AI for the robot age
Vicarious is developing artificial general intelligence for robots. By combining insights from generative probabilistic models and systems neuroscience, our architecture trains faster, adapts more readily, and generalizes more broadly than AI approaches commonly used today.
- Mark Zuckerberg
- Jeff Bezos
- Marc Benioff
- Founders Fund
- Good Ventures
- Khosla Ventures
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- Formation 8
- Jerry Yang
- Ashton Kutcher
- OS Fund
- Initialized Capital
- Sam Altman
- Adam D’Angelo
- Derek Collison
- Elon Musk
- Janus Friis
- Dustin Moskovitz
- Felicis Ventures
- Aaron Levie
- Open Field Capital
- Data Collective
- A Grade Investments
- Peter Diamandis
- Zarco Group
- Wipro Ventures
Prof. Fei-Fei Li
Dr. Li is the Director of the Stanford AI Lab, as well as an Associate Professor at Stanford. Prior to joining Stanford, she was on faculty at Princeton University and University of Illinois Urbana-Champaign. Research by Fei-Fei and her colleagues has been published in top-tier journals and conferences such as Nature, PNAS, Journal of Neuroscience, CVPR, ICCV, NIPS, ECCV, IJCV, IEEE-PAMI, and others. Fei-Fei is a recipient of the 2011 Alfred Sloan Faculty Award, 2012 Yahoo Labs FREP award, 2009 NSF CAREER award, the 2006 Microsoft Research New Faculty Fellowship and a number of Google Research awards.
Prof. Bruno Olshausen
Professor Olshausen is the Director of the Redwood Center for Theoretical Neuroscience at UC Berkeley, as well as a Professor of the Helen Wills Neuroscience Institute. He serves on the Editorial Board of Vision Research and the Journal of Computational Neuroscience and was Chair of the Gordon Research Conference on Sensory Coding and the Natural Environment in 2004. In 2002, he co-edited the book Probabilistic Models of Perception and Brain Function (MIT Press).
Prof. Alan Yuille
Professor Yuille is the Director of the UCLA Center for Cognition, Vision, and Learning, as well as a Professor at the UCLA Department of Statistics, with courtesy appointments at the Departments of Psychology, Computer Science, and Psychiatry. He is affiliated with the UCLA Staglin Center for Cognitive Neuroscience, the Center for Brains, Minds and Machines, and the NSF Expedition in Computing, Visual Cortex On Silicon.
D. Scott Phoenix
Before cofounding Vicarious, Scott was CEO of Frogmetrics (Y Combinator S2008), Entrepreneur in Residence at Founders Fund, CXO at OnlySecure (acquired by NetShops) and MarchingOrder (Ben Franklin Partners). Scott’s design work has been featured in 16 magazines and 3 museums, including the Institute for Contemporary Art in Philadelphia. Scott earned his BAS in Computer Science and Entrepreneurship from the University of Pennsylvania.
Dileep George, PhD
Before cofounding Vicarious, Dileep was CTO of Numenta, an AI company he cofounded with Jeff Hawkins and Donna Dubinsky. Before Numenta, Dileep was a Research Fellow at the Redwood Neuroscience Institute. Dileep has authored 22 patents and several influential papers on the mathematics of brain circuits. Dileep’s research on hierarchical models of the brain earned him a PhD in Electrical Engineerings from Stanford University. He earned in MS in EE from Stanford and his BS from IIT in Bombay.
Ken previously earned a BS degree in Computer Science and Physics at Stanford University, where he completed his thesis on image recognition under Peter Norvig. Before attending Stanford, Ken built laser control circuitry for condensed matter experiments at the University of Georgia, as well as a remote-control flamethrower, an electromagnetic levitator, and a flex sensor suit for controlling robotic arms.
Wolfgang Lehrach, PhD
Wolfgang was previously Director of Sequencing at Halcyon Molecular, where he developed image processing and bioinformatics algorithms to sequence DNA with electron microscopes. Before that, he worked at Alacris Theranostics, using deep sequencing and machine learning to enable personalized cancer treatment. Wolfgang did his post-doc in Machine Learning at Microsoft Research, earned his PhD and MSc from the University of Edinburgh, and his BS from Cambridge.
