San Francisco Bay Area Chapter of
American Statistical Association (SFASA)
2017 Annual Social Event with Career
Development Panel Discussion
Thursday,
December 7th, 6-8:30pm
(RSVP
required)
Please join us at 2017 SFASA annual social event with career
development panel discussion on Thursday, December 7th. Our panel, featuring distinguished statisticians and data scientists in
academia, pharmaceutical, and high tech industries, will offer practical tips
and guidance on making the right choice for your career or taking your career
path to the next level. They will answer any questions and clarify any
confusion that you may have. (Huge thanks to our panelists!) This event will also serve as a great opportunity for social networking
among our members. |
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Time: |
6:00
– 8:30 pm, Thursday, December 7th, 2017 á Check-in begins at 5:30pm á 6:00-6:30pm: dinner á 6:30-8:00pm: panel discussion á 8:00-8:30pm: social networking |
Location: |
UCSF
Mission Hall: Global Health & Clinical Sciences Building 550 16th
Street, Rooms 1401 & 1402, San Francisco, CA 94158 Nearest parking garage ($8/2hrs): UCSF Mission Bay Campus – 1630 Third Street Garage 1630 3rd St., San Francisco, CA 94158 Visitors can also park on the street (9am–6pm, $1.25/hr) |
Food: |
Complimentary
food and beverage will be served |
Fee: |
Free
to all current SFASA members For non-members, you are welcome to join the
chapter on site, and then enjoy this event at no additional cost. Regular
annual membership fee is $9 and student annual membership fee is $3. Payment is accepted by cash (please have the exact amount) or
check (payable to ÒSFASAÓ and please have a photo ID ready for this
purpose). |
Registration: |
Register
online at https://sfasacareerdevelopment2017.splashthat.com/ |
Panelists: |
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Deepak Agarwal VP Engineering and Head of Artificial Intelligence |
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Annette Molinaro Professor in Neurosurgery and Epidemiology and Biostatistics UCSF |
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Tara Maddala Head of Biostatistics and Data Management GRAIL |
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Imola K Fodor Deputy Global Head, Oncology Biostatistics HER Franchise Genentech/Roche |
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Brad Klingenberg VP Data Science Stitch Fix |
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Jizhou Fu Data Science Manager Uber |
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Anirban Deb Data Science Manager Uber |
Panelist
Bios: Deepak Agarwal,
Ph.D. Deepak
Agarwal is a vice president of engineering at
LinkedIn where he is responsible for all AI efforts across the company. He is
well known for his work on recommender systems and has published a book on
the topic. He has published extensively in top-tier computer science
conferences and has coauthored several patents. He is a Fellow of the
American Statistical Association and has served on the Executive Committee of
Knowledge Discovery and Data Mining (KDD). Deepak regularly serves on program
committees of various conferences in the field of AI and computer science. He
is also an associate editor of two flagship statistics journals. Annette
Molinaro, Ph.D. Annette
Molinaro is a principal investigator in the UCSF
Brain Tumor Center, the Director of the Division of Biomedical Statistics and
Informatics within the Department of Neurological Surgery, the Co-Director of
the Clinical and Biostatistics Core for the UCSF Brain SPORE, and the
Co-Director of the Biospecimen and Biostatistics
Core for the UCSF Brain Tumor Program Project Grant (P01). Dr. MolinaroÕs research interests are primarily focused on
statistical genetics and computational biology, including prediction,
survival analysis, classification, and causal inference with additional
curiosities in cancer epidemiology and in the estimation of absolute risk in
stratified case-cohort studies. Her research has pertained to predicting
clinical outcomes with high-dimensional explanatory variables, such as SNP
and methylation arrays, and large-scale epidemiology studies. This has
included an adaptation to Classification and Regression Trees (CART) for
survival outcomes, the introduction of partDSA, a novel data-adaptive algorithm that builds Boolean
combinations of explanatory variables as individual trees as well as an aggregate
learner, and a non-parametric method for point estimation based on a
stratified case-cohort study design. In addition, she has worked with
collaborators at the National Cancer Institute (NCI) on comparing
