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Recent Submissions

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    Towards Green AI: Evaluating Energy Efficiency for Quantized LLM Inference
    (University of Waterloo, 2026-02-09) Basta, Petar
    While it is known that quantization of large language models (LLMs) reduces memory usage via lower-bit weights, studies quantifying the resulting impact on energy usage and carbon intensity are scarce. We present the first unified evaluation of weight-only quantization strategies for LLM inference across varying input/output lengths and GPUs, highlighting differences in energy efficiency in addition to accuracy degradation and runtime. We study quantized inference on Llama 2 7B, Phi 3.5 Mini Instruct, OLMo 1B, Qwen 2.5 7B, and Qwen 2.5 14B across GLUE MNLI, MMLU, HumanEval, and GSM8K datasets, evaluating 10 post-training quantization (PTQ) strategies on NVIDIA H100, A6000, A100, and L4 GPUs. We identify a novel trend showing that quantization techniques tend to exhibit peak energy efficiency relative to full-precision baselines when inputs are sufficiently long and outputs are short. Furthermore, we show that quantization strategies become marginally more energy-efficient relative to full-precision models as batch sizes increase, though gains are modest. Notably, fused-kernel implementations such as EETQ int8 and Bitsandbytes int8 offer the highest energy savings, up to 4× compared to FP32 on short text generation, with negligible accuracy loss. Finally, we observe that energy usage closely tracks runtime on our evaluated benchmarks, indicating that, in practice, latency optimization can serve as a reliable proxy for reducing the environmental footprint of LLM services. We conclude with suggested directions for strategically selecting low-carbon quantization strategies based on specific inference requirements.
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    Lower Bounds on Average-Case and Non-Local Quantum Computation
    (University of Waterloo, 2026-02-06) Asadi, Vahid Reza
    This thesis studies computational and information-theoretic limitations in both classical and quantum models of computation. It is organized into two parts, each addressing a different aspect of computational hardness. Despite their differences, both parts share a common goal: understanding how structural and physical constraints shape what classical and quantum algorithms can achieve. In Part I, we present lower bounds on the average-case complexity of certain tasks in classical and quantum settings. We develop a general framework for constructing efficient worst-case to average-case reductions. Applying this framework, we obtain such reductions for fundamental problems in a variety of computational models; namely, algorithms for matrix multiplication, streaming algorithms for the online matrix-vector multiplication problem, and static data structures for all linear problems, as well as the multivariate polynomial evaluation problem. We further extend this framework to the setting of quantum algorithms, along the way obtaining a tight bound on the average-case quantum query complexity of the matrix-vector multiplication problem. Our techniques rely crucially on tools from additive combinatorics. In particular, we show local correction lemmas that rely on new probabilistic and noise-robust versions of the quasi-polynomial Bogolyubov-Ruzsa lemma. In Part II, we give quantum gate and entanglement lower bounds on certain non-local tasks. A non-local quantum computation (NLQC) replaces direct interaction between two quantum systems with a single simultaneous round of communication and shared entanglement. We study two classes of NLQC, f-routing and f-BB84, which are of relevance to classical information theoretic cryptography and quantum position-verification. We give the first non-trivial lower bounds on entanglement in both settings, under the assumption of perfect correctness. Within this setting, we give a lower bound on the Schmidt rank of any entangled state that completes these tasks for a given function f(x,y) in terms of the rank of a matrix G whose entries are zero when f(x,y)=0 and strictly positive otherwise. This also leads to a lower bound in terms of the non-deterministic quantum communication complexity of f. Due to a relationship between f-routing and the conditional disclosure of secrets (CDS) primitive studied in information theoretic cryptography, we obtain a new technique for lower bounding the randomness complexity of CDS. Finally, we show that the number of quantum gates plus single-qubit measurements required to implement a function f is at least linear in the entanglement-assisted simultaneous-message-passing communication complexity of f. As a consequence, we derive a linear lower bound for the inner-product function.
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    Directional dependence of phase transition splitting by uniaxial strain: A new test to determine strontium ruthenate superconducting states
    (University of Waterloo, 2014-06) Chen, Anffany
    Since the discovery of superconductivity in strontium ruthenate, there has been a widespread effort to understand its properties on a phenomenological level. To this date, there is not enough coherent experimental evidence to conclude if the superconducting phase has broken time-reversal symmetry. In this article we propose a new test based on the fact that for a multi-component order parameter, the application of uniaxial stress could cause phase transition splitting. Our analysis shows that such splitting depends on the direction of applied stress, and such directional dependence is different for every predicted superconducting phase. In particular, splitting occurs at all directions if and only if the superconducting phase is the one with broken time-reversal symmetry.
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    Ancestry Deconvolution via Differential Privacy
    (University of Waterloo, 2026-02-05) Chowdhury, Raiyan
    This thesis presents the first study of ancestry determination under differential privacy (DP). Direct-to-consumer genomics companies, such as 23andMe, offer ancestry testing to millions of individuals, yet remain vulnerable to severe data breaches. Such incidents are especially concerning because genomic data is uniquely identifying, highly correlated, and permanent once exposed. At the time of writing, 23andMe disclosed a catastrophic breach in October 2023 that compromised the genetic profiles of an estimated 6.9 million users, underscoring the urgent need for stronger privacy guarantees in genomic analysis. In this work, we investigate the application of DP to ancestry deconvolution. Using the 1000 Genomes dataset and Gnomix, a state-of-the-art ancestry inference model, we evaluate how privatizing single nucleotide polymorphism (SNP) data affects ancestry classification accuracy. We implement both naïve and correlation-aware local differential privacy (LDP) mechanisms across varying privacy budgets, enabling a systematic study of the privacy-utility trade-off in ancestry inference. Our results demonstrate that while naïve DP perturbations significantly degrade accuracy, correlation-aware LDP mechanisms preserve substantially more predictive power by accounting for linkage disequilibrium (LD). This thesis establishes a foundation for private ancestry deconvolution, providing an empirical benchmark of state-of-the-art DP methods in genomics and highlighting both the challenges and potential of integrating DP into ancestry testing.
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    Cultural ecosystem services and sense of place: post-rehabilitation assessment of Wadi Hanifah’s performance from a socio-cultural lens
    (Taylor & Francis Ltd., 2025-12-24) Kashmiri, Shahad; Khirfan, Luna
    This paper evaluates the socio-cultural performance of urban ecological rehabilitation through the lens of cultural ecosystem services (CES) and sense of place. Accordingly, it introduces a dynamic, context-sensitive evaluative framework that integrates place image, identity, and attachment as key performance indicators. The framework is applied to the rehabilitation of Wadi Hanifah in Riyadh, Saudi Arabia, using a mixed-methods approach that combines document reviews, online surveys, and semi-structured interviews to assess shifts in the Wadi’s meaning and use post-rehabilitation. Findings reveal a transformation in the Wadi’s place image – from associations with degradation and utilitarian function to one centered on recreation and heritage. The results underscore how ecological rehabilitation can foster socio-cultural value and strengthen collective identity. The proposed framework not only supports future evaluations of Wadi Hanifah but also offers a transferable model for assessing similar projects elsewhere, advancing more holistic understandings of the interplay between ecosystems and human experience.