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William H. Aeberhard

Senior Data Scientist

Swiss Data Science Center

About me

I obtained my PhD in Statistics in 2015 in a cotutelle between the University of Geneva and the University of Sydney. I then worked as a post-doctoral fellow at Dalhousie University as part of a Canadian Statistical Sciences Institute collaborative research team. I was an Assistant Professor of Statistics and Coordinator of the MS in Data Science in the Department of Mathematical Sciences at Stevens Institute of Technology before joining the Swiss Data Science Center, affiliated with ETH Zurich, in 2020 as Senior Data Scientist.

My research interests lie in robust statistics, semi-parametric methods, computational statistics, and spatio-temporal modeling with ecological applications. My current methodological research includes robust smoothing parameter selection for non-parametric additive models and implementations of outlier detection for general state space models. Ongoing cross-disciplinary collaborations include growth estimation methods for scarce mark-recapture data and large-scale spatio-temporal modeling of the impact of volcanic eruptions on vegetation density from satellite imagery.

Interests

  • Robust Statistics
  • Non- and Semi-Parametric Methods
  • Spatio-Temporal Modeling with Ecological Applications

Education

  • PhD in Statistics, 2015

    University of Geneva and University of Sydney

  • MSc in Statistics, 2010

    University of Geneva

  • BSc in Psychology, 2008

    University of Geneva

Submitted Papers and Preprints

Kerri M. Scolardi, Krystan A. Wilkinson, William H. Aeberhard, Carolyn Cush, Dee Semeyn, Candice Wilkins (2022). Long-term aerial monitoring of Florida manatees, Trichechus manatus latirostris, shows stable trends in counts in a diverse Gulf Coast environment. In Preparation.

Published Papers

Lakshminarayanan Subramanian, Shiva Iyer, Ananth Balashankar, William H. Aeberhard, Sujoy Bhattacharyya, Giuditta Rusconi, Jose Lejo, Nita Soans, Anant Sudarshan, Rohini Pande (2022). Modeling fine-grained spatio-temporal pollution maps with low-cost sensors. Accepted in npj Climate and Atmospheric Science.

Atlantine Boggio-Pasqua, Kim Bassos-Hull, William H. Aeberhard, Lisa A. Hoopes, Denise A. Swider, Krystan A. Wilkinson, Manuel Dureuil (2022). Whitespotted eagle ray (Aetobatus narinari) age and growth in wild (in-situ) versus aquarium-housed (ex-situ) individuals: Implications for conservation and management. Frontiers in Marine Science 9.

DOI

Manuel Dureuil, William H. Aeberhard, Michael Dowd, Rainer Froese, Sebastián A. Pardo, Frederick G. Whoriskey, Boris Worm (2022). Reliable growth estimation from mark–recapture tagging data in elasmobranchs. Fisheries Research 256, 106488.

DOI

Sebastien Biass, Susanna Jenkins, William H. Aeberhard, Pierre Delmelle, Tom Wilson (2022). Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data. Natural Hazards and Earth System Sciences 22 (9), 2829–2855.

DOI

William H. Aeberhard, Eva Cantoni, Chris Field, Hans R. Kuensch, Joanna Mills Flemming, Ximing Xu (2021). Robust Estimation for Discrete-Time State Space Models. Scandinavian Journal of Statistics 48 (4), 1127–1147.

PDF DOI

Manuel Dureuil, William H. Aeberhard, Kirsti A. Burnett, Robert E. Hueter, John P. Tyminski, Boris Worm (2021). Unified natural mortality estimation for teleosts and elasmobranchs. Marine Ecology Progress Series 667, 113–129.

DOI

William H. Aeberhard, Eva Cantoni, Giampiero Marra, Rosalba Radice (2021). Robust Fitting for Generalized Additive Models for Location, Scale and Shape. Statistics and Computing 31 (11), 1–16.

