See opportunities at Uber AI Labs
AI that moves the world
Uber AI Labs gathers top scientists and engineers to create the next generation of machine learning algorithms to improve the lives of millions of people worldwide.
Come help us turn the dreams of science fiction into reality. We conduct basic and applied research into problems such as self-driving cars, optimizing cities, and keeping riders safe.
The lab offers a unique set of challenges; vast computational resources; talented, diverse colleagues; the ability to publish, open-source, and otherwise share your research; and tremendous potential for real-world impact in one of the world’s most exciting technology companies.
Uber AI Labs News
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
Motion-sensor cameras can cheaply & unobtrusively gather vast amounts of data on wild animals. We show that deep learning can automate animal identification for 99.3% of the Snapshot Serengeti dataset while performing at the same 96.6% accuracy of humans.
VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution
Visual Inspector for Neuroevolution (VINE) is an interactive data visualization tool that offers fresh insight into the underlying dynamics of evolution to help those who are interested better understand and explore this family of algorithms.
Accelerating Deep Neuroevolution: Train Atari in Hours on a Single Personal Computer
We have open sourced much faster code for evolving deep neural networks. Such neuroevolution approaches can solve challenging deep reinforcement learning tasks. This code allows training Atari in a few hours on a single modern desktop.
Measuring the Intrinsic Dimension of Objective Landscapes
Curious about what it is like to traverse the high-dimensional loss landscapes of modern neural networks? Check out Uber AI Labs’ latest research on measuring intrinsic dimension to learn more.
A New Method for Learning to Learn
Differentiable Plasticity is a new method for training neural networks to change their connection weights adaptively even after training is completed, allowing a form of learning inspired by the lifelong plasticity of biological brains.
Introducing the Uber AI Residency
Interested in accelerating your career by tackling some of Uber’s most challenging AI problems? Apply for the Uber AI Residency, a research fellowship dedicated to fostering the next generation of AI talent.
Science Covers Uber AI Labs' Neuroevolution Research
The journal Science writes about our recent research on evolving neural networks, which shows they are competitive with deep reinforcement learning algorithms, how they can be improved, and how they relate to gradient-based learning algorithms.
Welcoming the Era of Deep Neuroevolution
In five papers, we show that evolving deep neural networks is a promising, competitive method for hard, deep reinforcement learning problems. We also study the power of evolution strategies and show how to improve genetic algorithms with gradients.
Welcome, Peter Dayan!
Ghahramani introduces AI Labs’ newest team member, award-winning neuroscientist Peter Dayan.
Uber AI Labs Open Sources Pyro, a Deep Probabilistic Programming Language
Pyro is an open source probabilistic programming language that unites modern deep learning with Bayesian modeling for a tool-first approach to AI.
Zoubin Ghahramani: a natural choice for Chief Scientist
The Chief Product Officer Jeff Holden, now part of the Executive Leadership Team at Uber, welcomes Zoubin as the Chief Scientist at Uber, overseeing all AI and ML strategy globally across the company. He explains why Uber is a “natural fit” for this role.
Ken’s life mission: Evolving brains inside machines
Ken Stanley is the world expert in the computational modeling of evolution, the only optimizer known to produce strong AI.
Jeff Clune discusses recent progress in generative models
A Nature News story features Jeff Clune describing the dramatic recent progress in generative models, which increasingly can synthesize photo-realistic images, including his work at Uber AI Labs with Jason Yosinski and Anh Nguyen .
Nature: Zoubin and Jeff on opening the black box of AI
Nature article features Chief Scientist Zoubin Ghahramani, his insights on AI interpretability, and more transparent reasoning with his Automatic Statistician.
Science: Jason’s new role as AI Detective deciphering deep learning
Science magazine interviews Jason Yosinski at Uber HQ on his new role as the “AI detective”, cracking open the black box of deep learning and pushing toward AI Neurosicence as a proper discipline.
Jeff Clune discusses robots that can rapidly adapt if damaged
The BBC interviews Jeff Clune about a Nature cover article he co-authored on using machine learning to enable robots to gracefully adapt to damage in under two minutes.
Geometric Intelligence takes the driver seat at Uber as Uber AI labs
Wired reports on the efforts at Uber to re-invent itself as an “AI-first” company with products ranging from self-driving trucks to self-flying cars. Geometric Intelligence was acquired to lead this company-wide transformation as the Uber AI labs.
Meet the team
Zoubin Ghahramani

