The Machine Learning Conference
The ML Conference gathers people to discuss and research and application of algorithms, tools, and platforms related to analyzing massive data sets.
Sharing Lessons Learned in Machine Learning Best Practices
San Francisco, CA • November 8, 2019
Register NowMLconf is a single-day, single-track machine learning conference event designed to gather the community to discuss the recent research and application of Algorithms, Tools, and Platforms to solve the hard problems that exist within organizing and analyzing massive and noisy data sets.
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Who Should Attend?
Professionals across many industries and professions attend the MLconf machine learning conference to stay current on today’s application of machine learning techniques and practices. Individuals with backgrounds in Data Science, Engineering, Software, Computer Vision, Research, Machine Learning, Technical Leadership, Co-Founders of Startups, Professors and Students attend to stay current with what techniques their peers are using to solve today’s problems that exist in industry.
What You’ll Learn
MLconf is a single-day, single-track machine learning conference event designed to gather the community to discuss the recent research and application of Algorithms, Tools, and Platforms to solve the hard problems that exist within organizing and analyzing massive and noisy data sets. Our next conference will host talks on today’s use of ML algorithms and techniques from companies such as Google Brain, AWS, Facebook, Slack & more. Additional tracks will cover intriguing topics on applications of machine learning within medical applications and within data science for social good.
Tickets start at $400 – Don't miss out on this event!
Meet Our Speakers
The following speakers will be presenting at our upcoming event. Check back regularly, as we're always adding new speakers!
Jamila Smith-Loud
Google
User Researcher
Jamila Smith-Loud
Google
User Researcher
Jamila Smith-Loud is a User Researcher on Google’s Trust and Safety team. She uses research to advocate for diverse needs and perspectives. Her work helps shape how Google puts our AI Principles for fairness and inclusion into action. Prior to joining Google, Jamila was the Manager of Strategic Initiatives at a Los Angeles-based civil rights […]
Upcoming Abstract Summary
Understanding Human Impact: Social and Equity Assessments for AI Technologies
Social and Equity Impact Assessments have broad applications but can be a useful tool to explore and mitigate for Machine Learning fairness issues and can be applied to product specific questions as a way to generate insights and learnings about users, as well as impacts on society broadly as a result of the deployment of […]
June Andrews
Stitch Fix
AI Instruments
June Andrews
Stitch Fix
AI Instruments
Dr. June Andrews works on AI Instruments at Stitch Fix. Previously, she led building a Monitoring & Diagnostics platform for GE’s airplane engines in use today. The platform has since been extended to turbines in renewable energy and power plants. At Pinterest, June created the Signals Program, a feature store, supporting over 50 ML engineers. […]
Upcoming Abstract Summary
The Uncanny Valley of ML
Every so often, the conundrum of the Uncanny Valley re-emerges as advanced technologies evolve from clearly experimental products to refined accepted technologies. We have seen its effects in robotics, computer graphics, and page load times. The debate of how to handle the new technology detracts from its benefits. When machine learning is added to human […]
Ted Willke
Intel
Director of the Brain-Inspired Computing Lab
Ted Willke
Intel
Director of the Brain-Inspired Computing Lab
Ted is currently the Director of the Brain-Inspired Computing Lab at Intel. The lab’s mission is to make Intel a leader in brain-inspired artificial intelligence by working closely with leaders in neuroscience to develop new algorithms and systems. Ted is also co-principal investigator of a 5-year program on real-time brain decoding with the Princeton Neuroscience Institute and an […]
Upcoming Abstract Summary
The Brain’s Guide to Dealing with Context in Language Understanding
Like the visual cortex, the regions of the brain involved in understanding language represent information hierarchically. But whereas the visual cortex organizes things into a spatial hierarchy, the language regions encode information into a hierarchy of timescale. This organization is key to our uniquely human ability to integrate semantic information across narratives. More and more, […]
Franziska Bell
Uber
Director of Data Science, Head of Platform Data Science
Franziska Bell
Uber
Director of Data Science, Head of Platform Data Science
Franziska Bell is the Director of Data Science, Head of Platform Data Science at Uber, where she founded the Anomaly Detection, Forecasting Platform and Natural Language Platform teams. In addition, she leads Applied Machine Learning, Behavioral Science, and Customer Support Data Science. Before Uber, Franziska was a Postdoc at Caltech where she developed a novel, […]
Upcoming Abstract Summary
Uber’s Intelligent Insights Assistant
Wouldn’t it be amazing to have highly accurate forecasts, anomaly detection and intelligent exploratory data analysis at a touch-of-a-button? The Platform Data Science team at Uber builds scalable platforms and tools that are making this a reality, resulting in faster innovation cycles and more accurate insights.
