https://murray.cds.caltech.edu/api.php?action=feedcontributions&feedformat=atom&user=Yhori 2026-05-23T05:17:03Z User contributions MediaWiki 1.44.2 https://murray.cds.caltech.edu/index.php?title=Hiroki_Miyazako,_Feb_2016&diff=19295 2016-02-21T17:03:37Z

<p>Yhori: </p> <hr /> <div>* Visitor: Hiroki Miyazako (U. Tokyo)<br /> * Date: February 22nd (Mon), 23rd (Tue)<br /> <br /> === Schedule ===<br /> ==== Feb 22 (Mon) ====<br /> * 9:30 - Yutaka - ANB 218<br /> * 11:00 - <br /> * 11:45 - Lunch<br /> * 13:00 - <br /> * 13:45 - seminar set up<br /> * 14:00 - seminar - Steele 110<br /> * 15:15 -<br /> * 16:00 - <br /> * 16:45 -<br /> * 17:30 - Done<br /> <br /> === Seminar ===<br /> '''Title:''' In-situ control of chemical reactions and transportation in nanofluidics using an inverted electron-beam lithography system<br /> <br /> '''Abstract:''' Manipulation techniques of chemical reactions and transportation, such as microfluidic devices and optical manipulation, have been widely studied for biochemical analysis. However, these techniques have still limitations on degrees-of-freedoms and spatiotemporal resolution. In this talk, we present a new method for control of chemical reactions and transportation in liquid samples using an electron beam (EB). By irradiating low-energy EBs to liquid samples through a 100-nm-thick silicon nitride membrane, electrical and chemical effects of the EBs can be applied to the samples with a resolution of 120 nm. We show our EB system can achieve several in-situ manipulations such as fluid control, fabrication of cell scaffolds and manipulation of cell membrane. Moreover, we describe some applications of our EB technique to single cell analysis and manipulation of biomolecules.<br /> <br /> '''Bio:''' Hiroki Miyazako received the B.E degree in engineering and the M.S. degree in information science and technology from the University of Tokyo, Tokyo, Japan, in 2013 and 2015, respectively. He is currently a Doctoral Student at the University of Tokyo. He is working as a JSPS Research Fellow (DC1). He is focusing on directed self-assembly of molecular and cell systems using nanotechnology. He was the recipient of the Conference Award at the 13th SICE Conference on Control Systems in 2014, and JSME Fellow Award for Outstanding Young Engineers in 2015.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Hiroki_Miyazako,_Feb_2016&diff=19292 2016-02-17T20:35:05Z

<p>Yhori: </p> <hr /> <div>* Visitor: Hiroki Miyazako (U. Tokyo)<br /> * Date: February 22nd (Mon), 23rd (Tue)<br /> <br /> === Schedule ===<br /> ==== Feb 22 (Mon) ====<br /> * 9:30 - Yutaka<br /> * 11:00 - <br /> * 11:45 - Lunch<br /> * 13:00 - <br /> * 13:45 - seminar set up<br /> * 14:00 - seminar - Steele 110<br /> * 15:15 -<br /> * 16:00 - <br /> * 16:45 -<br /> * 17:30 - Done<br /> <br /> ==== Feb 23 (Tue) ====<br /> * 9:30 - <br /> * 10:15 - <br /> * 11:00 - <br /> * 12:15 - Lunch<br /> * 13:30 - <br /> * 14:15 -<br /> * 15:00 -<br /> * 15:45 -<br /> * 16:30 - <br /> * 17:15 - Done<br /> <br /> === Seminar ===<br /> '''Title:''' In-situ control of chemical reactions and transportation in nanofluidics using an inverted electron-beam lithography system<br /> <br /> '''Abstract:''' Manipulation techniques of chemical reactions and transportation, such as microfluidic devices and optical manipulation, have been widely studied for biochemical analysis. However, these techniques have still limitations on degrees-of-freedoms and spatiotemporal resolution. In this talk, we present a new method for control of chemical reactions and transportation in liquid samples using an electron beam (EB). By irradiating low-energy EBs to liquid samples through a 100-nm-thick silicon nitride membrane, electrical and chemical effects of the EBs can be applied to the samples with a resolution of 120 nm. We show our EB system can achieve several in-situ manipulations such as fluid control, fabrication of cell scaffolds and manipulation of cell membrane. Moreover, we describe some applications of our EB technique to single cell analysis and manipulation of biomolecules.<br /> <br /> '''Bio:''' Hiroki Miyazako received the B.E degree in engineering and the M.S. degree in information science and technology from the University of Tokyo, Tokyo, Japan, in 2013 and 2015, respectively. He is currently a Doctoral Student at the University of Tokyo. He is working as a JSPS Research Fellow (DC1). He is focusing on directed self-assembly of molecular and cell systems using nanotechnology. He was the recipient of the Conference Award at the 13th SICE Conference on Control Systems in 2014, and JSME Fellow Award for Outstanding Young Engineers in 2015.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Hiroki_Miyazako,_Feb_2016&diff=19289 2016-02-16T09:29:12Z

<p>Yhori: </p> <hr /> <div>* Visitor: Hiroki Miyazako (U. Tokyo)<br /> * Date: February 22nd (Mon), 23rd (Tue)<br /> <br /> === Schedule ===<br /> ==== Feb 22 (Mon) ====<br /> * 9:30 - Yutaka<br /> * 11:00 - <br /> * 11:45 - Lunch<br /> * 13:00 - <br /> * 13:45 - seminar set up<br /> * 14:00 - seminar<br /> * 15:15 -<br /> * 16:00 - <br /> * 16:45 -<br /> * 17:30 - Done<br /> <br /> ==== Feb 23 (Tue) ====<br /> * 9:30 - <br /> * 10:15 - <br /> * 11:00 - <br /> * 12:15 - Lunch<br /> * 13:30 - <br /> * 14:15 -<br /> * 15:00 -<br /> * 15:45 -<br /> * 16:30 - <br /> * 17:15 - Done<br /> <br /> === Seminar ===<br /> '''Title:''' In-situ control of chemical reactions and transportation in nanofluidics using an inverted electron-beam lithography system<br /> <br /> '''Abstract:''' Manipulation techniques of chemical reactions and transportation, such as microfluidic devices and optical manipulation, have been widely studied for biochemical analysis. However, these techniques have still limitations on degrees-of-freedoms and spatiotemporal resolution. In this talk, we present a new method for control of chemical reactions and transportation in liquid samples using an electron beam (EB). By irradiating low-energy EBs to liquid samples through a 100-nm-thick silicon nitride membrane, electrical and chemical effects of the EBs can be applied to the samples with a resolution of 120 nm. We show our EB system can achieve several in-situ manipulations such as fluid control, fabrication of cell scaffolds and manipulation of cell membrane. Moreover, we describe some applications of our EB technique to single cell analysis and manipulation of biomolecules.<br /> <br /> '''Bio:''' Hiroki Miyazako received the B.E degree in engineering and the M.S. degree in information science and technology from the University of Tokyo, Tokyo, Japan, in 2013 and 2015, respectively. He is currently a Doctoral Student at the University of Tokyo. He is working as a JSPS Research Fellow (DC1). He is focusing on directed self-assembly of molecular and cell systems using nanotechnology. He was the recipient of the Conference Award at the 13th SICE Conference on Control Systems in 2014, and JSME Fellow Award for Outstanding Young Engineers in 2015.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Hiroki_Miyazako,_Feb_2016&diff=19288 2016-02-16T09:27:26Z