RESEARCH AREA

  • Probabilistic Machine Learning

Zoubin Ghahramani is Chief Scientist at Uber. He is also a Professor of Information Engineering at the University of Cambridge, and Deputy Academic Director of the Leverhulme Centre for the Future of Intelligence. Until recently he was also the founding Cambridge Director of the Alan Turing Institute, the UK's national institute for Data Science. He studied and did postdoctoral work at University of Pennsylvania, MIT (with Michael Jordan), the University of Toronto (with Geoffrey Hinton). His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His research and studies span computer science, engineering, cognitive science and Neuroscience. Zoubin's current research interests include statistical machine learning, interpretable artificial intelligence, Bayesian optimisation, scalable inference, probabilistic programming, and building an automatic statistician (a project that won a Google Focused Research Award). He has published over 250 papers, which have received a total of over 37,000 citations (and has an h-index of 83). He has served as programme and general chair of the leading international conferences in machine learning: AISTATS, ICML, and NIPS. He was co-founder of Geometric Intelligence (now Uber AI Labs). In 2015, he was elected a Fellow of the Royal Society.

Doug Bemis

RESEARCH AREA

  • NLP
  • Deep Learning
  • Applications

Doug manages the many facets of research at AI Labs and directs the connection between innovation and application. He has served as CTO/Co-Founder for several startups, including Syntracts LLC (NLP processing), Windward Mark Interactive (video game development), and Geometric Intelligence, which became Uber AI Labs. Doug received a PhD from NYU in neurolinguistics, for work using magnetoencephalography to investigate the neural bases of semantic composition. This led to further post-graduate work at Neurospin in France with Stanislas Dehaene and then to the co-founding of Geometric Intelligence with Gary Marcus, Zoubin Ghahramani, and Ken Stanley.

Ken Stanley

RESEARCH AREA

  • Neuroevolution
  • Neural Networks
  • Evolutionary Computation

Before joining Uber AI Labs full time, Ken was an associate professor of computer science at the University of Central Florida (he is currently on leave). He is a leader in neuroevolution (combining neural networks with evolutionary techniques), where he helped invent prominent algorithms such as NEAT, CPPNs, HyperNEAT, and novelty search. His ideas have also reached a broader audience through the recent popular science book, Why Greatness Cannot Be Planned: The Myth of the Objective.

RESEARCH AREA

  • Probabilistic Machine Learning

Zoubin Ghahramani is Chief Scientist at Uber. He is also a Professor of Information Engineering at the University of Cambridge, and Deputy Academic Director of the Leverhulme Centre for the Future of Intelligence. Until recently he was also the founding Cambridge Director of the Alan Turing Institute, the UK's national institute for Data Science. He studied and did postdoctoral work at University of Pennsylvania, MIT (with Michael Jordan), the University of Toronto (with Geoffrey Hinton). His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His research and studies span computer science, engineering, cognitive science and Neuroscience. Zoubin's current research interests include statistical machine learning, interpretable artificial intelligence, Bayesian optimisation, scalable inference, probabilistic programming, and building an automatic statistician (a project that won a Google Focused Research Award). He has published over 250 papers, which have received a total of over 37,000 citations (and has an h-index of 83). He has served as programme and general chair of the leading international conferences in machine learning: AISTATS, ICML, and NIPS. He was co-founder of Geometric Intelligence (now Uber AI Labs). In 2015, he was elected a Fellow of the Royal Society.

