Jonathan Siddharth
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CEO & Co-founder
California, United States
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Board MemberQuoraJan 2022 - Dec 2022 (1 year)Quora is the place to share knowledge and better understand the world.- Mentor, Admissions, InvestorStartXMar 2018 - Current (8 years 6 months)StartX runs the world's top startup accelerator and founder community for Stanford‑affiliated entrepreneurs. StartX is a 501(c)(3) Stanford-affiliated nonprofit in Silicon Valley that runs one of the world’s top startup accelerator programs. Its mission is to advance the development of the best entrepreneurs through experiential education and peer learning. Since launching in 2010, StartX has supported more than 400 companies and 900 entrepreneurs, from early to pre-IPO stage, working across a wide spectrum of industries. To date, StartX has supported over 700 companies including Snap, Lime, Branch Metrics, Life 360, Periscope, Kodiak (NYSE:KOD), Eargo (Nasdaq: EAR). StartX companies have raised over $2.2B with a $5.1M+ average per compan
- Founder & CEOturingMar 2018 - Current (8 years 6 months)Our mission is to accelerate AGI advancement and its deployment in the world. The bottleneck for AGI progress used to be compute and data. Now, it’s human intelligence. We use humans to make AI smarter: Turing trains AGI by working with all the major AI foundation model companies to improve model performance and fine-tuning for coding, data analysis, advanced reasoning, problem solving, multi-modality, function calling, agentic workflows, STEM and advanced knowledge work requiring industry domain expertise. We use AI to make humans smarter: Turing deploys AGI by working with Fortune 500 enterprises and high-growth startups to build custom models fine tuned on proprietary data, co-pilots, agents and custom AI applications that create str
- Entrepreneur In ResidenceFoundation CapitalNov 2017 - Mar 2018 (5 months)
- Entrepreneur In ResidenceFoundation CapitalNov 2017 - Mar 2018 (5 months)
- Senior Vice President Of TechnologyRevcontentJan 2017 - Dec 2017 (1 year)Joined Revcontent, following the acquisition of my company, Rover where I served as CEO. Rover which has offices in Silicon Valley and Mumbai is now a wholly owned subsidiary of Revcontent. More details on the acquisition here: https://techcrunch.com/2017/02/23/revcontent-acquires-rover/ We use state of the art Machine Learning and Natural Language Processing to power the Recommendation Engine that serves over 250 billion content recommendations/month across the top publishers in the US. To, *Help users discover content they love through Deep Personalization (through rich Document and User modeling) *Help publishers drive increased eCPM/revenues (Machine Learned CTR Prediction) *Help publishers and content marketers discover their most
- CEO & Co-founderRover AppJan 2008 - Dec 2016 (9 years)Rover uses Machine Learning for Deep Personalization to help people discover content they love. Formerly known as Flipora/Infoaxe. Rover has a Consumer business and an Enterprise business. Rover Consumer: *Apps for iPhone, Android and Web with over 40 Million registered users. *Featured by Apple under Best Apps for iPhone *Featured on TechCrunch, Recode, VentureBeat, Forbes, Huffington Post etc. Rover Enterprise: *Offers Personalized Content Recommendations to users via widgets on Publisher sites *Helps Publishers with monetization and traffic acquisition *Via Revcontent, reaches 97% of US households (per Quantcast) Technology: • Large Scale Modeling of a user's interests through content they engage with • Innovations in dimensi
- PScientist, Ranking & Search RelevancePowersetJul 2007 - Jan 2008 (7 months)Powerset was a Natural Language Search Engine where the goal was to read and understand every sentence on the Web to answer queries posed by users in natural language. This required deep syntactic parsing of documents & queries, followed by semantic analysis and markup leveraging resources like WordNet, FreeBase etc. At Powerset, I co-designed the Ranking Algorithms for the Natural Language Search engine that outperformed Google, Yahoo! and Live Search on Wikipedia on the Discounted Cumulative Gain metric. My work involved coming up with and combining various ranking features that were the result of deep syntactic and semantic analysis of the content of the documents, as well as keyword based and Web graph derived features. I was also res
- InternYahooJun 2006 - Sep 2006 (4 months)At Yahoo!'s Machine Learned Search Ranking group I worked on Automatic Search Regionalization where my changes resulted in one of Yahoo!'s biggest search relevance gains that year for a single feature. This is the problem of deciding what subset of the query stream needs regional/local content boosting eg. "buying digital camera" or "driver's license", so that the ranking function scores regional/local content more heavily. On the flip side, a query like "neural networks" needs no regional/local content boosting since we want to surface the most globally relevant results. I built a Query Classifier for regional intent that used cues from the user query stream and clickstream data and signals from. I then retrained the search ranking alg
