The document is a resume for David Grawoig, Ph.D. It summarizes that he has a Ph.D. in Chemistry from the University of North Carolina-Chapel Hill and experience in laboratory automation, chemical biology, virology, drug discovery, and bioinformatics. He is seeking opportunities in diagnostics, drug discovery, and synthetic biology that utilize his skills in areas like RNA structure experiments, high-throughput screening, and bioinformatics tools.
DNA Barcoding: A simple way of identifying species by DNAmarkstoeckle
DNA barcoding makes it easier for experts and non- experts to identify species including from bits and pieces, immature forms, and those with many close look-alikes. Applications include health, environment, and education. High school students are using DNA barcoding to explore the world around them and make scientific discoveries. Like a giant Wikipedia entry, the multitude of researchers depositing DNA barcodes in GenBank are creating the first large-scale maps of the genetic structure of biodiversity.
Deep Learning for Domain-Specific Entity Extraction from Unstructured Text wi...Databricks
Entity extraction, also known as named-entity recognition (NER), entity chunking and entity identification, is a subtask of information extraction with the goal of detecting and classifying phrases in a text into predefined categories. While finding entities in an automated way is useful on its own, it often serves as a preprocessing step for more complex tasks, such as relationship extraction. For example, biomedical entity extraction is a critical step for understanding the interactions between different entity types, such as the drug-disease relationship or the gene-protein relationship. Feature generation for such tasks is often complex and time consuming. However, neural networks can obviate the need for feature engineering and use original data as input.
We will demonstrate how to build a domain-specific entity extraction system from unstructured text using deep learning. In the model, domain-specific word embedding vectors are trained with word2vec learning algorithm on a Spark cluster using millions of Medline PubMed abstracts and then used as features to train an LSTM recurrent neural network for entity extraction, using Keras with TensorFlow or CNTK on a GPU-enabled Azure Data Science Virtual Machine (DSVM). Results show that training a domain-specific word embedding model boosts performance when compared to embeddings trained on generic data such as Google News. While we use biomedical data as an example, the pipeline is generic and can be applied to other domains.
State of the Art Natural Language Processing at Scale with Alexander Thomas a...Databricks
This is a deep dive into key design choices made in the NLP library for Apache Spark. The library natively extends the Spark ML pipeline API’s which enables zero-copy, distributed, combined NLP, ML & DL pipelines, leveraging all of Spark’s built-in optimizations. The library implements core NLP algorithms including lemmatization, part of speech tagging, dependency parsing, named entity recognition, spell checking and sentiment detection.
With the dual goal of delivering state of the art performance as well as accuracy, primary design challenges that we’ll cover are:
Using efficient caching, serialization & key-value stores to load large models (in particular very large neural networks) across many executors
Ensuring fast execution on both single machine and cluster environments (with benchmarks)
Providing simple, serializable, reproducible, optimized & unified NLP + ML + DL pipelines, since NLP pipelines are almost always part of a bigger machine learning or information retrieval workflow
Simple extensibility API’s for deep learning training pipelines, required for most real-world NLP problems require domain-specific models.
This talk will be of practical use to people using the Spark NLP library to build production-grade apps, as well as to anyone extending Spark ML and looking to make the most of it.
DNA Barcoding: A simple way of identifying species by DNAmarkstoeckle
DNA barcoding makes it easier for experts and non- experts to identify species including from bits and pieces, immature forms, and those with many close look-alikes. Applications include health, environment, and education. High school students are using DNA barcoding to explore the world around them and make scientific discoveries. Like a giant Wikipedia entry, the multitude of researchers depositing DNA barcodes in GenBank are creating the first large-scale maps of the genetic structure of biodiversity.
Deep Learning for Domain-Specific Entity Extraction from Unstructured Text wi...Databricks
Entity extraction, also known as named-entity recognition (NER), entity chunking and entity identification, is a subtask of information extraction with the goal of detecting and classifying phrases in a text into predefined categories. While finding entities in an automated way is useful on its own, it often serves as a preprocessing step for more complex tasks, such as relationship extraction. For example, biomedical entity extraction is a critical step for understanding the interactions between different entity types, such as the drug-disease relationship or the gene-protein relationship. Feature generation for such tasks is often complex and time consuming. However, neural networks can obviate the need for feature engineering and use original data as input.