Bhaskara Marthi, PhD
Bhaskara Marthi was previously a Research Scientist at Willow Garage, where he devised algorithms for robots to build 3D maps of their surroundings, assemble Ikea furniture, and tidy rooms in an optimal way. Before that, Bhaskara completed his post-doc in artificial intelligence at MIT and earned his PhD in Computer Science from UC Berkeley. His favorite learning agent is two years old and has already achieved state of the art results on the task of detecting candy in cluttered images.
Charlotte Bowell, PhD
Before joining Vicarious as Director of Operations, Charlotte was a researcher at Integrated Plasmonics and Halcyon Molecular, where she manipulated and imaged the nanoworld. Before that we was a post-doc in the Electron Microscopy Group at the University of Cambridge. She first got a taste for manipulating sub-atomic particles during her PhD at the University of Birmingham in the Condensed Matter Group. Charlotte earned her MSci in Experimental and Theoretical Physics at the University of Cambridge.
Devin previously earned his BA in Psychology at the University of Notre Dame, and his MFA in Fiction from the University of Alabama, where he was a Truman Capote fellow. He has worked in UX research and design at Verizon Laboratories and got his first experience coding at Harvard University’s Smithsonian Astrophysical Observatory.
Huayan Wang, PhD
Huayan earned his PhD in Computer Science at Stanford University, under Daphne Koller. His thesis was on MAP inference methods in rich-structured graphical models. He has been a reviewer for NIPS, and author for ICML, AAAI, CVPR, and ECCV. He worked briefly at Coursera as a software engineer. Then he decided that building intelligent machines is what he really cares about. Before Stanford, he earned his M.E. and B.S. degree at Peking University.
Xinghua Lou, PhD
Xinghua Lou received his PhD. in Computer Science at Universität Heidelberg, under Fred Hamprecht. He worked on graphical models and structured learning for biomedical data analysis. He has published in NIPS, ICML, CVPR, Bioinformatics, IEEE-TMI, among others. Though initially trained as an experimental physicist in Engineering Physics at Tsinghua University, he is very excited about the current path towards building machines that can learn and reason.
Dennis Park, PhD
Dennis received his PhD in Computer Science from UC Irvine, where he was advised by Deva Ramanan. His research interests span computer vision and machine learning with a focus on visual recognition. His work on tracking people and their 2D body poses in videos was published in CVPR, ECCV, and ICCV. During his PhD, he worked as a research intern in Los Alamos National Laboratory and Microsoft Research, and was a recipient of ARCS award. He received his B.S in Physics from Seoul National University, South Korea.
Miguel Lazaro Gredilla, PhD
Miguel previously earned his PhD in machine learning at Universidad Carlos III de Madrid. In his thesis, Miguel developed a sparse Gaussian process model which has become the de facto benchmark for fast regression algorithms. Since then, Miguel has been working on approximate inference algorithms for other Bayesian models. He has been a visiting scholar at the University of Cambridge and the University of Manchester, and has authored and reviewed for NIPS, JMLR, and several IEEE journals.
Zhaoshi Meng, PhD
Zhaoshi (Josh) received his PhD from the University of Michigan, under the supervision of Alfred Hero and Long Nguyen. During his PhD, Zhaoshi has worked on probabilistic graphical models and co-authored the Best Paper at ICML 2014, a Notable Paper at AISTATS 2013, and a Best Student Paper at CAMSAP 2013. Zhaoshi earned his BS in EE from Tsinghua University, and worked on recommendation algorithms at Microsoft Research Asia. He enjoys discovering latent patterns from noisy observations.
Michael Stark, PhD
Michael was previously a member of the Max Planck Center for Visual Computing and Communication, both at Stanford University and MPII. He earned his PhD from TU Darmstadt for his research on knowledge transfer for object class recognition. Michael’s work has been published in CVPR, ICCV, ECCV, ICLR, and he received back-to-back Best Paper Awards at CVPR-3dRR. He is thrilled to be able to teach intelligent machines how to see.
