Ryan Reece, Ph.D.
Machine learning engineer
/ data scientist / physicist
Mountain View, CA
Experience
Machine Learning Engineer | Apr 2018 - Aug 2022 (4
yrs 4 mos)
Cerebras Systems, Sunnyvale,
CA
- Unicorn startup building high-performance machine learning
accelerators, the first to achieve Wafer-Scale
Integration
- Developed end-to-end model references in both pytorch and tensorflow, including the input data
pipeline for Cerebras Wafer-Scale Engines
- Trained benchmark models and did exploratory optimization of various
models for computer vision (ResNets) and NLP (GNMT, Transformer, Linformer, BERT, RoBERTa, GPT-2); explored impacts of using mixed precision, bucketing
by sequence length, activation sparsity
- Model references and data pipeline code delivered to customers in
the Model Zoo with several examples and detailed documentation
- Helped develop a new normalization layer, OnlineNorm,
that uses streaming statistics to allow normalization of activations
with small batch sizes [NeurIPS
2019]
- Triaged, explored, and tested customer-shared models; represented
customer requirements to compiler engineers
- Directly engaged and supported customers (from both national labs
and industry) in meetings and on-sites; helped in the development of
demos; debugged model and data pipeline issues for customers
- Co-authored a blog about Getting
started with PyTorch BERT models on the Cerebras CS-2 System
Artificial Intelligence Fellow | Jan 2018 - Mar 2018
(2 mos)
Insight Data Science, Palo Alto,
CA
- Learned about data science and machine learning applications in a
variety of business domains
- Developed cloud-based hyperparameter optimization platform: HYPR.AI, for automating the
testing of many ML models using AWS/Paperspace in docker containerized
jobs
Postdoctoral Research Fellow | Jul 2013 - Aug 2017
(4 yrs 2 mos)
Santa Cruz Institute for Particle
Physics, The University of California, Santa Cruz, and
The European Council for Nuclear Research (CERN), Geneva, Switzerland
- 10 years (postdoc and Ph.D.) as a member of the ATLAS experiment, a
3000+ person collaboration looking for new physics in high energy
proton-proton collisions at the Large Hadron Collider (LHC)
- Long involvement in codebase of more than 10 million lines of C++
and almost as many lines of Python
- Expert in petabyte data
reduction (ATLAS ~10 PB/year), world-wide grid computing, and
data visualization as a user
and primary supporter of our group’s 200-CPU computing cluster,
accumulated more than 350k
CPU-hours
- Lead analysis groups as “Editor” in different searches for signals
of supersymmetry and exotic decays, contributed to 6 research
publications, and defended their approval
- 2015-17, full-time support the operations of the data acquisition system (DAQ) and
detector monitoring systems of the SCT (a tracking sub-detector in
ATLAS)
- 2016-17, built more expertise in
machine learning techniques, deep learning frameworks using Keras
to build CNNs for particle
classification, and another project using sklearn for anomaly
detection by clustering with
Gaussian Mixture Models
Graduate Researcher | Jun 2006 - Jul 2013 (7
yrs)
The University of
Pennsylvania, Philadelphia, PA, and
The European Council for Nuclear Research (CERN), Geneva, Switzerland
- First summers as a student with Penn (2006-08) at CERN participating
in the integration
and commissioning of custom electronics for the Transition Radiation
Tracker (TRT), the outermost sub-detector of the ATLAS tracker
- 2009-12, throughout most of the running of the LHC, rotated the
on-call responsibility for the
TRT DAQ
- Ph.D. research with the data from ATLAS focused on the
identification of decays of tau leptons and their use in searches for
new physics, a pattern
recognition problem to identify a type of particle
- 2009-10, was the lead developer of the cut-based tau identification
used with the first ATLAS data
- 2010-12, helped develop advanced tau identification using Boosted Decision Trees (BDTs) which
superseded the above
- Knack for developing data analysis frameworks: pyframe has been used by
several analyses in ATLAS
- The ATLAS and CMS experiments at the LHC discovered
the long-sought-after Higgs boson, evidence of which was announced
on July 4, 2012 [Physics
Letters B, arxiv:1207.7214]
Education
- Ph.D. Experimental Particle Physics, The University
of Pennsylvania (Philadelpha, PA), Jun 2006 - Jul 2013
thesis: “A search
for new physics in high-mass ditau events in the ATLAS
detector”
- B.S. Physics with Honors, The University of Texas
(Austin, TX), Aug 2003 - May 2006
thesis: “Late pulsing in the Hamamatsu R1408 PMT used in the Sudbury
Neutrino Observatory”
Publications
- Chiley, V. et al. (2019). Online normalization for training
neural networks. NeurIPS
2019. [arxiv:1905.05894]
- Albertsson, K. et al. (2018). Machine learning in high
energy physics community white paper. [arxiv:1807.02876]
- As a member of the ATLAS collaboration since June 1, 2008, I am an
“author” of more than 800 publications (google
scholar, inspire),
however, my list of selected publications is here: rreece.github.io/publications,
but in particular:
- Search for supersymmetry in a final state containing two photons and
missing transverse momentum in √s = 13 TeV pp collisions at the LHC
using the ATLAS detector. European
Physical Journal C, 76, 517 (2016). [arxiv:1606.09150]
- Identification and energy calibration of hadronically decaying tau
leptons with the ATLAS experiment in pp collisions at
√s = 8 TeV. European
Physical Journal C, 75, 303 (2015). [arxiv:1412.7086]
- A search for high-mass resonances decaying to τ+τ−
with the ATLAS detector. Physics
Letters B, 719, 242-260 (2013). [arxiv:1210.6604]
- Performance of the ATLAS detector using first collision data. Journal
of High Energy Physics, 9, 56 (2010). [arxiv:1005.5254]
Skills
- General: deep learning (NLP and CV), statistical
analysis, data visualization, data-driven modeling, anomaly detection,
neural network classifiers, boosted decision trees, petabyte data
reduction, object-oriented design, polymorphic interfaces, writing
technical reports, working independently and in groups, presenting my
ideas, graduate level physics and mathematics
- Programming languages (fluent): C/C++/STL (17+ years), Python (15+
years);
(experienced): javascript, SQL; Markup
languages: LaTeX, Markdown, (x)html with css
- ML / Data science software: pytorch, tensorflow, keras, HuggingFace, matplotlib, numpy,
scipy, scikit-learn, pandas, jupyter, AWS (EC2, S3), docker,
singularity, ROOT, RooStats, TMVA
- General software: Linux, bash, git, svn, UML, QT,
Mathematica
- Hobbies: poker, philosophy, cycling, climbing
Last updated: August 27, 2022
A pdf version of this resume is
here.