2026 CMP Academic Projects

There are a few kinds of hearing technologies that help people with hearing loss to hear. These include hearing aids that acoustically amplify sound entering someone’s ear. While hearing aids are normally prescribed and programmed by a healthcare professional, many over-the-counter hearing aids have recently become available as well, and even Air Pods contain hearing-assistive programs. However, these types of devices do not help those with more severe hearing loss. A more complex hearing device involves bypassing the outermost parts of the auditory system and are more like bionic ears: these are cochlear implants.

A cochlear implant is neural prosthetic that provide people with a profound hearing impairment with auditory perception by directly electrically stimulating the auditory nerve. It requires a surgical operation wherein a small string of electrodes is inserted into the patient’s inner ear. These electrodes are then controlled using a speech processor with a microphone that sits behind the patient’s ear. Most recipients can perceive speech well with their implant, and some enjoy music. They are arguably the most successful auditory prosthetic in existence today, with over 1 million users globally.

However, many struggle to understand speech with their implants, especially in challenging listening conditions with background noise. This may be due to the fact that individual cochlear implant users lose their hearing for different reasons, and the cochlear implant software and settings are not optimized for their unique pattern of hearing loss. We have developed a diagnostic tool called the Panoramic ECAP method that is designed to provide patient-specific indicators of the interaction between a patient’s implant and their auditory system. It involves applying a non-linear optimisation algorithm to electrophysiological measurements of neural activity in the cochlea. These measurements are called ‘Electrically Evoked Compound Action Potentials’ or ‘ECAPs’ for short. It is our hope that this tool can be used in a clinical setting to personalise and optimize cochlear-implant software and enable patients to achieve their hearing potential with their device.

The Panoramic ECAP (PECAP) method provides two estimates that describe the interaction between a cochlear implant patient’s inner ear (a.k.a. cochlea [1]) and their implant: the health of the auditory nerves and the spread of electrical current. The two PECAP estimates are quantified for each electrode of the cochlear implant. Areas of poor neural responsiveness or wide current spread reduce the efficiency of delivering auditory information from the neuroprosthetic device to the brain. The PECAP method estimates the neural responsiveness and the current spread at each electrode separately, using a nonlinear optimisation algorithm based on sequential quadratic programming whose structure is based on a theoretical framework [2]. We have conducted experiments to validate that these estimates are accurate and indeed separate from each other. While the results of our previous work are encouraging for clinical translation, long neural-response recording times and noisiness of some recorded data are barriers to translating this research to the clinic.

This project will have two facets that must be balanced with each other:

1. Building on a previous CMP student's work, determine if we can record less neural data (i.e. requiring a shorter amount of time to record) to accurately reproduce the PECAP algorithm's current-spread and neural-responsiveness estimates
2. Evaluate the potential and effectiveness of a recently-published denoising technique [3] to reduce measurement noise and thereby increase the percentage of cochlear-implant patents with whom the PECAP technique can be used (i.e. in cases of noisy neural data).

Two previous CMP students - Taren Rughooputh (2018) and Scott Hislop (2023) - conducted similar projects in our laboratory in the past whose work contributed to academic publications, for which they are on the author lists. (Rughooputh [1], Hislop [Garcia, et al, 2024, DOI: 10.1007/s10162-024-00966-x])

[1] Background Coursework from Duke University describing the peripheral auditory system:
https://gb.coursera.org/lecture/medical-neuroscience/peripheral-auditory-mechanisms-part-1-SQ6H7.
I recommend watching the first 3 videos of week 7 for a review of the human auditory system

[2] The publication describing the ‘PECAP’ algorithm that this project is based on:
Garcia, C., Goehring, T., Cosentino, S. et al. The Panoramic ECAP Method: Estimating Patient-Specific Patterns of Current Spread and Neural Health in Cochlear Implant Users. JARO 22, 567–589 (2021).
https://doi.org/10.1007/s10162-021-00795-2

[3] The publication describing the de-noising technique:
Kung F. J. (2025). An Integrated Spatial-Spectral Denoising Framework for Robust Electrically Evoked Compound Action Potential Enhancement and Auditory Parameter Estimation. Sensors (Basel, Switzerland), 25(11), 3523.
https://doi.org/10.3390/s25113523

Since the landmark detection of GW170817, gravitational wave astronomers have been searching for additional "bright sirens" - gravitational wave events with electromagnetic counterparts. However, LIGO's frequentist methodology using false alarm rates may have missed signals buried just below detection thresholds. These subthreshold signals could provide crucial cosmological information, including independent measurements of the Hubble constant to address the Hubble tension.

