Adrian Lange

employment

Vice President of Computation June 2022 — Present
Senior Data Scientist September 2021 — June 2022
Evozyne Chicago, IL

• Oversight of ML and software engineering initiatives, project planning, hiring/recruitment, establishing team culture, management of 10+ direct reports
• Research partnership with Nvidia resulting in cutting-edge generative large language model (LLM) for protein engineering
• Architected and directed the development of two cloud-based systems: a laboratory data and analytics system and a scalable ML system [Python, PyTorch, MetaFlow, Kubernetes]
• Researched semi-supervised generative AI models (VAE, transformer) to create optimized synthetic proteins
• Generated novel protein sequences to successfully enhance enzymatic activity and thermostability phenotypes by as much as 10x

Senior Data Scientist February 2021 — September 2021
Data Scientist, Applied Machine Learning May 2019 — February 2021
Tempus Labs Chicago, IL

• Development of ML survival models for precision medicine prediction for risk of metastasis in cancer patients based on patient DNA and RNA genomics data
• Built general purpose data science platform for conducting ML experiments [Python, sklearn, TensorFlow, lifelines, RedShift, MetaFlow]

Senior Data Scientist June 2017 — May 2019
Sprout Social Chicago, IL

• Research and development of data science and machine learning systems to understand customer usage and analyze social media data [Python, Java, Spark, Redshift]

Lead Data Scientist September 2016 — June 2017
GE Transportation Chicago, IL

• Created descriptive and predictive analytics solutions for asset performance (e.g. fuel optimization) utilizing modern machine learning and big data technologies [Python, Spark, Hadoop, Zeppelin]
• Analytics and data engineering pipeline for container ship manifests importing goods to the Port of Los Angeles [Java, PostgreSQL]

Big Data Engineer, iTunes Analytics May 2015 — August 2016
Apple, Inc. Cupertino, CA

• Developed analytics infrastructure to generate insights into customer experiences on products such as the iTunes Store, App Store, Apple Music, and Apple TV [Java, Python, Splunk, Cassandra, Hadoop, JavaScript]
• Utilized machine learning, statistics, and data mining to perform data analysis, segmentation, and hypothesis testing

Software Developer August 2013 — April 2015
Signal (formerly known as BrightTag) Chicago, IL

• Developed data models, algorithms, and back-end services to build and analyze user profile networks for millions of users per day; stored in NoSQL database with billions of records (∼50 TB) [Java, Cassandra, Python, Spark, Kafka, R]
• Created real-time anomaly detection and network traffic forecasting system using Fourier analysis capable of predicting regular traffic patterns for upcoming week with >90% accuracy [Java, Python, Storm]

Postdoctoral Appointee March 2012 — July 2013
Argonne National Laboratory Leadership Computing Facility Chicago, IL
University of Chicago

• Optimized massively parallel physics/chemistry simulations on IBM Blue Gene/Q supercomputer (3 on Top500); increased simulation speed over 8x, scalability to ∼0.4 million CPU cores [C++, C, MPI, OpenMP, Python]
• Invented novel quantum mechanical proton transport model based on fragment electronic structure theory; model fitting via statistical optimization techniques (simulated annealing, regression, swarm intelligence, etc.)

Ph.D. Student Researcher June 2007 — March 2012
The Ohio State University Columbus, OH

• Published 10 first author journal articles (see publications); presented at 20+ professional and academic events
• Researched quantum chemistry and statistical thermodynamics; mathematical theory, computation, and algorithms
• Implemented theoretical physics/chemistry models into efficient code [C++, C, Fortran, MPI, OpenMP]

education

Ph.D. Physical Chemistry June 2007 — March 2012
The Ohio State University Columbus, OH
Dissertation: Multi-layer Methods for Quantum Chemistry in the Condensed Phase: Combining Density Functional Theory, Molecular Mechanics, and Continuum Solvation Models

B.S. Chemistry, minor in Microbiology August 2003 — June 2007
The Ohio State University Columbus, OH

Quantum Mechanics, Statistical Thermodynamics, Computational Chemistry, Chemical Physics, Multivariable Calculus, Linear Algebra, Differential Equations, Computer Programming, Numerical Methods

• Udacity: Web Development, Programming Languages, Parallel Programming (GPU/CUDA), Machine Learning, Artifical Intelligence
• Coursera: Data Science Signature Track (R Programming, Statistics, Data Wrangling), Machine Learning, Algorithms, Databases, Neural Networks

technical skills

projects & additional experience

To see some code I have written, visit my GitHub account.