Education
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- Bachelor’s Degree, Computer ScienceAnna UniversityGraduated at the top of my class (1st Rank) in the Computer Science Department at SVCE Published my first peer reviewed IEEE paper on Artificial Neural Networks for Self Driving Cars as a Sophomore. Presented my work at the IEEE Conference on A.I in Singapore. Merit Awards for 1st Rank in Computer Science (semesters 6,8 and overall) CAT Prize- 1st Rank in Continuous Assessment tests (semesters 5,6,7,8) Lucas TVS Merit Award
- Masters with Distinction in Research, Computer ScienceStanford UniversityAwarded the Christopher Stephenson Memorial Award for Best Masters Research in the Computer Science Department at Stanford University Research Assistant at the Stanford InfoLab Artificial Intelligence Track Collaborated on a Research Project between the Stanford Artificial Intelligence Lab (Rion Snow & Andrew Ng) with Powerset
- Bachelor’s Degree, Computer ScienceAnna University ChennaiGraduated at the top of my class (1st Rank) in the Computer Science Department at SVCE Published my first peer reviewed IEEE paper on Artificial Neural Networks for Self Driving Cars as a Sophomore. Presented my work at the IEEE Conference on A.I in Singapore. Merit Awards for 1st Rank in Computer Science (semesters 6,8 and overall) CAT Prize- 1st Rank in Continuous Assessment tests (semesters 5,6,7,8) Lucas TVS Merit Award
Publications
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- ASpotSigs: Robust and Efficient Near Duplicate Detection in. Large Web Collections.ACM SIGIRMay 2008Motivated by our work with political scientists who need to manually analyze large Web archives of news sites, we present SpotSigs, a new algorithm for extracting and matching signatures for near duplicate detection in large Web crawls. Our spot signatures are designed to favor naturallanguage portions of Web pages over advertisements and navigational bars. The contributions of SpotSigs are twofold: 1) by combining stopword antecedents with short chains of adjacent content terms, we create robust document signatures with a natural ability to filter out noisy components of Web pages that would otherwise distract pure n-gram-based approaches such as Shingling; 2) we provide an exact and efficient, self- tuning matching algorithm that exploits
- MSpotSigs: Near Duplicate Detection in Web Page CollectionsMasters Thesis Best Thesis Award in Computer Science at Stanford UniversityJun 2007Motivated by our work with political scientists we present an algorithm that detects near-duplicate Web pages. These scientists analyze Web archives of news sites. The archives were collected with crawlers and contain a large number of pages that look very different because the frame around their core content differs. However, the news stories in the pages are nearly identical. The close proximity of unrelated items on the pages makes the detection of content overlap difficult. Our SpotSigs algorithm generates signatures that are spread across each document. Places for these signatures are determined by the placement of common words, like 'is' and 'the' in the documents. We can vary our method of computing the signatures. Using hash collisi
- MSQUINT - SVM for Identification of Relevant Sections in Web Pages for Web SearchMachine Learning Course Project CSAug 2006We propose SQUINT – an SVM based approach to identify sections (paragraphs) of a Web page that are relevant to a query in Web Search. SQUINT works by generating features from the top most relevant results returned in response to a query from a Web Search Engine, to learn more about the query and its context. It then uses an SVM with a linear kernel to score sections of a Web page based on these features. One application of SQUINT we can think of is some form of highlighting of the sections to indicate which section is most likely to be interesting to the user given his query. If the result page has a lot of (possibly diverse) content sections, this could be very useful to the user in terms of reducing his time to get the information he need
- SContext Driven Ranking for Information RetrievalStanford InfoLab Independent Research under Prof Hector GarciaMolina Dr Andreas PaepckeJan 2006Improving search relevance by obtaining more ‘context’ (contextually related words) automatically for the search query, weighting it appropriately and using it to improve search relevance on the Discounted Cumulative Gain metric. Eg. For the search query "photography", contextually related words would be "pictures", "camera","film" etc. The presence of these contextually related words in a document is scored positively for relevance to the query.