Bayesian model comparison offers a principled framework for assessing whether multi-messenger information (such as gamma-ray burst sky localizations) can help identify gravitational wave signals that would otherwise be classified as noise. Recent advances in GPU-accelerated inference using JAX-based tools like BlackJAX and JimGW make this computationally tractable.

The student will develop GPU-accelerated nested sampling pipelines to search for subthreshold gravitational wave signals by conditioning on sky localizations from gamma-ray bursts. Specific tasks include:

1. Learning the fundamentals of gravitational wave data analysis and Bayesian nested sampling
2. Implementing GPU-accelerated inference using JAX, BlackJAX, and JimGW 
3. Generating realistic mock gravitational wave data using BILBY and injecting signals at various signal-to-noise ratios
4. Comparing Bayesian evidence between unconditioned and sky-conditioned models
5. Applying the pipeline to real LIGO data coincident with short gamma-ray bursts from the Fermi GRB catalogue

A successful outcome would be a validated pipeline capable of identifying candidate subthreshold signals and quantifying the evidential support for their astrophysical origin. The project uses mathematical skills in probability theory, Bayesian inference, and high-dimensional integration (nested sampling). Programming skills in Python and familiarity with numerical computing are essential.

The Hubble tension - the 4-6σ disagreement between early-universe (CMB) and late-universe (supernovae) measurements of the cosmic expansion rate H₀ - is one of the most pressing problems in modern cosmology. Gravitational wave "standard sirens" offer a completely independent method to measure H₀, as the luminosity distance is encoded directly in the gravitational wave signal amplitude.

GW170817, the first binary neutron star merger with an electromagnetic counterpart, provided the first standard siren measurement of H₀. However, standard parameter estimation pipelines require thousands of CPU hours, limiting rapid multi-messenger follow-up. GPU-accelerated inference using JAX can reduce this to minutes, enabling real-time cosmological constraints from future detections.

The student will develop and validate a GPU-accelerated pipeline for joint inference of gravitational wave source parameters and the Hubble constant. Specific tasks include:

1. Understanding the theoretical framework connecting gravitational wave observations to cosmological distance measurements
2. Implementing joint parameter estimation for GW source properties and H₀ using JAX-based tools
3. Validating the pipeline on the benchmark event GW150914
4. Reproducing the H₀ posterior from GW170817 and comparing with published LVK collaboration results
5. Investigating prior sensitivity by comparing direct sampling approaches with reweighting techniques
6. Benchmarking GPU vs CPU performance

A successful outcome would be a validated, fast pipeline for H₀ inference that reproduces published results while providing insight into statistical methodology. The project uses mathematical skills in Bayesian inference, cosmology, and high-dimensional sampling. Strong programming skills in Python are essential.

Transmissible cancers are infectious tumours that spread through animal populations as clonal allografts [1]. Three are known to affect mammals: Canine Transmissible Venereal Tumour (CTVT) [2], and two forms of Tasmanian Devil Facial Tumour disease (DFT1 and DFT2) [3,4]. Several more forms have been discovered as disseminated neoplasias in marine bivalves [5].

One common feature of transmissible cancers is that they have outlived the progenitor that founded them. Subsequently they have been exposed to new, temporary hosts of diverse genotype and microbiota. DNA from one such host has been permanently incorporated into a transmissible cancer genome on at least one occasion [6].

However, we don’t know the extent to which microbial and viral material has been included. This project aims to assess this through the analysis of several high-coverage, short read sequencing data derived from CTVT, DFT1 and DFT2 tumour samples, and from normal tissue obtained from their matched hosts.

