Amit Sheth
Highlights [Oct 2017]
* As a researcher: With an H-index of 112, Prof. Sheth is was the top 100 computer scientists and engineers in the world (68th in the US) in 2018, placing him among a small handful of top researchers in the US in his areas of AI and Big Data. The majority of his $30+million in research funds has come from the most competitive and prestigious sources: NIH and NSF. This level of funding places him among the top-funded academic leaders worldwide.
* As an educator: Prof. Sheth has fostered exceptional careers for his 30Ph.D., about the same number of MS Thesis advisees, and >15 postdocs— they have typically become tenured/tenure-track faculty at Carnegie R1 universities, or begin their career as researchers/scientists in top companies where they compete successfully with graduates from top 10/20 schools, or (a few) launch start-ups as successful entrepreneurs. During the 10 years starting his move to Wright State University, his research grants funded 1,036 months of GRA (which translates to 148 quarters (until 2012) and 148 semesters of GRA support. He has led major initiatives at key times to start new academic programs.
* As an entrepreneur, leader, and administrator: He has founded three startups by licensing his university research-derived technology, and has cofounded/advised two additional companies. He has consistently recruited high-quality and exceptionally productive team members when he was a manager in industry, an entrepreneur, and in academia (especially as an Ohio Eminent Scholar with faculty lines and as the director of a lab, a center and an institute. He has executive experience as the CEO/President, Founder, or Board Member of technology companies, with his roles spanning all aspects of organization building, including investment raising, finance, marketing and sales, recruiting, and technology development. His startups have received millions in investment and have invested many millions more in local job creation/payroll where his companies have operated.
More info at:
http://aiisc.ai/amit
http://www.linkedin.com/in/amitsheth
https://scholar.google.com/citations?hl=en&user=2T3H4ekAAAAJ
http://www.slideshare.net/apsheth/
Twitter: http://twitter.com/#!/amit_p
Address: Search for "AIISC" on Google Map
* As a researcher: With an H-index of 112, Prof. Sheth is was the top 100 computer scientists and engineers in the world (68th in the US) in 2018, placing him among a small handful of top researchers in the US in his areas of AI and Big Data. The majority of his $30+million in research funds has come from the most competitive and prestigious sources: NIH and NSF. This level of funding places him among the top-funded academic leaders worldwide.
* As an educator: Prof. Sheth has fostered exceptional careers for his 30Ph.D., about the same number of MS Thesis advisees, and >15 postdocs— they have typically become tenured/tenure-track faculty at Carnegie R1 universities, or begin their career as researchers/scientists in top companies where they compete successfully with graduates from top 10/20 schools, or (a few) launch start-ups as successful entrepreneurs. During the 10 years starting his move to Wright State University, his research grants funded 1,036 months of GRA (which translates to 148 quarters (until 2012) and 148 semesters of GRA support. He has led major initiatives at key times to start new academic programs.
* As an entrepreneur, leader, and administrator: He has founded three startups by licensing his university research-derived technology, and has cofounded/advised two additional companies. He has consistently recruited high-quality and exceptionally productive team members when he was a manager in industry, an entrepreneur, and in academia (especially as an Ohio Eminent Scholar with faculty lines and as the director of a lab, a center and an institute. He has executive experience as the CEO/President, Founder, or Board Member of technology companies, with his roles spanning all aspects of organization building, including investment raising, finance, marketing and sales, recruiting, and technology development. His startups have received millions in investment and have invested many millions more in local job creation/payroll where his companies have operated.
More info at:
http://aiisc.ai/amit
http://www.linkedin.com/in/amitsheth
https://scholar.google.com/citations?hl=en&user=2T3H4ekAAAAJ
http://www.slideshare.net/apsheth/
Twitter: http://twitter.com/#!/amit_p
Address: Search for "AIISC" on Google Map
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Talks by Amit Sheth
contexts: the US, European Union and India. For example, he is involved in the CityPulse project
that aims to provides innovative smart city applications by adopting an integrated approach to the
Internet of Things and the Internet of People. Prof. Sheth is the Executive Director of the Ohio
Center of Excellence in Knowledge-enabled Computing at Wright State University, which has a very
high global research impact, and he is currently on a six week visit to the University of Otago. Prof.
Sheth is involved in multidisciplinary research with real-world impacts across a range of fields
including: healthcare and life sciences; defence and intelligence, manufacturing science; and,
human, social and economic development. In addi5on to his academic career, Prof. Sheth has
founded and managed three start-up companies and held senior positions in the research and
development teams at Bellcore, Unisys, and Honeywell. You can read more about Professor Sheth’s
work at hSp://knoesis.wright.edu/amit/
I will review: 1) understanding and analysis of informal text, esp. microblogs (e.g., issues of cultural entity extraction and role of semantic/background knowledge enhanced techniques), and 2) how we built Twitris, a comprehensive social media analytics (social intelligence) platform.