PDF DOI

Ethan Lawler, Kim Whoriskey, William H. Aeberhard, Joanna Mills Flemming, Chris Field (2019). The conditionally autoregressive hidden Markov model (CarHMM): Inferring behavioural states from animal tracking data exhibiting conditional autocorrelation. Journal of Agricultural, Biological, and Environmental Statistics 24 (4), 651–668.

DOI

Yihao Yin, William H. Aeberhard, Stephen J. Smith, Joanna Mills Flemming (2019). Identifiable state-space models: A case study of the Bay of Fundy sea scallop fishery. Canadian Journal of Statistics 47 (1), 27–47.

DOI

William H. Aeberhard, Joanna Mills Flemming, Anders Nielsen (2018). Review of State-Space Models for Fisheries Science. Annual Review of Statistics and Its Application 5, 215–235.

DOI

Omar Defeo, Carlos A. M. Barboza, Francisco R. Barboza, William H. Aeberhard, Tatiana M. B. Cabrini, Ricardo S. Cardoso, Leonardo Ortega, Viviane Skinner, Boris Worm (2017). Aggregate Patterns of Macrofaunal Diversity: An Interocean Comparison. Global Ecology and Biogeography 26 (7), 823–834.

DOI

William H. Aeberhard, Eva Cantoni, Stephane Heritier (2017). Saddlepoint Tests for Accurate and Robust Inference on Overdispersed Count Data. Computational Statistics & Data Analysis 107, 162–175.

DOI

Sebastien Biass, Gholamhossein Bagheri, William H. Aeberhard, Costanza Bonadonna (2014). TError: Towards a Better Quantification of the Uncertainty Propagated during the Characterization of Tephra Deposits. Statistics in Volcanology 1 (2), 1–27.

DOI

William H. Aeberhard, Eva Cantoni, Stephane Heritier (2014). Robust Inference in the Negative Binomial Regression Model with an Application to Falls Data. Biometrics 70 (4), 920–931.

DOI

Other Publications

International Council for the Exploration of the Sea (ICES) (2020). Workshop on the review and future of state space stock assessment models in ICES (WKRFSAM). ICES Scientific Report 2 (32), 1–23.

DOI

William H. Aeberhard, Eva Cantoni (2015). Le Modèle Linéaire Généralisé (GLM) Robuste. Méthodes Robustes en Statistique. Ed. by J.-J. Droesbeke, G. Saporta, and C. Thomas-Agnan. Paris, France: Technip, 109–128. ISBN: 9782710811497.

Software

RobSSM

R code for robust estimation for state space models, supplementary material to Aeberhard, Cantoni, Field, Kuensch, Mills Flemming and Xu (2021)

spglmetm

R package for fitting of semi-parametric generalized linear models based on exponentially tilted mixtures

SSM-SeaScallops

R code for simulating and fitting the alternative state space model of Yin, Aeberhard, Smith and Mills Flemming (2019)

trNB

Fully-documented R package for truncated negative binomial and truncated Poisson family objects

robNB

Fully-documented R package for robust estimation and accurate inference for the negative binomial model, implementing the methods presented in Aeberhard, Cantoni and Heritier (2017)

glmrob.nb

R code for fitting a robust negative binomial generalized linear model, supplementary material to Aeberhard, Cantoni and Heritier (2014)

Teaching

Lecturer at Stevens Institute of Technology

  • MA 540 Introduction to Probability Theory: probability theory for MSc in Data Science (Spring ‘20)
  • MA 611 Probability: measure-theoretic probability theory for MSc and PhD in Mathematics (Fall ‘18, Fall ‘19)
  • MA 701 Statistical Inference: fundamental statistics for PhD in Business Administration and other MSc/PhD programs (Fall ‘19)
  • MA 331 Intermediate Statistics: basics of statistical inference for 2nd year BSc in many science and engineering curricula (Spring ‘19)

Lecturer at Dalhousie University

  • 1060 Introductory Statistics for Science and Health Science: basics of explorartory data analysis and statistical inference for 1st year BSc in health sciences (Summer ‘16, Winter ‘17)