CS Theses & Dissertations 2021

For 2021 graduation dates (in alphabetical order by last name):

A principled approach to automated road network conflation
Agarwal, Gorisha
DOI : 10.14288/1.0398182
URI : http://hdl.handle.net/2429/78446
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Laks Lakshmanan

Investigating the impact of methodological choices on source code maintenance analyses
Ahmad, Syed Ishtiaque
DOI : 10.14288/1.0401968
URI : http://hdl.handle.net/2429/79697
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Reid Holmes

[no title]
Basava, Ramya Rao
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Alan Wagner 

On label-efficient computer vision: building fast and effective few-shot image classifiers
Bateni, Peyman
DOI : 10.14288/1.0402554
URI : http://hdl.handle.net/2429/79995
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Frank Wood

[no title]
Bouchard, Bronson Raoul
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Alan Wagner

Improved normal estimation from cross-section drawings
Burla, Luciano Silver
DOI : 10.14288/1.0395345
URI : http://hdl.handle.net/2429/76839
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Alla Sheffer

COMET: Tractable Reactive Program Synthesis
Chen, Christopher Kyin-Hwa
DOI : 10.14288/1.0395746
URI : http://hdl.handle.net/2429/77177
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Mark Greenstreet & Dr. Margo Seltzer

“@alex, this fixes #9” : analysis of referencing patterns in Pull Request discussions
Chopra, Ashish
DOI : 10.14288/1.0395948
URI : http://hdl.handle.net/2429/77372
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Dongwook Yoon

On competitive strategies for external exploration of a convex polygon
Clarkson, Kyle Patrick
DOI : 10.14288/1.0395351
URI :  http://hdl.handle.net/2429/76856
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Will Evans

Security through isolation for cloud and mobile
Colp, Patrick
DOI : 10.14288/1.0401793
URI : http://hdl.handle.net/2429/79510
Degree : Doctor of Philosophy - PhD
Graduation Date : 2021-11
Supervisor : Dr. Margo Seltzer

People store increasing amounts of personal data digitally, from emails to credit cards. Two prevalent places this data is stored are on cloud platforms hosted by third parties and on mobile devices, which are easily lost or stolen and which run any of millions of untrusted third-party applications. We explore security through isolation as a means to protect the sensitive data residing on cloud and mobile platforms. We carefully consider the attributes of each platform and the specifics of the attacks we are trying to protect against to select isolation mechanisms that provide the necessary security benefit without incurring an undue performance penalty. Today's cloud platforms provide isolation through virtualization boundaries, which are typically managed by a monolithic control VM. We decompose such monolithic entities to reduce the attack surface. We break apart the control VM of Xen, a mature virtualization platform, into least-privilege components. We leverage this disaggregation to restart these components frequently, reducing the time window for attacks. Today's mobile platforms provide isolation through passwords and process boundaries. However, these protection mechanisms do little once an attacker can access the physical memory directly. We encrypt sensitive data while it is in memory to prevent direct, physical access to it. We leverage cache locking to provide a safe location embedded within the system chip itself to decrypt application data as it is required. Sharing data between applications is crucial for mobile platforms and is achieved using inter-process communication (IPC). An attacker that gains control of the OS also gains access to all this shared data. We encrypt IPC using a security monitor that operates outside the OS. Leveraging previous work on strong application boundaries, we provide end-to-end encrypted IPC, preventing a compromised OS from being able to access this sensitive data. We demonstrate three systems. First, we disaggregate Xen's monolithic control VM, improving security and reducing performance by 2% or less for most benchmarks. Second, we protect sensitive data on mobile devices from physical memory attacks while preserving performance within 5% for normal Android application usage. Third, we protect all IPC on Android devices incurring no noticeable performance overhead.

