Ashish Goel

Ashish Goel is an American professor whose research focuses on the design, analysis and applications of algorithms. He is a professor of Management Science and Engineering (and by courtesy Computer Science) at Stanford University.[1]

Early life and early education

Ashish Goel was born in Uttar Pradesh in India. He did his schooling at Uttar Pradesh including at St. Peter's, Agra. He was ranked first in IIT JEE 1990.[2][3] He graduated with a B.Tech in Computer Science from IIT Kanpur in 1994. He then went on to obtain a Ph.D. in Computer Science from Stanford University in 1999.[1]

Academic work

Ashish Goel's research has spanned algorithmic problems in several areas of computer science and computational social science including computer networks, theoretical computer science, molecular self-assembly, algorithmic game theory, and computational social choice.

Ashish Goel's early work resolved several open algorithmic problems in graph theory and computer networks including showing that the scheduling protocol FIFO can result in instability at arbitrarily low rates in a packet network;[4] showing that matching in regular bipartite graphs can be computed in time nearly linear in the number of vertices (i.e. without looking at all the edges);[5] showing that every monotone graph property has a sharp threshold in geometric random graphs;[6] and showing that in a packet switch, output queuing (the gold standard) can be simulated using a fabric that is twice as fast as an input-queued switch.[7]

Goel along with Rajeev Motwani and Gagan Aggarwal gave the first comprehensive analysis of how the auction used by Google to price search keywords can be made truthful.[8] This work was co-awarded the ACM SigECOMM test of time award in 2018.[9] Another paper in computational advertising received the best paper award at The Web Conference 2009.[10]

Career

Goel has had made contributions to algorithms and software related to personalization, online advertising and decentralized finance, and has been associated with companies such as Twitter, Stripe, Coinbase, and Infosys[11][12] as an advisor/consultant.

From 2009 to 2010, he worked for Twitter when the company was small. He designed all of Twitter's early personalization products and was credited by ex-Twitter CEO Dick Costolo for designing its monetization model.[13] His research have also received coverage in mainstream media.[14][15]

Civic impact

Goel's research has focused on building software systems that enable constructive online conversation and collaboration on important, often contentious, socio-political issues.

  • Applied Social Choice: Goel's work on the role of confirmation bias in increasing political polarization and the role of recommender systems in exacerbating it is widely cited.[16][17][18] In addition to doing theoretical research in social choice, Goel has also translated this research into online platforms.The Stanford Participatory Budgeting Platform has become the de facto platform for participatory budgeting in the US, and has been used over 100 times, including major cities like New York, Boston, Seattle, and Chicago.[19][20]
  • Censorship vs Free Speech: Working with colleagues including the political scientist Frank Fukuyama, Goel came up with an architecture that could allow social networks such as Facebook to outsource their editorial decisions on censorship to a third party, called a "middleware". Their paper appeared in Foreign Affairs, a political science magazine,[21] and led to discussion in the popular press.[22][23][24]

References

  1. 1 2 "Ashish Goel". web.stanford.edu.
  2. ↑ "Last 38 Years IIT JEE Toppers". Studentigiri. April 27, 2016.
  3. ↑ "Where are They Now?". July 16, 2009.
  4. ↑ Bhattacharjee, Rajat; Goel, Ashish; Lotker, Zvi (January 1, 2005). "Instability of FIFO at Arbitrarily Low Rates in the Adversarial Queueing Model". SIAM Journal on Computing. 34 (2): 318–332. doi:10.1137/S0097539703426805 – via epubs.siam.org (Atypon).
  5. ↑ Goel, Ashish; Kapralov, Michael; Khanna, Sanjeev (January 1, 2013). "Perfect Matchings in $O(n\log n)$ Time in Regular Bipartite Graphs". SIAM Journal on Computing. 42 (3): 1392–1404. arXiv:0909.3346. doi:10.1137/100812513 – via epubs.siam.org (Atypon).
  6. ↑ Goel, Ashish; Rai, Sanatan; Krishnamachari, Bhaskar (November 30, 2005). "Monotone properties of random geometric graphs have sharp thresholds". The Annals of Applied Probability. 15 (4): 2535–2552. arXiv:math/0310232. doi:10.1214/105051605000000575 – via Project Euclid.
  7. ↑ Chuang, Shang-Tse; Goel, A.; McKeown, N.; Prabhakar, B. (March 31, 1999). "Matching output queueing with a combined input output queued switch". pp. 1169–1178 vol.3. doi:10.1109/INFCOM.1999.751673 – via IEEE Xplore.
  8. ↑ Aggarwal, Gagan; Goel, Ashish; Motwani, Rajeev (June 11, 2006). "Truthful auctions for pricing search keywords". Association for Computing Machinery. pp. 1–7. doi:10.1145/1134707.1134708 – via ACM Digital Library.
  9. ↑ "ACM SIGecom: Test of Time Award". www.sigecom.org.
  10. ↑ "www 2009 Madrid". thewebconf.org.
  11. ↑ Sood, Varun (May 18, 2016). "Infosys to tie-up with online education firms". mint.
  12. ↑ "Infosys ropes in former Twitter executive Ashish Goel as scientific adviser - ET Telecom". ETTelecom.com.
  13. ↑ "Twitter, the Startup That Wouldn't Die". March 1, 2012 – via www.bloomberg.com.
  14. ↑ Cruz, University of California-Santa (April 18, 2020). "Widely Used AI Machine Learning Methods Don't Work as Claimed". SciTechDaily.
  15. ↑ "Predicting what topics will trend on Twitter". MIT News | Massachusetts Institute of Technology.
  16. ↑ "Preaching to the choir". www.nationalaffairs.com.
  17. ↑ "New mathematical model shows how society becomes polarized". ScienceDaily.
  18. ↑ "Researchers create social systems to reduce political polarization". April 28, 2013.
  19. ↑ Efrati, Amir. "Crowdsourcing Tough Decisions on Deficit Reduction". WSJ.
  20. ↑ magazine, STANFORD (March 4, 2022). "Deliberation Nation". stanfordmag.org.
  21. ↑ Fukuyama, Francis; Richman, Barak; Goel, Ashish (January 26, 2021). "How to Save Democracy From Technology" – via www.foreignaffairs.com.
  22. ↑ "How to save democracy from technology". November 24, 2020.
  23. ↑ https://www.livemint.com/opinion/columns/what-we-must-regulate-when-we-regulate-social-media-platforms-11612109656267.html
  24. ↑ "Want to limit digital platform power? Use middleware, Stanford professors say". November 10, 2021.
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