Experimental neural network and deep learning library; SGD and backpropagation analytic gradient implemented from scratch for multi-layer perceptron, 1-D convolution net, particle network (my own invented flavor of ANN); exploring data parallelization via Spark and GPU acceleration [Python, Spark, numpy, PyCUDA, PyOpenCL]

Convolution neural network model for distinguishing between pictures of bacon and/or Kevin Bacon; web app interface for uploading and classifying pictures (formerly hosted at www.isitbacon.net) [Python, Flask, Lasagne, Theano, HTML, CSS, JavaScript, Twitter Bootstrap]

Open-source parallel JavaScript math and statistics library built around HTML5 Web Workers and Node.js cluster library capable of speeding up computations on multi-core devices; accompanying documentation website: mathworkersjs.org, available for install on npm [JavaScript, Node.js, HTML5, CSS/SCSS, Python, Flask, Apache Server]

Full stack coding, back-end to front-end; dynamic blog database. [HTML, CSS/SCSS, JavaScript/jQuery/Node.js, MySQL, Skeleton]

Recreational mathematics and programming problems from Project Euler; currently solved more than 110 problems, 99th percentile [Python, C++]

Parallel interface to Q-Chem program for propagating chemically reactive proton transport simulations with analytic gradients; demonstrated scalability to >200 CPUs [C++, C, MPI]

open source & community contributions

Simple error handling for input server connection list [Python]

Lead author of PCM solvent modeling, QM/MM, parallel linear algebra solvers, and Fast Multipole Method code; software design committee; 7th author of 161 co-authors on software white paper [C++, C, Fortran]

Multi-copy communication interface to open-source molecular dynamics software for parallel tempering/replica exchange (LAMMPS Ensembles); optimized compute kernel for pairwise interactions [C++, C, MPI, OpenMP, Python]

honors & awards

publications

Google Scholar Statistics: 4000+ total citations, h-index 13

1. Emre Sevgen, Joshua Moller, Adrian W. Lange, John Parker, et al. ProT-VAE: Protein Transformer Variational AutoEncoder for Functional Protein Design bioRxiv (2023).
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2. Yihan Shao, Zhengting Gan, Evgeny Epifanovsky, Andrew T.B. Gilbert, Michael Wormit, Joerg Kussmann, Adrian W. Lange et al. Advances in molecular quantum chemistry contained in the Q-Chem 4 program package Mol. Phys. 1-32 (2014).
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3. John M. Herbert and Adrian W. Lange. Book chapter: Polarizable Continuum Models for (Bio)Molecular Electrostatics: Basic Theory and Recent Developments for Macromolecules and Simulations (2014).
4. Adrian W. Lange and Gregory A. Voth. Multi-state Approach to Chemical Reactivity in Fragment Based Quantum Chemistry Calculations J. Chem. Theory Comput. 9, 4018-4025 (2013).
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5. Adrian W. Lange, Gard Nelson, Christopher Knight, and Gregory A. Voth. Multiscale Molecular Simulations at the Petascale (Parallelization of Reactive Force Field Model for Blue Gene/Q): ALCF-2 Early Science Program Technical Report Argonne National Laboratory (2013).
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6. Adrian W. Lange and John M. Herbert. Improving generalized Born models by exploiting connections to polarizable continuum models. II. Corrections for salt effects. J. Chem. Theory Comput. 8, 4381-4392 (2012).
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7. Adrian W. Lange and John M. Herbert. Improving generalized Born models by exploiting connections to polarizable continuum models. I. An improved effective Coulomb operator. J. Chem. Theory Comput. 8, 1999-2011 (2012).
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8. Adrian W. Lange and John M. Herbert. A Simple Polarizable Continuum Solvation Model for Electrolyte Solutions. J. Chem. Phys. 134, 204110 (2011).
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9. Adrian W. Lange and John M. Herbert. Symmetric Versus Asymmetric Discretization of the Integral Equations in Polarizable Continuum Solvation Models. Chem. Phys. Lett. 509, 77 (2011).
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10. Adrian W. Lange and John M. Herbert. Response to “Comment on ‘A Smooth, Nonsingular, and Faithful Discretization Scheme for Polarizable Continuum Models: The Switching/Gaussian Approach."’. J. Chem. Phys. 134, 117102 (2011).
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11. Adrian W. Lange and John M. Herbert. A Smooth, Nonsingular, and Faithful Discretization Scheme for Polarizable Continuum Models: The Switching/Gaussian Approach. J. Chem. Phys. 133, 244111 (2010).
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12. Adrian W. Lange and John M. Herbert. Polarizable Continuum Reaction-field Solvation Models Affording Smooth Potential Energy Surfaces. J. Phys. Chem. Lett. 1, 556-561 (2010).
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13. Adrian W. Lange and John M. Herbert. Both Intra- and Interstrand Charge-Transfer Excited States in Aqueous B-DNA Are Present at Energies Comparable to or Just Above the 1ππ* Excitonic Bright States. J. Am. Chem. Soc. 131, 3913-3922 (2009).
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14. Adrian W. Lange, Mary A. Rohrdanz, and John M. Herbert. Charge-Transfer Excited States in a π-Stacked Adenine Dimer, As Predicted Using Long-Range-Corrected Time-Dependent Density Functional Theory. J. Phys. Chem. B 112, 6304 (2008).
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15. Adrian Lange and John M. Herbert. Simple Methods to Reduce Charge-Transfer Contamination in Time-Dependent Density-Functional Calculations of Clusters and Liquids. J. Chem. Theory Comput. 3, 1680 (2007).
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