Thomas Wiecki, PhD

Current: Lead Data Scientist at Quantopian Inc.

Ph.D., Cognitive Sciences, 2014
Brown University, Providence, Rhode Island

M.S., Computer Science (Bioinformatics), 2010
University of Tübingen, Germany

Research Interests


My main interest lies in computational psychiatry and Bayesian statistics. I use models of the basal ganglia, the Drift-Diffusion model and machine learning techniques to gain further understanding of psychiatric disorders such as Parkinson's disease and schizophrenia.

I sometimes blog about Bayesian statistics, Python and my research here.

Publications

Wiecki, T.V., Campbell, A., Lent, J., Stauth J., (submitted) All that Glitters Is Not Gold: Comparing Backtest and Out-of-Sample Performance on a Large Cohort of Trading Algorithms (SSRN)

Salvatier, J., Wiecki, T.V., Fonnesbeck, C., (2016) Probabilistic programming in Python using PyMC3. PeerJ Computer Science 2:e55 https://doi.org/10.7717/peerj-cs.55 (pdf, software)

Wiecki, T.V., Antoniades, C.A., Stevenson, A., Kennard, C., Borowsky, B., Owen, G., Leavitt, B., Roos, R., Durr, A., Tabrizi, S.J. & Frank, M.J. (2016). A computational cognitive biomarker for early-stage Huntington's disease. PLOS ONE. (pdf)

Wiecki, T.V., Poland, J.S. & Frank, M.J. (2015). Model-based cognitive neuroscience approaches to computational psychiatry: clustering and classification. Clinical Psychological Science. (main pdf, supp pdf)

D. G. Dillon*, T. V. Wiecki*, P. Pechtel, C. Webb, F. Goer, L. Murray, M. Trivedi, M. Fava, P. J. McGrath, M. Weissman, R. Parsey, B. Kurian, P. Adams, T. Carmody, S. Weyandt, K. Shores-Wilson, M. Toups, M. McInnis, M. A. Oquendo, C. Cusin, P. Deldin, G. Bruder and D. A. Pizzagalli (2015). A computational analysis of flanker interference in depression. Psychological Medicine (pdf) (* both authors contributed equally)

Frank, M.J., Gagne, C., Nyhus, E., Masters, S., Wiecki, T.V., Cavanagh, J.F. & Badre, D.
(2015). fMRI and EEG Predictors of Dynamic Decision Parameters during Human Reinforcement Learning. Journal of Neuroscience. (pdf)

Cavanagh, J.F., Wiecki, T.V., Kochar, A. & Frank, M.J. (2014)
. Eye tracking and pupillometry are indicators of dissociable latent decision processes. Journal of Experimental Psychology: General(pdf)

Matzke D., Love J. , Wiecki T. V., Brown S. D., Logan G. D., & Wagenmakers E. J., (2013). Release the BEESTS: Bayesian Estimation of Ex-Gaussian STop-Signal Reaction Time Distributions. Front. Psychol. 4:918. doi: 10.3389/fpsyg.2013.00918 (pdf, software).

Wiecki T. V., Sofer I. & Frank M.J. (2013). HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. Frontiers in Neuroinformatics 7:14. doi: 10.3389/fninf.2013.00014 (pdf)

Wiecki T. V., & Frank M. J. (2013). A computational model of inhibitory control in frontal cortex and basal ganglia. Psychological review120(2), 329–55. doi:10.1037/a0031542 (pdf)

Cavanagh J.F., Wiecki T. V., Cohen, M. X., Figueroa, C. M., Samanta, J., Sherman, S.J. & Frank, M.J. (2011). Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nature Neuroscience 14(11), 1462-7.(pdf)

Waltz J.A., Frank M.J., Wiecki T.V., & Gold J.M. (2011). Altered probabilistic learning and response biases in schizophrenia: Behavioral evidence and neurocomputational modeling. Neuropsychology 25(1), 86-97 (pdf).

Wiecki T. V., and Frank, M.J. (2010). Neurocomputational models of motor and cognitive deficits in Parkinson's disease. Progress in Brain Research.(pdf)

Wiecki T. V., K. Riedinger, A. Ameln-Mayerhofer, W. J. Schmidt and M. J. Frank (2009). A neurocomputational account of catalepsy sensitization induced by D2 receptor blockade in rats: context dependency, extinction, and renewal. Psychopharmacology 1-13.(pdf)

Drewing, K., Wiecki T. V., and M. O. Ernst. (2008) Material Properties Determine How Force and Position Signals Combine in Haptic Shape Perception Acta Psychologica 128(2), 264-273 (06 2008).

PhD thesis

Wiecki, T. V. (2015) Computational Psychiatry: 
Combining multiple levels of 
analysis to understand brain 
disorders. (pdf)

Conference Papers (Conference abstracts not listed)


Bethge, M., Wiecki, T. V., and F. A. Wichmann. The Independent Components of Natural Images are Perceptually Dependent. Human Vision and Electronic Imaging XII: Proceedings of the SPIE Human Vision and Electronic Imaging Conference 2007, 1-12. (Eds.) Rogowitz, B. E. SPIE, Bellingham, WA, USA (02 2007)

Drewing, K., M. O. Ernst and Wiecki, T. V. Material Properties Determine How we Integrate Shape Signals in Active Touch. Proceedings of the 1st Joint Worldhaptic Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, 1-6 (03 2005)

Software
        HDDM: Hierarchical Bayesian estimation of the drift-diffusion model (https://github.com/hddm-devs/hddm)
        Kabuki: Building hierarchical Bayesian models in Python using PyMC (https://github.com/hddm-devs/kabuki)
          Pynopticon: Creating and training visual object recognition systems in Python (http://code.google.com/p/pynopticon/)
        PyMC3: Bayesian Modeling in Python (https://github.com/pymc-devs/pymc3)
        BEESTS: Hierarchical Bayesian estimation of the Stop-Signal model (http://dora.erbe-matzke.com/software.html)

Contact Information:

Email:    thomas.wiecki@gmail.com
Twitter: @twiecki
GitHub: twiecki