Before joining Vicarious as its Resident Chef, Bryan worked as a private chef to Silicon Valley executives since 2001. He started his culinary adventure while living in Japan and working for the US Navy where he toured Asia and was exposed to new foods and cultures. After his return to the Bay Area, he attended the California Culinary Academy of San Francisco and graduated in 1994. Outside of work, Bryan spends his time with his children, as well as glassblowing and woodworking.
Nick Hay, PhD
Nick Hay earned his PhD at UC Berkeley under Stuart Russell. His research applied reinforcement learning and Bayesian analysis to the metalevel control problem: how can an agent learn to control its own computations. More broadly, he is interested in how AI systems can be safely developed for the benefit of humanity, and how this pursuit might be informed by the cognitive sciences. Born in New Zealand, Nick is still getting used to walking upside down.
Wenzhao Lian, PhD
Wenzhao received his PhD in machine learning at Duke University, advised by Dr. Lawrence Carin. He has worked on probabilistic graphical models and Bayesian statistics, focusing on time series and point processes. During his PhD, Wenzhao interned at Microsoft Research and Yahoo Labs; he has authored and reviewed for ICML, NIPS, AAAI, and IEEE journals. He earned his Master’s degree in statistics at Duke and Bachelor’s degree in ECE at Shanghai Jiao Tong University.
Before joining the Operations Team at Vicarious, Sandhya worked in executive support and operations in both corporate and start-up environments. Channeling her previous experience in SEO, she expanded those roles to include marketing and brand-storytelling. Sandhya earned a BA in Neuroscience and History from Kenyon College, where she completed theses on traumatic brain injury and sub-Saharan mineral economies.
Roman was previously a senior system engineer at EPAM, where he was responsible for databases and applications deployment automations. Roman has also served as the lead consultant for a tech security team and worked as both a system and network administrator. Roman currently resides with his lovely wife and daughter and a big red cat named Oscar.
Anna was previously at Quora, where she applied ML to learn the semantics of millions of questions, and built the first system to detect paraphrased questions. Before that she earned her MS in Computer Science at University of Michigan, where she worked with Edmund Durfee and Satinder Singh on multi-agent cooperative planning under a reinforcement learning framework. In her free time she likes to paint, currently focusing on watercolor and oil painting.
Scott earned his BS in Applied and Computational Mathematics at Brigham Young University. While there, he completed undergraduate research in handwriting recognition, created possibly the world’s best competitive bot for the game Puyo Puyo, programmed a cryptanalysis engine to attack a variety of common cyphers, and wrote a multi-threaded program to decipher a symmetric-key cryptography challenge. Scott loves to cook, solve online math challenges, and work on coding projects. He especially enjoys saving time by making computer programs that play online games autonomously, so that he doesn’t have to.
Tom Silver earned his BA with highest honors in Computer Science and Mathematics at Harvard. His undergraduate thesis proposed a new crowdsourced benchmark for artificial intelligence. He previously interned at Numenta and Google. He also worked in the Sabeti Lab, publishing research on machine learning methods for Ebola prognostic prediction. Cooking, corgis, and coffee are a few of his favorite things.
Alex Schlegel, PhD
Alex completed his PhD in Cognitive Neuroscience with Peter Tse at Dartmouth College, where his work sought mechanistic links between the distributed networks of the brain and the mind they create. The questions that drive him are: How does the human brain mediate flexible, creative cognitive behaviors including artistic and scientific thought? Why is our species the only one (so far) to demonstrate these abilities? And how are these abilities realized via dynamic interactions in distributed neural networks? In his free time, Alex enjoys hiking, gardening, and creating sculpture that reveals hidden processes in the environment. He looks forward to the day when machines begin imagining a better world.
David A. Mely, PhD
David completed his Ph.D. in Cognitive Science with Dr. Thomas Serre at Brown University, elaborating computational models of cortical micro-circuitry to explain contextual integration in human vision. Previously, David’s interest in thinking machines was sparked by Isaac Asimov’s novels; he went on to study mathematics, theoretical physics, and biology at the École Polytechnique. When he is not working towards AI, David is a bon vivant who always enjoys craft beer, jazz music, and swimming.
