I'm graduating this May with degrees in Computer Science and Cognitive Neuroscience from Rutgers University. I'm fortunate to be advised by Kostas Bekris and to be a member of the Physics-aware Research for Autonomous Computational Systems Lab (PracSys) at Rutgers. Here, I work on generative 3D for robotics with Shiyang Lu. I think of robotics as a quest to understand the human mind, and computer vision specifically pieces apart our subjective experience of qualia.

My personal philosophy has changed a lot. Currently it most closely aligns with pyrrhonian skepticism, empty individualism, and absurdism.

As we get closer to a post-scarcity world, I've thought a lot about what motivates me. For now, it's accelerating our understanding of cognition and more broadly building deeptech companies-- ones that tackle frontier markets and involve extremely high scientific risk.

Tasha Pais, Shiyang Lu, Kostas Bekris
In progress
Github

Tasha Pais*, Nikhilesh Belulkar*, Jingxi Xu
July 2023
Github

Tasha Pais*, Nikhilesh Belulkar*, Huy Ha
December 2022
PDF   •   Video   •   Github

* indicates equal contribution, active exploration video by REAL

The project delves into sensor technologies for robotics, emphasizing localization and Bayesian reasoning for accurate robot positioning. It explores Bayesian filtering techniques, applying them to occupancy grids for spatial representation and navigation. The study further investigates particle filters and Kalman filters for dynamic system state estimation and noise filtering.
December 2023
Report   •   Github

The project starts by implementing probabilistic roadmaps (PRM) and rapidly-exploring random trees (RRT). It advances into asymptotically optimal sampling-based planners, integrating potential functions for improved path efficiency. Additionally, it encompasses the challenges posed by non-holonomic and under-actuated systems, to build a steerable kinematic model.
November 2023
Report   •   Github

The project encapsulates the study and application of robotic path planning techniques, exploring grid-based search algorithms, visibility graphs assessment, and combinatorial planning. It delves into spatial analysis through trapezoidal decomposition and C-space introduction, abstracts these configuration spaces to simplify complex planning scenarios, and applies the foundational concepts of sampling-based motion planning strategies.
October 2023
Report   •   Github

Bayes risk, gaussian based generative classifiers, EM for GMM
Proofs   •   Colab

Softmax over negative margins of the ensemble to derive adaboost
Proofs   •   Colab

SVM in dual and primal, kernel trick, stochastic subgradient descent pegasos
Proofs   •   Colab   •   Handwritten notes

l2 regularization, cross entropy loss, Large margin learning, representer theorem
Proofs   •   Colab

Least squares estimation, maximum likelihood, asymmetric squared loss
Proofs   •   Colab

* all projects were implemented on datasets like MNIST, CIFAR10, California housing, etc. and completed Sep-Dec 2023

I built a Tile Stacking Game leveraging Three.js for its 3D graphics, making the gameplay visually engaging. On the frontend, I wrote the game logic in JavaScript, ensuring a smooth user experience. For the backend, I chose Node.js and Express.js to handle server-side operations and API requests. To integrate blockchain functionalities, especially for minting high scores as NFTs, I employed Solana's web3.js library and connected to the Phantom Wallet.
May 2022
Github  •   Link to play

Quadratic voting application deployed on polygon, developed in Hardhat and Next.js, smart contract tests written in solidity and javascript, inspired by Vitalik's blog post advocating for nonlinear cost functions
December 2022
Github   •   Deployed link

Custom malloc for virtual address allocation; two-level page table for 32-bit address translation; direct-mapped TLB for efficient address translation caching; bit manipulation for efficient memory tracking; designed a 4-level page table for 64-bit addressing; ensured thread safety and compatibility with various page sizes, benchmarked using matrix multiplication.
November 2023
Report   •   Github

Thread creation, yielding, exiting, joining, and synchronization using mutexes; scheduling policies including Pre-emptive Shortest Job First (PSJF) and Multi-Level Feedback Queue (MLFQ); thread context management through makecontext, swapcontext, and ucontext APIs
October 2023
Report   •   Github

Advanced socket programming, multitasking (or select() for I/O multiplexing), and thread synchronization for secure, simultaneous game state management. Extended functionality includes interruption handling via signals, specifically using pthread_kill() to signal threads and pthread_sigmask() for signal reception control, leveraging SIGUSR1 and SIGUSR2 for managing blocked system calls.
April 2023
Github

I designed and implemented a command-line shell akin to bash or zsh. I utilized Posix stream I/O for unbuffered input and output operations, manipulated the working directory, and spawned child processes to execute user commands while capturing their exit statuses. My implementation leveraged dup2() and pipe() for redirecting standard input and output, enabling the construction of pipelines between commands.
March 2023
Github

This simulator dealt with simulating memory operations like reading and writing individual bytes, employing different cache mapping strategies, and implementing replacement policies. Specifically, it simulated write-through, write-allocate cache behavior, and introduced prefetching to improve spatial locality benefits.
December 2021
Github

This program interprets a custom specification language describing circuits' inputs, outputs, and the logical gates connecting them. By efficiently parsing these descriptions, "truthtable" computes and prints all possible input combinations alongside their corresponding outputs, offering insights into the digital logic underlying the specified circuitry.
November 2021
Github

Programming in C, Unix environment, File I/O, dynamic memory allocation, machine-learning algorithm implementation, Gauss-Jordan elimination, matrix handling, "one-shot" learning algorithm application, weight calculation from attributes, matrices for attribute and price representation, matrix transformation and inversion.
October 2021
Github

In partnership with the UN, presented at 2021 general assembly; building low-cost, cloud computing institutes in Dharavi, Mumbai; refer to slides 28 and 29 for break-even calculations with net output and capital cushions
May 2021
Deck