We will demonstrate how to build a domain-specific entity extraction system from unstructured text using deep learning. In the model, domain-specific word embedding vectors are trained with word2vec learning algorithm on a Spark cluster using millions of Medline PubMed abstracts and then used as features to train an LSTM recurrent neural network for entity extraction, using Keras with TensorFlow or CNTK on a GPU-enabled Azure Data Science Virtual Machine (DSVM). Results show that training a domain-specific word embedding model boosts performance when compared to embeddings trained on generic data such as Google News. While we use biomedical data as an example, the pipeline is generic and can be applied to other domains.
State of the Art Natural Language Processing at Scale with Alexander Thomas a...Databricks
This is a deep dive into key design choices made in the NLP library for Apache Spark. The library natively extends the Spark ML pipeline API’s which enables zero-copy, distributed, combined NLP, ML & DL pipelines, leveraging all of Spark’s built-in optimizations. The library implements core NLP algorithms including lemmatization, part of speech tagging, dependency parsing, named entity recognition, spell checking and sentiment detection.
With the dual goal of delivering state of the art performance as well as accuracy, primary design challenges that we’ll cover are:
Using efficient caching, serialization & key-value stores to load large models (in particular very large neural networks) across many executors
Ensuring fast execution on both single machine and cluster environments (with benchmarks)
Providing simple, serializable, reproducible, optimized & unified NLP + ML + DL pipelines, since NLP pipelines are almost always part of a bigger machine learning or information retrieval workflow
Simple extensibility API’s for deep learning training pipelines, required for most real-world NLP problems require domain-specific models.
This talk will be of practical use to people using the Spark NLP library to build production-grade apps, as well as to anyone extending Spark ML and looking to make the most of it.
Current CV .
My objective is to obtain a rewarding and challenging research scientist position where my background and experience will contribute to the success of a growing company or research center.
Currently, I am a Senior Associate Scientist at Amgen Inc. and certified Molecular Biologist with the American Society of Clinical Pathology MB (ASCP). I have more than 10 years of experience in the biotechnology/ pharmaceutical industry. I am highly proficient in various lab techniques, technologies, and automation. I demonstrated consistent success in the execution of assay development and method validation activities supporting clinical stage programs within GCP and GLP regulated environments. I possess extensive experience in optimization and validation of drug potency assays (ELISA and cell based assays), protein purification and characterization, and DNA/RNA extraction and quantitation. I am a subject matter expertise in the areas of human and rodent cell lines propagation and tissue dis-aggregation. I have proven operational capabilities in the establishment of standard operating procedures to ensure our laboratory meets regulatory and business requirements.
I am a self-motivated professional who works effectively as an individual contributor or within a team matrix. As a quick learner, I can efficiently deliver results, easily adapt to changing environment and provide fresh ideas. My strengths include statistical analysis/guidance, report writing, and communication.
Thank you in advance for your consideration. Please feel free to call me at (805-990-6258), or by e-mail at (mahawally46@gmail.com) if you have questions or would like a list of references.
Sincerely,
Maha Rizk
Anh Nguyen | Laboratory Technician Resume in San Diego CA | Biotech/Pharmaceu...starshaper
Search for a career in the field of biotech, pharmaceutical or related field. Have more than 3 years experience in the industry, strong background in science especially biology and chemistry.
Current CV .
My objective is to obtain a rewarding and challenging research scientist position where my background and experience will contribute to the success of a growing company or research center.
