About
Rishi Saha
Systems engineer, security researcher, and AI practitioner. Building at the intersection of low-level computing, adversarial intelligence, and infrastructure that earns trust by design.
YESS I'M BROWN HOMIEB.Tech in Computer Science & Engineering · 1st Year — IIIT Kalyani
Everything he studies and builds points toward a single organizing idea: understanding systems so thoroughly that they can be taken apart, analyzed, reshaped, and improved. Grounded in a deep fascination with how computers behave at their lowest levels and how intelligence — both artificial and human-designed — can emerge from deterministic machinery.
Systems Thinking
01Reasoning about performance, security, and intelligence as properties of entire stacks — from hardware initialization to userland.
Software is not isolated code — it is a living component of a machine that includes firmware, operating systems, networks, and users. This perspective allows reasoning about kernel behavior, boot chains, and memory management with the same fluency as neural networks, probabilistic models, or distributed systems.
Reverse Engineering
02Analyzing compiled binaries, Android apps, and protected software to recover intent from artifacts.
Active analysis of compiled binaries, Android applications, and protected software to understand internal logic, control flow, and security mechanisms. Unpacking obfuscated code, identifying packers and encryption layers, reconstructing algorithms, and documenting behavior — applied computer science archaeology when source is unavailable.
Security Research
03Vulnerabilities as consequences of design decisions, assumptions, and trade-offs.
Studying web servers, network services, operating systems, and applications to understand where abstractions leak. Focus on authentication systems, cryptographic usage, sandbox escapes, misconfigurations, memory corruption classes, logic flaws, and privilege boundaries. Motivated by discovering novel vulnerabilities and responsibly reporting them.
Artificial Intelligence
04AI models as computational structures — parameterized functions shaped by data, optimization, and constraints.
Machine learning fundamentals, large language models, probabilistic reasoning, and hybrid systems that combine symbolic logic with statistical learning. Data leakage, model inversion, adversarial inputs, and alignment are concrete engineering problems — not abstract concerns.
Low-Level & Firmware
05How computers boot, how trust is established from power-on to userland.
BIOS and UEFI concepts, bootloaders, secure boot chains, hardware initialization, and firmware storage mechanisms. Exploring how proprietary firmware can be studied, emulated, or replaced using open alternatives and programmable logic such as FPGAs.
Infrastructure & OS
06Server deployments, containerization, kernel tuning, and privacy-focused network architectures.
Linux kernel behavior, memory management, filesystems, process scheduling, and security modules. Self-hosted infrastructure, cloud VMs, hybrid deployments. VPN systems, secure tunnels, and minimal userland setups — shaping the operating system into a precise instrument.
Ethics & Philosophy
Ethics are inseparable from competence. Understanding systems deeply carries responsibility — the same knowledge that enables exploitation also enables defense, auditing, and improvement. Work emphasizes consent, ownership, and responsible disclosure. Critical of security theater and shallow compliance — preferring real understanding over checklists.
An emerging systems researcher and AI-focused engineer with a reverse engineer's eye and a scientist's patience. Representing a generation of technologists who refuse to accept black boxes, who value understanding over convenience, and who see the future of computing not as magic, but as something built — carefully, rigorously, and consciously — from the ground up.