Work with experts from University of Cambridge

Researching and implementing SOTA model-based deep reinforcement/active learning methods such as Adalead, Generative Flow Networks (GFlowNets) with Bayes Opt, and Model Predictive Control for optimal control of selection pressures within company’s bioreactors. Building production-quality (documented, tested, maintainable) python package of machine learning algorithms for strain engineering/optimization. Designing biophysical simulations of evolutionary dynamics and fitness landscapes to simulate bioreactor and test optimization methods. Developing in house simulation benchmarks, utilizing Ray to automate benchmark testing and hyper-parameter optimization on AWS, and logging output and model configs to wandb.

Deep Alpha

Machine Learning Engineer / March, 2021 — March, 2022

Built a cloud-based algorithmic trading system using deep learning methods. Adapting attention-based sequence to sequence models for time series forecasting. Researching deep reinforcement learning ‘actor-critic’ techniques for dynamic portfolio optimization. Finalist at Innospark Ventures AI pitch competition.

Massachusetts Institute of Technology

Post-Doctoral Associate / April, 2018 — April, 2021

Project: Quantifying non-equilibrium behavior using neural network estimators. Keywords: neural networks, machine learning. Project: Designing optimal vaccination protocols. Keywords: kinetic Monte Carlo, information theory, chemical master equations. Project: Understanding how immune cells discriminate between healthy and infected cells. Keywords: ordinary differential equations, systems biology modeling, kinetic proofreading, channel capacity, mutual information.

University of Cambridge

Ph.D. Candidate / January, 2014 — January, 2018

Project: Deriving the theoretical origins of thermo- and diffusio-osmotic flow. Keywords: molecular dynamics, non-equilibrium thermodynamics.

Research Expertise

Condensed Matter Physics

Atomic and Molecular Physics, and Optics

Electrical and Electronic Engineering

Biophysics

Surfaces and Interfaces

And 2 more

About

I've spent the past few years as a Machine Learning Scientist and Engineer. At Melonfrost, a synthetic biology startup, I employed deep generative modeling techniques such as Generative Flow Networks (GFlowNets) in a reinforcement/active learning setting and machine learning guided evolutionary algorithms like Adalead (Adapt-with-the-Leader) with the aim of directing the evolution of microbial populations within the company’s high-throughput system of bioreactors. These methods were originally developed to optimize DNA and protein sequence design as well as for applications in drug discovery. In addition to the ML research effort, I designed bespoke biophysical simulation benchmarks, utilized Ray to automate benchmark testing and hyper-parameter optimization on Amazon Web Services, and tracked experiments and model configs with Weights & Biases. As a Ph.D. student in the Department of Theoretical and Computational Chemistry at the University of Cambridge, I developed molecular dynamics simulations to make fundamental theoretical contributions towards understanding non-equilibrium nano-scale flow. Projects included some of the first non-equilibrium calculations of nanoscale flow profiles induced by temperature and chemical potential gradients. Subsequently, as a postdoctoral associate at the Massachusetts Institute of Technology, I used methods from information theory to quantify immune cell signal discrimination between healthy and infected cells. I also developed kinetic Monte Carlo methods to optimize vaccination protocols against highly mutable pathogens. All of this work resulted in numerous publications.

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Jennifer J. Bitting, Ph.D., P.E.

Ph.D. from Cambridge University Engineering Department with a research focus of sustainable solutions to water scarcity. in California.

Education

University of Cambridge

Ph.D., Engineering / April, 2022

Cambridge

University of Cambridge

M.Phil., Engineering for Sustainable Development / August, 2006

Cambridge

California Polytechnic State University

B.S., Environmental Engineering / June, 1999

San Luis Obispo, California, United States of America

Research Expertise

Water Quantity

Sustainability

Material Flow Analysis

Water Quality

Low Impact Development (LID)

And 2 more

About

Rachel Carson, author of the book *Silent Spring*, is my scientific hero because she communicates technically complex concepts in a manner anyone could understand. Her influence led me to focus my Ph.D. thesis on bringing together separate tables of water quantity and water quality data into a single graph that allows members of the public, local water managers, and elected city officials to make data-informed decisions, without needing a scientific background to understand and interpret the data. To test the results of my proposed method, I surveyed engineers to determine whether they saw a benefit in combing separate data sets, and whether they preferred tables of data or a graph. Ninety-two percent of the engineers preferred a single diagram depicting multiple data sets and found the combination of data in a single graph synergistic, and useful as a communication aid. The American Society of Civil Engineers published this research in the *Journal of Sustainable Water in the Built Environment* under the title, “[Pairing Quality and Quality in a Mass Balance of Water in California](https://ascelibrary.org/doi/10.1061/JSWBAY.SWENG-483).”