- PKnowledge discovery in Clinical Databases with Neural Network Evidence CombinationProceedings of International Conference on Intelligent Sensing and Information ProcessingJan 2005Diagnosis of diseases and disorders afflicting mankind has always been a candidate for automation. Numerous attempts made at classification of symptoms and characteristic features of disorders have rarely used neural networks due to the inherent difficulty of training with sufficient data. But, the inherent robustness of neural networks and their adaptability in varying relationships of input and output justifies their use in clinical databases. To overcome the problem of training under conditions of insufficient and incomplete data, we propose to use three different neural network classifiers, each using a different learning function. Consequent combination of their beliefs by Dempster-Shafer evidence combination overcomes weaknesses exhib
- NA System for Power-aware Agent-based Intrusion Detection (SPAID) in Wireless Ad hoc NetworksNetworking and Mobile Computing Springer Berlin Heidelberg APAJan 2005In this paper, we propose a distributed hierarchical intrusion detection system for ad hoc wireless networks, based on a power level metric for potential ad hoc hosts, which is used to determine the duration for which a particular node can support a network monitoring node. We propose an iterative power-aware power-optimal solution to identifying nodes for distributed agent-based intrusion detection. The advantages that our approach entails are several, not least of which is the inherent flexibility SPAID provides. We consider minimally mobile networks in this paper, and considerations apt for mobile ad hoc networks and issues related to dynamism are earmarked for future research. Comprehensive simulations were carried out to analyze and cl
- SA Swarm Intelligence based Task Allocation Algorithm (SITA)Senior Thesis Research Report Best Paper Award at Abacus National level Tech Symposium at Anna UniversityJan 2005This paper proposes the use of a Swarm Intelligence based approach (SITA) for Task Allocation and scheduling in a dynamically reconfigurable environment such as the computational Grid. SITA is a massively distributed task allocation algorithm that draws inspiration from the hugely efficient foraging and food hunting paradigm of ants. We employ the ant colony optimization (ACO), a population based search technique for the solution of combinatorial optimization problems for resource discovery in the Grid. Making use of evaporating pheromone trails, the algorithm adapts effortlessly to transient network conditions like congestion, node failure, link failure etc. The use of the distributed agents (ants) working in parallel and independent of ea
- ISentient Autonomous Vehicle using Advanced Neural Net TechnologyIEEE International Conference on Cybernetics and Intelligent Systems CISDec 2004SAVANT uses a multi-layer feed-forward neural network with back propagation learning to guide a mobile agent through a hostile and unfamiliar domain after being trained by a human user with domain expertise. The system learns to negotiate turns and implement lane-changing maneuvers to avoid or overtake obstacles.
- PA Minimal Fragmentation Algorithm for Task Allocation in Mesh-Connected MulticomputersProceedings of the IEEE International Conference on Advances in Intelligent Systems Theory and Applications AISTAJan 2004Efficient allocation of processors to incoming tasks in tightly coupled systems is crucial for achieving high performance. A good allocation algorithm should identify available processors with minimum overhead. In addition, it should be submesh recognition complete and should minimize fragmentation as far as possible. In this paper, we propose an efficient task allocation mechanism called the Minimal Fragmentation Algorithm (MFA). By weighting the available nodes on the basis of their adjacency to existing busy submeshes or the mesh boundary, we identify nodes that, if chosen as the base for task allocation, would result in minimal external fragmentation. An analysis of the complexity of the proposed algorithm reveals that our scheme provid