BodyData: a modular system for the design and implementation of complex multistep experiments
Dietrich, Matthew Evan
DOI : 10.14288/1.0401377
URI :  http://hdl.handle.net/2429/79266
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Dinesh Pai

[no title]
Fadaviardakani, Mona
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Jim Little

First-order methods for structured optimization
Fang, Huang
DOI : 10.14288/1.0402562
URI :  http://hdl.handle.net/2429/80005
Degree : Doctor of Philosophy - PhD
Graduation Date : 2021-11
Supervisor : Dr. Michael Friedlander

First-order methods are gaining substantial interest in the past two decades because of their superior performance in solving today's large-scale problems. In this thesis, we study some widely used first-order methods for problems that satisfy certain structures. Specifically, in the first part, we contribute to coordinate optimization and show that greedy coordinate descent (GCD) has an implicit screening ability that usually selects coordinates that are nonzero at the solution, which explains why GCD works exceptionally well for problems that admit sparse solutions. We also extend the elegant safe-screening rule that depends on duality gap to atomic-norm regularized problems. In the second part, we study online mirror descent (OMD) with unknown time horizon and unbounded domain, which is known to suffer from linear regret. We provide a stabilization technique and show that the stabilized-OMD can achieve sublinear regret. We also build the connection between stabilized-OMD and dual averaging. In the third part, we derive improved iteration complexity of the stochastic subgradient method for over-parameterized models that satisfy an interpolation condition. The obtained iteration complexity matches the rate of the stochastic gradient method applied to smooth problems that also satisfy an interpolation condition. Our analysis partially explains the empirical observation that nonsmoothness in modern machine learning models sometimes does not slow down the training process.

From neural discourse parsing to content structuring: towards a large-scale data-driven approach to discourse processing
Guz, Grigorii
DOI : 10.14288/1.0397220
URI :  http://hdl.handle.net/2429/78087
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Giuseppe Carenini

Architectures and learning algorithms for data-driven decision making
Hartford, Jason Siyanda
DOI : 10.14288/1.0397441
URI :  http://hdl.handle.net/2429/78301
Degree : Doctor of Philosophy - PhD
Graduation Date : 2021-11
Supervisor : Dr. Kevin Leyton-Brown

To design good policy, we need accurate models of how the decision makers that operate within a given system will respond to policy changes. For example, an economist reasoning about the design of an auction needs a model of human behavior in order to predict how changes to the auction design will be reflected in outcomes; or a doctor deciding on treatments needs a model of people's health responses under different treatments to select the best treatment policy. We would like to leverage the accuracy of modern deep learning approaches to estimate these models, but this setting brings two non-standard challenges. First, decision problems often involve reasoning over sets of items, so we need deep networks that reflect this structure. The first part of this thesis develops a deep network layer that reflects this structural assumption, and shows that the resulting layer is maximally expressive among parameter tying schemes. We then evaluate deep network architectures composed of these layers on a variety of decision problems from human decision making in a game theory setting, to algorithmic decision making on propositional satisfiability problems. The second challenge is that predicting the effect of policy changes involves reasoning about shifts in distribution: any policy change will, by definition, change the conditions under which decision makers operate. This violates the standard machine learning assumption that models will be evaluated under the same conditions as those under which they were trained (the ``independent and identically distributed'' data assumption). The second part of this thesis shows how we can train deep networks that make valid predictions of the results of such policy interventions, by adapting the classical causal inference method of instrumental variables. Finally, we develop methods that are robust to some violations of the instrumental variable assumptions in settings with multiple instrumental variables.

Human pose and stride length estimation
Hedlin, Eric
DOI : 10.14288/1.0401772
URI :  http://hdl.handle.net/2429/79506
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Kwang Moo Yi & Dr. Helge Rhodin

Cooperative virtual machine and spot instance scheduling for greater spot instance revenue
Iqbal, Syed
DOI : 10.14288/1.0401110
URI :  http://hdl.handle.net/2429/79161
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Alan Hu

Improving prediction of user cognitive abilities and performance for user-adaptive narrative visualizations by leveraging eye-tracking data from multiple user studies
Iranpour, Alireza
DOI : 10.14288/1.0402581
URI : http://hdl.handle.net/2429/80022
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Cristina Conati

Hierarchical part-based disentanglement of pose and appearance
Javadi Fishani, Farnoosh
DOI : 10.14288/1.0395356
URI :  http://hdl.handle.net/2429/76854
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Jim Little & Dr. Helge Rhodin