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Eric Appel, Ph.D.

Palo Alto, California, United States of America

Professor of Materials Science & Engineering at Stanford University

Education

University of Cambridge

Ph.D., Chemistry / January, 2013

Cambridge

Research Expertise

Biomaterials, Drug Delivery Technologies, Immunoengineering, Wildfire

About

Eric A. Appel is an Associate Professor of Materials Science & Engineering at Stanford University. He received his BS in Chemistry and MS in Polymer Science from California Polytechnic State University in San Luis Obispo, CA. Eric performed his MS thesis research with Dr Jim Hedrick and Dr Robert Miller on the synthesis of polymers for drug delivery applications at the IBM Almaden Research Center in San Jose, CA. He then obtained his PhD in Chemistry with Prof. Oren A. Scherman at the University of Cambridge. His PhD research focused on the preparation of dynamic and stimuli-responsive supramolecular polymeric materials. For his PhD work, Eric was the recipient of the Jon Weaver PhD prize from the Royal Society of Chemistry and a Graduate Student Award from the Materials Research Society. Upon graduating from Cambridge, he was awarded a National Research Service Award from the NIBIB and a Wellcome Trust Postdoctoral Fellowship to work with Prof. Robert Langer at MIT on the development of supramolecular biomaterials for applications in tissue engineering and drug delivery. Eric’s research at Stanford focuses on the development of biomimetic polymeric materials that can be used as tools to better understand fundamental biological processes and to engineer advanced healthcare solutions. His research has led to more than one hundred publications and 40 patents. He has been awarded young faculty awards from the Hellman Foundation, American Diabetes Association, American Cancer Society, and PhRMA Foundation. Eric received the 2022 IUPAC Hanwha-TotalEnergies Young Polymer Scientist Award, the 2023 Society for Biomaterials Young Investigator Award, and the 2023 Biomaterials Science Lectureship Award. In 2024, Eric was elected a Fellow of the American Institute for Medical & Biological Engineering.

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ARNOLD RAYMOND

Arnold Palmer Hospital for Children, University of Central Florida, Embry-Riddle Aeronautical

Education

University of Cambridge

PhD, English Department / July, 2001

Cambridge

Harvard University

AM, Celtic Languages and Literatures / July, 1997

Cambridge, Massachusetts, United States of America

University of East Anglia

BA, English and American Studies / July, 1994

Norwich

Research Expertise

Computer Science Applications

And 17 more

About

Arnold Raymond is a professor of English at Swansea University. He received his PhD in English from the University of Cambridge in 2001, and his AM in Celtic Languages and Literatures from Harvard University in 1997. He also holds a BA in English and American Studies from the University of East Anglia.

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Dr. Joshua Johnson, Ph.D.

Cambridge Postdoctoral Researcher with publications in DNA nanotechnology and experience with creating artificial cells.

Experience

Imperial College London

Research Associate / March, 2020 — Present

Designed, built, and programmed custom instrumentation needed for precise and varied annealing protocols as cheaper and higher throughput alternative to existing equipment. Designed novel methods for integrating DNA nanomachines with artificial cells and nanopores for enhanced cancer detection

The Ohio State University

Graduate Research Associate / May, 2014 — February, 2020

Research focused on design and characterization of DNA origami nanomachines. Emphasis on combining organic and inorganic nanomaterials using thermal or optical inputs as control mechanisms for dynamic DNA nanostructures.

University of Cambridge

Postdoctoral Research Associate / October, 2022 — April, 2023

Transferred from Imperial College to continue with research in DNA nanotechnology. During the moving process I helped establish laboratory infrastructure including the development and approval of standard operating procedures and safety protocols to ensure compliance with regulations within the Department of Chemical Engineering and Biotechnology.