RESEARCH AREA

  • NLP
  • Deep Learning
  • Applications

Doug manages the many facets of research at AI Labs and directs the connection between innovation and application. He has served as CTO/Co-Founder for several startups, including Syntracts LLC (NLP processing), Windward Mark Interactive (video game development), and Geometric Intelligence, which became Uber AI Labs. Doug received a PhD from NYU in neurolinguistics, for work using magnetoencephalography to investigate the neural bases of semantic composition. This led to further post-graduate work at Neurospin in France with Stanislas Dehaene and then to the co-founding of Geometric Intelligence with Gary Marcus, Zoubin Ghahramani, and Ken Stanley.

RESEARCH AREA

  • Neuroevolution
  • Neural Networks
  • Evolutionary Computation

Before joining Uber AI Labs full time, Ken was an associate professor of computer science at the University of Central Florida (he is currently on leave). He is a leader in neuroevolution (combining neural networks with evolutionary techniques), where he helped invent prominent algorithms such as NEAT, CPPNs, HyperNEAT, and novelty search. His ideas have also reached a broader audience through the recent popular science book, Why Greatness Cannot Be Planned: The Myth of the Objective.

Jeff Clune

RESEARCH AREA

  • Deep reinforcement learning
  • Deep learning
  • Evolving neural networks (neuroevolution)
  • Computational biology
  • Generative models

Jeff is on leave from the University of Wyoming, where he is the Loy and Edith Harris Associate Professor in Computer Science and directs the Evolving AI Lab (http://EvolvingAI.org). He researches robotics and creating artificial intelligence in neural networks, either via deep learning or evolutionary algorithms. In the last three years a robotics paper he coauthored was on the cover of Nature, he won an NSF CAREER award, he received the Distinguished Young Investigator Award from the International Society for Artificial Life, and deep learning papers he coauthored were awarded oral presentations at NIPS, CVPR, ICLR, and an ICML workshop.

Noah Goodman

RESEARCH AREA

  • Probabilistic Modeling

In addition to working at Uber AI Labs, Noah is also an Associate Professor of Psychology, Computer Science, and Linguistics at Stanford University, where he runs the Computation and Cognition Lab. He studies the computational basis of natural and artificial intelligence, merging behavioral experiments with formal methods from statistics and programming languages. His research topics include language understanding, social reasoning, and concept learning, as well as applications of these ideas and enabling technologies such as probabilistic programming languages. Professor Goodman has published more than 150 papers in fields including psychology, linguistics, computer science, and mathematics.

Jason Yosinski

RESEARCH AREA

  • Machine Learning

Jason Yosinski is a machine learning researcher and founding member of Uber AI Labs, where he uses neural networks to build more capable and more understandable AI. He suspects scientists and engineers will build increasingly powerful AI systems faster than we can understand them, motivating much of his work on what has been called "AI Neuroscience" -- an emerging field that may become increasingly important in the next several years. Dr. Yosinski was previously a PhD student and NASA Space Technology Research Fellow working at the Cornell Creative Machines Lab, the University of Montreal, the Caltech Jet Propulsion Laboratory, and Google DeepMind. His work on AI has been featured on NPR, Fast Company, the Economist, TEDx, and on the BBC. In his free time, Jason enjoys cooking, sailing, reading, and sometimes pretending he’s an artist.

RESEARCH AREA

  • Deep reinforcement learning
  • Deep learning
  • Evolving neural networks (neuroevolution)
  • Computational biology
  • Generative models

Jeff is on leave from the University of Wyoming, where he is the Loy and Edith Harris Associate Professor in Computer Science and directs the Evolving AI Lab (http://EvolvingAI.org). He researches robotics and creating artificial intelligence in neural networks, either via deep learning or evolutionary algorithms. In the last three years a robotics paper he coauthored was on the cover of Nature, he won an NSF CAREER award, he received the Distinguished Young Investigator Award from the International Society for Artificial Life, and deep learning papers he coauthored were awarded oral presentations at NIPS, CVPR, ICLR, and an ICML workshop.