Bradley Voytek
UC San Diego
Associate Professor
Bradley Voytek
UC San Diego
Associate Professor
Bradley Voytek is an Associate Professor in Cognitive Science, Data Science, and the Neurosciences Graduate Program at UC San Diego. He is an Alfred P. Sloan Neuroscience Research Fellow and National Academies Kavli Fellow, and a founding faculty member of the UC San Diego Data Science major and the Halıcıoğlu Data Science Institute. He was […]
Upcoming Abstract Summary
The Art of Parameterization
One of the most remarkable aspects of data science is the ability to quantify observations of the world around us and, by using analytic and machine learning approaches, infer physical laws and make predictions about the future. In this talk, I will draw on my experiences, both from my time in industry as well as from running a neuroscience research lab at UC San Diego, […]
Xavier Amatriain
Curai
CTO
Xavier Amatriain
Curai
CTO
Xavier Amatriain is co-founder and CTO of Curai, a health-tech startup using AI to provide the world’s best healthcare to everyone. Previous to this, he was VP of Engineering at Quora, and Research/engineering Director at Netflix, where he led the team building the famous Netflix recommendation algorithms. Before going into leadership positions in industry, Xavier […]
Upcoming Abstract Summary
AI for healthcare: Scaling Access and Quality of Care for Everyone
Half of the world’s population lack access to healthcare services. Just in the US alone, 30% of the working-age adult population have inadequate health insurance coverage to get even basic access to services. Meanwhile, the healthcare system is known to have large inefficiencies that current technology hasn’t been able to address. In this talk, we […]
Mihajlo Grbovic
Airbnb
ML and Data Science
Mihajlo Grbovic
Airbnb
ML and Data Science
Mihajlo Grbovic holds a Ph.D in Machine Learning from Temple University in Philadelphia. He has more than 10 years of technical experience in applied Machine Learning, acting as a Science Lead in a portfolio of projects at Yahoo and now at Airbnb. During his time at Yahoo, from 2012 to 2016, he worked on integrating […]
Upcoming Abstract Summary
Machine Learning-Powered Search Ranking of Airbnb Experiences
Airbnb Experiences are handcrafted activities designed and led by expert hosts that offer a unique taste of local scene and culture. Since the launch of Airbnb Experiences in November 2016 we managed to bring Experiences to more than 1,000 destinations worldwide, including unique places like Easter Island, Tasmania, and Iceland. As the marketplace grew, Search […]
Josh Wills
Slack
Director of Data Engineering
Josh Wills
Slack
Director of Data Engineering
Josh Wills is a Software Engineer at Slack. Previously, Josh was the Head of Data Engineering at Slack. Prior to Slack, he built and led data science teams at Cloudera and Google. He is the founder of the Apache Crunch project, co-authored an O’Reilly book on advanced analytics with Apache Spark, and wrote a popular […]
Upcoming Abstract Summary
Data Labeling as Religious Experience
One of the most common places to deploy a production machine learning systems is as a replacement for a legacy rules-based system that is having a hard time keeping up with new edge cases and requirements. I’ll be walking through the process and tooling we used to help us design, train, and deploy a model […]
Jekaterina Novikova
Winterlight Labs
Director of ML
Jekaterina Novikova
Winterlight Labs
Director of ML
Jekaterina Novikova is a Director of Machine Learning at Winterlight Labs. Winterlight Labs is a Toronto-based Canadian company that is developing a novel AI-based diagnostic platform that can objectively assess and monitor cognitive health. Jekaterina’s work explores artificial intelligence in the context of language understanding, characterizing speaker’s cognitive, acoustic and linguistic state, as well as […]
Upcoming Abstract Summary
Machine Learning Methods in Detecting Alzheimer’s Disease from Speech and Language
Alzheimer’s disease affects millions of people worldwide, and it is important to predict the disease as early and as accurate as possible. In this talk, I will discuss development of novel ML models that help classifying healthy people from those who develop Alzheimer’s, using short samples of human speech. As an input to the model, […]
Igor Markov
University of Michigan
Professor
Igor Markov
University of Michigan
Professor
Igor L. Markov is a Professor at University of Michigan and has occasionally taught at Stanford. Dr. Markov previously worked at Google and he currently works at Facebook. He received his M.A. in Mathematics and Ph.D. in Computer Science from UCLA. He is an IEEE Fellow and an ACM Distinguished Scientist. Igor is interested in […]
Upcoming Abstract Summary
Quantum Computing: a Treasure Hunt, not a Gold Rush
Quantum computers promise a significant step up in computational power over conventional computers, but also suffer a number of counterintuitive limitations — both in their computational model and in leading lab implementations. In this talk, we review how quantum computers compete with conventional computers and how conventional computers try to hold their ground. Then we […]
Sneha Rajana
Amazon
Software Development Engineer
Sneha Rajana
Amazon
Software Development Engineer
Sneha is a Software Development Engineer with the Personalization team at Amazon. Her work involves using customer behavior data to guide hypothesis-driven experimentation and developing highly personalized recommendation experiences for Amazon customers. She holds a Master’s degree in Computer Science (Machine Learning) from the University of Pennsylvania. She’s passionate about AI and working with data. […]
