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鈥� Nghi锚n c峄﹗ v岷璶 h脿nh
鈥� T峄慽 瓢u h贸a m岷�nh m岷�
鈥� Nghi锚n c峄﹗ s芒u v峄� c谩c m么 h矛nh m谩y t铆nh v脿 thu岷璽 to谩n
鈥� H峄峜 m谩y th峄憂g k锚, t岷璸 trung v脿o t铆nh th瓢a th峄泃 (sparsity).

1. Fangda Gu, Laurent El Ghaoui, Guillaume Darmet. Grid Topology Estimation in Electrical Markets with Diagonally Dominant Laplacian. Submitted to 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe 2021), April 2021.
2. Laurent El Ghaoui, Fangda Gu, Bertrand Travacca, Armin Askari. Implicit Deep Learning. Posted on Arxiv, and submitted to SIAM J. Mathematics of Data Science, February 2021.
3. Armin Askari, Alexandre d鈥橝spremont, Laurent El Ghaoui. Naive Feature Selection: Sparsity in Na茂ve Bayes. Posted on Arxiv, and submitted to SIAM J. Mathematics of Data Science, March 2021.
4. Calafiore, G.C., El Ghaoui, L., Preziosi, A. and Russo, L., 2017. Topic analysis in news via sparse learning: a case study on the 2016 US Presidential Elections. IFAC-PapersOnLine, 50(1), pp.13593-13598.
5. Pauphilet, Jean, Diego Kiner, Damien Faille, and Laurent El Ghaoui. A tractable numerical strategy for robust MILP and application to energy management. In 2016 IEEE 55th Conference on Decision and Control (CDC), pp. 1490-1495.
6. Mert Pilanci, Martin Wainwright, Laurent El Ghaoui. Sparse learning via Boolean relaxations. In Math Programming, Series B, (2015), 151:63-87.
7. L. El Ghaoui, V. Pham, G.-C. Li, V.-A. Duong, A. Srivastava, and K. Bhaduri. Understanding Large Text Corpora via Sparse Machine Learning. Jour. Statistical Anal. & Data Mining, to appear. Short version in Proc. Conference on Intelligent Data Understanding, October 2011.
8. Mert Pilanci, Laurent El Ghaoui, and Venkat Chandrasekaran. Recovery of sparse probability measures via convex programming. In Proc. Conf. Neural Information and Processing Systems, December 2012.
9. Y. Zhang and L. El Ghaoui. Large-scale sparse principal component analysis and application to text data. Proc. NIPS, 2011

1990: Ti岷縩 s末 H脿ng kh么ng v农 tr峄�, 膼岷�i h峄峜 Stanford
1986: Th岷�c s末 膼i峄乽 khi峄僴 t峄� 膽峄檔g, Sup鈥橝茅ro, Ph谩p
1985: C峄� nh芒n Khoa h峄峜 To谩n h峄峜 v脿 V岷璽 l媒, Ecole Polytechnique, Ph谩p

Huy ch瓢啤ng 膼峄搉g Khoa h峄峜 K峄� thu岷璽, Trung t芒m Nghi锚n c峄﹗ Khoa h峄峜 Qu峄慶 gia, Ph谩p, 1999.
T脿i tr峄� nghi锚n c峄﹗ c峄�a Qu峄� Okawa, 2001.
Gi岷�i th瓢峄焠g NSF Career, 2002.
Gi岷�i t峄慽 瓢u h贸a SIAM cho t谩c ph岷﹎ hay nh岷�t n膬m 2004-2007.

https://scholar.google.com/citations?user=GDe65XEAAAAJ&hl=en

Ho岷�t 膽峄檔g gi谩o d峄�c

• 2012-nay: Ph谩t tri峄僴 v脿 h瓢峄沶g d岷玭 m么n h峄峜 鈥淜hoa h峄峜 D峄� li峄噓 T脿i ch铆nh鈥� trong ch瓢啤ng tr矛nh Th岷�c s末 K峄� thu岷璽 T脿i ch铆nh, Tr瓢峄漬g Kinh doanh Haas, UC Berkeley.
• 2018-nay: 膼i峄乽 tra vi锚n ch铆nh t岷�i H峄峜 vi峄噉 Thanh Hoa-Berkeley 峄� Th芒m Quy岷縩鈥�, v峄沬 s峄� tham gia c峄�a kho岷�ng 20 gi岷�ng vi锚n t峄� UC Berkeley; T么i hi峄噉 ch峄� tham gia v脿o c么ng t谩c tuy峄僴 d峄�ng c峄�a h峄峜 vi峄噉.
• 2017-nay: Ph谩t tri峄僴 gi谩o tr矛nh t瓢啤ng t谩c v峄� t峄慽 瓢u h贸a. N峄乶 t岷�ng n脿y hi峄噉 kh么ng ho岷�t 膽峄檔g, s谩ch gi谩o tr矛nh 膽i k猫m c贸 t岷�i .
• 2012: Ph谩t tri峄僴 kh贸a h峄峜 ng岷痭 h岷�n v峄� T峄慽 瓢u h贸a m岷�nh m岷�, 膽瓢峄�c tr矛nh b脿y t岷�i h峄檌 th岷�o v峄� nh峄痭g ti岷縩 b峄� trong t峄慽 瓢u h贸a, Zinal.
• 2011-2017: Th脿nh vi锚n s谩ng l岷璸 Berkeley Center for New Media, UC Berkeley.
  Ch峄� tr矛 phi锚n h峄峱 t岷�i nhi峄乽 h峄檌 ngh峄� (Int. Symposium Math. Programming, CDC, ACC).
• Th谩ng 9, 2010: Ban t峄� ch峄ヽ, h峄檌 th岷�o v峄� t峄慽 瓢u h贸a to谩n h峄峜, Vi峄噉 To谩n h峄峜 thu岷�n t煤y v脿 峄╪g d峄�ng, UCLA.

Ho岷�t 膽峄檔g di峄卬 thuy岷縯

• 2013-nay: 膼瓢峄�c m峄漣 n贸i chuy峄噉 t岷�i Twitter, Exxon, Walmart, EDF, GE, Google, Amazon, Bay Learn, Voleon, c霉ng nhi峄乽 c么ng ty v脿 t峄� ch峄ヽ kh谩c.
• Th谩ng 4, 2016: H峄檌 th岷�o Bay Area Optimization.
• Th谩ng 7, 2015: Th岷�o lu岷璶 to脿n th峄� t岷�i INFORMS.