RESEARCH AREA

  • Probabilistic Modeling

In addition to working at Uber AI Labs, Noah is also an Associate Professor of Psychology, Computer Science, and Linguistics at Stanford University, where he runs the Computation and Cognition Lab. He studies the computational basis of natural and artificial intelligence, merging behavioral experiments with formal methods from statistics and programming languages. His research topics include language understanding, social reasoning, and concept learning, as well as applications of these ideas and enabling technologies such as probabilistic programming languages. Professor Goodman has published more than 150 papers in fields including psychology, linguistics, computer science, and mathematics.

RESEARCH AREA

  • Machine Learning

Jason Yosinski is a machine learning researcher and founding member of Uber AI Labs, where he uses neural networks to build more capable and more understandable AI. He suspects scientists and engineers will build increasingly powerful AI systems faster than we can understand them, motivating much of his work on what has been called "AI Neuroscience" -- an emerging field that may become increasingly important in the next several years. Dr. Yosinski was previously a PhD student and NASA Space Technology Research Fellow working at the Cornell Creative Machines Lab, the University of Montreal, the Caltech Jet Propulsion Laboratory, and Google DeepMind. His work on AI has been featured on NPR, Fast Company, the Economist, TEDx, and on the BBC. In his free time, Jason enjoys cooking, sailing, reading, and sometimes pretending he’s an artist.

Eli Bingham

RESEARCH AREA

  • Probabilistic Modeling

Eli is a research scientist working on probabilistic programming, approximate Bayesian inference, and grounded language understanding. He has previously worked on condensed matter physics, computational biology, climatology, multiscale dictionary learning, and deep learning for computer vision. In his spare time he hangs out in a lab tinkering with his nanopore DNA sequencer.

Jonathan Chen

RESEARCH AREA

  • Probabilistic Modeling
  • Bayesian Inference
  • Probabilistic Programming

Jonathan is a researcher, focusing on using probabilistic graphical models to solve spatio-temporal problems. He holds an undergraduate degree from the University of Pennsylvania.

Olcay Cirit

RESEARCH AREA

  • Probabilistic Modeling
  • Approximate Inference
  • Probabilistic Programming

Olcay's career sits squarely at the intersection of engineering and data science. He received his undergraduate degree in EECS as a UC Berkeley Chancellor's Scholar and subsequently served as engineering lead on multiple DARPA-backed research projects ranging from brain-computer interfaces to neuroevolution of robotic controls. Before joining Uber he worked at Google where he designed improved models for predicting ad clicks and online purchases. He was the first engineer on Uber's Sensing and Perception team where he lead the development of ML models for extracting knowledge from sensor data. At AI Labs he is thinking up new ways of working with massive datasets to amplify the productivity of scientists across all of Uber's businesses.

RESEARCH AREA

  • Probabilistic Modeling

Eli is a research scientist working on probabilistic programming, approximate Bayesian inference, and grounded language understanding. He has previously worked on condensed matter physics, computational biology, climatology, multiscale dictionary learning, and deep learning for computer vision. In his spare time he hangs out in a lab tinkering with his nanopore DNA sequencer.

RESEARCH AREA

  • Probabilistic Modeling
  • Bayesian Inference
  • Probabilistic Programming

Jonathan is a researcher, focusing on using probabilistic graphical models to solve spatio-temporal problems. He holds an undergraduate degree from the University of Pennsylvania.

RESEARCH AREA

  • Probabilistic Modeling
  • Approximate Inference
  • Probabilistic Programming

Olcay's career sits squarely at the intersection of engineering and data science. He received his undergraduate degree in EECS as a UC Berkeley Chancellor's Scholar and subsequently served as engineering lead on multiple DARPA-backed research projects ranging from brain-computer interfaces to neuroevolution of robotic controls. Before joining Uber he worked at Google where he designed improved models for predicting ad clicks and online purchases. He was the first engineer on Uber's Sensing and Perception team where he lead the development of ML models for extracting knowledge from sensor data. At AI Labs he is thinking up new ways of working with massive datasets to amplify the productivity of scientists across all of Uber's businesses.