<p>Yhori: /* Feb 23 (Tue) */</p> <hr /> <div>* Visitor: Hiroki Miyazako (U. Tokyo)<br /> * Date: February 22nd (Mon)<br /> <br /> === Schedule ===<br /> ==== Feb 22 (Mon) ====<br /> * 9:30 - Yutaka<br /> * 11:00 - <br /> * 11:45 - Lunch<br /> * 13:00 - <br /> * 13:45 - seminar set up<br /> * 14:00 - seminar<br /> * 15:15 -<br /> * 16:00 - <br /> * 16:45 -<br /> * 17:30 - Done<br /> <br /> ==== Feb 23 (Tue) ====<br /> * 9:30 - <br /> * 10:15 - <br /> * 11:00 - <br /> * 12:15 - Lunch<br /> * 13:30 - <br /> * 14:15 -<br /> * 15:00 -<br /> * 15:45 -<br /> * 16:30 - <br /> * 17:15 - Done<br /> <br /> === Seminar ===<br /> '''Title:''' In-situ control of chemical reactions and transportation in nanofluidics using an inverted electron-beam lithography system<br /> <br /> '''Abstract:''' Manipulation techniques of chemical reactions and transportation, such as microfluidic devices and optical manipulation, have been widely studied for biochemical analysis. However, these techniques have still limitations on degrees-of-freedoms and spatiotemporal resolution. In this talk, we present a new method for control of chemical reactions and transportation in liquid samples using an electron beam (EB). By irradiating low-energy EBs to liquid samples through a 100-nm-thick silicon nitride membrane, electrical and chemical effects of the EBs can be applied to the samples with a resolution of 120 nm. We show our EB system can achieve several in-situ manipulations such as fluid control, fabrication of cell scaffolds and manipulation of cell membrane. Moreover, we describe some applications of our EB technique to single cell analysis and manipulation of biomolecules.<br /> <br /> '''Bio:''' Hiroki Miyazako received the B.E degree in engineering and the M.S. degree in information science and technology from the University of Tokyo, Tokyo, Japan, in 2013 and 2015, respectively. He is currently a Doctoral Student at the University of Tokyo. He is working as a JSPS Research Fellow (DC1). He is focusing on directed self-assembly of molecular and cell systems using nanotechnology. He was the recipient of the Conference Award at the 13th SICE Conference on Control Systems in 2014, and JSME Fellow Award for Outstanding Young Engineers in 2015.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Hiroki_Miyazako,_Feb_2016&diff=19287 2016-02-16T09:16:15Z

<p>Yhori: /* Feb 22 (Mon) */</p> <hr /> <div>* Visitor: Hiroki Miyazako (U. Tokyo)<br /> * Date: February 22nd (Mon)<br /> <br /> === Schedule ===<br /> ==== Feb 22 (Mon) ====<br /> * 9:30 - Yutaka<br /> * 11:00 - <br /> * 11:45 - Lunch<br /> * 13:00 - <br /> * 13:45 - seminar set up<br /> * 14:00 - seminar<br /> * 15:15 -<br /> * 16:00 - <br /> * 16:45 -<br /> * 17:30 - Done<br /> <br /> ==== Feb 23 (Tue) ====<br /> * 9:30 - <br /> * 10:15 - <br /> * 11:00 - <br /> * 11:45 - Lunch<br /> * 13:00 - <br /> * 13:45 -<br /> * 14:00 -<br /> * 15:15 -<br /> * 16:00 - <br /> * 16:45 -<br /> * 17:30 - Done<br /> <br /> === Seminar ===<br /> '''Title:''' In-situ control of chemical reactions and transportation in nanofluidics using an inverted electron-beam lithography system<br /> <br /> '''Abstract:''' Manipulation techniques of chemical reactions and transportation, such as microfluidic devices and optical manipulation, have been widely studied for biochemical analysis. However, these techniques have still limitations on degrees-of-freedoms and spatiotemporal resolution. In this talk, we present a new method for control of chemical reactions and transportation in liquid samples using an electron beam (EB). By irradiating low-energy EBs to liquid samples through a 100-nm-thick silicon nitride membrane, electrical and chemical effects of the EBs can be applied to the samples with a resolution of 120 nm. We show our EB system can achieve several in-situ manipulations such as fluid control, fabrication of cell scaffolds and manipulation of cell membrane. Moreover, we describe some applications of our EB technique to single cell analysis and manipulation of biomolecules.<br /> <br /> '''Bio:''' Hiroki Miyazako received the B.E degree in engineering and the M.S. degree in information science and technology from the University of Tokyo, Tokyo, Japan, in 2013 and 2015, respectively. He is currently a Doctoral Student at the University of Tokyo. He is working as a JSPS Research Fellow (DC1). He is focusing on directed self-assembly of molecular and cell systems using nanotechnology. He was the recipient of the Conference Award at the 13th SICE Conference on Control Systems in 2014, and JSME Fellow Award for Outstanding Young Engineers in 2015.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Hiroki_Miyazako,_Feb_2016&diff=19285 2016-02-16T00:22:57Z

<p>Yhori: /* Feb 23 (Tue) */</p> <hr /> <div>* Visitor: Hiroki Miyazako (U. Tokyo)<br /> * Date: February 22nd (Mon)<br /> <br /> === Schedule ===<br /> ==== Feb 22 (Mon) ====<br /> * 9:30 - Yutaka<br /> * 10:15 - <br /> * 11:00 - <br /> * 11:45 - Lunch<br /> * 13:00 - <br /> * 13:45 - seminar set up<br /> * 14:00 - seminar<br /> * 15:15 -<br /> * 16:00 - <br /> * 16:45 -<br /> * 17:30 - Done<br /> <br /> ==== Feb 23 (Tue) ====<br /> * 9:30 - <br /> * 10:15 - <br /> * 11:00 - <br /> * 11:45 - Lunch<br /> * 13:00 - <br /> * 13:45 -<br /> * 14:00 -<br /> * 15:15 -<br /> * 16:00 - <br /> * 16:45 -<br /> * 17:30 - Done<br /> <br /> === Seminar ===<br /> '''Title:''' In-situ control of chemical reactions and transportation in nanofluidics using an inverted electron-beam lithography system<br /> <br /> '''Abstract:''' Manipulation techniques of chemical reactions and transportation, such as microfluidic devices and optical manipulation, have been widely studied for biochemical analysis. However, these techniques have still limitations on degrees-of-freedoms and spatiotemporal resolution. In this talk, we present a new method for control of chemical reactions and transportation in liquid samples using an electron beam (EB). By irradiating low-energy EBs to liquid samples through a 100-nm-thick silicon nitride membrane, electrical and chemical effects of the EBs can be applied to the samples with a resolution of 120 nm. We show our EB system can achieve several in-situ manipulations such as fluid control, fabrication of cell scaffolds and manipulation of cell membrane. Moreover, we describe some applications of our EB technique to single cell analysis and manipulation of biomolecules.<br /> <br /> '''Bio:''' Hiroki Miyazako received the B.E degree in engineering and the M.S. degree in information science and technology from the University of Tokyo, Tokyo, Japan, in 2013 and 2015, respectively. He is currently a Doctoral Student at the University of Tokyo. He is working as a JSPS Research Fellow (DC1). He is focusing on directed self-assembly of molecular and cell systems using nanotechnology. He was the recipient of the Conference Award at the 13th SICE Conference on Control Systems in 2014, and JSME Fellow Award for Outstanding Young Engineers in 2015.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Hiroki_Miyazako,_Feb_2016&diff=19284 2016-02-16T00:22:38Z