Last iterate convergence in network zero-sum games
Kadan, Amit
DOI : 10.14288/1.0395553
URI :  http://hdl.handle.net/2429/77049
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Hu Fu

[no title]
Kim, Nam Hee
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Michiel van de Panne

Datacenter resource scheduling for networked cloud applications
Kodirov, Nodir
DOI : 10.14288/1.0402558
URI :   http://hdl.handle.net/2429/80003
Degree : Doctor of Philosophy - PhD
Graduation Date : 2021-11
Supervisor : Dr. Ivan Beschastnikh & Dr. Alan Hu

Cloud computing is an integral part of modern life, which became increasingly apparent during the COVID-19 pandemic. Applications that run on the cloud facilitate many of our daily activities, including education, retail, and high quality video calls that keep us connected. These applications run on one or more Virtual Machines (VM), where networked cloud applications can benefit from inter-VM network bandwidth guarantees. For example, an entire class of network-intensive big-data processing applications run more quickly with sufficient network bandwidth guarantees. However, offering inter-VM bandwidth guarantees creates challenges both for resource allocation latency and datacenter utilization, because the resource scheduler must satisfy per-VM resource demands and inter-VM bandwidth requirements. This dissertation demonstrates that it is feasible to offer inter-VM bandwidth guarantees as a first class cloud service. We develop several algorithms that allow efficient sharing of datacenter network bandwidth across tenants. These algorithms maintain high datacenter utilization while offering low allocation latency. Specifically, we propose constraint-solver-based algorithms that scale well to datacenters with hundreds of servers and heuristic-based algorithms that scale well to large-scale datacenters with thousands of servers. We demonstrate the practicality of these algorithms by integrating them into the OpenStack cloud management framework. We also construct a realistic cloud workload with bandwidth requirements, which we use to evaluate the efficiency of our resource scheduling algorithms. We demonstrate that selling inter-VM network bandwidth guarantees as a service increases cloud provider revenue. Furthermore, it is possible to do so without changing cloud affordability for the tenants due to shortened job completion times for the tenant applications. Savings from the shortened VM lifetimes can be used to cover the network bandwidth guarantees service cost, which allows tenants to complete their job faster without paying extra. For example, we show that cloud providers can generate up to 63% extra revenue compared to the case when they do not offer network bandwidth guarantees.

ANF preserves dependent types up to extensional equality
Koronkevich, Paulette
DOI : 10.14288/1.0401431
URI :  http://hdl.handle.net/2429/79315
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. William Bowman

Visualizing multi-level structures in data
Liu, Zipeng
DOI : 10.14288/1.0401773
URI :  http://hdl.handle.net/2429/79497
Degree : Doctor of Philosophy - PhD
Graduation Date : 2021-11
Supervisor : Dr. Tamara Munzner

Visualization is an important tool to analyze data, but there emerge various challenges from complex analytical data and tasks. In this dissertation, I present four projects that were motivated by these challenges, situated in the nested model proposed by Munzner, which consists of four layers to describe the components in visualization: domain, data and task, encoding design, and algorithm. In ADVIEW, to address the challenges of comparing many phylogenetic trees in the domain of biology, I propose a visual encoding to compress a tree representation, design and implement a multi-view interactive tool to handle the multiple levels of detail in a tree collection dataset, ranging from the whole collection, through subsets of trees, individual trees, subtrees, to leaf nodes. In SPRAWLTER, to address the existing visual encoding problems of readability metrics for node-link graphs, I propose two novel metrics to measure a finer-grained clutter and to balance the geometric sparseness and clutter. These metrics recognize different levels of visual saliency such as metanodes and leaf nodes in multi-level graphs. In LOGSEG, to fulfill user demands for chunking actions in the domain of image editing software, I propose a segmentation model for the action logs to serve the demands that require different chunking granularities. For example, smart undo for going back to a previous user task needs a low-level chunking, while managing an overview of milestones needs a high-level one. In CORGIE, to fill the gap in visual qualitative evaluation of graph neural networks (GNNs) in the domain of machine learning, I propose an approach and design a tool to explore correspondences between a graph and its embedding to check how different levels of structures are preserved from the input graph to the output embedding. I also design a new graph layout to reveal how a GNN leverages node neighbors and computes an embedding. I identify a common theme among these projects: multi-level structures. They consist of nesting subsets of data points that are relevant to the analytical tasks. I demonstrate how to exploit them in the visualization if provided in hierarchical data, or to compute them for non-hierarchical data.