1. Ní Leathlobhair M, Lenski RE. Population genetics of clonally transmissible cancers. Nat Ecol Evol. 2022;6: 1077–1089.
2. Murchison EP, Wedge DC, Alexandrov LB, Fu B, Martincorena I, Ning Z, et al. Transmissible [corrected] dog cancer genome reveals the origin and history of an ancient cell lineage. Science. 2014;343: 437–440.
3. Pye RJ, Pemberton D, Tovar C, Tubio JMC, Dun KA, Fox S, et al. A second transmissible cancer in Tasmanian devils. Proc Natl Acad Sci U S A. 2016;113: 374–379.
4. Hawkins CE, Baars C, Hesterman H, Hocking GJ, Jones ME, Lazenby B, et al. Emerging disease and population decline of an island endemic, the Tasmanian devil Sarcophilus harrisii. Biol Conserv. 2006;131: 307–324.
5. Metzger MJ, Villalba A, Carballal MJ, Iglesias D, Sherry J, Reinisch C, et al. Widespread transmission of independent cancer lineages within multiple bivalve species. Nature. 2016. Available: http://www.nature.com/nature/journal/vaop/ncurrent/full/nature18599.html
6. Gori K, Baez-Ortega A, Strakova A, Stammnitz MR, Wang J, Chan J, et al. Horizontal transfer of nuclear DNA in transmissible cancer. Proc Natl Acad Sci U S A. 2025;122: e2424634122.

This project will investigate whether we can detect the inclusion of foreign DNA in the genomes of CTVT, DFT1 and DFT2. In the case of CTVT, we are already aware of a novel retrovirus that has implanted in the genome, and that papillomavirus is a common coinfection. The project will initially focus on detecting and characterising these viruses, before expanding to look for evidence of acquisition from any source.

Sequence that has inserted into the genome is of particular interest; a challenging part of the project will be to determine whether foreign DNA is integrated into the tumour genome, or is instead associated with the cellular environment. The project uses high-coverage, short read sequencing data derived from CTVT, DFT1 and DFT2 tumour samples, and from normal tissue obtained from their matched hosts. It will develop skills in bioinformatics, especially in handling genomic data, running sequence assembly, and comparing and annotating sequence contigs. Sequence analysis is an inherently mathematical topic. Assembling contigs is a longest common substring problem: by representing short sequence fragments as nodes in a graph in which the edges represent their overlaps, this amounts to finding the Euler path through the graph [7]. Sequence evolution models changes in sequence as a continuous time Markov process operating over a tree [8]. The large amount of data produced by sequencing projects necessitates use of statistics to simplify and identify patterns in the data. This project, though biological, has scope for the application and development of mathematical techniques, and would be of interest to a mathematician.

7. Pevzner PA, Tang H, Waterman MS. An Eulerian path approach to DNA fragment assembly. Proc Natl Acad Sci U S A. 2001;98: 9748–9753.
8. Felsenstein J. Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol. 1981;17: 368–376.

The biological motivation of the project is the study of the formation and maintenance of the apical hook, including its response to biochemical perturbations [4], as well as the gravitropic behavior of shoots and roots [5,6]. From a mathematical perspective, the project aims to develop and implement numerical methods for integrating continuum morphoelastic models of rod-like plant organs.

The project will focus on the design and implementation of a numerical scheme to integrate the model proposed in [7]. By assuming morphoelastic roles for morphogens, the model directly links axial growth and bending to spatial morphogen distributions within a quasi-static formulation of growth mechanics. The organ is represented as an array of concentric cylindrical morphoelastic shells connected in parallel, yielding a mathematically rich framework that couples elasticity, growth, and geometry.

To relate this continuum description to cellular processes, gene regulatory networks, and active morphogen transport, the model will be discretized and implemented within an existing custom numerical solver for plant morphodynamics written in C++ [8].

The student will be hosted within the Jönsson group, led by Professor Henrik Jönsson, Director of the Sainsbury Laboratory. The group offers a collaborative and intellectually stimulating research environment, with weekly group meetings in which members present and discuss ongoing research.

The student will receive regular one-on-one supervision through weekly meetings with Dr. Amir Porat, with additional guidance and support available as needed throughout the internship.

The Sainsbury Laboratory also provides a vibrant and welcoming environment for summer students, hosting a range of social events and activities designed to foster interaction among students from different programs and research backgrounds.

Plant morphogenesis arises from the interplay of growth, mechanics, and genetic regulation. A central mathematical challenge is understanding how spatially distributed biochemical morphogens generate stable growth patterns. This project focuses on plant organs exhibiting self-similar growth, where global geometry is preserved as cells grow and divide.