I will describe the analysis capabilities along three dimensions: spatio-temporal-thematic, people-content-network, and sentiment-emption-intent. I will couple technical insights with identification of computational techniques and real-world examples using live demos of Twitris (http://twitris2.knoesis.org).
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Opening talk at Singapore Symposium on Sentiment Analysis (S3A), February 6, 2015, Singapore. http://s3a.sentic.net/#s3a2015
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is human health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information, etc.). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will forward the concept of Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If I am an asthma patient, for all the data relevant to me with the four V-challenges, what I care about is simply, “How is my current health, and what is the risk of having an asthma attack in my personal situation, especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city. I will present examples from a couple of these.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. Lastly, for Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships and uses them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response and smart city. I will present examples from a couple of these.
At Kno.e.sis, we have collaborations with clinicians in growing number of specializations (Cardiovascular, Pulmonology, Gastroenterology) to study personalized health decision making that involve the use of real-world patient data, deep background knowledge and well targeted clinical applications. For example:
For a patient discharged from hospital with Acute Decompensated Heart Failure, can we compute post hospital discharge risk factor to reduce 30-day readmissions?
For children with Asthma, can we predict an impending attack to enable actions that prevent an attack reducing the need for post-attack symptomatic relief?
For Parkinson’s Disease, can we characterize the progression to adjust medication and therapeutic changes?
The above provides the context for a research agenda around what I call Smart Data, which (a) provides value from harnessing the challenges posed by volume, velocity, variety and veracity of Big Data, in-turn providing actionable information and improve decision making, and/or (b) is focused on the actionable value achieved by human involvement in data creation, processing and consumption phases for improving the Human experience. In describing Smart Data approach to above heath applications, I will cover the following technical capabilities that adds semantics to enhance or complement traditional NLP and ML centric solutions:
* Semantic Sensor Web- including semantic computation infrastructure, ability to semi-automatically create domain specific background knowledge (ontology) from unstructured data (e.g., EMR), and automatically do semantic annotation of multimodal and multisensory data
* Semantic perception – convert low level signals into higher level abstractions using IntellegO framework that utilizes domain knowledge and hybrid abductive/deductive reasoning
* Intelligence at Edge - perform scalable and efficient semantic computation on resource constrained devices
For achieving energy sustainability, Smart Grids are known to transform the way we generate, distribute, and consume power. Unprecedented amount of data is being collected from smart meters, smart devices, and sensors all throughout the power grid. I will discuss the central question of deriving Value from the entire smart grid data deluge by discussing novel algorithms and techniques such as Semantic Perception for dealing with Velocity, use of ontologies and vocabularies for dealing with Variety, and Continuous Semantics for dealing with Velocity. I will discuss scenarios that exemplify the process of deriving Value from Big Data in the context of Smart Grid.
"Amit Sheth, "Transforming Big Data into Smart Data: Deriving Value via harnessing Volume, Variety and Velocity using semantics and Semantic Web," keynote at the 21st Italian Symposium on Advanced Database Systems,
June 30 - July 03 2013, Roccella Jonica, Italy. Also invited talks given in Universities in Spain and Italy in June 2013.
Highlight: How to harness Smart Data that is actionable, from the Voluminous Big Data with Velocity and Variety-- using Semantics and the Semantic Web core to bring Human-Centric Computing in practice.
Abstract from: http://www.sebd2013.unirc.it/invitedSpeakers.html
Big Data has captured much interest in research and industry, with anticipation of better decisions, efficient organizations, and many new jobs. Much of the emphasis is on technology that handles volume, including storage and computational techniques to support analysis (Hadoop, NoSQL, MapReduce, etc), and the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity. However, the most important feature of data, the raison d'etre, is neither volume, variety, velocity, nor veracity -- but value. In this talk, I will emphasize the significance of Smart Data, and discuss how it is can be realized by extracting value from Big Data. To accomplish this task requires organized ways to harness and overcome the original four V-challenges; and while the technologies currently touted may provide some necessary infrastructure-- they are far from sufficient. In particular, we will need to utilize metadata, employ semantics and intelligent processing, and leverage some of the extensive work that predates Big Data. For Volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration, and discuss how this can not simply be wished away using NoSQL. Lastly, for Velocity, I will discuss somewhat more recent work on Continuous Semantics , which seeks to use dynamically created models of new objects, concepts, and relationships and uses them to better understand new cues in the data that capture rapidly evolving events and situations.
Additional background at: http://knoesis.org/vision > SmartData and "Semantics-empowered Approaches to Big Data Processing for Physical-Cyber-Social Applications," http://www.knoesis.org/library/resource.php?id=1889 ."