World’s Fastest Time-of-Flight Camera
02 December 2019
Time-of-flight 3D imaging is the key technology in autonomous cars, robotics, and remote sensing. Building on more than 20 years of research on photonic time stretch data acquisition, our laboratory recently demonstrated the world’s fastest time of flight 3D camera. The Time Stretch Lidar scans orders of magnitude faster than today’s commercial line-scanning pulsed-LiDAR systems. Here is the link to our paper and on nature news featuring our work.
Research on Deep Cytometry Published in Scientific Reports
July 31, 2019, Los Angeles
Our recent work "Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry" which shows that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time stretch quantitative phase imaging, microfluidics and deep learning is published in Scientific Reports.
Invited Talk presented by Prof. Jalali at the Label-Free Biomedical Imaging and Sensing (LBIS) Conference at SPIE Photonics West
February 2, 2019, San Francisco
Our recent work has shown that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time stretch quantitative phase imaging, microfluidics and deep learning. Such a technology holds promise for early detection of primary cancer or metastasis by finding rare diseased cells among a large population of normal cells in blood or other bodily fluids. In his February 2, 2019 talk at Photonics West, Prof. Jalali described two implementations of deep convolutional neural networks in time stretch imaging flow cytometry. In the first mode, the network operates on features extracted from cell images constructed from temporal waveforms. In the second mode, the network directly maps the raw temporal waveforms into output classes. This eliminates the image processing pipeline resulting in significantly faster runtime to enable cell sorting decisions to be made in real time.
Jalali-Lab Research on Deep Learning in Digital Engineering News
September 21, 2018
Mathworks® has recently featured our work on Deep Learning (DL) in Digital Engineering News. The article titled as Deep Learning Accelerates Product Development was posted by Randall Newton in Engineering Computing on May 1, 2018. Few words from the article "UCLA researchers used MathWorks Deep Learning tools to create a new diagnostic product for examining cancer cells that gives superior results over existing methods. The researchers say modeling the system with DL saved months of experimental time. The cancer cell neural net model was then repurposed for algal cell classification, by providing new data to the algorithm."
Jalali-Lab Releases Python Code for Image Super Resolution
July 28, 2018
RAISR (Rapid and Accurate Image Super Resolution) is an image processing algorithm reported by Google Research in 2016. The algorithm creates high resolution images from lower resolution images and is reportedly deployed on Google phones. The source code released on Github is the Jalali-Lab’s independent implementation of the RAISR algorithm written in Python 3.x. The code was developed by Sifeng He, under the guidance of Prof. Bahram Jalali. The implementation achieved performance results that are comparable to that reported by Google's research paper (with less than ± 0.1 dB in PSNR). Just-in-time (JIT) compilation employing JIT numba is used to speed up the Python code. A very parallelized Python code employing multi-processing capabilities is used to speed up the testing process. The code has been tested on GNU/Linux and Mac OS X 10.13.2 platforms.
Phase Stretch Transform features as one of the top-ten most read stories on Mathworks this year
December 29, 2017
Mathworks® recently featured our physics-inspired edge detection algorithm, Phase Stretch Transform (PST) as a potential candidate for feature detection in biometrics, medical and computer vision applications in their article. The article received a lot of attention from the image processing community making it one of the top-ten most read stories on Mathworks this year.
Time stretch and its applications
October 18, 2017
Professor Jalali and collaborators from four continents publish a thorough review of the Time Stretch technology in Nature Photonics. The review article explains how Time Stretch overcomes the speed limitations of electronic digitizers and enables ultrafast single-shot spectroscopy, imaging, and other measurements at refresh rates reaching billions of frames per second with non-stop recording, which spans trillions of continuous frames. The paper is available here.
Phase Stretch Transform for biometrics and other computer vision applications
September 22, 2017
Mathworks® has featured our physics-inspired edge detection algorithm, Phase Stretch Transform (PST) as a potential candidate for feature detection in biometrics, medical and computer vision applications in their recent article. In the article, various example images are exhibited where PST has shown significant improvement in edge detection for various applications. You can read more about it here.
Deep Learning Microscope
July 27, 2017
Prof. Bahram Jalali presented a keynote talk entitled "Deep Learning Microscope" at the The 7th International Multidisciplinary Conference on Optofluidics 2017 in Singapore. The talk described our group's success in detection of cancer cells in blood using the time stretch microscope and deep learning: Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack accuracy. Also, the sample size analyzed by these assays is limited due to their low throughput. We have integrated feature extraction and deep learning with high-throughput quantitative phase imaging enabled by photonic time stretch, achieving record high accuracy in label-free blood cell classification. Our system captures quantitative phase and intensity images and extracts 16 biophysical features from each cell. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.
Book on Artificial-Intelligence Microscope
April 27, 2017
Professor Jalali and his students Ata Mahjoubfar and Claire Chen have published a book that details the world's fastest and most accurate AI-powered microscope for identification of biological cells. Used with a microfluidics chip, the microscope has proven applications in cancer detection and renewable energy. The book is published by Springer and can be obtained as an ebook or a hardcover here.