Such systems exhibit approximately stationary material flows in suitable coordinates, making them analytically tractable [1]. We have developed a novel continuous geometric framework for self-similar growing manifolds, in which growth is described by the Lie derivative of a Lagrangian metric [2,3]. Under the assumption of shearless growth along orthogonal curvilinear coordinates, the Lie derivative simplifies, providing a compact and powerful description of tissue deformation and growth kinematics.

The project is split into two complementary tracks, with the option to focus primarily on one while exploring the other as time permits.

Track 1: 3D Cellularization of Self-Similar Growth (Numerical Modelling)
Extending an existing 2D C++ implementation of self-similar growth in the shoot apical meristem (SAM) [4] to three dimensions. This involves modelling cellular growth and division, developing algorithms to match simulated cell shapes and sizes to live-imaging data [5], and using the resulting geometries to analyse gene patterning and tissue mechanics.

Track 2: Geometric Perturbations of Self-Similar Growth (Analytical Modelling)
Investigating geometric perturbations of self-similar growth, motivated by phyllotaxis - the periodic formation of leaves and flowers on the flanks of the SAM [6]. Starting from the Lie derivative of the Lagrangian metric in self-similarity, we wish to examine how combined perturbations of the velocity and metric fields can generate biologically relevant growth patterns. The work begins with interpreting custom axisymmetric shearless perturbations of shells using the Lie derivative formalism, followed by periodic or more general perturbations, with the aim of establishing a new geometric framework for studying shape dynamics and morphogenesis.

The student will be hosted within the Jönsson group, led by Professor Henrik Jönsson, Director of the Sainsbury Laboratory. The group offers a collaborative and intellectually stimulating research environment, with weekly group meetings in which members present and discuss ongoing research.

The student will receive regular one-on-one supervision through weekly meetings with Dr. Amir Porat, with additional guidance and support available as needed throughout the internship.

The Sainsbury Laboratory also provides a vibrant and welcoming environment for summer students, hosting a range of social events and activities designed to foster interaction among students from different programs and research backgrounds.

Our general long-term aim is to develop an ‘inference engine’ using machine learning methods that extracts interpretable mechanistic laws directly from biological data to bridge the gap between modern large biological datasets and mechanistic biophysical models. This process includes two stages: (i) learning a ‘meaningful’ representation of the data (e.g. using unsupervised learning [1, 2]) and (ii) inferring connections and ultimately physical models from this learned representation [3, 4]. We recently demonstrated an example of this approach (LMRL25, ICLR workshop [5]). Depending on the student’s interests this project can focus on either or both of these complementary strands:

Strand 1: Data Alignment. Biological data is captured across different modalities (morphology vs. gene expression). This strand focuses on the "representation" problem: using unsupervised methods (e.g. Matrix Factorisation [1] or Autoencoders [2]) to project high-dimensional data into a unified, low-dimensional "latent manifold" that represents the tissue's state.

Strand 2: Differentiable Simulations & Model Inference. This strand focuses directly on the "inference" problem. Given a representation of the tissue state and its dynamics, we will use differentiable simulations (e.g. using Graph Neural Networks [6]) combined with identifiability techniques [3, 4] to infer possible governing equations.

[1] Argelaguet, Ricard, et al. "MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data." Genome biology 21 (2020).
[2] Yang, Karren Dai, et al. "Multi-domain translation between single-cell imaging and sequencing data using autoencoders." Nature communications 12.1 (2021).
[3] Maddu, Suryanarayana, et al. "Learning physically consistent differential equation models from data using group sparsity." Physical Review E 103.4 (2021).
[4] Brunton, Steven L., Joshua L. Proctor, and J. Nathan Kutz. "Discovering governing equations from data by sparse identification of nonlinear dynamical systems." Proceedings of the national academy of sciences 113.15 (2016).
[5] Zardilis, Argyris, Alexandra Budnikova, and Henrik Jönsson. "Learning a mechanical growth model of flower morphogenesis." LMRL25, ICLR workshop.
[6] Choi, Jeongwhan, et al. "Gread: Graph neural reaction-diffusion networks." International conference on machine learning. PMLR, 2023.
Refahi, Yassin, Zardilis, Argyris et al. "A multiscale analysis of early flower development in Arabidopsis provides an integrated view of molecular regulation and growth control." Developmental Cell 56.4 (2021).
Wang, Hanchen, et al. "Scientific discovery in the age of artificial intelligence." Nature 620.7972 (2023): 47-60.
Villoutreix, Paul. "What machine learning can do for developmental biology." Development 148.1 (2021): dev188474.