<p>Yhori: /* Feb 22 (Mon) */</p> <hr /> <div>* Visitor: Hiroki Miyazako (U. Tokyo)<br /> * Date: February 22nd (Mon)<br /> <br /> === Schedule ===<br /> ==== Feb 22 (Mon) ====<br /> * 9:30 - Yutaka<br /> * 10:15 - <br /> * 11:00 - <br /> * 11:45 - Lunch<br /> * 13:00 - <br /> * 13:45 - seminar set up<br /> * 14:00 - seminar<br /> * 15:15 -<br /> * 16:00 - <br /> * 16:45 -<br /> * 17:30 - Done<br /> <br /> ==== Feb 23 (Tue) ====<br /> * 9:30 - <br /> * 10:15 - <br /> * 11:00 - <br /> * 11:45 - Lunch<br /> * 13:00 - <br /> * 13:45 -<br /> * 14:00 -<br /> * 15:15 -<br /> * 16:00 - <br /> * 16:45 -<br /> * 17:30 - Done<br /> <br /> === Seminar ===<br /> '''Title:''' In-situ control of chemical reactions and transportation in nanofluidics using an inverted electron-beam lithography system<br /> <br /> '''Abstract:''' Manipulation techniques of chemical reactions and transportation, such as microfluidic devices and optical manipulation, have been widely studied for biochemical analysis. However, these techniques have still limitations on degrees-of-freedoms and spatiotemporal resolution. In this talk, we present a new method for control of chemical reactions and transportation in liquid samples using an electron beam (EB). By irradiating low-energy EBs to liquid samples through a 100-nm-thick silicon nitride membrane, electrical and chemical effects of the EBs can be applied to the samples with a resolution of 120 nm. We show our EB system can achieve several in-situ manipulations such as fluid control, fabrication of cell scaffolds and manipulation of cell membrane. Moreover, we describe some applications of our EB technique to single cell analysis and manipulation of biomolecules.<br /> <br /> '''Bio:''' Hiroki Miyazako received the B.E degree in engineering and the M.S. degree in information science and technology from the University of Tokyo, Tokyo, Japan, in 2013 and 2015, respectively. He is currently a Doctoral Student at the University of Tokyo. He is working as a JSPS Research Fellow (DC1). He is focusing on directed self-assembly of molecular and cell systems using nanotechnology. He was the recipient of the Conference Award at the 13th SICE Conference on Control Systems in 2014, and JSME Fellow Award for Outstanding Young Engineers in 2015.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Hiroki_Miyazako,_Feb_2016&diff=19283 2016-02-16T00:22:16Z

<p>Yhori: /* Schedule */</p> <hr /> <div>* Visitor: Hiroki Miyazako (U. Tokyo)<br /> * Date: February 22nd (Mon)<br /> <br /> === Schedule ===<br /> ==== Feb 22 (Mon) ====<br /> * 9:30 - Yutaka<br /> * 10:15 - <br /> * 11:00 - <br /> * 11:45 - Lunch<br /> * 13:00 - <br /> * 13:45 - seminar set up<br /> * 14:00 - seminar<br /> * 15:15 -<br /> * 16:00 - <br /> * 16:45 -<br /> * 17:30 - Done<br /> <br /> ==== Feb 23 (Tue) ====<br /> * 9:30 - <br /> * 10:15 - <br /> * 11:00 - <br /> * 11:45 - Lunch<br /> * 13:00 - <br /> * 13:45 -<br /> * 14:00 -<br /> * 15:15 -<br /> * 16:00 - <br /> * 16:45 -<br /> * 17:30 - Done<br /> <br /> === Seminar ===<br /> '''Title:''' In-situ control of chemical reactions and transportation in nanofluidics using an inverted electron-beam lithography system<br /> <br /> '''Abstract:''' Manipulation techniques of chemical reactions and transportation, such as microfluidic devices and optical manipulation, have been widely studied for biochemical analysis. However, these techniques have still limitations on degrees-of-freedoms and spatiotemporal resolution. In this talk, we present a new method for control of chemical reactions and transportation in liquid samples using an electron beam (EB). By irradiating low-energy EBs to liquid samples through a 100-nm-thick silicon nitride membrane, electrical and chemical effects of the EBs can be applied to the samples with a resolution of 120 nm. We show our EB system can achieve several in-situ manipulations such as fluid control, fabrication of cell scaffolds and manipulation of cell membrane. Moreover, we describe some applications of our EB technique to single cell analysis and manipulation of biomolecules.<br /> <br /> '''Bio:''' Hiroki Miyazako received the B.E degree in engineering and the M.S. degree in information science and technology from the University of Tokyo, Tokyo, Japan, in 2013 and 2015, respectively. He is currently a Doctoral Student at the University of Tokyo. He is working as a JSPS Research Fellow (DC1). He is focusing on directed self-assembly of molecular and cell systems using nanotechnology. He was the recipient of the Conference Award at the 13th SICE Conference on Control Systems in 2014, and JSME Fellow Award for Outstanding Young Engineers in 2015.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Hiroki_Miyazako,_Feb_2016&diff=19282 2016-02-16T00:14:53Z

<p>Yhori: /* Seminar */</p> <hr /> <div>* Visitor: Hiroki Miyazako (U. Tokyo)<br /> * Date: February 22nd (Mon)<br /> <br /> === Schedule ===<br /> * Feb 22 (Mon)<br /> <br /> <br /> <br /> === Seminar ===<br /> '''Title:''' In-situ control of chemical reactions and transportation in nanofluidics using an inverted electron-beam lithography system<br /> <br /> '''Abstract:''' Manipulation techniques of chemical reactions and transportation, such as microfluidic devices and optical manipulation, have been widely studied for biochemical analysis. However, these techniques have still limitations on degrees-of-freedoms and spatiotemporal resolution. In this talk, we present a new method for control of chemical reactions and transportation in liquid samples using an electron beam (EB). By irradiating low-energy EBs to liquid samples through a 100-nm-thick silicon nitride membrane, electrical and chemical effects of the EBs can be applied to the samples with a resolution of 120 nm. We show our EB system can achieve several in-situ manipulations such as fluid control, fabrication of cell scaffolds and manipulation of cell membrane. Moreover, we describe some applications of our EB technique to single cell analysis and manipulation of biomolecules.<br /> <br /> '''Bio:''' Hiroki Miyazako received the B.E degree in engineering and the M.S. degree in information science and technology from the University of Tokyo, Tokyo, Japan, in 2013 and 2015, respectively. He is currently a Doctoral Student at the University of Tokyo. He is working as a JSPS Research Fellow (DC1). He is focusing on directed self-assembly of molecular and cell systems using nanotechnology. He was the recipient of the Conference Award at the 13th SICE Conference on Control Systems in 2014, and JSME Fellow Award for Outstanding Young Engineers in 2015.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Hiroki_Miyazako,_Feb_2016&diff=19281 2016-02-15T21:06:12Z