Eric Frank

RESEARCH AREA

  • Neuroevolution

Before joining Uber AI Labs as a researcher, Eric invented AI oriented toys for Kite and Rocket Research. He was also a research assistant at the University of Rochester and makes art in his free time.

Martin Jankowiak

RESEARCH AREA

  • Approximate Bayesian Inference
  • Probabilistic Programming languages (PPLs)
  • Spatiotemporal modeling
  • Gaussian Processes

Martin is a former particle physicist whose interest in data and modeling goes back to the Large Hadron Collider. After physics stops at Stanford and Heidelberg, he became a Research Scientist at the Center for Urban Science and Progress at NYU with a focus on applied machine learning research. Martin then joined a small machine learning start-up (Geometric Intelligence) with the happy end result that he joined AI Labs in March 2017.

Theofanis Karaletsos

RESEARCH AREA

  • Probabilistic Modeling
  • Approximate Inference
  • Probabilistic Programming

Theofanis took his first steps as a machine learner at the Max Planck Institute For Intelligent Systems in collaboration with Microsoft Research Cambridge with work focused on unsupervised knowledge extraction from unstructured data, such as generative modeling of images and phenotyping for biology. He then moved to Memorial Sloan Kettering Cancer Center in New York, where he worked on machine learning in the context of cancer therapeutics. He joined a small AI startup Geometric Intelligence in 2016 and with his colleagues formed the new Uber AI Labs. Theofanis' research interests are focused on rich probabilistic modeling, approximate inference and probabilistic programming. His main passion are structured models, examples of which are spatio-temporal processes, models of image formation, deep probabilistic models and the tools needed to make them work on real data. His past in the life sciences has also made him keenly interested in how to make models interpretable and quantify their uncertainty, non-traditional learning settings such as weakly supervised learning and model criticism.

RESEARCH AREA

  • Neuroevolution

Before joining Uber AI Labs as a researcher, Eric invented AI oriented toys for Kite and Rocket Research. He was also a research assistant at the University of Rochester and makes art in his free time.

RESEARCH AREA

  • Approximate Bayesian Inference
  • Probabilistic Programming languages (PPLs)
  • Spatiotemporal modeling
  • Gaussian Processes

Martin is a former particle physicist whose interest in data and modeling goes back to the Large Hadron Collider. After physics stops at Stanford and Heidelberg, he became a Research Scientist at the Center for Urban Science and Progress at NYU with a focus on applied machine learning research. Martin then joined a small machine learning start-up (Geometric Intelligence) with the happy end result that he joined AI Labs in March 2017.

RESEARCH AREA

  • Probabilistic Modeling
  • Approximate Inference
  • Probabilistic Programming

Theofanis took his first steps as a machine learner at the Max Planck Institute For Intelligent Systems in collaboration with Microsoft Research Cambridge with work focused on unsupervised knowledge extraction from unstructured data, such as generative modeling of images and phenotyping for biology. He then moved to Memorial Sloan Kettering Cancer Center in New York, where he worked on machine learning in the context of cancer therapeutics. He joined a small AI startup Geometric Intelligence in 2016 and with his colleagues formed the new Uber AI Labs. Theofanis' research interests are focused on rich probabilistic modeling, approximate inference and probabilistic programming. His main passion are structured models, examples of which are spatio-temporal processes, models of image formation, deep probabilistic models and the tools needed to make them work on real data. His past in the life sciences has also made him keenly interested in how to make models interpretable and quantify their uncertainty, non-traditional learning settings such as weakly supervised learning and model criticism.

Joel Lehman

RESEARCH AREA

  • Neuroevolution
  • Deep Reinforcement Learning

Joel Lehman was previously an assistant professor at the IT University of Copenhagen, and researches neural networks, evolutionary algorithms, and reinforcement learning.

Rosanne Liu

RESEARCH AREA

  • Computer Vision
  • Deep Learning

Rosanne obtained her PhD in Computer Science at Northwestern University, where she used neural networks to help discover novel materials. Months before graduation she started working as a machine learning researcher at a New York based startup Geometric Intelligence, which later became Uber AI Labs. She has a diverse background in optimization, automotive control, materials informatics and social media mining. She loves reading, standup comedies and playing with dogs.

