astronomy

Vasant Honavar

Vasant Honavar is a astronomy topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Vasant Honavar rather than just read about it. In short: Vasant G. Honavar is an Indian-American computer scientist, and artificial intelligence, machine learning, big data, data science, causal inference, knowledge representation, bioinformatics and health informatics researcher and professor.

Key takeaways

Vasant Honavar belongs to astronomy; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Vasant Honavar to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Vasant Honavar from memory before moving on to harder problems.

Reference excerpt

Vasant G. Honavar is an Indian-American computer scientist, and artificial intelligence, machine learning, big data, data science, causal inference, knowledge representation, bioinformatics and health informatics researcher and professor.

Early life and education Vasant Honavar was born at Pune, India to Bhavani G. and Gajanan N. Honavar. He received his early education at the Vidya Vardhaka Sangha High School and M.E.S. College in Bangalore, India. He received a B.E. in Electronics & Communications Engineering from the B.M.S. College of Engineering in Bangalore, India in 1982, when it was affiliated with Bangalore University, an M.S. in electrical and computer engineering in 1984 from Drexel University, and an M.S. in computer science in 1989, and a Ph.D. in 1990, respectively, from the University of Wisconsin–Madison, where he studied Artificial Intelligence and worked with Leonard Uhr.

Career Honavar is on the faculty of Informatics and Intelligent Systems Department in the Penn State College of Information Sciences and Technology at Pennsylvania State University where he currently holds the Dorothy Foehr Huck and J. Lloyd Huck Chair in Biomedical Data Sciences and Artificial Intelligence and previously held the Edward Frymoyer Endowed Chair in Information Sciences and Technology. He serves on the faculties of the graduate programs in Computer Science, Informatics, Bioinformatics and Genomics, Neuroscience, Operations Research, Public Health Sciences, and of undergraduate programs in Data Science and Artificial Intelligence methods and applications. Honavar serves as the director of the Artificial Intelligence Research Laboratory, Director of Strategic Initiatives for the Institute for Computational and Data Sciences and the director of the Center for Artificial Intelligence Foundations and Scientific Applications at Pennsylvania State University. Honavar served on the Leadership Team of the Northeast Big Data Innovation Hub. Honavar served on the Computing Research Association's Computing Community Consortium Council during 2014-2017, where he chaired the task force on Convergence of Data and Computing, and was a member of the task force on Artificial Intelligence. Honavar was the first Sudha Murty Distinguished Visiting Chair of Neurocomputing and Data Science by the Indian Institute of Science, Bangalore, India. Honavar was named a Distinguished Member of the Association for Computing Machinery for "outstanding scientific contributions to computing"; and elected a Fellow of the American Association for the Advancement of Science for his "distinguished research contributions and leadership in data science". As a Program Director in the Information Integration and Informatics program in the Information and Intelligent Systems Division of the Computer and Information Science and Engineering Directorate of the US National Science Foundation during 2010-13, Honavar led the Big Data Program. Honavar was a professor of computer science at Iowa State University where he led the Artificial Intelligence Research Laboratory which he founded in 1990 and was instrumental in establishing an interdepartmental graduate program in Bioinformatics and Computational Biology (and served as its Chair during 2003–2005). Honavar has held visiting professorships at Carnegie Mellon University, the University of Wisconsin–Madison, and at the Indian Institute of Science.

Research Honavar's research has contributed to advances in artificial intelligence, machine learning, causal inference, knowledge representation, neural networks, semantic web, big data analytics, and bioinformatics and computational biology. He was a program chair of the Association for the Advancement of Artificial Intelligence(AAAI)'s 36th Conference on Artificial Intelligence. He has published over 300 research articles, including many highly cited ones, as well as several books on these topics. His recent work has focused on federated machine learning algorithms for constructing predictive models from distributed data and linked open data, learning predictive models from high dimensional longitudinal data, reasoning with federated knowledge bases, detecting algorithmic bias, big data analytics, analysis and prediction of protein-protein, protein-RNA, and protein-DNA interfaces and interactions, social network analytics, health informatics, secrecy-preserving query answering, representing and reasoning about preferences, and causal inference from complex, e.g., relational, data, large language models, diffusion models, and meta analysis. Honavar has been active in fostering national and international scientific collaborations in Artificial Intelligence, Data Sciences, and their applications in addressing national, international, and societal priorities in accelerating science, improving health, transforming agriculture through partnerships that bring together academia, non-profits, and industry. He is also active in making the science policy case for major national research initiatives such as AI for accelerating science and AI for combating the epidemic of diseases of despair.

Honors National Science Foundation Director's Award for Superior Accomplishment, 2013 National Science Foundation Director's Award for Collaborative Integration, 2012 Margaret Ellen White Graduate Faculty Award, Iowa State University, 2011 Outstanding Career Achievement in Research Award, College of Liberal Arts and Sciences, Iowa State University, 2008 Regents Award for Faculty Excellence, Iowa Board of Regents, 2007 Edward Frymoyer Endowed Chair in Information Sciences and Technology, Penn State College of Information Sciences and Technology, Pennsylvania State University, 2013 Senior Faculty Research Excellence Award, Penn State College of Information Sciences and Technology, Pennsylvania State University, 2016 125 People of Impact, Department of Electrical and Computer Engineering, University of Wisconsin-Madison, 2016 Sudha Murty Distinguished (Visiting) Chair of Neurocomputing and Data Science, Indian Institute of Science, 2016-2021 ACM Distinguished Member, 2018 AAAS Fellow American Association for the Advancement of Science, 2018 EAI Fellow European Alliance for Innovation, 2019 Dorothy Foehr Huck and J. Lloyd Huck Chair in Biomedical Data Sciences and Artificial Intelligence, Pennsylvania State University, 2021

References

External links Personal home page List of publications from Penn State's profile List of publications on Google Scholar

Worked examples

Example 1 — a first encounter with Vasant Honavar

Start with the simplest possible case. Write down what Vasant Honavar claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Vasant Honavar before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Vasant Honavar ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Vasant Honavar

In research: Vasant Honavar appears in astronomy research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Vasant Honavar in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Vasant Honavar is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1960 births, American bioinformaticians, American cognitive scientists, so understanding it makes those chapters shorter.
In everyday life: Look for Vasant Honavar outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Vasant Honavar in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Vasant Honavar means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Vasant Honavar out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Vasant Honavar in simple terms?

Why does Vasant Honavar matter?

Because it connects several astronomy ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Vasant Honavar?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Vasant Honavar.