Semantic Machines is made up of a team of proven researchers, engineers and entrepreneurs with extensive track records in AI development. Our team has unique experience, building core AI technology for Siri and Google Now as well as leading award-winning academic research. Collectively, we have made pioneering contributions to natural language processing, speech recognition, speech synthesis, deep learning, semantic understanding, machine learning and linguistics.
Co-founder and CEO
Dan is a serial technology entrepreneur with a focus on building organizations that excel at research-intensive product innovation. His first startup, Voice Signal Technologies, was the industry leader in the development of proprietary speech recognition and synthesis interfaces for mobile devices including the revolutionary iPhone. After Voice Signal was acquired by Nuance Communications [NASDAQ:NUAN] in 2007 for $300M, Dan founded Shaser BioScience, a developer of miniaturized laser technology for dermatological applications. Shaser BioScience was acquired by Spectrum Brands [NYSE:SPB] in 2012 in a deal valued at $100M. Dan received his B.S. in Biology from Trinity College, and is an inventor on more than 90 U.S. and foreign patents.
Co-founder and CFO
Damon has been a startup CFO for nearly 20 years. Prior to co-founding Semantic Machines, Damon was CFO of TeraDiode, Inc., Shaser BioScience Inc., and NeoSaej Corp. Before that, Damon was VP Finance and later CFO of Voice Signal, where he led the complex and successful acquisition by Nuance Communications, Inc. Damon began his career at PriceWaterhouseCoopers. He received his B.S. in Accounting from Southern New Hampshire University and is a C.P.A.
Co-founder and CTO
Larry has more than 30 years of experience at the forefront of speech and language R&D. He was the Vice President of Research at Dragon Systems, the premier innovator in the field of speech recognition for personal computers. Under his leadership, Dragon released Dragon NaturallySpeaking, the first continuous speech dictation product. He went on to serve as Vice President of Core Technology at Voice Signal Technologies, a successful startup that developed speech recognition and synthesis for mobile phones. After the acquisition of Voice Signal by Nuance Communications, Larry became Nuance’s Vice President of Research for mobile devices. Most recently, he was Chief Speech Scientist for Siri at Apple. He is the author of more than 25 patents and many influential scientific papers. Larry has a BA from Swarthmore College, an MA from Columbia University, and a PhD in applied mathematics from MIT.
Co-founder, Chief Scientist and VP of Research
Dan Klein is co-founder, Chief Scientist and VP of Research at Semantic Machines and a recognized leader in the field of natural language processing. Dan is a full professor of computer science at UC Berkeley and was previously Chief Scientist at Adap.tv. Dan has published over 100 papers on a wide range of NLP and machine learning topics, including multiple award-winning results. He is a Microsoft Faculty Fellow, a Sloan Fellow, and a Marshall Scholar. He has received many awards including the ACM’s Grace Murray Hopper award (given to a single CS researcher each year), the NSF CAREER award, and the UC Berkeley Distinguished Teaching Award. Dan holds a PhD in computer science from Stanford University and a Masters in linguistics from Oxford.
Percy Liang is Lead Scientist at Semantic Machines and Assistant Professor of Computer Science at Stanford University. His research spans theoretical machine learning to practical natural language processing; topics include semantic parsing, question answering, machine translation, online learning, method of moments, approximate inference, Bayesian modeling, and deep learning. He has over 60 publications appearing in top venues and has received several paper awards. Percy received a B.S. in computer science from MIT and a Ph.D. from UC Berkeley in 2011. After graduation, he spent one year at Google, where he was one of the founding members of the semantic parsing team. His awards include an IJCAI Computers and Thought Award (2016, given to a single AI researcher every two years), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).
Co-founder, VP of Technology
For the past several years Jordan has been an independent technology consultant for companies including Apple, Cisco, Audience, Telenav and RIM. Prior to consulting, Jordan was a Senior Scientist at SRI, acting as the Principal Investigator for GALE, a large DARPA program in speech recognition, language translation, and information delivery. Jordan was the CTO of Voice Signal, a leader in speech recognition and synthesis technology acquired by Nuance in 2007. He has been on the research technical staff of the Institute for Defense Analyses, IBM, and the National Security Agency. He has served on the Technical Advisory Board of Sensory, IDIAP (the Swiss research agency), and the Center of Excellence for Human Language Technology at Johns Hopkins. Jordan has a Master’s degree in Electrical Engineering from the University of Illinois and a PhD in Linguistics from the University of Connecticut. He has co-authored more than 15 patents, and published dozens of papers and popular descriptions of technical issues.