<p>Yhori: Created page with &quot;* Visitor: Hiroki Miyazako (U. Tokyo) * Date: February 22nd (Mon) === Schedule === * Feb 22 (Mon) === Seminar ===&quot;</p> <hr /> <div>* Visitor: Hiroki Miyazako (U. Tokyo)<br /> * Date: February 22nd (Mon)<br /> <br /> === Schedule ===<br /> * Feb 22 (Mon)<br /> <br /> <br /> <br /> === Seminar ===</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Research_meetings,_Jan/Feb_2016&diff=19190 2016-01-19T19:22:22Z

<p>Yhori: /* 28 Jan 2016 (Thu) */</p> <hr /> <div>Please sign up for a slot below. __NOTOC__<br /> <br /> {| border=1 width=100%<br /> |- valign = top<br /> |<br /> === 25 Jan 2016 (Mon) ===<br /> * Richard in SF<br /> |<br /> === 26 Jan 2016 (Tue) ===<br /> * 1-2 pm: Andrey Shur<br /> * 2-3 pm: Anders Knight<br /> * 3-4 pm: Reed McCardell<br /> * 5:30-6:30 pm: Ania Baetica<br /> |<br /> <br /> === 27 Jan 2016 (Wed) ===<br /> * 8:30-9:30 am: Vipul Singhal<br /> * 9:30-10:30 am: Anandh Swaminathan<br /> * 4-5 pm: Shaobin<br /> * 5-6 pm: Ivan (flexible)<br /> |<br /> <br /> === 28 Jan 2016 (Thu) ===<br /> * 5:30-6:30 pm: Hold: Michele Colledanchise<br /> * 6:30-7:30 pm: Yutaka<br /> |<br /> <br /> === 29 Jan 2016 (Fri) ===<br /> * 2-3 pm: Victoria Hsiao<br /> * 3-4 pm: Clare<br /> * 4:30-5:30 pm: Ioannis Filippidis<br /> * 5:30-6:30 pm: George Artavanis<br /> |- valign = top<br /> |<br /> <br /> === 25 Jan 2016 (Mon) ===<br /> * Richard in Hartford<br /> |<br /> === 2 Feb 2016 (Tue) ===<br /> * 9-10 am: Yong W.<br /> * 2-3 pm: Tony Fragoso<br /> * 3-4 pm: Sean Sanchez<br /> * 4-5 pm: James Parkin<br /> * 5:30-6:30 pm: Daniel Naftalovich<br /> * 6:30-7:30 pm: Anu Thubagere <br /> |<br /> <br /> === 3 Feb 2016 (Wed) ===<br /> * 8:30-9:30 am: open<br /> * 9:30-10:30 am: open<br /> |<br /> === 4 Feb 2016 (Thu) ===<br /> * Richard in SF<br /> |<br /> === 5 Feb 2016 (Fri) ===<br /> * BE visiting day<br /> |}<br /> &lt;!--<br /> === 7 Feb 2016 (Sun) ===<br /> * 2-3 pm: open (if needed)<br /> * 3-4 pm: open (if needed)<br /> * 4-5 pm: open (if needed)<br /> * 5-6 pm: open (if needed)<br /> --&gt;</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Soheil_Feizi,_Oct_2015&diff=18928 2015-10-19T16:19:50Z

<p>Yhori: /* Schedule */</p> <hr /> <div>* Visitor: Soheil Feizi (MIT)<br /> * Date: October 19th (Mon)<br /> === Schedule ===<br /> * Oct 19 (Mon)<br /> * 9:15 am - Yutaka Hori (218 ANB; ex 3552)<br /> * 10:15 am - Enrique Mallada (ANB 338)<br /> * 11 am - Vipul Singhal (Keck 139)<br /> * 11:45 am - seminar set up<br /> * 12-1:15 pm - seminar (121 ANB)<br /> * 1:15 pm - Dj (ANB 218)<br /> * 2pm - Michelle Effros (Moore 250)<br /> * 3 pm - Enoch Yeung (Keck 139)<br /> * 4 pm - Richard Murray (Steele 109)<br /> * 4:30 pm - Anandh Swaminathan (Steele 103)<br /> * 5:00 pm - Joel Tropp (ANB 307)<br /> <br /> * Oct 20th (Tues)<br /> * 2:30pm - Adam Wierman<br /> * 3:15pm - Houman Owhadi<br /> <br /> === Seminar ===<br /> Title: Learning (from) networks: fundamental limits, algorithms, and applications<br /> <br /> Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. <br /> <br /> In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Soheil_Feizi,_Oct_2015&diff=18903 2015-10-12T17:22:52Z

<p>Yhori: /* Schedule */</p> <hr /> <div>* Visitor: Soheil Feizi (MIT)<br /> * Date: October 19th (Mon)<br /> === Schedule ===<br /> * 9:15 am - Yutaka Hori (218 ANB; ex 3552)<br /> * 10:15 am - Enrique Mallada (ANB 338)<br /> * 11 am - Vipul Singhal (Keck 139)<br /> * 11:45 am - seminar set up<br /> * 12-1:15 pm - seminar (121 ANB)<br /> * 1:15 pm -<br /> * 2pm - Michelle Effros (Moore 250)<br /> * 3 pm - Enoch Yeung (Keck 139)<br /> * 4 pm - Richard Murray (Steele 109)<br /> * 4:30 pm - Anandh Swaminathan (Steele 103)<br /> * 5:15 pm - done<br /> <br /> === Seminar ===<br /> Title: Learning (from) networks: fundamental limits, algorithms, and applications<br /> <br /> Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. <br /> <br /> In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Soheil_Feizi,_Oct_2015&diff=18902 2015-10-12T17:21:25Z

<p>Yhori: /* Schedule */</p> <hr /> <div>* Visitor: Soheil Feizi (MIT)<br /> * Date: October 19th (Mon)<br /> === Schedule ===<br /> * 9:15 am - Yutaka Hori (218 ANB; ex 3552)<br /> * 10:15 am - Enrique Mallada (ANB 338)<br /> * 11 am - Vipul Singhal (Keck 139)<br /> * 11:45 am - seminar set up<br /> * 12-1:15 pm - seminar (121 ANB)<br /> * 1:15 pm -<br /> * 2pm - Prof. Effros (Moore 250)<br /> * 3 pm - Enoch Yeung (Keck 139)<br /> * 4 pm - Richard Murray (Steele 109)<br /> * 4:30 pm - Anandh Swaminathan (Steele 103)<br /> * 5:15 pm - done<br /> <br /> === Seminar ===<br /> Title: Learning (from) networks: fundamental limits, algorithms, and applications<br /> <br /> Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. <br /> <br /> In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Soheil_Feizi,_Oct_2015&diff=18901 2015-10-12T15:54:46Z