cross-validation approaches to validating predictors in small sample sizes
and the power of data mining methods for detecting genetic associations and
interactions. Dr. Molinaro was awarded an R01 by
the NCI to expand partDSA for building risk models
accommodating various study designs and competing risks. Tara Maddala,
Ph.D. Tara Maddala is the Head of Biostatistics and Data Management
at GRAIL. GRAILÕs mission is to
detect cancer early—when it can be cured—by combining the
techniques of modern data-science with the value of cfNA
signals from high-intensity sequencing of unprecedented breadth and
depth. TaraÕs team collaborates
with clinicians, scientists, and information technologists to design and
execute on one of the largest clinical trial programs ever pursued in genomic
medicine. Before GRAIL, Tara led
the Clinical Biostatistics team at Genomic Health responsible for design and
analysis of large-scale oncology biomarker studies that resulted in
algorithm-based, clinically-actionable commercial
diagnostics. Before Genomic
Health, Tara was Director of Biostatistics at Clinimetrics,
where she supported the development and approval of several
therapeutics for small-to-medium biotechnology companies. She holds a
bachelorÕs degree in Industrial and Systems Engineering from the University
of Florida, a master's degree in Health Systems Engineering from Georgia
Tech, and a Ph.D. in Biostatistics from The University of Texas. Imola K Fodor,
Ph.D. Imola K
Fodor is the Global Head of Oncology Biostatistics for Research and Early
Development at Genentech and for the Breast and Gynecological Cancer
Franchise in late stage development across Genentech/Roche. She joined
Genentech in 2007, starting as a Senior Statistical Scientist in the
Nonclinical Biostatistics group. Since then, she held positions of increasing
responsibilities including Director of the Nonclinical and Statistical
Methods and Research groups. Her experience spans from research and early
development through technical operations and late-stage clinical
development. Prior
to joining Genentech, Imola was a research staff
scientist at Lawrence Livermore National Laboratory for seven years,
developing statistical methods to obtain scientific insights from large and
complex datasets. Imola obtained
undergraduate degrees in mathematics and statistics with minor in physics
from Rutgers University, and a Ph.D. in statistics from the University of
California at Berkeley. Brad Klingenberg,
Ph.D. Brad Klingenberg is VP of Data Science at Stitch Fix in San
Francisco. Stitch Fix is an online personal styling service that bets on it
recommendations by physically delivering inventory to clients. Brad is an
applied statistician at heart, and at Stitch Fix his team uses data and
statistics to improve the algorithmic management of inventory and the
human-in-the-loop recommendation system used to select items for clients.
Prior to Stitch Fix Brad received his PhD in Statistics at Stanford and
worked in tech and finance. Jizhou Fu, M.S. Jizhou Fu is
a data science manager in growth marketing at Uber.
She leads a team that uses statistical and machine learning approaches to
empower informed decision making and to enhance
marketing/advertising efficacy. These models have been leveraged to inform
optimal allocation of Uber's multi-million
marketing budget across multiple dimensions, predict user conversion
probabilities to better inform marketing tactics, detect advertising fraud to
enhance ad partner quality as well as recommend content to boost user
conversion and engagement. Prior
to Uber, Jizhou worked in
an AdTech startup in Los Angeles that develops
machine learning powered analytics platform, where she managed a team in
building advanced statistical models to help optimize marketing investment
and translate insights into revenue-growing strategies and actions. She
earned her Master in Statistics from the University of Illinois at
Urbana-Champaign. |
Anirban Deb, MBA
Anirban Deb is a data science manager in
experimentation at Uber in San Francisco. Anirban leads three teams (Experimentation Data Science,
Mobile, and Data Infra) in building platforms for statistical experiments with Uber. The internal Uber teams use
the platform to test and analyze their new products. Currently, the teams focus
on several topics, such as AB testing, continuous experiments with bandits and
rollout, Bayesian optimization, segmentation, sequential testing, and
observational studies on causal inferences.
Prior to Uber,
Anirban had several management positions in Adobe,
Intuit, and Paypal. Anirban
received his MBA degree from Haas, UC Berkeley.