Our team at Quantum focused on cloud and cold storage technologies. Data integrity and protection are among the mainstream objectives of the data storage business. The reliability, durability, and availability of coded system architectures remain central (and often debated) concerns in the market.

Our aim was to develop algorithms and approximate random processes to predict the reliability and durability of Quantum’s product line, and to help other research groups assess their own systems by establishing a common set of tools. Erasure-correction coding is used to add redundancy and protect data against abnormalities and failures. Our research emphasized modern erasure codes with linear-time encoding/decoding when possible, spanning designs from Cauchy Reed–Solomon codes to fountain codes and locally decodable linear codes.

A key challenge was adapting elegant theory to real product constraints and system-level requirements, including efficient implementations that improve customer experience. Deliverables included patents, technical papers, and system-level implementations.

Modern networked systems are typically designed as a stack of layers. In progressive multimedia coding, a base layer carries essential “skeleton” information, while enhancement layers refine reconstruction quality. In wireless environments, strict layering can lead to inefficiencies due to time-varying channels, interference, and heterogeneous receiver conditions. Cross-layer approaches have long been known to be beneficial, but are often avoided in practice due to complex inter-layer interactions and concerns about robustness. More recently, it has been shown that cross-layer optimization can be achieved while preserving robustness.

I studied progressive source transmission systems combining a progressive source coder, a rate-compatible punctured convolutional (RCPC) channel coder, and hierarchical modulation. Under bandwidth constraints, we developed a novel packetization strategy and optimized system parameters in a mean-distortion sense. See publications for details.

A key contribution of my thesis was a simple, constructive concatenated coding method for embedded bit streams that achieved performance very close to Shannon-capacity limits for binary symmetric channels (BSCs), within approximately 0.25–0.3 dB in PSNR.

The method couples a novel packetization paradigm with RCPC and rate-compatible LDPC (RC-LDPC) codes, together with carefully designed interleavers for burst-error randomization. We developed a tractable functional optimization procedure enabling constrained exhaustive search to identify globally optimal designs for the stated objective.

The above scenarios often assume the transmitter has perfect or imperfect channel state information (CSI). In many multimedia applications, CSI may be unavailable or unreliable; in multicast settings, receivers experience different channels, so there is no single CSI to adapt to.

Fountain codes are a natural fit in such cases. They are rateless: an essentially unbounded number of encoded packets can be generated, and the actual number transmitted can be determined on the fly. In my thesis, we proposed a general fountain-code design suited to multimedia transmissions, showing how to tune parameters for progressive transmission and emphasizing unequal iteration time (URI) performance via a joint design of the distributions governing code behavior. We also investigated limiting performance bounds for simple LT-code designs under representative source-transmission scenarios.

In summer 2009, I worked with the imaging group at Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA. I first developed a tissue simulation program (C-based engine) using a finite element method to morph an object (a tumor) within a volume. This enabled the generation of synthetic images and videos for testing tracking and classification pipelines.

We evaluated algorithms for detection and tracking in both visible and invisible tumor scenarios, focusing on accuracy and reliability. I also developed additional methods including: (1) improved temporal random-walk tracking, (2) seedless image segmentation using repeated random walks or graph cuts, and (3) learning and tracking via regression on different Lie groups.

Founsure is an open-source erasure coding library for fault-tolerant distributed storage systems. It implements multi-dimensional, graph-based erasure codes using efficient XOR-based operations and multi-core optimizations, enabling fast encoding, decoding, repair, and update in large-scale storage architectures.

The library supports fountain-style (LT) coding with flexible parameterization, bridging elegant erasure-coding theory with practical, deployable system-level implementations for modern distributed storage platforms.