<p>Yhori: /* Schedule */</p> <hr /> <div>* Visitor: Soheil Feizi (MIT)<br /> * Date: October 19th (Mon)<br /> === Schedule ===<br /> * 9:15 am - Yutaka Hori (218 ANB; ex 3552)<br /> * 10:15 am - Enrique Mallada (ANB 338)<br /> * 11 am - Vipul Singhal (Keck 139)<br /> * 11:45 am - seminar set up<br /> * 12-1:15 pm - seminar (121 ANB)<br /> * 1:15 pm (pending 4:30?-) - Anandh Swaminathan (Steele 103)<br /> * 2pm - Prof. Effros (Moore 250)<br /> * 3 pm - Enoch Yeung (Keck 139)<br /> * 4 pm - Richard Murray (Steele 109)<br /> * 4:30 pm - <br /> * 5:00 pm - done<br /> <br /> === Seminar ===<br /> Title: Learning (from) networks: fundamental limits, algorithms, and applications<br /> <br /> Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. <br /> <br /> In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Soheil_Feizi,_Oct_2015&diff=18900 2015-10-12T15:51:33Z

<p>Yhori: /* Schedule */</p> <hr /> <div>* Visitor: Soheil Feizi (MIT)<br /> * Date: October 19th (Mon)<br /> === Schedule ===<br /> * 9:15 am - Yutaka Hori (218 ANB; ex 3552)<br /> * 10:15 am - Enrique Mallada (ANB 338)<br /> * 11 am - Vipul Singhal (Keck 139)<br /> * 11:45 am - seminar set up<br /> * 12-1:15 pm - seminar (121 ANB)<br /> * 1:15 pm (pending) - Anandh Swaminathan (Steele 103)<br /> * 2pm - Effros (Moore 250)<br /> * 3 pm - Enoch Yeung (Keck 139)<br /> * 4 pm - Richard Murray (Steele 109)<br /> * 4:30 pm - <br /> * 5:00 pm - done<br /> <br /> === Seminar ===<br /> Title: Learning (from) networks: fundamental limits, algorithms, and applications<br /> <br /> Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. <br /> <br /> In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Soheil_Feizi,_Oct_2015&diff=18892 2015-10-10T23:40:00Z

<p>Yhori: /* Schedule */</p> <hr /> <div>* Visitor: Soheil Feizi (MIT)<br /> * Date: October 19th (Mon)<br /> === Schedule ===<br /> * 9:15 am - Yutaka Hori (218 ANB; ex 3552)<br /> * 10:15 am - Enrique Mallada (ANB 338)<br /> * 11 am - Vipul Singhal (Keck 139)<br /> * 11:45 am - seminar set up<br /> * 12-1:15 pm - seminar (121 ANB)<br /> * 1:30 pm - <br /> * 2:15 pm - Anandh Swaminathan (Steele 103)<br /> * 3 pm - Enoch Yeung (Keck 139)<br /> * 4 pm - Richard Murray (Steele 109)<br /> * 4:45 pm - Done<br /> <br /> === Seminar ===<br /> Title: Learning (from) networks: fundamental limits, algorithms, and applications<br /> <br /> Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. <br /> <br /> In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Soheil_Feizi,_Oct_2015&diff=18891 2015-10-10T23:39:24Z

<p>Yhori: /* Schedule */</p> <hr /> <div>* Visitor: Soheil Feizi (MIT)<br /> * Date: October 19th (Mon)<br /> === Schedule ===<br /> * 9:15 am - Yutaka Hori (218 ANB; ex 3552)<br /> * 10:15 am - Enrique Mallada (ANB 338)<br /> * 11 am - Vipul Singhal (Keck 139)<br /> * 11:45 am - seminar set up<br /> * 12-1:15 pm - seminar (213 ANB; room changed!!)<br /> * 1:30 pm - <br /> * 2:15 pm - Anandh Swaminathan (Steele 103)<br /> * 3 pm - Enoch Yeung (Keck 139)<br /> * 4 pm - Richard Murray (Steele 109)<br /> * 4:45 pm - Done<br /> <br /> === Seminar ===<br /> Title: Learning (from) networks: fundamental limits, algorithms, and applications<br /> <br /> Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. <br /> <br /> In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Soheil_Feizi,_Oct_2015&diff=18885 2015-10-09T20:28:55Z

<p>Yhori: /* Schedule */</p> <hr /> <div>* Visitor: Soheil Feizi (MIT)<br /> * Date: October 19th (Mon)<br /> === Schedule ===<br /> * 9:15 am - Yutaka Hori (218 ANB; ex 3552)<br /> * 10:15 am - Enrique Mallada<br /> * 11 am - Vipul Singhal (Keck 139)<br /> * 11:45 am - seminar set up<br /> * 12-1:15 pm - seminar (213 ANB; room changed!!)<br /> * 1:30 pm - <br /> * 2:15 pm - Anandh Swaminathan (Steele 103)<br /> * 3 pm - Enoch Yeung (Keck 139)<br /> * 4 pm - Richard Murray (Steele 109)<br /> * 4:45 pm - Done<br /> <br /> === Seminar ===<br /> Title: Learning (from) networks: fundamental limits, algorithms, and applications<br /> <br /> Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. <br /> <br /> In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Soheil_Feizi,_Oct_2015&diff=18884 2015-10-09T17:43:02Z

<p>Yhori: /* Schedule */</p> <hr /> <div>* Visitor: Soheil Feizi (MIT)<br /> * Date: October 19th (Mon)<br /> === Schedule ===<br /> * 9:15 am - Yutaka Hori (218 ANB; ex 3552)<br /> * 10:15 am - Enrique Mallada<br /> * 11 am - Vipul Singhal (Keck 139)<br /> * 11:45 am - seminar set up<br /> * 12-1:15 pm - seminar (121 ANB)<br /> * 1:30 pm - <br /> * 2:15 pm - Anandh Swaminathan (Steele 103)<br /> * 3 pm - Enoch Yeung (Keck 139)<br /> * 4 pm - Richard Murray (Steele 109)<br /> * 4:45 pm - Done<br /> <br /> === Seminar ===<br /> Title: Learning (from) networks: fundamental limits, algorithms, and applications<br /> <br /> Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. <br /> <br /> In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Soheil_Feizi,_Oct_2015&diff=18875 2015-10-06T20:40:37Z

<p>Yhori: /* Schedule */</p> <hr /> <div>* Visitor: Soheil Feizi (MIT)<br /> * Date: October 19th (Mon)<br /> === Schedule ===<br /> * 9:15 am - Yutaka Hori (218 ANB; ex 3552)<br /> * 10:15 am - <br /> * 11 am - <br /> * 11:45 am - seminar set up<br /> * 12-1:15 pm - seminar (121 ANB)<br /> * 1:30 pm - <br /> * 2:15 pm - <br /> * 3 pm (~3:15) - Richard Murray (Steele 12)<br /> * 4 pm - <br /> * 5 pm - Done<br /> <br /> === Seminar ===<br /> Title: Learning (from) networks: fundamental limits, algorithms, and applications<br /> <br /> Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. <br /> <br /> In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Soheil_Feizi,_Oct_2015&diff=18850 2015-10-04T02:45:12Z

<p>Yhori: /* Seminar */</p> <hr /> <div>* Visitor: Soheil Feizi (MIT)<br /> * Date: October 19th (Mon)<br /> === Schedule ===<br /> * 9:15 am - Yutaka (218 ANB; ex 3552)<br /> * 10:15 am - <br /> * 11 am - <br /> * 11:45 am - seminar set up<br /> * 12-1:15 pm - seminar (213 ANB)<br /> * 1:30 pm - <br /> * 2:15 pm - <br /> * 3 pm (~3:15) - Richard<br /> * 4 pm - <br /> * 5 pm - Done<br /> <br /> === Seminar ===<br /> Title: Learning (from) networks: fundamental limits, algorithms, and applications<br /> <br /> Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. <br /> <br /> In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Soheil_Feizi,_Oct_2015&diff=18849 2015-10-04T02:13:40Z