Carter Wendelken, PhD
Carter was previously a research scientist at UC Berkeley where his work focused on neural mechanisms of reasoning, memory, and cognitive control. He has authored publications in journals including Cerebral Cortex, Neuron, Journal of Neuroscience, Developmental Science, and Neurocomputing. Prior to working as a neuroscientist, Carter earned his Ph.D. in Computer Science (Artificial Intelligence), under Lokendra Shastri and Jerry Feldman. Now that he has figured out how human brains work (if only), Carter is excited to apply what he’s learned to the task of creating artificial brains.
Nan Rong, PhD
Nan completed her Ph.D. in Computer Science with Dr. Joe Halpern at Cornell University, where her work focused on enabling machines to automatically learn new actions that they weren’t aware of previously. She also worked on designing equilibrium concepts that explain and motivate people’s cooperative behavior in a game theoretic setting. She is pretty serious about theories and at the same time excited by building robots. Her passion for creating robots started from reading Asimov’s Three Laws. In her free time, she enjoys reading novels, archery, and tree climbing.
Alex studied mechanical and aerospace engineering at Cornell, then pursued robotics in Carnegie Mellon’s MS in Mechanical Engineering program, where he led a team to building a lunar rover. Previously, he was a Senior Software Engineer with Numenta, developing biologically-derived AI algorithms. In 2016, Alex was selected to the Forbes 30 Under 30 List for Science. In his free time, Alex enjoys running, yoga, live music, and reading sci-fi and theoretical physics books.
Jimmy Baraglia, PhD
Jimmy earned his Ph.D. in Engineering at the Osaka University under the supervision of Minoru Asada and Yukie Nagai. His work allowed robots to develop prosocial behavior similar to infants’ based on a prediction error minimization mechanism. The system is based on Bayesian inference and the Free Energy principle. Previously, Jimmy was student at Paul Sabatier University in France where he studied automatism, signal processing and electronics. Jimmy loves cooking, traveling and playing all kind of sports.
Eyrún Eyjólfsdóttir, PhD
Eyrún’s research interests lie on the intersection of Artificial Intelligence and Neuroscience. She completed her PhD at Caltech where she worked with Pietro Perona on automated analysis of behavior, particularly that of fruit flies, in a collaboration with the David Anderson lab. During her PhD, she interned at Qualcomm Research Center, where she developed a power-efficient context-classification algorithm for smartphones. Prior to Caltech, she received her MS in Computer Science from UCSB and her BS in Mathematics from the University of Iceland. Her favorite pastime activities involve the backcountry, home brewing, and knitting.
Jaldert Rombouts, PhD
Jaldert earned his PhD at the Free University of Amsterdam under Pieter Roelfsema and Sander Bohte for his work on biologically plausible learning in neural networks. At CMU’s Robotics Institute he worked on the SnackBot, and at Brain Corporation he helped develop indoor navigation solutions for large wheeled robots. Besides playing with state of the art learning systems, Jaldert likes cooking (especially breakfast, his favorite meal), biking, hiking, reading, and Krav Maga.
Nathaniel earned his AB/SM in Computer Science from Harvard. He previously worked as a Quant and Trader at Jane Street and Goldman Sachs before transitioning into the pure tech industry. Nathaniel worked as a Data Scientist at Facebook, a Product Manager at Microsoft and a Software Engineer at Google before joining Vicarious. He is an avid reader and learner. He teaches part time at General Assembly and is developing open source teaching material for data science, machine learning, and web development.
Before joining Vicarious, Amit worked at a healthcare software company building integrated systems for hospitals to help provide quality care for patients. Prior to that, he developed accurate and robust handwriting recognition algorithms with Thomas Binford at Read-Ink Technologies. Amit earned a B.Tech in Computer Science at IIT Madras where he became interested in intelligent systems while doing research in camera surveillance and object recognition. In his free time, Amit enjoys exploring new places and playing music.
Klas Kronander, PhD
Klas Kronander completed his PhD in Robotics at Ecole Polytechnique Federale de Lausanne under the supervision of Aude Billard. He has done research on robot learning from demonstration, specifically in the domains of compliant manipulation motions and dynamical systems. Outside of work, Klas enjoys spending time with his family, the outdoors, cooking and listening to jazz.