Upcoming Abstract Summary
Deep Learning Architectures for Semantic Relation Detection Tasks
Recognizing and distinguishing specific semantic relations from other types of semantic relations is an essential part of language understanding systems. Identifying expressions with similar and contrasting meanings is valuable for NLP systems which go beyond recognizing semantic relatedness and require to identify specific semantic relations. In this talk, I will first present novel techniques for […]
Anitha Kannan
Curai
Founding Member
Anitha Kannan
Curai
Founding Member
Anitha Kannan is a founding member at Curai where she works on AI-driven disruptive solutions to healthcare. Prior to Curai, she has held senior research positions at Facebook AI research and at Microsoft research. Her research impacted products at Microsoft for which she has received many technical awards, including multiple Gold Star awards. She holds […]
Upcoming Abstract Summary
AI for healthcare: Scaling Access and Quality of Care for Everyone
Half of the world’s population lack access to healthcare services. Just in the US alone, 30% of the working-age adult population have inadequate health insurance coverage to get even basic access to services. Meanwhile, the healthcare system is known to have large inefficiencies that current technology hasn’t been able to address. In this talk, we […]
Justin Armstrong
Compology
Senior Backend Engineer - Applied ML
Justin Armstrong
Compology
Senior Backend Engineer - Applied ML
Justin Armstrong is a subject matter expert in the industrial application of machine learning technology. With over 12 years of professional software experience, Armstrong is currently Compology’s Senior Backend Engineer specializing in applied machine learning, algorithm design and data quality engineering. Armstrong holds a BSe from Tulane University in Mechanical Engineering, graduating with departmental honors. […]
Upcoming Abstract Summary
Applying Computer Vision to Reduce Contamination in the Recycling Stream
With China’s recent refusal of most foreign recyclables, North American waste haulers are scrambling to figure out how to make on-shore recycling cost-effective in order to continue providing recycling services. Recyclables that were once being shipped to China for manual sorting are now primarily being redirected to landfills or incinerators. Without a solution, a nearly […]
Meghana Ravikumar
SigOpt
Machine Learning Engineer
Meghana Ravikumar
SigOpt
Machine Learning Engineer
Meghana is a machine learning engineer at SigOpt with a particular focus on novel applications of deep learning across academia and industry. In particular, Meghana explores the impact of hyperparameter optimization and other techniques on model performance and evangelizes these practical lessons for the broader machine learning community. Prior to SigOpt, she worked in biotech, […]
Upcoming Abstract Summary
Optimized Image Classification on the Cheap
In this talk, we anchor on building an image classifier trained on the Stanford Cars dataset to evaluate two approaches to transfer learning -fine tuning and feature extraction- and the impact of hyperparameter optimization on these techniques. Once we define the most performant transfer learning technique for Stanford Cars, we will double the size of […]
Anoop Deoras
Netflix
Researcher
Anoop Deoras
Netflix
Researcher
Anoop Deoras is an applied researcher at Netflix where he leads various deep learning projects spanning several products from recommendations to search to home page construction to machine translation for localization. Before joining Netflix, he was working with Microsoft working on Cortana, a virtual personal assistant, innovating and applying advances in spoken language understanding technologies. […]
Upcoming Abstract Summary
Building an Incrementally Trained, Local Taste Aware, Global Deep Learned Recommender System Model
At Netflix, our main goal is to maximize our members’ enjoyment of the selected show by minimizing the amount of time it takes for them to find it. We try to achieve this goal by personalizing almost all the aspects of our product — from what shows to recommend, to how to present these shows […]
Martin Isaksson
PerceptiLabs
CEO
Martin Isaksson
PerceptiLabs
CEO
Martin Isaksson is Co-Founder and CEO of PerceptiLabs, a startup focused on making machine learning easy. PerceptiLabs created a flexible machine learning platform that gives developers full transparency into the process of machine learning models’ development. Martin received his M.Sc. in Robotics/Artificial Intelligence from the Royal Institute of Technology and UPC Barcelona in 2017. In […]
Upcoming Abstract Summary
How to Enable Warp Speed for Machine Learning Modeling
Machine Learning frameworks undeniably went through a mind-blowing evolution over the last 4 years. The multiple iterations created various trends, one of which has put a bright spotlight on the importance of the framework’s interface. Developers want the flexibility and transparency of low-level APIs in their work with high-level APIs. TensorFlow 2.0, Keras and PyTorch […]
Noam Finkelstein
Johns Hopkins University
PhD Student
Noam Finkelstein
Johns Hopkins University
PhD Student
Noam Finkelstein is a PhD student in the computer science department of the Johns Hopkins University. His research is in health care applications of machine learning, from cancer genetics to acute deterioration monitoring with electronic medical record data.