<p>Yhori: Created page with &quot;* Visitor: Soheil Feizi (MIT) * Date: October 19th (Mon) === Schedule === * 9:15 am - Yutaka (218 ANB; ex 3552) * 10:15 am - * 11 am - * 11:45 am - seminar set up * 12-1:15...&quot;</p> <hr /> <div>* Visitor: Soheil Feizi (MIT)<br /> * Date: October 19th (Mon)<br /> === Schedule ===<br /> * 9:15 am - Yutaka (218 ANB; ex 3552)<br /> * 10:15 am - <br /> * 11 am - <br /> * 11:45 am - seminar set up<br /> * 12-1:15 pm - seminar (213 ANB)<br /> * 1:30 pm - <br /> * 2:15 pm - <br /> * 3 pm (~3:15) - Richard<br /> * 4 pm - <br /> * 5 pm - Done<br /> <br /> === Seminar ===<br /> Title: Learning (from) networks: fundamental limits, algorithms, and applications<br /> <br /> Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. <br /> In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Group_Schedule,_Spring_2015&diff=18319 2015-03-18T18:57:59Z

<p>Yhori: /* Week 2: 6 Apr - 10 Apr */</p> <hr /> <div>This page contains information about various upcoming events that are of interest to the group. __NOTOC__<br /> {| width=60%<br /> |- valign=top<br /> | width=50% |<br /> * [[Schedule|Richard's calendar (travel)]]<br /> | width=50% |<br /> * [[Group Schedule, Winter 2015]]<br /> |}<br /> <br /> The schedule for group and subgroup meetings is given below. Contact Richard if you need to change the schedule. Unless otherwise noted, here are the locations of the meetings:<br /> <br /> :{| width=100%<br /> |- valign=top<br /> | width=33% |<br /> * Group meetings - 213 ANB<br /> | width=33% |<br /> * Biocircuits subgroup - 111 Keck<br /> | width=33% |<br /> * NCS subgroup - 243 ANB<br /> |}<br /> <br /> {| width=100% border=1<br /> <br /> |- valign=top<br /> | width=33% |<br /> === Week 1: 30 Mar - 3 Apr ===<br /> '''NCS: 30 Mar (Mon), 4p-5:30p'''<br /> * Anthony Fragoso (main)<br /> * James Bern (short)<br /> '''Biocircuits: 1 Apr (Wed), 10a-12p'''<br /> * Lab safety (Clare, Richard)<br /> * Ania Baetica (short)<br /> <br /> | width=33% |<br /> <br /> === Week 2: 6 Apr - 10 Apr ===<br /> '''NCS: 6 Apr (Mon), 2:30p-4p'''<br /> * Catharine McGhan (main)<br /> * Open (short)<br /> '''Biocircuits: 8 Apr (Wed), 10a-12p'''<br /> * Emzo de los Santos (long)<br /> * Yutaka Hori (short)<br /> | width=33% |<br /> <br /> === Week 3: 13 Apr - 17 Apr ===<br /> * Richard out of town, Mon-Thu<br /> * Biocircuits lab cleanup, Wed @ 10 am<br /> |- valign=top<br /> | width=33% |<br /> <br /> === Week 4: 20 Apr - 24 Apr ===<br /> '''NCS: 20 Apr (Mon), 2:30p-4p<br /> * Yilin Mo (main)<br /> * Open (short)<br /> '''Biocircuits: 22 Apr (Wed), 10a-12p<br /> * Anandh Saminathan (long)<br /> * Victoria Hsiao (short)<br /> | width=33% |<br /> <br /> === Week 5: 27 Apr - 1 May ===<br /> '''Biocircuits: 29 Apr (Wed), 10a-12p'''<br /> * Anu Thubagere (long)<br /> * Dan Siegal (short)<br /> &lt;hr&gt;<br /> * NASA Formal Methods Symposium, Mon-Wed<br /> <br /> | width=33% |<br /> <br /> === Week 6: 4 May - 8 May ===<br /> '''NCS: 4 May (Mon), 2:30p-4p<br /> * Ivan Papusha (main)<br /> * Open (short)<br /> '''Biocircuits: 6 May (Wed), 10a-12p'''<br /> * Clare Hayes (long)<br /> * Vipul Singhal (short)<br /> |- valign=top<br /> | width=33% |<br /> <br /> === Week 7: 11 May - 15 May ===<br /> '''NCS: 11 May (Mon), 2:30p-4p<br /> * Vasu Raman (main)<br /> * Open (short)<br /> '''Biocircuits: 13 May (Wed), 10a-12p'''<br /> * Shaobin Guo (long)<br /> * Yong Wu (short)<br /> <br /> | width=33% |<br /> <br /> === Week 8: 18 May - 22 May ===<br /> '''Biocircuits: 20 May (Wed), 10a-12p'''<br /> * Zach Sun (long)<br /> * Enoch Yeung (short)<br /> '''NCS: 20 May (Wed), 4-5:30p<br /> * Samira Farahani (main)<br /> * Open (short)<br /> <br /> | width=33% |<br /> <br /> === Week 9: 25 May - 29 May ===<br /> '''NCS: 25 May (Mon), 2:30p-4p<br /> * Scott Livingston (main)<br /> * Open (short)<br /> '''Biocircuits: 27 May (Wed), 10a-12p'''<br /> * Rotation student (long)<br /> * Sean Sanchez (short)<br /> <br /> |- valign=top<br /> | width=33% |<br /> <br /> === Week 10: 1 Jun - 5 Jun ===<br /> '''NCS: 1 Jun (Mon), 2:30p-4p<br /> * Ioannis Filippidis (main)<br /> * Open (short)<br /> '''Biocircuits: 4 Jun (Thu), 10a-12p'''<br /> * Rotation student (long)<br /> * Tiffany Zhou (short)<br /> <br /> | width=33% |<br /> <br /> === Week 11: 8 Jun - 12 Jun ===<br /> '''NCS: 8 Jun (Mon), 2:30p-4p<br /> * Benson Christalin (short)<br /> * Daniel Naftalovich (short)<br /> &lt;hr&gt;<br /> * SEED: Wed-Sat in Boston<br /> | width=33% |<br /> <br /> |}</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Group_Schedule,_Spring_2015&diff=18318 2015-03-18T18:57:42Z

