I am a machine learning researcher. I work as a consulting
member of technical staff at Oracle
Labs in Boston. I am a senior member of the ACM. A long time ago I
worked on computer science topics that were not machine learning.
Research Interests
 Safe machine learning
 Bayesian machine learning
 Scalable machine learning and dimentionality reduction
Safe Machine Learning
My main research interest is safe machine learning. This covers common
topics such as fairness, accountability, specifications, and
assurance.

NeurIPS'19 Unlocking Fairness: a Tradeoff Revisited (PDF)
Michael L. Wick, Swetasudha Panda, JeanBaptiste Tristan

ROBUST AI in FS'19 (NIPS workshop) Using Bayes Factors to Control for Fairness A Case Study on Learning To Rank (PDF)
Swetasudha Panda, Jeanbaptiste Tristan, Haniyeh Mahmoudian, Pallika Kanani, Michael Wick

AAAI'19 Gradientbased Inference for Networks with Output Constraints (PDF)
JayYoon Lee, Sanket Mehta, Michael L. Wick, JeanBaptiste Tristan, Jaime Carbonell

AKBC'17 (NIPS workshop) Enforcing Output Constraints via SGD: A Step Towards Neural Lagrangian Relaxation (PDF)
JayYoon Lee, Michael L. Wick, JeanBaptiste Tristan, Jaime Carbonell

PLDI'17 Flexible Compilation for Probabilistic Programs (PDF)
Daniel Huang, JeanBaptiste Tristan, Greg Morrisett

NIPS'14 Augur: dataparallel probabilistic modeling
(PDF)
JeanBaptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam Pocock, Stephen J. Green, Guy L. Steele Jr.
Spotlight
Bayesian Machine Learning
I'm interested in the design of Bayesian inference algorithms. One of
the key challenge in Bayesian inference algorithms is that there is
often a complex tradeoff between computational and statistical
properties. For example, marginalizing a random variable in a model
might improve convergence of a Gibbs sampler, but at the cost of
making the sampler sequential. In my work I consider statitistical
performance in light of computational complexity, parallelism, and
memory footprint.

AISTATS'19 Sketching for Latent DirichletCategorical Models (PDF)
Joseph Tassarotti, JeanBaptiste Tristan, Michael L. Wick

LearningSys'17 (NIPS workshop) Sketchy LDA: Towards Streaming Inference (PDF)
JeanBaptiste Tristan, Michael L. Wick, Joseph Tassarotti

AISTSATS'16 Exponential stochastic cellular automata for massively parallel inference (PDF)
Manzil Zaheer, Michael L. Wick, JeanBaptiste Tristan, Alex Smola, Guy L. Steele Jr.

OPT'15 (NIPS workshop) Comparing Gibbs, EM and SEM for MAP inference in mixture models (PDF)
Manzil Zaheer, Michael L. Wick, Satwick Kotur, JeanBaptiste Tristan

LearningSys'15 (NIPS workshop) Exponential stochastic cellular automata for
massively parallel inference (PDF)
Manzil Zaheer, Michael L. Wick, JeanBaptiste Tristan, Alex Smola, Guy L. Steele Jr.
Spotlight

ICML'15 Efficient training of LDA on a GPU by meanformode estimation (PDF)
JeanBaptiste Tristan, Joseph Tassarotti, Guy L. Steele Jr.
Scalable Machine Learning
Some of my work focuses on scaling machine learning algorithms to
large datasetes and large models either by using randomized methods
for dimentionality reduction or parallelization and distribution.

TOPC'19 Using ButterflyPatterned Partial Sums to Draw from Discrete Distributions (To appear)
Guy L. Steele Jr., JeanBaptiste Tristan

AKBC'19 Scaling Hierarchical Coreference with Homomorphic Compression (PDF)
Michael L. Wick, Swetasudha Panda, Joseph Tassarotti, JeanBaptiste Tristan

TOPC'17 Adding Approximate Counters (PDF)
Guy L. Steele Jr., JeanBaptiste Tristan

PPOPP'17 Using ButterflyPatterned Partial Sums to Draw from Discrete Distributions (PDF)
JeanBaptiste Tristan, Guy L. Steele Jr.
Selected to appear in TOPC

PPOPP'16 Adding approximate counters
(PDF)
JeanBaptiste Tristan, Guy L. Steele Jr.
Selected to appear in TOPC
Talks Highlights
 Keynote speaker at the first International Conference on Probabilistic Programming
 Seminar and colloquium talks at INRIA, ENS, Harvard, Yale, MIT, Northeastern, UMass
Teaching
 Lecturer for the Advanced Machine Learning class at Harvard (CS281) in 2019
 Lecturer for the compiler engineering class at Harvard (CS153) in 2015
 Teaching Fellow (course assistant) for both introductions to CS at Harvard (cs50 and CS51)
Service Highlights
 PC member, HOPL 4 (History of programming languages)
 PC member, PLDI 2018 (Programming Language Design and Implementation)
 PC member, PPS 2018 (Probabilistic Programs and Systems)
 PC member, SNAPL 2017 (Summit in advances in programming languages)
 PC member, PPOPP 2016 (Principles and Practice of Parallel Programming)
 3 times NSF panelist