San Francisco, CA • November 8, 2019Register Now
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.
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.
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.
The following speakers will be presenting at our upcoming event. Check back regularly, as we're always adding new speakers!
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 […]
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 […]
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. […]
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 […]
Sneha is a software development engineer at Amazon where she works on developing personalized recommendation experiences for Amazon customers. She has previously worked for Audible, Pluribus Networks, VMware, and Avaya. She holds a Master’s degree in CS from University of Pennsylvania and a Bachelor’s degree in CS. She’s passionate about AI and working with data […]
Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network
Recognizing and distinguishing antonyms from other types of semantic relations is an essential part of language understanding systems. Identifying antonymy and expressions with contrasting meanings is valuable for NLP systems which go beyond recognizing semantic relatedness and require to identify specific semantic relations. In this paper, we present a novel method for deriving antonym pairs […]
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.
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 […]
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. […]
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 […]
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. […]
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 […]
Franziska Bell is a Senior Data Science Manager on the Platform Team 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, highly […]
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, […]
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 […]
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 […]
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, […]
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 […]
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 […]
Ted Willke leads a team that researches large-scale machine learning and data mining techniques in Intel Labs. His research interests include parallel and distributed systems, image processing, machine learning, graph analytics, and cognitive neuroscience. Ted is also a co-principal investigator in a multi-year grand challenge project on real-time brain decoding with the Princeton Neuroscience Institute. […]
I love MLconf. Highest signal to noise ratio of any conference I attend.Josh Wills, Head of Data Engineering, Slack