<p>Yhori: /* Week 1: 30 Mar - 3 Apr */</p> <hr /> <div>This page contains information about various upcoming events that are of interest to the group. __NOTOC__<br /> {| width=60%<br /> |- valign=top<br /> | width=50% |<br /> * [[Schedule|Richard's calendar (travel)]]<br /> | width=50% |<br /> * [[Group Schedule, Winter 2015]]<br /> |}<br /> <br /> The schedule for group and subgroup meetings is given below. Contact Richard if you need to change the schedule. Unless otherwise noted, here are the locations of the meetings:<br /> <br /> :{| width=100%<br /> |- valign=top<br /> | width=33% |<br /> * Group meetings - 213 ANB<br /> | width=33% |<br /> * Biocircuits subgroup - 111 Keck<br /> | width=33% |<br /> * NCS subgroup - 243 ANB<br /> |}<br /> <br /> {| width=100% border=1<br /> <br /> |- valign=top<br /> | width=33% |<br /> === Week 1: 30 Mar - 3 Apr ===<br /> '''NCS: 30 Mar (Mon), 4p-5:30p'''<br /> * Anthony Fragoso (main)<br /> * James Bern (short)<br /> '''Biocircuits: 1 Apr (Wed), 10a-12p'''<br /> * Lab safety (Clare, Richard)<br /> * Ania Baetica (short)<br /> <br /> | width=33% |<br /> <br /> === Week 2: 6 Apr - 10 Apr ===<br /> '''NCS: 6 Apr (Mon), 2:30p-4p'''<br /> * Catharine McGhan (main)<br /> * Open (short)<br /> '''Biocircuits: 8 Apr (Wed), 10a-12p'''<br /> * Emzo de los Santos (long)<br /> * Ania Baetica (short)<br /> | width=33% |<br /> <br /> === Week 3: 13 Apr - 17 Apr ===<br /> * Richard out of town, Mon-Thu<br /> * Biocircuits lab cleanup, Wed @ 10 am<br /> |- valign=top<br /> | width=33% |<br /> <br /> === Week 4: 20 Apr - 24 Apr ===<br /> '''NCS: 20 Apr (Mon), 2:30p-4p<br /> * Yilin Mo (main)<br /> * Open (short)<br /> '''Biocircuits: 22 Apr (Wed), 10a-12p<br /> * Anandh Saminathan (long)<br /> * Victoria Hsiao (short)<br /> | width=33% |<br /> <br /> === Week 5: 27 Apr - 1 May ===<br /> '''Biocircuits: 29 Apr (Wed), 10a-12p'''<br /> * Anu Thubagere (long)<br /> * Dan Siegal (short)<br /> &lt;hr&gt;<br /> * NASA Formal Methods Symposium, Mon-Wed<br /> <br /> | width=33% |<br /> <br /> === Week 6: 4 May - 8 May ===<br /> '''NCS: 4 May (Mon), 2:30p-4p<br /> * Ivan Papusha (main)<br /> * Open (short)<br /> '''Biocircuits: 6 May (Wed), 10a-12p'''<br /> * Clare Hayes (long)<br /> * Vipul Singhal (short)<br /> |- valign=top<br /> | width=33% |<br /> <br /> === Week 7: 11 May - 15 May ===<br /> '''NCS: 11 May (Mon), 2:30p-4p<br /> * Vasu Raman (main)<br /> * Open (short)<br /> '''Biocircuits: 13 May (Wed), 10a-12p'''<br /> * Shaobin Guo (long)<br /> * Yong Wu (short)<br /> <br /> | width=33% |<br /> <br /> === Week 8: 18 May - 22 May ===<br /> '''Biocircuits: 20 May (Wed), 10a-12p'''<br /> * Zach Sun (long)<br /> * Enoch Yeung (short)<br /> '''NCS: 20 May (Wed), 4-5:30p<br /> * Samira Farahani (main)<br /> * Open (short)<br /> <br /> | width=33% |<br /> <br /> === Week 9: 25 May - 29 May ===<br /> '''NCS: 25 May (Mon), 2:30p-4p<br /> * Scott Livingston (main)<br /> * Open (short)<br /> '''Biocircuits: 27 May (Wed), 10a-12p'''<br /> * Rotation student (long)<br /> * Sean Sanchez (short)<br /> <br /> |- valign=top<br /> | width=33% |<br /> <br /> === Week 10: 1 Jun - 5 Jun ===<br /> '''NCS: 1 Jun (Mon), 2:30p-4p<br /> * Ioannis Filippidis (main)<br /> * Open (short)<br /> '''Biocircuits: 4 Jun (Thu), 10a-12p'''<br /> * Rotation student (long)<br /> * Tiffany Zhou (short)<br /> <br /> | width=33% |<br /> <br /> === Week 11: 8 Jun - 12 Jun ===<br /> '''NCS: 8 Jun (Mon), 2:30p-4p<br /> * Benson Christalin (short)<br /> * Daniel Naftalovich (short)<br /> &lt;hr&gt;<br /> * SEED: Wed-Sat in Boston<br /> | width=33% |<br /> <br /> |}</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Yasuaki_Wasa,_Dec_2014&diff=17798 2014-12-16T19:17:46Z

<p>Yhori: /* Schedule */</p> <hr /> <div>* Visitor: Yasuaki Wasa (Tokyo Institute of Technology, graduate student)<br /> * Date: December 19th (Fri)<br /> === Schedule ===<br /> * 10 am - Yutaka (218 ANB; ex 3552)<br /> * 11 am - Yorie?<br /> * 12-1:15 pm - lunch<br /> * 1:30 pm - John<br /> * 2:15 pm - Richard<br /> * 3 pm -<br /> * 4 pm - <br /> * 5 pm - Done<br /> <br /> === Bio ===<br /> Mr. Wasa received B.Eng. and M.Eng degrees in the Department of Mechanical and Control Engineering at Tokyo Institute of Technology, Japan in 2011 and 2013 respectively. He is currently a Ph.D. student at Tokyo Institute of Technology, where his advisor is Prof. Masayuki Fujita, and a research fellow of the Japan Society for the Promotion of Science. His research interests include game theoretic cooperative control and distributed control for energy and environment.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Yasuaki_Wasa,_Dec_2014&diff=17749 2014-12-01T17:34:11Z

<p>Yhori: Created page with &quot;* Visitor: Yasuaki Wasa (Tokyo Institute of Technology, graduate student) * Date: December 19th (Fri) === Schedule === * 9 am - Yutaka (218 ANB; ex 3552) * 10 am - * 11 am - ...&quot;</p> <hr /> <div>* Visitor: Yasuaki Wasa (Tokyo Institute of Technology, graduate student)<br /> * Date: December 19th (Fri)<br /> === Schedule ===<br /> * 9 am - Yutaka (218 ANB; ex 3552)<br /> * 10 am - <br /> * 11 am -<br /> * 12-1:15 pm - lunch<br /> * 1:30 pm - John<br /> * 2:15 pm - Richard<br /> * 3 pm -<br /> * 4 pm - <br /> * 5 pm - Done<br /> <br /> === Bio ===<br /> Mr. Wasa received B.Eng. and M.Eng degrees in the Department of Mechanical and Control Engineering at Tokyo Institute of Technology, Japan in 2011 and 2013 respectively. He is currently a Ph.D. student at Tokyo Institute of Technology, where his advisor is Prof. Masayuki Fujita, and a research fellow of the Japan Society for the Promotion of Science. His research interests include game theoretic cooperative control and distributed control for energy and environment.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Tomoaki_Hashimoto,_Aug_2014&diff=17562 2014-07-24T17:41:08Z