Co-founder, Research Scientist
David received his PhD in Computer Science from UC Berkeley advised by Dan Klein. He is the recipient of the 2012 Google PhD Fellowship in Natural Language Processing, an NSF graduate research fellowship, the 2011 EECS Outstanding Graduate Student Instructor award, and a distinguished paper at EMNLP 2012. He has authored 15 publications at top conferences and has built and released numerous software systems, including the fastest high-accuracy constituency parser in the world, state-of-the-art parsers for 10 languages, the Breeze scientific computing library, and the award-winning Overmind StarCraft agent. He has a B.S. and M.S. from Stanford University, both in Symbolic Systems.
VP of Engineering
Jesse Rusak is VP of Engineering at Semantic Machines, leading development of iOS applications, conversational data acquisition, and cloud infrastructure for distributed AI agent technology. Jesse has expertise across the gamut of software development, from building delightful user interfaces to optimizing the last few microseconds out of low-level code. Jesse started in the games industry, building UI toolkits and real-time machine learning algorithms for the Wii. He left to work as a software contractor, where he built websites of every sort, Mac apps for scientific research, and a cat-entertaining robot (it used lasers). Over the last five years, Jesse has led teams building world-class iPhone and iPad apps for flight pricing, photo manipulation, event planning, news reading, and many more, which are routinely featured by Apple and receive rave reviews.
Steven Wegmann is a Research Scientist at Semantic Machines leading development of proprietary speech recognition technology. Steven has worked in academic and industrial research laboratories on problems in speech processing for more than two decades. Steven started his career at Dragon Systems working on large vocabulary continuous speech recognition. He then joined Voice Signal, and after its acquisition by Nuance, became a member of its mobile device speech recognition research organization. He later joined the Speech and Language Technology Group within Cisco System's Emerging Technologies. Prior to joining Semantic Machines he was the Director of Speech Research at the International Computer Science Institute (ICSI). Earlier in his career, he was a mathematician who specialized in algebraic topology. He obtained his PhD in mathematics at the University of Warwick and was a Marshall Scholar. Steven is an author of many influential papers in the field of speech and language technology.
Adam received a B.S. in Computer Science and Physics from the University of British Columbia. As an undergraduate, he published papers in both planetary astronomy and natural language processing. He received his PhD in 2012 from UC Berkeley in Computer Science, specializing in machine learning and natural language processing. His studies were funded by fellowships from Google, UC Berkeley, and the Natural Sciences and Engineering Research Council of Canada (NSERC). His work on optimal parsing algorithms won the Best Paper Award from the Association for Computational Linguistics in 2009. After graduation, he worked as a research scientist at Google, focusing on natural language understanding and question answering. Outside of academics, Adam remains passionate about language and speaks 7 languages at varying degrees of fluency.
Michael Newman is a Research Scientist at Semantic Machines working on novel speech recognition technology. He started research in speech recognition over 20 years ago as an early member of Dragon Systems working on the first version of Dragon NaturallySpeaking. He then joined Voice Signal as a senior researcher and later led a research team at Nuance. Michael has worked on a broad range of speech technology projects from very large research systems (DARPA SwitchBoard and Broadcast News evaluations) to commercial applications such as LVCSR, speaker identification, wake-up word recognition, and audio mining. He received a BA in Mathematics from Cambridge University, and a PhD in Theoretical Physics from Princeton, and held a two-year research fellowship at Harvard.