Photoacoustic imaging is an emerging medical imaging technique that combines light and ultrasound to non-invasively measure blood oxygenation and vascular structure, providing valuable biomarkers for early cancer detection (e.g. breast cancer). However, a key challenge for its clinical use is systematic bias caused by skin pigmentation. Melanin in the skin absorbs light strongly, particularly in individuals with darker skin tones, which will reduce the amount of light reaching deeper tissue. This leads to underestimation of physiological parameters such as blood oxygenation and introduces bias that is unrelated to true underlying biology.

In our lab, we have previously undertaken large-scale healthy volunteer studies spanning a wide range of skin tones, generating unique clinical photoacoustic datasets that clearly demonstrate pigmentation-dependent bias in quantitative measurements. Current correction methods rely on simplified optical models or empirical calibration and often fail to generalise across diverse populations. Recent advances in machine learning and computational modelling therefore offer promising new approaches to explicitly model and correct pigmentation-induced bias by combining physics-based simulations with data-driven inference.

The project is partly determinate and partly open-ended. The overall aim is to develop quantitative methods to reduce skin-tone-dependent bias in clinical photoacoustic imaging, building on large-scale datasets previously acquired in our lab.

Possible Project Directions:

1. Exploratory analysis of simulated and clinical data to characterise how pigmentation-related bias varies with wavelength, tissue depth, and physiological parameters.
2. Modelling the gap between simulations and clinical images using machine learning approaches, including conditional diffusion models, adversarial image-to-image translation methods (e.g. CycleGAN), and domain adaptation techniques to learn realistic noise, artefacts, and distribution shifts.
3. Developing physics-informed AI methods for bias correction by incorporating physical constraints or simulation-based priors from optical and acoustic models building on existing approaches to improve quantitative accuracy across skin tones.
4. Gaining experience with optical and acoustic computational modelling, including Monte Carlo light transport models and acoustic wave propagation (e.g. using in-house SIMPA framework), to understand the physical origins of bias in photoacoustic imaging.

Physics-based simulations are widely used in the development and interpretation of medical imaging and spectroscopic techniques, including Raman spectroscopy and photoacoustic imaging. Simulated data provide controlled access to underlying physical parameters and are commonly used for algorithm development and training machine-learning models. However, simulated spectrum often differ systematically from experimental measurements due to instrument response, measurement noise, calibration effects, and unmodelled experimental conditions.

This simulation-to-experiment gap limits the direct transfer of algorithms trained on simulated data to real clinical or experimental settings. In spectral and signal-domain measurements, these discrepancies can manifest as wavelength-dependent distortions, changes in noise structure, baseline shifts, and amplitude scaling, all of which are not fully captured by simplified physical models.

Recent advances in machine learning and data-driven modelling offer new opportunities to explicitly learn and characterise the gap between simulated and experimental spectral data. By leveraging paired or partially paired datasets, it becomes possible to model realistic noise processes, instrument-induced distortions, and distribution shifts, improving the fidelity of synthetic data and enabling more robust downstream analysis in real-world applications.

The project is partly determinate and partly open-ended. The overall aim is to develop quantitative methods to model the gap between simulated and experimental spectral data, using paired Raman spectroscopy data and photoacoustic datasets available in our lab. While the problem setting is well defined, the appropriate level of model complexity is intentionally left open.

Possible Project Directions:

1. Starting with simple, interpretable models to capture systematic differences between simulated and experimental data, such as additive or multiplicative noise models, wavelength-dependent distortions, baseline shifts, or low-rank spectral transformations.
2. Exploring more flexible data-driven models, such as probabilistic regression models, conditional diffusion models, and adversarial learning approaches (e.g. GAN-based image or spectrum translation), to capture complex noise structures, nonlinear distortions, and distribution shifts between simulated and experimental data.
3. Comparing different classes of models to understand trade-offs between interpretability, physical plausibility, and predictive performance.
4. (Optional) Incorporating physical knowledge through constraints or simulation-based priors, ensuring that learned mappings remain consistent with known physical behaviour.