Vashisht Madhavan

RESEARCH AREA

  • Reinforcement Learning

Vashisht (Vash) is a recent graduate of UC Berkeley, where he received his BS and MS in Computer Science, with a focus in Computer Vision and Artificial Intelligence. At Berkeley, his work focused on perception systems for autonomous vehicles. His interests lie at the intersection of computer vision, machine learning, and reinforcement learning.

RESEARCH AREA

  • Neuroevolution
  • Deep Reinforcement Learning

Joel Lehman was previously an assistant professor at the IT University of Copenhagen, and researches neural networks, evolutionary algorithms, and reinforcement learning.

RESEARCH AREA

  • Computer Vision
  • Deep Learning

Rosanne obtained her PhD in Computer Science at Northwestern University, where she used neural networks to help discover novel materials. Months before graduation she started working as a machine learning researcher at a New York based startup Geometric Intelligence, which later became Uber AI Labs. She has a diverse background in optimization, automotive control, materials informatics and social media mining. She loves reading, standup comedies and playing with dogs.

RESEARCH AREA

  • Reinforcement Learning

Vashisht (Vash) is a recent graduate of UC Berkeley, where he received his BS and MS in Computer Science, with a focus in Computer Vision and Artificial Intelligence. At Berkeley, his work focused on perception systems for autonomous vehicles. His interests lie at the intersection of computer vision, machine learning, and reinforcement learning.

Piero Molino

RESEARCH AREA

  • Natural Language Processing
  • Deep Learning
  • Unsupervised Learning
  • Dialog Systems

Piero is a research scientist focusing on Natural Language Processing. He received a PhD in computer science from the University of Bari in Italy with a thesis on distributional semantics in question answering. He worked for Yahoo Labs in Barcelona on learning to rank on community question answering mixing language, user modeling and social network analysis techniques. He then joined IBM Watson in New York on and worked on question answering, query autocomplete, misspell correction using deep learning models. Before joining Uber he worked at Geometric Intelligence, where his main focus was on grounded language understanding mixing computer vision and language using deep learning approaches. He is now working on natural language understanding tasks and dialog systems.

Fritz Obermeyer

RESEARCH AREA

  • Probabilistic Programming

Fritz is a research engineer focusing on probabilistic programming. His interests lie at the intersection of probabilistic machine learning and programming language research. After an early career in Bayesian modeling, he returned to academia to study program induction, receiving a PhD from Carnegie-Mellon in 2009. Since then he has developed probabilistic inference software at Google Research, Salesforce.com, Prior Knowledge, and various defense contractors.

Neeraj Pradhan

RESEARCH AREA

  • Probabilistic Modeling
  • Software Systems for Machine Learning

Neeraj is a software engineer on Pyro, a deep probabilistic programming language built over PyTorch. He was earlier an engineer on uberPool, working on algorithms and infrastructure for pooling riders. He is interested in synthesizing techniques from machine learning research to build intelligent systems that scale to real world problems. Prior to Uber, Neeraj worked on search, personalized recommendations, and learning to rank at Groupon. He did his masters in Operations Research from Stanford University, and undergraduate in Physics and EE from BITS Pilani.

RESEARCH AREA

  • Natural Language Processing
  • Deep Learning
  • Unsupervised Learning
  • Dialog Systems

Piero is a research scientist focusing on Natural Language Processing. He received a PhD in computer science from the University of Bari in Italy with a thesis on distributional semantics in question answering. He worked for Yahoo Labs in Barcelona on learning to rank on community question answering mixing language, user modeling and social network analysis techniques. He then joined IBM Watson in New York on and worked on question answering, query autocomplete, misspell correction using deep learning models. Before joining Uber he worked at Geometric Intelligence, where his main focus was on grounded language understanding mixing computer vision and language using deep learning approaches. He is now working on natural language understanding tasks and dialog systems.