Taylor Berg-Kirkpatrick is a Research Scientist at Semantic Machines working on machine learning applications in natural language processing and speech synthesis. He recently completed his PhD in computer science at UC Berkeley. He worked with professor Dan Klein on using machine learning to understand structured human data, including language but also sources like music, document images, and other complex artifacts. Taylor completed his undergraduate degree in mathematics and computer science at Berkeley as well, where he won the departmental Dorothea Klumpke Roberts Prize in mathematics. As a graduate student, Taylor has received both the Qualcomm Innovation Fellowship and the National Science Foundation Graduate Research Fellowship.
Dan Povey is a Research Consultant at Semantic Machines working on speech recognition and machine learning. He completed his PhD at Cambridge University in 2003, and after spending just under ten years working for industry research labs (IBM Research and then Microsoft Research), joined Johns Hopkins University in 2012. His thesis work introduced several practical innovations for discriminative training of models for speech recognition, and made those techniques widely popular. At IBM Research he introduced feature-space discriminative training, which has become a common feature of state-of-the art systems. He also devised the Subspace Gaussian Mixture Model – a modeling technique which enhances the Gaussian Mixture Model framework by using subspace ideas similar to those used in speaker identification. At Microsoft Research and then at Johns Hopkins University, he has been creating a speech recognition toolkit "Kaldi", which aims to make state-of-the-art speech recognition techniques widely accessible.
David Talkin is a Research Consultant at Semantic Machines working on novel speech synthesis technology. He brings to bear knowledge of speech science and technology accumulated during his 45 years of research and development activity. He has been active in physiological models of voice production, psychoacoustics, voice transformation, face modeling, and speech analysis, coding, recognition and synthesis. His speech analysis tools have seen worldwide use in hundreds of laboratories. David has previously held research scientist positions at Google, Dragon Systems, Rhetorical Systems, Nuance and Entropic. He holds several speech-related patents, and has authored and co-authored several research papers and book chapters.
Jacob Andreas is a Research Scientist at Semantic Machines working at the intersection of semantic analysis, deep learning and conversational AI. He is a third-year PhD student in computer science at UC Berkeley. His research focuses on grounded models for natural language understanding; other interests include graph automata and general machine learning theory. Jacob received a BS from Columbia in 2012 and an MPhil from Cambridge in 2013, where he studied as a Churchill scholar and won the Computer Laboratory’s outstanding dissertation award. Jacob has also worked with Microsoft Live Labs, Google, and the Information Sciences Institute.
Charles received a BA in computer science and mathematics from UC Berkeley, where he won the departmental Dorothea Klumpke Prize for outstanding mathematics scholarship. He has published papers on algebraic combinatorics and artificial intelligence applied to malware detection. After graduating from Berkeley, he worked at Google as a software engineer, where he focused on social search, voice search, and natural language understanding.
Chuck Wooters is a Research Scientist at Semantic Machines. Chuck has worked in academic, industrial, and government research laboratories on problems in the field of human language technology for more than two decades. Chuck’s research has spanned several areas including: speech recognition, natural language understanding, dialog systems, speaker diarization, and speaker identification. He has a broad range of experience including the following institutions: the Siri team at Apple, the International Computer Science Institute (ICSI) in Berkeley, the Human Language Technology Center of Excellence (HLT/COE) at Johns Hopkins University, and BBN Technologies. Chuck received his PhD from UC Berkeley in an interdisciplinary program incorporating the departments of Computer Science, Linguistics, and Psychology. He also holds a BA and MA from UC Berkeley, both in Linguistics. Chuck has published more than 70 papers and has authored several patents within the field of human language technology.
David Burkett is a Research Scientist at Semantic Machines working on novel applications of structured prediction and machine learning for building conversational agents. He earned a B.S. in Computer Science and Psychology from the University of Chicago in 2004, and a PhD in computer science from UC Berkeley in 2012. His research has focused primarily on syntactic analysis, especially as it relates to machine translation, but ranges over a wide variety of topics in AI, including variational inference for structured prediction, heuristic search for optimal planning, and video game AI (despite his expertise as an NLP researcher, his most widely publicized project to date is the Berkeley Overmind StarCraft agent). He also has a wide range of professional experience, including work as a software engineer at PEAK6 Investments in Chicago building systems for automated stock trading, as a research intern at SRI and Google working on applications of research in machine translation and natural language processing, and as a data scientist at Twitter working on user behavior analytics, text analysis, and information retrieval.