Currently, I am a Senior Associate Scientist at Amgen Inc. and certified Molecular Biologist with the American Society of Clinical Pathology MB (ASCP). I have more than 10 years of experience in the biotechnology/ pharmaceutical industry. I am highly proficient in various lab techniques, technologies, and automation. I demonstrated consistent success in the execution of assay development and method validation activities supporting clinical stage programs within GCP and GLP regulated environments. I possess extensive experience in optimization and validation of drug potency assays (ELISA and cell based assays), protein purification and characterization, and DNA/RNA extraction and quantitation. I am a subject matter expertise in the areas of human and rodent cell lines propagation and tissue dis-aggregation. I have proven operational capabilities in the establishment of standard operating procedures to ensure our laboratory meets regulatory and business requirements.
I am a self-motivated professional who works effectively as an individual contributor or within a team matrix. As a quick learner, I can efficiently deliver results, easily adapt to changing environment and provide fresh ideas. My strengths include statistical analysis/guidance, report writing, and communication.
Thank you in advance for your consideration. Please feel free to call me at (805-990-6258), or by e-mail at (mahawally46@gmail.com) if you have questions or would like a list of references.
Sincerely,
Maha Rizk
Anh Nguyen | Laboratory Technician Resume in San Diego CA | Biotech/Pharmaceu...starshaper
Search for a career in the field of biotech, pharmaceutical or related field. Have more than 3 years experience in the industry, strong background in science especially biology and chemistry.
Anh Nguyen | Laboratory Technician Resume in San Diego CA | Biotech/Pharmaceu...
David Grawoig resume 061215
1. David Grawoig, Ph.D.
16
Royce
Rd
#1
Boston,
MA
02134
(847)-‐927-‐9245
grawoig@gmail.com
SUMMARY
Innovative
scientist
with
experience
in
complex
laboratory
automation,
chemical
biology,
virology,
drug
discovery,
and
bioinformatics.
Seeking
challenging
opportunities
in
diagnostics,
drug
discovery,
and
synthetic
biology.
EDUCATION
Ph.D.,
Chemistry,
University
of
North
Carolina-‐Chapel
Hill
2014
Dissertation:
SAR
by
SHAPE:
Towards
small
molecules
that
target
RNA
B.S.,
Biochemistry,
University
of
Wisconsin-‐Madison
2007
RESEARCH SKILLS
Laboratory
–
SHAPE
(selective
2'-‐OH
acylation
analyzed
by
primer
extension)
RNA
structure
experiments,
PCR,
DNA/RNA
sequencing,
protein/RNA
expression
&
purification,
cell
culture,
radioligand
binding
assays,
western
blotting
Assay
development
–
automated
magnetic
bead
nucleic
acid
purification,
dual
luciferase
assays,
high-‐throughput
screening
(design
and
execution),
SOP
development
Automation
–
Tecan
Freedom
Evo,
robotic
liquid
handling,
MCA96
programming
Bio/chemoinformatics
–
JMP,
Python,
Schrodinger
(Maestro,
Canvas),
C,
C++,
Java,
Python,
SQL,
UNIX
shell,
SHAPE
analysis
(ShapeFinder,
QuShape),
XRNA
Data
analysis
–
Kaleidagraph,
Prism,
Word,
Excel,
SAS,
Illustrator,
Acrobat
Grant
writing
–NIH
R01
and
NIH
training
grants
RESEARCH EXPERIENCE
Predoctoral
Research
Assistant
2009–2014
Weeks
lab,
Dept.
of
Chemistry,
University
of
North
Carolina-‐Chapel
Hill
Developed
and
executed
automated
screen
for
small
molecules
binding
viral
RNA.
Undergraduate
and
Post-‐graduate
Researcher
2006–2008
Ruoho
lab,
Dept.
of
Pharmacology,
University
of
Wisconsin-‐Madison
Researched
membrane
protein
ligand
binding,
structure
and
function.
Undergraduate
Researcher
2005–2006
Cho
Lab,
Dept.
of
Chemistry,
University
of
Illinois
at
Chicago
Expressed
and
purified
peripheral
membrane
proteins
(advisor:
Dr.
R.V.
Stahelin).
HONORS AND AWARDS
NIH
Virology
Training
Grant
Trainee
2009-‐2010
UNC
Biological
and
Biomedical
Sciences
Program
Trainee
2008-‐2009
David Grawoig, Ph.D.