RESEARCH AREA

  • Probabilistic Programming

Fritz is a research engineer focusing on probabilistic programming. His interests lie at the intersection of probabilistic machine learning and programming language research. After an early career in Bayesian modeling, he returned to academia to study program induction, receiving a PhD from Carnegie-Mellon in 2009. Since then he has developed probabilistic inference software at Google Research, Salesforce.com, Prior Knowledge, and various defense contractors.

RESEARCH AREA

  • Probabilistic Modeling
  • Software Systems for Machine Learning

Neeraj is a software engineer on Pyro, a deep probabilistic programming language built over PyTorch. He was earlier an engineer on uberPool, working on algorithms and infrastructure for pooling riders. He is interested in synthesizing techniques from machine learning research to build intelligent systems that scale to real world problems. Prior to Uber, Neeraj worked on search, personalized recommendations, and learning to rank at Groupon. He did his masters in Operations Research from Stanford University, and undergraduate in Physics and EE from BITS Pilani.

Yunus Saatci

RESEARCH AREA

  • Probabilistic Machine Learning

Yunus did his PhD with Carl Rasmussen and Zoubin Ghahramani at the University of Cambridge. His research focused on scalable inference for Bayesian nonparametrics models, with a particular emphasis on Gaussian Processes. Since completing his PhD Yunus has applied machine learning to quite a variety of application domains, including high frequency trading, self-driving cars and trucks, natural language processing and even venture capital. He continues his adventures in the world of AI and ML at Uber, where probabilistic inference, deep learning and huge datasets meet to generate the next wave of innovation in the field.

Rohit Singh

RESEARCH AREA

  • Machine Learning for Software Systems
  • Programming Languages and Databases

Rohit is an AI researcher with a PhD from MIT Computer Science and Artificial Intelligence Lab (CSAIL). He is currently working on applications of various AI techniques with the Pyro programming language across product teams at Uber. His previous work has involved applications of Machine Learning, Quantitative Game Theory and Program Synthesis in multiple domains from the fields of Compilers and Databases. Rohit has worked as an intern at Google where he used the Google Brain deep-learning framework for an application with the YouTube team and as a PM intern at Yelp where he worked on a Machine Learning application on Ad CTR prediction.

Felipe Petroski Such

RESEARCH AREA

  • Deep Learning
  • Reinforcement Learning
  • and Neuroevolution

Felipe Petroski Such received his BS and MS in Computer Engineering at the Rochester Institute of Technology, New York, USA, in 2017. His interest in machine intelligence ranges from hardware acceleration to applicable software. During his time at RIT he worked as a Teaching Assistant for deep learning as well as a Research Assistant where he developed state-of-the-art handwriting recognition and graph filtering software.

RESEARCH AREA

  • Probabilistic Machine Learning

Yunus did his PhD with Carl Rasmussen and Zoubin Ghahramani at the University of Cambridge. His research focused on scalable inference for Bayesian nonparametrics models, with a particular emphasis on Gaussian Processes. Since completing his PhD Yunus has applied machine learning to quite a variety of application domains, including high frequency trading, self-driving cars and trucks, natural language processing and even venture capital. He continues his adventures in the world of AI and ML at Uber, where probabilistic inference, deep learning and huge datasets meet to generate the next wave of innovation in the field.

RESEARCH AREA

  • Machine Learning for Software Systems
  • Programming Languages and Databases

Rohit is an AI researcher with a PhD from MIT Computer Science and Artificial Intelligence Lab (CSAIL). He is currently working on applications of various AI techniques with the Pyro programming language across product teams at Uber. His previous work has involved applications of Machine Learning, Quantitative Game Theory and Program Synthesis in multiple domains from the fields of Compilers and Databases. Rohit has worked as an intern at Google where he used the Google Brain deep-learning framework for an application with the YouTube team and as a PM intern at Yelp where he worked on a Machine Learning application on Ad CTR prediction.