One of our more specific areas of research is phylogenetic networks. These are directed graphs describing complex evolutionary histories. We use Markov models to probabilistically evaluate genome evolution histories along phylogenetic networks. We use algebraic statistics to understand these models, which are viewed as varieties from algebraic geometry, allowing us to efficiently infer phylogenetic networks from DNA data. Possible projects in this area could be to extend existing work [1] on inferring small phylogenetic networks, or developing further understanding of the geometry of small phylogenetic network models [2]. Experience in python programming and knowledge of algebraic geometry and statistical inference is desirable.

Another area of the group’s research is massive-scale phylogenetics. Progress in sequencing technologies enables the generation of datasets of thousands, and sometimes millions of pathogen genomes. This data can reveal essential details of infectious disease evolution and spread, but existing mathematical and computational methods for analysing such data are not scalable enough. We address this challenge by developing new scalable and accurate approaches [3,4]. Internships on this subject would focus on phylogeography, which uses genetic data to reconstruct transmissions between countries, species, or generally groups of hosts. Possible projects include contributing to the development of simulation methods (good coding skills in Python or C++ would be beneficial), benchmarking of existing methods (some coding skills would be useful), or the development of new efficient mathematical pathogen spread models robust to biases in sample collection (probabilistic mathematical modeling skills and some coding experience would be desirable).

Lastly, our group develops mathematical models and algorithms for the improvement of sequencing technologies - machines that read DNA data from biological samples. In particular, we use information theory principles to improve adaptive sampling methods, which focus sequencing resources on reading DNA fragments of interest: see [5]. This makes sequencing more efficient, allowing researchers to achieve more accurate genetic data at lower costs. Possible projects in this area would focus on simulations and benchmarking, determining scenarios in which adaptive sampling is useful. Skills in probabilistic mathematical modeling and some coding experience would be desirable.

Most cancers die when the individual carrying them dies. In very rare cases, however, cancer cells can behave like parasites: they pass directly from one individual to another and continue living in new hosts. These are known as transmissible cancers.

Tasmanian devils, carnivorous marsupials living on the island of Tasmania, are affected by two transmissible cancers. These cancers spread when devils bite each other, transferring living cancer cells that grow into facial tumours. One of these cancers has already spread across almost the entire population and is threatening the survival of the species.

A key question is why these tumours are generally not recognized by the devils immune system and are some devils able to slow down or resist the disease.

Tasmanian devils carry certain genes that produce proteins displayed on the surface of their cells. These proteins differ slightly between individuals and help the body distinguish its own cells from foreign cells. Recent work (Batley et al. 2025. bioRXiv) suggests that devils whose versions of these genes differ more strongly from those carried by the cancer cells are more likely to mount an immune response to the tumours, and overall have better outcomes. We aim to dig further on that question, using a larger dataset, and for that we will:

1. Characterize genetic variation
We will analyze DNA sequence data from hundreds of Tasmanian devils, focusing on a small group of highly variable immune genes shared between hosts and cancer cells. This will involve bioinformatics analysis of whole-genome short-read DNA sequencing data, complemented by long-read PacBio data available for a subset of individuals. We will identify genetic variants, including single nucleotide and structural variants, and link these results with gene expression data.
The goal is to reliably infer each individual’s gene variants from ambiguous sequencing data, using partial high-quality information. This will involve testing existing computational methods—or developing our own—to improve genotyping accuracy across the short-read dataset.

2. Link genes to disease outcomes
Using gene expression data, we will use statistical modelling to test whether devils carrying certain gene variants tend to:

• show more tumour regression,
• survive longer,
• have more immune cells infiltrating the tumour.

3. Study cancer adaptation
If having such genes is helpful for the devils survival, we might expect to see the frequency of those variants increase in the population. We will test for that. On the other hand, if certain gene variants in devils make it harder for the cancer to survive, this creates an evolutionary arms race: as the host population evolves traits that improve resistance, the cancer cells may in turn evolve ways to avoid detection. We will examine cancer DNA and gene expression data to see whether some cancer lineages have lost or reduced the production of these surface proteins, and similarly, see how frequent that is.

Overall, this project offers hands-on experience with genomic and transcriptomic datasets, method development, and quantitative analysis in a setting where clear hypotheses will be tested against data, which directly connects to questions in cancer biology, epidemiology, and species conservation.