Upcoming Abstract Summary
The Importance of Modeling Data Collection
Data sets used in machine learning are often collected in a systematically biased way – certain data points are more likely to be collected than others. We call this “observation bias”. For example, in health care, we are more likely to see lab tests when the patient is feeling unwell than otherwise. Failing to account […]
Vinay Prabhu
UnifyID Inc.
Chief Scientist
Vinay Prabhu
UnifyID Inc.
Chief Scientist
Vinay Prabhu is currently on a mission to model human kinematics using motion sensors on smartphones paving the way for numerous breakthroughs in areas such as passive password-free authentication, geriatric care, neuro-degenerative disease modeling, fitness and augmented reality. He is currently the Chief Scientist at UnifyID Inc and has over 30 peer reviewed publications in […]
Upcoming Abstract Summary
Project GaitNet: Ushering in the ImageNet moment for human Gait kinematics
The emergence of the upright human bipedal gait can be traced back 4 to 2.8 million years ago, to the now extinct hominin Australopithecus afarensis. Fine grained analysis of gait using the modern MEMS sensors found on all smartphones not just reveals a lot about the person’s orthopedic and neuromuscular health status, but also has […]
Vito Ostuni
Pandora
Staff Scientist on the Listeners Science Team
Vito Ostuni
Pandora
Staff Scientist on the Listeners Science Team
Dr. Vito Ostuni is a Staff Scientist on the Listeners Science team at Pandora where he leads the science behind voice and search. He holds a Ph.D. in Electrical and Information Engineering from Polytechnic University of Bari, Italy. His main interests are recommender systems, search and machine learning. He is author of 15 peer reviewed […]
Upcoming Abstract Summary
‘The Voice’: New Challenges in a Zero UI world
The adoption of voice-enabled devices has seen an explosive growth in the last few years and music consumption is among the most popular use cases. Music personalization and recommendation plays a major role at Pandora in providing a daily delightful listening experience for millions of users. In turn, providing the same perfectly tailored listening experience […]
Puntis Palazzolo
Event Emcee
Sr. Product Manager, Data Science, SAP
Puntis Palazzolo
Event Emcee
Sr. Product Manager, Data Science, SAP
Puntis Palazzolo is a Sr. Data Scientist at SAP where she manages the SAP Big Data solution, SAP Data Hub, Developer-focused and Advanced Analytics topics in her role in SAP Data Hub/SAP Data Intelligence Product Management team. She has more than a decade of experience in software design and development, machine learning systems and database […]
Bill Wright
Event Emcee
Head of AI/ML business development and technical strategy for global verticals at Red Hat
Bill Wright
Event Emcee
Head of AI/ML business development and technical strategy for global verticals at Red Hat
Bill Wright is the head of AI/ML business development and technical strategy for global verticals at Red Hat. He is also a member of the Red Hat AI ILT, and participates in the ETSI ENI working group, to establish standards for the use of AI/ML in mobile network operations. He was the former Director of […]
Andrea Gagliano
Event Emcee
Sr. Data Scientist, Getty Images
Andrea Gagliano
Event Emcee
Sr. Data Scientist, Getty Images
Andrea Gagliano is a Senior Data Scientist responsible for building ML/AI to fuel new products for creatives at Getty Images in Seattle, Washington. Previously a designer of ML tools at Apple, she works closely with UX designers keeping the user at the heart of her ML/AI models to inspire, encourage, and support the end creative […]
Roger Pincombe
Event Emcee
Lead Platform Engineer, Salesforce
Roger Pincombe
Event Emcee
Lead Platform Engineer, Salesforce
Roger is a lead platform engineer at Salesforce, with diverse engineering, product, and evangelism experience. He has broad experience with technologies, industries, and scales of operation, including winning over 15 hackathons, founding a startup which went on to Techstars and acquisition, consulting on machine learning projects, and sharing his expertise at conferences and meetups.
Tickets start at $400 – Don't miss out on this event!
I love MLconf. Highest signal to noise ratio of any conference I attend.
Josh Wills, Head of Data Engineering, Slack
View Past MLConf Talks
Using Network Analysis to Detect Kickback Schemes Among Medical Providers
LeAnna Kent at MLconf 2019 New York City
Machine Learning to Detect Illegal Online Sales of Prescription Opioids
Janani Kalyanam at MLconf 2019 New York City
Deep Learning Applications to Online Payment Fraud Detection
Nitin Sharma at MLconf 2019 New York City
Building Machine Learning Models with Strict Privacy Boundaries
Renaud Bourassa at MLconf 2019 New York City
