Vaneet Aggarwal

Last updated

Vaneet Aggarwal is a researcher and academic in the field of machine learning. He currently holds the position of Full Professor at Purdue University. [1] He leads the maChine Learning and quANtum computing (CLAN) research labs at Purdue. [2]

Aggarwal earned his B.Tech. degree in 2005 from Indian Institute of Technology, Kanpur, India. He obtained M.A. and Ph.D. degrees in 2007 and 2010, respectively, from Princeton University in Princeton, NJ, USA. His Ph.D. dissertation was supervised by Prof. Robert Calderbank. [3]

Aggarwal joined Purdue University in January 2015 and is currently a Full Professor. Prior to this, he was a researcher at AT&T Labs-Research, Florham Park, NJ (2010-2014). He was Adjunct Assistant Professor at Columbia University (EE, 2013-2014), VAJRA Chair Professor at IISc Bangalore (ECE, 2018-2019), [4] Adjunct Faculty at IIIT Delhi (CS, 2022-2023), and Visiting Professor at KAUST, Saudi Arabia (CS, 2022-2023).

His work has been recognized with numerous awards and honors. Notably, he was the recipient of Princeton University's prestigious Porter Ogden Jacobus Honorific Fellowship in 2009, which is the highest honor bestowed upon a graduate student at Princeton University. [5] Purdue University awarded him the Most Impactful Faculty Innovator Award in 2021. [6] Dr. Aggarwal's contributions to the academic community have been acknowledged with several prestigious awards, including the 2017 Jack Neubauer Memorial Award for the Best Systems Paper published in the IEEE Transactions on Vehicular Technology, [7] the 2018 IEEE Infocom Workshop Best Paper Award, [8] and the 2021 NeurIPS Workshop Best Paper Award. [9] [10]

His work "HADAR: Heat-Assisted Detection and Ranging" has appeared on the cover of NATURE, and has been covered in NATURE podcast episode and multiple news. [11] [12] [13] His work on understanding the natural language of DNA with foundation models [14] is mentioned in Axios. [15]

He was on the Editorial Board of the IEEE Transactions on Green Communications and Networking and the IEEE Transactions on Communications. He is currently serving on the Editorial Board of the IEEE/ACM Transactions on Networking [16] and is co-Editor-in-Chief of the ACM Journal on Transportation Systems [17]

Related Research Articles

The Association for Computing Machinery (ACM) is a US-based international learned society for computing. It was founded in 1947 and is the world's largest scientific and educational computing society. The ACM is a non-profit professional membership group, reporting nearly 110,000 student and professional members as of 2022. Its headquarters are in New York City.

<span class="mw-page-title-main">Conference on Neural Information Processing Systems</span> Machine learning and computational neuroscience conference

The Conference and Workshop on Neural Information Processing Systems is a machine learning and computational neuroscience conference held every December. The conference is currently a double-track meeting that includes invited talks as well as oral and poster presentations of refereed papers, followed by parallel-track workshops that up to 2013 were held at ski resorts.

John Chi-Shing Lui is a Hong Kong computer scientist. He was the chairman of the Department of Computer Science & Engineering in the Chinese University of Hong Kong. He received his Ph.D. in computer science from UCLA. When he was a Ph.D. student at UCLA, he spent a summer working in IBM's Thomas J. Watson Research Center. After his graduation, he joined the IBM Almaden Research Laboratory/San Jose Laboratory and participated in various research and development projects on file systems and parallel I/O architectures. He later joined the Department of Computer Science and Engineering at the Chinese University of Hong Kong. For the past several summers, he has been a visiting professor in computer science departments at UCLA, Columbia University, University of Maryland at College Park, Purdue University, University of Massachusetts Amherst and Universita' degli Studi di Torino in Italy.

<span class="mw-page-title-main">Rangasami L. Kashyap</span> Indian applied mathematician

Rangasami Lakshminarayan Kashyap was an Indian applied mathematician and a Professor of Electrical Engineering at Purdue University.

<span class="mw-page-title-main">Mung Chiang</span> Current president of Purdue University

Mung Chiang is a Chinese-American academic administrator, telecommunications engineer, and professor. He currently serves as the 13th President of Purdue University.

<span class="mw-page-title-main">John T. Riedl</span> American computer scientist

John Thomas Riedl was an American computer scientist and the McKnight Distinguished Professor at the University of Minnesota. His published works include highly influential research on the social web, recommendation systems, and collaborative systems.

Philip S. Yu is an American computer scientist and professor of information technology at the University of Illinois at Chicago. He is a prolific author, holds over 300 patents, and is known for his work in the field of data mining.

<span class="mw-page-title-main">Fei-Fei Li</span> American computer scientist (born 1976)

Fei-Fei Li is an American computer scientist, who was born in China and is known for establishing ImageNet, the dataset that enabled rapid advances in computer vision in the 2010s. She is the Sequoia Capital Professor of Computer Science at Stanford University and former board director at Twitter. Li is a Co-Director of the Stanford Institute for Human-Centered Artificial Intelligence, and a Co-Director of the Stanford Vision and Learning Lab. She served as the director of the Stanford Artificial Intelligence Laboratory (SAIL) from 2013 to 2018.

<span class="mw-page-title-main">Bedrich Benes</span> Computer scientist (born 1967)

Bedrich Benes is a computer scientist and a researcher in computer graphics.

Animashree (Anima) Anandkumar is the Bren Professor of Computing at California Institute of Technology. Previously, she was a director of Machine Learning research at NVIDIA. Her research considers tensor-algebraic methods, deep learning and non-convex problems.