[no title]
Ma, Mark (Ke)
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Rachel Pottinger

On path-greedy geometric spanners
Morais de Arruda, Siaudzionis Lucca
DOI : 10.14288/1.0402167
URI :  http://hdl.handle.net/2429/79736
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Will Evans

Diagnosing bias in the gender representation of HCI research participants : how it happens and where we are
Offenwanger, Anna Maria
DOI : 10.14288/1.0395335
URI :  http://hdl.handle.net/2429/76844
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Dongwook Yoon & Dr. Julia Bullard

Data-driven prototyping in information visualization
Oppermann, Michael
DOI : 10.14288/1.0401844
URI :  http://hdl.handle.net/2429/79576
Degree : Doctor of Philosophy - PhD
Graduation Date : 2021-11
Supervisor : Dr. Tamara Munzner

Prototyping is employed in information visualization to understand user needs and to iteratively implement, test, and refine possible solutions. Yet, data is often seen merely as a resource in this process, and toy or synthetic datasets can lead to incorrect data abstractions and less effective visualization designs. In this dissertation, we demonstrate how a data-driven prototyping process based on real-world data can lead to novel contributions in information visualization with high industry relevance. The design study on the Ocupado project describes the process of designing, implementing, and evaluating a suite of novel visualization tools for studying space utilization at scale. We reflect on the prototyping process that included multiple stakeholders and present generalizable design choices for visualizing non-trajectory spatiotemporal data related to indoor regions. The findings from the Ocupado study highlight the need for analyzing data concerning time periods of interest that are known in advance rather than determined on the fly. We provide a detailed characterization of non-contiguous time series slices and propose TimeElide, a domain-agnostic visual analysis tool and design space. Inspired by emerging large-scale visualization collections and the difficulty in finding relevant information, we investigate a content-based approach for visualization recommendation in the VizCommender study. We focus on text-based content that is representative of the subject matter of visualizations and compare different similarity measures. We identify that all existing visualization snippets—compact previews of visualizations in those collections—are characterized by their low information density and fail to help people judge the relevance. The VizSnippets study is the first systematic approach to visualization snippet design. We propose a design framework and computational pipeline for the lossy compression of visual and textual content into representative snippets. A critical reflection on our data-driven prototyping approach, and visualization design studies in general, reveals an alternative avenue for applied visualization projects that begins with real-world data rather than specific stakeholder analysis questions. We introduce the notion of data-first design studies and provide practical guidance.

StrokeStrip: Joint Parameterization and Fitting of Stroke Clusters
Pagurek van Mossel, David Eric
DOI : 10.14288/1.0396730
URI :  http://hdl.handle.net/2429/77804
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Alla Sheffer

Online contention resolution schemes for matchings and matroids
Ramezani, Iliad
DOI : 10.14288/1.0401971
URI :  http://hdl.handle.net/2429/79698
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Hu Fu & Dr. Bruce Shepherd

Pragmatic investigations of applied deep learning in computer vision applications
Shafaei, Alireza
DOI : 10.14288/1.0395340
URI :  http://hdl.handle.net/2429/76834
Degree : Doctor of Philosophy - PhD
Graduation Date : 2021-05
Supervisor : Dr. Jim Little & Dr. Mark Schmidt