RESEARCH AREA

  • Deep Learning
  • Reinforcement Learning
  • and Neuroevolution

Felipe Petroski Such received his BS and MS in Computer Engineering at the Rochester Institute of Technology, New York, USA, in 2017. His interest in machine intelligence ranges from hardware acceleration to applicable software. During his time at RIT he worked as a Teaching Assistant for deep learning as well as a Research Assistant where he developed state-of-the-art handwriting recognition and graph filtering software.

Paul Szerlip

RESEARCH AREA

  • Probabilistic Programming

Paul Szerlip earned his PhD with Dr. Kenneth Stanley at the University of Central Florida focusing on open-source infrastructure for collaborative evolutionary software. This open-source platform enables researchers to quickly integrate crowd-sourced human contributions with automated algorithms, while making the results easily accessible online. His later research highlighted new ways to integrate neuroevolutionary techniques like HyperNEAT and Novelty Search into deep learning frameworks.

Rui Wang

RESEARCH AREA

  • Neuroevolution
  • deep learning
  • deep reinforcement learning

Rui obtained his PhD in Electrical and Computer Engineering from University of Illinois at Ubana-Champaign, focused on high-performance computing and numerical analysis. Prior to Uber, he had worked at Analog Devices and Cadence Design Systems, where he was leading the development of industry-leading commercial-grade simulation software that powers the design of the world's most advanced integrated circuits. His current research interests lies in building intelligent algorithms and systems that advance the state of the art of artificial intelligence and solve large-scale real world problems

Johna Manibusan

Johna Manibusan is the Executive Assistant to the AI Labs. She graduated from San Francisco State University with a BA in communications. Before Uber she worked at a private Investment Bank for 6 years where she implemented new software programs, executed corporate events and supported the CEO and CFO. She enjoys implementing new strategies to enhance efficiency and always looking to improve her skills. When she isn't working, she enjoys spending time with her family and finding new ways to be adventerous.

RESEARCH AREA

  • Probabilistic Programming

Paul Szerlip earned his PhD with Dr. Kenneth Stanley at the University of Central Florida focusing on open-source infrastructure for collaborative evolutionary software. This open-source platform enables researchers to quickly integrate crowd-sourced human contributions with automated algorithms, while making the results easily accessible online. His later research highlighted new ways to integrate neuroevolutionary techniques like HyperNEAT and Novelty Search into deep learning frameworks.

RESEARCH AREA

  • Neuroevolution
  • deep learning
  • deep reinforcement learning

Rui obtained his PhD in Electrical and Computer Engineering from University of Illinois at Ubana-Champaign, focused on high-performance computing and numerical analysis. Prior to Uber, he had worked at Analog Devices and Cadence Design Systems, where he was leading the development of industry-leading commercial-grade simulation software that powers the design of the world's most advanced integrated circuits. His current research interests lies in building intelligent algorithms and systems that advance the state of the art of artificial intelligence and solve large-scale real world problems

Johna Manibusan is the Executive Assistant to the AI Labs. She graduated from San Francisco State University with a BA in communications. Before Uber she worked at a private Investment Bank for 6 years where she implemented new software programs, executed corporate events and supported the CEO and CFO. She enjoys implementing new strategies to enhance efficiency and always looking to improve her skills. When she isn't working, she enjoys spending time with her family and finding new ways to be adventerous.

Join us
We are located in San Francisco and are looking for talented researchers and engineers of all levels. Our work spans many techniques including deep learning, probabilistic modeling, Bayesian optimization, evolution, reinforcement learning, and more!
AI Labs Research Scientist
Research scientists create the next generation of artificial intelligence algorithms to help Uber improve the lives of millions of people worldwide.
AI Labs Conversational AI Research Scientist
Uber AI Labs gathers top scientists and engineers to create the next generation of machine learning algorithms to improve the lives of millions of people worldwide.
AI Labs Speech Research Scientist
Uber AI Labs gathers top scientists and engineers to create the next generation of machine learning algorithms to improve the lives of millions of people worldwide.
Get in touch
Interested in learning more? Please use the link below to get in touch!