Alon Orlitsky is an information theorist and the Qualcomm Professor for Information Theory and its Applications at University of California, San Diego. He received a BSc in Mathematics and Electrical Engineering from Ben Gurion University in 1981, and a PhD in Electrical Engineering from Stanford University in 1986. He was a member of Bell Labs from 1986 to 1996, and worked for D. E. Shaw from 1996 to 1997. He joined UCSD in 1997.

<span class="mw-page-title-main">Mauro Martino</span> Italian artist, designer and researcher

Mauro Martino is an Italian artist, designer and researcher. He is the founder and director of the Visual Artificial Intelligence Lab at IBM Research, and Professor of Practice at Northeastern University.

Mehdi Ashraphijuo (Medi Ash) is an Iranian-American mathematician, financial risk manager, academic and writer, residing in New York City. Ash is currently a vice president and executive director at Goldman Sachs and an adjunct assistant professor at Columbia University. He is a CFA and FRM charter-holder. In addition, he is a board member at business advisory board of School For Business at Metropolitan College of New York (MCNY).

<span class="mw-page-title-main">Salman A. Avestimehr</span>

Salman A. Avestimehr is a Dean's professor at the Electrical & Computer Engineering and Computer Science Departments of University of Southern California, where he is the inaugural director of the USC-Amazon Center for Secure and Trusted Machine Learning and the director of the Information Theory and Machine Learning (vITAL) research lab. He is also the CEO and Co-Founder of FedML. Avestimehr's contributions in research and publications are in the areas of information theory, machine learning, large-scale distributed computing, and secure/private computing and learning. In particular, he is best known for deterministic approximation approaches to network information theory and coded computing. He was a general co-chair of the 2020 International Symposium on Information Theory (ISIT), and is a Fellow of IEEE. He is also co-authors of four books titled “An Approximation Approach to Network Information Theory”, “Multihop Wireless Networks: A Unified Approach to Relaying and Interference Management”, “Coded Computing”, and “Problem Solving Strategies for Elementary-School Math.”

Wei Wang is a Chinese-born American computer scientist. She is the Leonard Kleinrock Chair Professor in Computer Science and Computational Medicine at University of California, Los Angeles and the director of the Scalable Analytics Institute (ScAi). Her research specializes in big data analytics and modeling, database systems, natural language processing, bioinformatics and computational biology, and computational medicine.

Abeba Birhane is an Ethiopian-born cognitive scientist who works at the intersection of complex adaptive systems, machine learning, algorithmic bias, and critical race studies. Birhane's work with Vinay Prabhu uncovered that large-scale image datasets commonly used to develop AI systems, including ImageNet and 80 Million Tiny Images, carried racist and misogynistic labels and offensive images. She has been recognized by VentureBeat as a top innovator in computer vision and named as one of the 100 most influential persons in AI 2023 by TIME magazine.

Mi Zhang is a computer scientist at Ohio State University, where he is an Associate Professor of Computer Science and Engineering and the director of AIoT and Machine Learning Systems Lab. He is best known for his work in Edge AI, Artificial Intelligence of Things (AIoT), machine learning systems, and mobile health.

Sébastien Bubeck is a French-American computer scientist and mathematician. He is currently a Senior Principal Research Manager at Microsoft Research in the Machine Learning Foundations group and was formerly professor at Princeton University.

<span class="mw-page-title-main">Edward Y. Chang</span> American computer scientist

Edward Y. Chang is a computer scientist, academic, and author. He is an adjunct professor of Computer Science at Stanford University, and Visiting Chair Professor of Bioinformatics and Medical Engineering at Asia University, since 2019.

Lester Mackey is an American computer scientist and statistician. He is a principal researcher at Microsoft Research and an adjunct professor at Stanford University. Mackey develops machine learning methods, models, and theory for large-scale learning tasks driven by applications from climate forecasting, healthcare, and the social good. He was named a 2023 MacArthur Fellow.

References

  1. ↑ "Vaneet Aggarwal".
  2. ↑ "CLAN Homepage".
  3. ↑ "Decisions in distributed wireless networks with imprecise information" (PDF). ProQuest .
  4. ↑ "Division of EECS, IISc Bangalore".
  5. ↑ "Princeton honors top students at Alumni Day".
  6. ↑ "Recognizing College of Engineering inventors and innovators". 31 January 2022.
  7. ↑ "Aggarwal receives IEEE TVT best paper award".
  8. ↑ "Aggarwal, Elgabli win HotPOST 2018 Best Paper Award".
  9. ↑ "Cooperative AI".
  10. ↑ "Aggarwal and team receive best paper award at 2021 NeurIPS".
  11. ↑ "HADAR: New Method Allows AI to See Through Pitch Darkness Like Broad Daylight". 3 August 2023.
  12. ↑ Bhattarai, Manish; Thompson, Sophia (2023). "Heat-assisted imaging enables day-like visibility at night". Nature. 619 (7971): 699–700. Bibcode:2023Natur.619..699B. doi:10.1038/d41586-023-02333-x. PMID   37495875. S2CID   260170762.
  13. ↑ Pile, David (2023). "Turning night into day". Nature Photonics. 17 (10): 843. Bibcode:2023NaPho..17..843P. doi:10.1038/s41566-023-01297-8. S2CID   263231960.
  14. ↑ Malusare, Aditya; Kothandaraman, Harish; Tamboli, Dipesh; Lanman, Nadia A.; Aggarwal, Vaneet (2023). "Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision". arXiv: 2311.02333 [cs.LG].
  15. ↑ "Axios Science: AI learns life's language" . Retrieved 16 December 2023.
  16. ↑ "IEEE/ACM Transactions on Networking - People".
  17. ↑ "ACM JOURNAL ON AUTONOMOUS TRANSPORTATION SYSTEMS Home".
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.