Deep neural networks have dominated performance benchmarks on numerous machine learning tasks. These models now power the core technology of a growing list of products such as Google Search, Google Translate, Apple Siri, and even Snapchat, to mention a few. We first address two challenges in the real-world applications of deep neural networks in computer vision: data scarcity and prediction reliability. We present a new approach to data collection through synthetic data via video games that is cost-effective and can produce high-quality labelled training data on a large scale. We validate the effectiveness of synthetic data on multiple problems through cross-dataset evaluation and simple adaptive techniques. We also examine the reliability of neural network predictions in computer vision problems and show that these models are fragile on out-of-distribution test data. Motivated by statistical learning theory, we argue that it is necessary to detect out-of-distribution samples before relying on the predictions. To facilitate the development of reliable out-of-distribution sample detectors, we present a less biased evaluation framework. Using our framework, we thoroughly evaluate over ten methods from data mining, deep learning, and Bayesian methods. We show that on real-world problems, none of the evaluated methods can reliably certify a prediction. Finally, we explore the applications of deep neural networks on high-resolution portrait production pipelines. We introduce AutoPortrait, a pipeline that performs professional-grade colour-correction, portrait cropping, and portrait retouching in under two seconds. We release the first large scale professional retouching dataset.

Digital social interaction in older adults during the COVID-19 pandemic
Sin, Frances Jihae
DOI : 10.14288/1.0397211
URI :  http://hdl.handle.net/2429/78092
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Dongwook Yoon

Learned acoustic reconstruction using synthetic aperture focusing
Straubinger, Tim
DOI : 10.14288/1.0402577
URI :  http://hdl.handle.net/2429/80019
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Robert Xiao & Dr. Helge Rhodin

Attacking transaction relay in MimbleWimble blockchains
Tabatabaee, Seyed Ali
DOI : 10.14288/1.0401254
URI :  http://hdl.handle.net/2429/79200
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Ivan Beschastnikh & Dr. Chen Feng

Large scale federated analytics and differential privacy budget preservation
Ulhoa Avelar Stolet, Matheus
DOI : 10.14288/1.0401936
URI :  http://hdl.handle.net/2429/79670
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Ivan Beschastnikh & Dr. Aline Talhouk

Designing familiar augmented and virtual reality environments and interactions through off-the-shelf real-world solutions
Unlu, Arda Ege
DOI : 10.14288/1.0401766
URI :  http://hdl.handle.net/2429/79481
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Robert Xiao

Tinkertoy: build your own operating systems for IoT devices
Wang, Bingyao
DOI : 10.14288/1.0396955
URI :  http://hdl.handle.net/2429/77951
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Margo Seltzer

Hierarchical structure and ordinal features in class-based linear models
Wang, Wan Shing Martin
DOI : 10.14288/1.0395817
URI :  http://hdl.handle.net/2429/77241
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. David Poole

Approximate extended formulations for multidimensional knapsack and the unsplittable flow problem on trees
Weninger, Noah John Beckie
DOI : 10.14288/1.0401745
URI :  http://hdl.handle.net/2429/79478
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Bruce Shepherd

Reproducibility as a service
Wonsil, Joseph
DOI : 10.14288/1.0398221
URI :  http://hdl.handle.net/2429/78501
Degree : Master of Science – MSc
Graduation Date : 2021-11
Supervisor : Dr. Margo Seltzer

[no title]
Wu, Junze
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. William Bowman

Recommendation-based approaches to unveil binding preferences of experimentally unexplored RNA-binding proteins
Yang, Shu
DOI : 10.14288/1.0396643
URI :  http://hdl.handle.net/2429/77744
Degree : Doctor of Philosophy - PhD
Graduation Date : 2021-05
Supervisor : Dr. Raymond Ng & Dr. Anne Condon