Greg Durrett is a Research Scientist at Semantic Machines. He received a B.S. in Computer Science and Mathematics from MIT and a Ph.D. in Computer Science at UC Berkeley, where he was advised by Dan Klein. His research covers a range of topics in statistical natural language processing, including coreference resolution, entity linking, syntactic parsing, and document summarization. In particular, he is interested in structured machine learning approaches to these problems, especially joint models that integrate multiple approaches or multiple tasks. His paper on coreference resolution was a Best Paper finalist at EMNLP 2013. Greg has also been the recipient of an NSF Graduate Research Fellowship and the Facebook Fellowship in Natural Language Processing.
Jason is a Research Scientist at Semantic Machines. He received a Ph.D. in Computer Science from UC Berkeley in 2011, advised by Prof. Stuart Russell. While at Berkeley, he published papers in leading venues on diverse topics including bioinformatics, machine learning, robotics, search algorithms, and AI planning. He has improved machine translation quality at Google, hacked robots at Willow Garage, and lead an engineering team to build machine learning and NLP systems for personalized news ranking at Prismatic. Jason is a Siebel Scholar, the lead author and maintainer of several widely used Clojure libraries, and an onsite finalist in the Google Code Jam international programming competition.
Michael I. Jordan
Michael I. Jordan is a Research Consultant at Semantic Machines. He is currently the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.
Dan Jurafsky is a Research Consultant at Semantic Machines. Dan is currently Professor and Chair of Linguistics and Professor of Computer Science at Stanford University. Dan and his colleagues in the Stanford NLP group and Computational Linguistics program study natural language processing— including speech, dialogue, and Chinese NLP—as well as applications to the behavioral and social sciences. Dan is a past MacArthur Fellow and also writes and teaches about the language of food.
Manager, AI Teachers
Abby manages the human AI Teachers at Semantic Machines. Abby moved to Boston after graduating from Florida State University with a B.A. in English Literature and Psychology. Prior to joining Semantic Machines she worked at MIT as a librarian.
Prior to joining Semantic Machines, Joshua worked as a Software Engineer at TripAdvisor where he focused on optimizing service data processing as well as developing models to predict click-through rates based on profile activity. Josh has a BS in computer engineering from Iowa State University where he also worked as a research assistant developing mobile applications, games for a local startup, and presented at an ICSE workshop on distributed software development.
Linda Arsenault is a Linguistic Data Scientist with Semantic Machines, working on transcription management, annotation, and data modeling. She has a background in automatic speech recognition via research positions with EnglishCentral and Nuance's Dragon Research group, where she most recently worked on competitive benchmarking of ASR systems. Linda holds a M.S. in Library and Information Science from Simmons College and a B.A. in English from California Polytechnic State University, San Luis Obispo. She previously taught English as a Foreign Language in Hiroshima and Kurashiki, Japan, and speaks Japanese and French.
Theo Lanman is a Software Engineer at Semantic Machines, working across the technology stack. Over his career, Theo has engineered distributed systems at scale, worked on highly concurrent financial systems, and built tools and processes to coordinate the efforts of hundreds of software engineers. Theo holds a G.E.D. from the State of Maine.
John Bufe is a Software Engineer at Semantic Machines. Previously, he worked on the Watson project at IBM specializing in a range of fields including machine learning, NLP, conversational systems, and front end development of tools and consumer applications. He received his B.S. in Computer Science, B.S. in Mathematics, and M.S. in Applied Mathematics and Statistics from Georgetown University.
Alan received a B.S. with Highest Distinction in Mathematics from Duke University in 2011, where he was a Faculty Scholar, awarded to three undergraduates across the university for outstanding scholarship, and where he won the departmental Julia Dale prize for excellence in mathematics. He went on to receive a PhD in Computer Science from MIT in 2015, funded by a National Science Foundation Graduate Research Fellowship. Alan has published over a dozen papers spanning mathematics, computer science, and medicine, including a paper proving NP-hardness of classic Nintendo games such as Mario and Zelda. After graduation, he worked as a quantitative analyst at a hedge fund in Boston.