Posts

I am a Machine Learning Researcher at Prowler.io since February 2018. I hold PhD from the Gatsby Computational neuroscience Unit. I have a broad interest in cognitive science, probabilistic machine learning and approximate inference. In my thesis work, I focused on auditory perception and explored how Bayesian theories of perception could account for various perceptual phenomena revealed in controlled psychophysical experiments. I also developped non-parametric methods for statistical data analysis.



Publications

Journal Papers

  • Chambers C, Akram S, Adam V, Pelofi C, Sahani M, Shamma S, Pressnitzer D (2017) Prior context in audition informs binding and shapes simple features. Nature Communications Nature, 8, 15027. doi:10.1038/ncomms15027

  • Bastian M, Lerique S, Adam ⁠V, Franklin M.S⁠, Schooler J.W, Sackur J (2017) Language facilitates introspection: Verbal mind-wandering has privileged access to consciousness Consciousness & Cognition. doi:10.1016/j.concog.2017.01.002

  • Daunizeau J, Adam V, Rigoux L (2014) VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data. PLoS Comput Biol 10(1): e1003441. doi:10.1371/journal.pcbi.1003441 [code]

Conference Papers

  • Adam V, Hensman J, Sahani M (2016) Scalable transformed additive signal decomposition by non-conjugate gaussian process inference. In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). [code]

  • Adam V, Structured Variational Inference for Coupled Gaussian Processes. arxiv e-prints 2017 [link][code], to be presented at the Workshop on Advances in Approximate Bayesian Inference, NIPS 2017.

Conference Posters

  • Lieder I, Adam V, Sahani M, Ahissar M (2017) Sensory history affects perception through online updating of prior expectations. Cosyne Abstracts 2017, Salt Lake City USA.

  • Adam V, Duncker L, Sahani M (2017) Continuous-time point-process GPFA using sparse variational methods. Cosyne Abstracts 2017, Salt Lake City USA.

  • Adam V, Chambers C, Sahani M, Pressnitzer D (2016) Pre-perceptual grouping accounts for contextual dependence in the perception of frequency shift. Cosyne Abstracts 2016, Salt Lake City USA.

  • Adam V, Soldado-Magraner J, Jitkrittum W, Strathmann H, Lakshminarayanan B, Ialongo A.D, Bohner G, Huh Ben.D, Goetz L, Dowling S, Serban I.V, Louis M (2015) Performance of synchrony and spectral-based features in early seizure detection: exploring feature combinations and effect of latency. International Workshop on Seizure Prediction (IWSP7). Award: Honourable Mention, [report]

  • Adam V, Sahani M (2014) Bayesian perception of the pitch of non-stationary natural sounds. Cosyne Abstracts 2014, Salt Lake City USA.

Working papers

  • Duncker L, Adam V, Sahani M, Sparse Variational Gaussian Processes for Non-Conjugate Latent Factor Models.

Talks

  • Scalable transformed additive signal decomposition by non-conjugate gaussian process inference., 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

  • Sparse Variational Gaussian Processes for Non-Conjugate Latent Factor Models., Janelia Junior Scientist Workshop on Machine Learning and Computer Vision 2017.




Education

  • 2012-2017, PhD Candidate at the Gatsby Computational neuroscience Unit. Supervisor: Maneesh Sahani

  • 2009-2011, Master’s Degree in Cognitive Science (Cogmaster), at ENS-EHESS-PARISV, France. Master’s thesis: Study of the implication of cognitive control in adaptation to variable environments (with Etienne Koechlin)

  • 2006-2009, Engineering Diploma at Ecole Centrale de Nantes (France), major: Signal processing.




Projects and other activities

I participated in the development of the scientific application Daydreaming, with financial help from the city of Paris through Science en poche. I started a scientific blog that I plan to revive soon. With the Gatsby Unit we participated in a Kaggle hosted seizure detection challenge and ranked 9/200 and we presented our work at a seizure focused conference. I’ve been involved in consultancy work with the company Swhere applying machine learning techniques to solve business problems.




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