<p>Yhori: /* Schedule */</p> <hr /> <div>* Visitor: Prof. Tomoaki Hashimoto (Osaka University, Assistant Professor)<br /> * Date: August 1st (Fri)<br /> === Schedule ===<br /> * 9 am - Yutaka (218 ANB; ex 3552)<br /> * 10 am - <br /> * 11 am -<br /> * 11:50 am - seminar set up<br /> * 12-1:15 pm - seminar (213 ANB)<br /> * 1:15 pm - Richard (until 2 pm)<br /> * 2 pm - <br /> * 3 pm - <br /> * 4 pm -<br /> * 5 pm - Done<br /> <br /> === Seminar ===<br /> Abstract: Model predictive control (MPC), also known as receding horizon control, is a type of optimal feedback control where control performance<br /> over a finite future is optimized with a performance index that has a moving initial time and a terminal time. This talk provides two topics about MPC. <br /> One is a design method of MPC for thermal fluid systems governed by nonlinear partial differential equations [1]. The other one is probabilistic constrained<br /> MPC for linear discrete-time stochastic systems. Both topics are related to the recent development of MPC. In particular, we would like to review the<br /> background and key idea of these studies [1, 2] rather than the technical details.<br /> <br /> References: <br /> <br /> [1] Tomoaki HASHIMOTO, Yusuke YOSHIOKA and Toshiyuki OHTSUKA: Receding Horizon Control with Numerical Solution for Nonlinear <br /> Parabolic Partial Differential Equations, IEEE Transactions on Automatic Control, Vol. 58, pp.725-730, 2013.<br /> <br /> [2] Tomoaki HASHIMOTO: Probabilistic Constrained Model Predictive Control for Linear Discrete-time Systems with Additive Stochastic Disturbances,<br /> Proceedings of the 52nd IEEE Conference on Decision and Control, pp.6434-6439, 2013. <br /> <br /> === Bio ===<br /> Dr. Hashimoto received the B.Eng., M.Eng., and D.Eng. degrees from the Tokyo Metropolitan Institute of Technology, Hino, Japan, in 2003, 2004, and 2007, respectively, all in aerospace engineering. He was a Research Assistant at the RIKEN Brain Science Institute, Wako, Japan, from 2007 to 2008, and an Assistant Professor at the Shinshu University, Nagano, Japan, from 2008 to 2009. Since 2009, he has been an Assistant Professor at Osaka University, Toyonaka, Japan. His recent research interests are in the area of model predictive control and its application.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Tomoaki_Hashimoto,_Aug_2014&diff=17559 2014-07-22T01:56:26Z

<p>Yhori: Created page with &quot;* Visitor: Prof. Tomoaki Hashimoto (Osaka University, Assistant Professor) * Date: August 1st (Fri) === Schedule === * 9 am - * 10 am - * 11 am * 11:50 am - seminar set up *...&quot;</p> <hr /> <div>* Visitor: Prof. Tomoaki Hashimoto (Osaka University, Assistant Professor)<br /> * Date: August 1st (Fri)<br /> === Schedule ===<br /> * 9 am - <br /> * 10 am - <br /> * 11 am<br /> * 11:50 am - seminar set up<br /> * 12-1:15 pm - seminar (213 ANB)<br /> * 1:30 pm - <br /> * 2:30 pm - <br /> * 3:30 pm - <br /> * 4:30 pm -<br /> * 5:30 pm - Done<br /> <br /> === Seminar ===<br /> Abstract: Model predictive control (MPC), also known as receding horizon control, is a type of optimal feedback control where control performance<br /> over a finite future is optimized with a performance index that has a moving initial time and a terminal time. This talk provides two topics about MPC. <br /> One is a design method of MPC for thermal fluid systems governed by nonlinear partial differential equations [1]. The other one is probabilistic constrained<br /> MPC for linear discrete-time stochastic systems. Both topics are related to the recent development of MPC. In particular, we would like to review the<br /> background and key idea of these studies [1, 2] rather than the technical details.<br /> <br /> References: <br /> <br /> [1] Tomoaki HASHIMOTO, Yusuke YOSHIOKA and Toshiyuki OHTSUKA: Receding Horizon Control with Numerical Solution for Nonlinear <br /> Parabolic Partial Differential Equations, IEEE Transactions on Automatic Control, Vol. 58, pp.725-730, 2013.<br /> <br /> [2] Tomoaki HASHIMOTO: Probabilistic Constrained Model Predictive Control for Linear Discrete-time Systems with Additive Stochastic Disturbances,<br /> Proceedings of the 52nd IEEE Conference on Decision and Control, pp.6434-6439, 2013. <br /> <br /> === Bio ===<br /> Dr. Hashimoto received the B.Eng., M.Eng., and D.Eng. degrees from the Tokyo Metropolitan Institute of Technology, Hino, Japan, in 2003, 2004, and 2007, respectively, all in aerospace engineering. He was a Research Assistant at the RIKEN Brain Science Institute, Wako, Japan, from 2007 to 2008, and an Assistant Professor at the Shinshu University, Nagano, Japan, from 2008 to 2009. Since 2009, he has been an Assistant Professor at Osaka University, Toyonaka, Japan. His recent research interests are in the area of model predictive control and its application.</div>

Yhori https://murray.cds.caltech.edu/index.php?title=Sebastian_Maerkl,_Apr_2013&diff=17147 2014-04-04T05:50:52Z

<p>Yhori: /* Schedule */</p> <hr /> <div>Sebastian Maerkl will visit Caltech on 7-8 April.<br /> <br /> === Schedule ===<br /> <br /> Monday<br /> * 10 am - meeting with Richard<br /> * 11 am - open (Zach?)<br /> * 12 pm - lunch with Richard and Zach?<br /> * 2 pm - Informal seminar<br /> * 3 pm - Open discussions<br /> * 4 pm - Individual meetings<br /> <br /> Tuesday<br /> * Individual meetings<br /> * ALL demo (Sean/Enoch)?<br /> * 2 pm - Yutaka (time flexible; any time in Tuesday afternoon)<br /> * 5 pm - Done for the day<br /> <br /> === Seminar ===<br /> <br /> '''In vitro Synthetic Biology (a cell-free talk)'''<br /> <br /> Sebastian J. Maerkl&lt;br&gt;<br /> École Polytechnique Fédérale de Lausanne<br /> <br /> 7 Apr (Mon), 2-3 pm&lt;br&gt;<br /> 111 Keck<br /> <br /> Living cells maintain a steady state of biochemical reaction rates by exchanging energy and matter with the environment. These exchanges usually do not occur in in vitro systems, which consequently go to chemical equilibrium. This in turn has severely constrained the complexity of biological networks that can be implemented in vitro. We developed nanoliter-scale microfluidic reactors that exchange reagents at dilution rates matching those of dividing bacteria. In these reactors we achieved transcription and translation at steady state for 30 h and implemented diverse regulatory mechanisms on the transcriptional, translational, and posttranslational levels, including RNA polymerases, transcriptional repression, translational activation, and proteolysis. We constructed and implemented an in vitro genetic oscillator and mapped its phase diagram showing that steady-state conditions were necessary to produce oscillations.<br /> <br /> One potential application of in vitro synthetic biology is rapid prototyping of genetic circuits. This in turn requires that components and systems can be transferred from in vitro to in vivo, which has not yet been demonstrated for more complex genetic circuits. I will present recent results indicating that it is possible to transfer a functional in vivo genetic circuit (the repressilator) to our in vitro environment as a first step towards closing the loop between in vitro characterization and optimization of genetic circuits and their in vivo implementation.</div>

Yhori