Dianhuan Lin, PhD
Dianhuan Lin received her PhD in Machine Learning at Imperial College London, advised by Stephen Muggleton. Her research focused on Symbolic Machine Learning, specifically Inductive Logic Programming. She developed approaches to automatically learn high-level abstraction, which leads to more compressed representation of concepts. She also worked with Josh Tenenbaum on one-shot program induction. Before joining Vicarious, she was at Amazon, working on Alexa. Outside of work, she enjoys all kinds of sports, including hiking, playing ping pong and swimming.
Matthieu Perrinel, PhD
Matthieu was previously a data scientist in Deepomatic, where he worked on the efficient building of similarity datasets, and designed deep learning based models to predict the similarity between objects. Before joining Deepomatic, Matthieu earned his Ph.D. in Computer Science at ENS Lyon, under Patrick Baillot. During his thesis, he created type systems for lambda-calculus corresponding to polynomial time. When he does not talk about french food, Matthieu enjoys sailing in the bay and brewing his own beers.
Anjali Rao was previously a Robotics researcher at Symbio Robotics, where she worked on robotic learning for task generalization. Before that she earned her Master’s degree from SUNY Stony Brook University. Her Master’s thesis was focused on User-guided Inverse Kinematics based Path planning for redundant manipulators. She completed her Bachelors in Mechanical Engineering in India. Besides understanding the early stages of human brain development, she enjoys reading, traveling and cooking.
Levi previously worked at Atlassian as a DevOps Engineer where he was responsible for infrastructure automation and continuous integration. He has a passion for “everything as code; cattle – not pets” when it comes to infrastructure. In his spare time, he enjoys making music on his guitar whilst also working on achieving his MS degree from Georgia Tech.
Swaroop Guntupalli, PhD
Swaroop completed his PhD in Cognitive Neuroscience at Dartmouth College under James Haxby. His work in the Haxby Lab focused on understanding neural representation of visual and auditory information and developing computational methods for probing and building models of representational spaces in the brain. Prior to Vicarious, he was a postdoc in HaxbyLab and a visiting scholar at Stanford. He received his MS in Computer Science from Texas A&M University under Bruce McCormick, and BS in Mechanical Engineering from IIT Madras. Swaroop enjoys running, hiking, reading math, science, and sci-fi, coffee, and hoppy IPAs.
Yasemin Bekiroglu, PhD
Yasemin completed her Ph.D. at the Royal Institute of Technology (KTH), in Stockholm, Sweden, in 2012. Her research is focused on data driven learning for robotics applications with a focus on Bayesian non-parametrics. In specific she is interested in data efficient learning from multisensory data. Prior to Vicarious she worked as a post-doctoral researcher at University of Birmingham, and as a research scientist at ABB, Corporate Research, Sweden, coordinating the EU project SARAFun on industrial assembly tasks.
Jeffrey graduated from Harvard with a BA in Computer Science and Mathematics. He became interested in AI from working in the Harvard NLP group, where he researched deep learning and natural language processing, and from an internship at Google Translate. Jeffrey likes food, sports, and puzzles.
Before joining Vicarious, Bianca held a variety of account management and sales roles at leading luxury fashion brands in New York. Since moving to San Francisco, her experience has expanded to include operations, executive support, and technical recruiting at Series C/D start-ups. Bianca earned a BA in Art History and Philosophy from the University of Wisconsin-Milwaukee, followed by graduate coursework in Philosophy of Physics at Columbia University in New York.
Prior to Vicarious, Laura spent a decade in the VC/growth equity space, mainly investing in and working with US and emerging markets for technology, resource efficiency, and infrastructure on behalf of multi-billion dollar organizations, such as the World Bank/IFC and OPIC, down to an early stage VC fund she helped launch out of a NYC family office. She received her MBA from UPenn’s Wharton School and MAs in International Economics and Energy, Resources, and Environment from the Johns Hopkins School of Advanced International Studies (SAIS). She is obsessed with efficiency, impact, and finding some legit pizza in the Bay.