RNA-binding proteins (RBPs) interact with their RNA targets to mediate various critical cellular processes in post-transcriptional gene regulations such as RNA splicing, modification, replication, localization, etc.. Characterizing the binding preferences of RBP is essential for us to decipher the underlying interaction code and to understand the functions of the interaction partners. However, currently only a minority of the numerous RBPs have RNA binding data available from in vivo or in vitro experiments. The binding preferences of experimentally unexplored RBPs remain largely unknown and are challenging to identify. In this thesis, we take machine learning based recommendation approaches to address this problem. We focus on leveraging the binding data currently available to infer the RNA preferences for RBPs that have not been experimentally explored. Firstly, we present a recommendation method based on co-evolutions to predict the RNA binding specificities for experimentally unexplored RBPs, waiving the need of the RBPs' binding data. We first demonstrate the co-evolutionary relationship between RBPs and their RNA targets. We then describe a K-nearest neighbors based algorithm to explore co-evolutions to infer the RNA binding specificity of an RBP using only the specificities information from its homologous RBPs. Secondly, we present a nucleic acid recommender system to predict probe-level binding profiles for unexplored or poorly studied RBPs. We first encode biological sequences to distributed feature representations by adapting word embedding techniques. We then build a neural network to recommend binding profiles for unexplored RBPs by learning the similarities between them and RBPs that have binding data available. Thirdly, we present a graph convolutional network for unexplored RBPs' binding affinities recommendation. Extending from the previous two approaches, this method adopts a transductive message passing setting to incorporate more information from the data. It predicts the interaction affinity between an unexplored RBP and an RNA probe by propagating information from other explored RBP-RNA interactions through a heterogeneous graph of RBPs and RNAs. Overall, the approaches presented here can help to improve the understanding of RBPs' binding mechanisms and provide new opportunities to investigate the complex post-transcriptional regulations.

Person in context synthesis with compositional structural space
Yin, Weidong
DOI : 10.14288/1.0395749
URI :  http://hdl.handle.net/2429/77183
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Leonid Sigal

[no title]
Zamprogno, Lucas Alan
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Reid Holmes

AudioViewer: learning to visualize sound
Zhang, Yuchi
DOI : 10.14288/1.0395431
URI : http://hdl.handle.net/2429/76929
Degree : Master of Science – MSc
Graduation Date : 2021-05
Supervisor : Dr. Helge Rhodin

Efficiently estimating kinetics of interacting nucleic acid strands modeled as continuous-time Markov chains
Zolaktaf, Sedigheh
DOI : 10.14288/1.0395346
URI :  http://hdl.handle.net/2429/76843
Degree : Doctor of Philosophy – PhD
Graduation Date : 2021-05
Supervisor : Dr. Anne Condon & Dr. Mark Schmidt

Nucleic acid molecules are vital constituents of living beings. These molecules are also utilized for building autonomous nanoscale devices with biological and technological applications, such as toehold switches, algorithmic structures, robots, and logic gates. Predicting the kinetics (non-equilibrium dynamics) of interacting nucleic acid strands, such as hairpin opening and strand displacement reactions, would assist with understanding the functionality of nucleic acids in the cell and with building nucleic-acid based devices. Continuous-time Markov chains (CTMC) are commonly used to predict the kinetics of these reactions. However, predicting kinetics with CTMC models is challenging. Because, first, the CTMCs should be defined with accurate and biophysically realistic kinetic models. Second, the state space of the CTMCs may be large, making predictions time-consuming, particularly for reactions that happen on a long time scale (rare events), such as strand displacement at room temperature. We introduce an Arrhenius kinetic model of interacting nucleic acid strands that relates the activation energy of a state transition with the immediate local environment of the affected base pair. Our model can be used in stochastic simulations to estimate kinetic properties and is consistent with existing thermodynamic models that make equilibrium predictions. We infer the model’s parameters on a wide range of reactions by using mean first passage time (MFPT) estimates. We estimate MFPTs using exact computations on simplified state spaces. We show that our new model surpasses the performance of the previously established Metropolis kinetic model. We further address MFPT estimation and the rapid evaluation of perturbed parameters for parameter inference in the full state space of reactions’ CTMCs. We show how to use a reduced variance stochastic simulation algorithm (RVSSA) to estimate MFPTs. We also introduce a fixed path ensemble inference (FPEI) approach for the rapid evaluation of perturbed parameters. These methods are promising, but they are not suitable for rare events. Thus, we introduce the pathway elaboration method, a time-efficient and probabilistic truncated-based approach for addressing both mentioned tasks. We demonstrate the effectiveness of our methods by conducting computational experiments on nucleic acid kinetics measurements that cover a wide range of rates for different type of reactions.