as per user requirements using K-meansFinancial planning of 0.4 billion USD Life and General Insurance business:◦ Forecasted monthly sales for 4,000+ pan-India branches using historical performance, peer growth, and industry indicators◦ Furnished monthly productivity targets across different channels and grades of 40,000+ branch and tele-sales personnelTech Stack: R, SAS, SQL

Pune Area, India

Reinforcement Learning (RL) of Optimal ControlI assessed the viability of using RL to replace conventional control systems with adaptive model-free closed-loop control on the following two use cases. The research work was also accepted for poster presentations at the ACODS 2018 conference.Optimal Insulin Dosing for Glucose Control in a Virtual Type-I Diabetes Patient◦ Trained an ’Artificial Pancreas’ capable of maintaining normal glucose levels with minimal interference in patient’s lifestyle◦ Simulated Q-learning, Dyna-Q, Q(λ) and Policy Gradient on two meal models and experimented with multiple reward functions◦ Formulated evaluation metrics and tuned parameters in presence of realistic disturbances in the amount and timings of mealOptimization of Fuel Consumption in a Hybrid Electric Vehicle (HEV)◦ Trained a controller to optimize fuel consumption by learning optimal load distribution between the engine and electric motor◦ Implemented Q-learning and Q(λ) on an HEV power-train model and used ARMAX time-series model for partial state estimation◦ Optimized for urban, rural, and semi-urban driving routines with battery charge constraints and disturbances in accelerationTech Stack: Python

Master Of Science - Ms, Computer Science

GPA: 3.9/4.0 Specialization: High-Performance Computing Coursework: Advanced Programming Parallel Programming Algorithms Databases Cloud.

Bachelor Of Technology - Btech, Chemical Engineering, 8.5/10

Activities and Societies: Dramatics Club, Chemineers SocietyCoursework: Computer Science: Natural Language Processing (NLP), Fundamentals.