As the Office Manager at Vicarious, Lindsey cares for the entire Vicarian sphere, from employee on-boarding to planning events, visa management, inventory and more. She brings good humor and empathy to her day-to-day work at the office. Her previous roles range from coordinating 300 volunteers at Strongwater Farm Therapeutic Equestrian Center to, most recently, running the bustling Boston start-up office of TrueMotion. Lindsey graduated Summa Cum Laude from Lesley University. Her passions include dancing West Coast Swing, planning events and elaborate meals, and continuing the search for an everlasting blue hair dye.
Jake previously lead Product Development at Bossa Nova Robotics where he spent 6 years working with the team to solve challenges relating to large scale SLAM, human-robot interaction and product recognition. Before entering the robotics space, Jake worked as a strategy consultant for LEK Consulting, where he specialized in aviation and biotech. Jake holds a Masters in Engineering Management from Dartmouth College, a BS in Civil Engineering, and BA in Economics from UCLA.
Siva Swaminathan, PhD
Siva is interested in bringing together ideas from probabilistic inference and fundamental physics. Before joining Vicarious, he earned a Ph.D. in Physics at UT Austin, advised by Can Kilic. His graduate research focused on topics in Fundamental Theoretical Physics, bringing together themes from Particle Physics, Cosmology, quantum gravity and the use of tensor networks to model emergent properties of high-dimensional quantum systems. Before graduate school, he spent four exciting years at the Indian Institute of Technology Madras, earning an undergraduate degree in Electrical Engineering and making wonderful friends along the way. In his free time, Siva enjoys indulging his curiosity, reading, playing sports, and pondering the nature of human learning.
Prior to joining Vicarious, Mike served as an IT consultant, in both the Bay Area and Southern California, designing secure networks and systems for everything from startups to publicly traded companies. He appreciates fully redundant network designs, flexible systems architecture, and the simplicity underlying the complexities of Linux. In his free time, Mike enjoys attending concerts and live sporting events, weekend getaways, and bonsai.
Abhishek enjoys working on intelligent systems that can interact with and reason about their world. Abhishek studied Mechanical Engineering and Computer Science at Cornell University, where he worked in Professor Hadas Kress-Gazit’s Verifiable Robotics Group. Previously, Abhishek worked in the much more heavily regulated world of medical robots at Neocis Inc. In his free time he enjoys reading and watching Roger Federer highlights.
Ashish has previously worked in the fields of augmented reality and surgical robotics. Prior to that, he earned an M.S. in Computer Science and Engineering at Santa Clara University, where his thesis research on panoramic stereovision received a hardware grant from Nvidia. He also holds a B.Eng. in EE from the University of Mumbai. Outside the Vicarian sphere of life, Ashish spends his time volunteering with local non-profit charitable organizations, playing soccer, trying to play the guitar, and building his 1/10th scale self-driving car, which he intends to race for street cred someday.
Lalin Theverapperuma, PhD
Lalin worked at Apple as the Signal processing/ ML Architect on the AirPods project. He started his graduate studies on neural correlates in the cerebellum during reach-to-grasp. He changed his thesis to Adaptive Digital Signal Processing and completed an industry-sponsored Ph.D. on the phenomenon of Entrainment in Adaptive Filters He was a leading technical member on countless wireless, audio, and DSP products for Starkey Labs, Bosch, and Intel before joining Apple in 2011, where he designed high-quality audio algorithms for iPhone 4 thru 6. He holds many patents in the field of Signal Processing and ML. He is passionate about computer vision and artificial general intelligence (AGI).
Nicholas previously worked at the Education Innovation Laboratory, where he analyzed data to determine the effects of education programs on student test scores. He earned his BA in Computer Science at Harvard with a minor in Economics. Previously, he interned at GiveWell and Chase bank. In his free time he enjoys rock climbing, hiking and running.
Before joining Vicarious, Elton received his M.S in Robotics from Northwestern University and B.S in Bioengineering from University of California, San Diego. For his Master’s project thesis, he integrated hardware and software components to build a four wheeled mecanum mobile robot that can be controlled with ROS through a Linux computer. During his free time, he enjoys cooking, playing video games, and keeping up with the latest TV shows.