AI time-stretch microscope solves big data problem in cancer diagnostics
Feb 14, 2017
Our technical article published by Mathworks® highlights that the TS-QPI system generates equivalent of 20 HD movies per second. For a single experiment, in which every cell in a 10-milliliter blood sample is imaged, the system generates from 10 to 50 terabytes of data. The research paper can be found here.
AI-augmented microscope uses deep learning to take on cancer
July 24, 2016
Mathworks® has featured our recently published research which combines microscopy and deep learning for cancer diagnosis. The article explains how Matlab® can be used for design, simulation, and modeling of quantitative phase imaging, amplified time-stretch, and big data analytics using deep learning. You can read more about it here.
AI Time Stretch Microscope covered in 70 media outlets
July 19, 2016
The time-stretch microscopy technique which was first developed in the Jalali lab has recently been combined with artificial intelligence for cancer cell detection. This research work was enthusiastically received in the scientific community and has been covered by 70 news outlets internationally. link
The Birth of Mode Locking
Cover Nature Photonics May 2016
June 2, 2016
The work of Jalali-Lab in collaboration with U. Gottingen in Germany appeared on the cover of Nature Photonics, May 2016. The cover shows the birth of laser mode-locking captured for the first time. The feat was enabled by Photonic Time Stretch technology invented at UCLA. Mode locked lasers are the gold standard in time and frequency standards and the workhorse of precision measurements and metrology. link
Madhuri Suthar receives five prestigious awards at her undergraduate alma mater ISM-Dhanbad
April 21, 2016
Graduate student Madhuri Suthar received the State Bank of India Student of the Year Award (for securing highest G.P.A in the institute) and ISM Gold Medal (for securing first position in the class of 85 students of Electronics and Communication Engineering B.Tech programme) conferred to her by the Honorable Minister of Human Resources and Development (HRD) Govt. of India, Mrs. Smriti Irani in the 37th annual commencement ceremony of her undergraduate alma mater Indian School of Mines (ISM), Dhanbad held on April 8, 2016. She also received Smt. Sneh Lata Srivasatva Memorial Gold Medal, on being selected as the best female student of B.Tech/ 5-year Dual Degree/ 5-year Integrated programmes. Other awards received by her include ISM Alumni Association (ISMAA) Award for best UG student admitted through IIT-JEE in ISM and Late Mrs. Poonam (Khanna) Singh Memorial Cash Award for being the best B.Tech Student of ISM for the session 2013-14. Congratulations!
Big Data Photonics Workshop 2016
February 13, 2016
Big Data Photonics is an annual workshop that focus on the theme, "Trends Shaping the Future of Photonics". The goal of the workshop is to bring industry and academia from the US, Denmark, and elsewhere together to showcase the newest trends and best practices within big data analytics and photonic technologies for Data Centers, Optical Computing, Internet of Things (IoT) in Agriculture and Smart City, and DNA/Genomics. The workshop is sponsored by Innovation Center Denmark, Silicon Valley and jointly organized by UCLA and DTU. Researchers from leading US and Danish companies and universities such as Google, Lumentum Operations LLC, Bifrost Communications ApS, The Studio - Reykjavik, Alexandra Institute, Aarhus University, Stanford University, Aalborg University Copenhagen, Del Rosario University, UC Davis, Anderson School of Management, Institute of the Risk Sciences at UCLA, etc. will speak at the workshop. Researchers from UCLA and DTU will be presenting posters. There will also be a demonstration of RogueScope, an optical scope with a frame rate of up to 100 million frames per second. The speaking opportunity is by invitation only and we are not soliciting papers.
25 Mar 2016 - 25 Mar 2016
Carnesale Commons (3rd floor), UCLA
Los Angeles, California, USA
Contact: Cejo Konuparamban Lonappan
Phone: (310) 206-4554
Venue: Carnesale Commons (3rd floor), UCLA
Sponsor: Innovation Center Denmark, Silicon Valley, University of California, Los Angeles
Powerful image detection algorithm source code released
February 13, 2016
The Phase Stretch Transform algorithm, as it is known, is a physics-inspired computational approach to processing images and information. The algorithm grew out of UCLA research on a technique called photonic time stretch, which has been used for ultrafast imaging and detecting cancer cells in blood. The algorithm also helps computers see features of objects that aren't visible using standard imaging techniques. For example, it might be used to detect an LED lamp's internal structure, which - using conventional techniques - would be obscured by the brightness of its light, and it can see distant stars that would normally be invisible in astronomical images. It is available for free download on two open source platforms, Github and Matlab File Exchange. Making it available as open source code will allow researchers to work together to study, use and improve the algorithm, and to freely modify and distribute it. It also will enable users to incorporate the technology into computer vision and pattern recognition applications and other image-processing applications. Please click here to read further.
Interactive Time-Stretch ADC and DFT Design Calculators Go Live
February 13, 2016
The Time Stretch Enhanced Recorder (TiSER) ADC takes a high speed analog electronic signal, slows it down by a photonic time-stretch preprocessor, and digitizes that with a relatively slow but high resolution electronic analog-to-digital converter. With this calculator, you will be able to determine the stretch factor, RF bandwidth, and required optical bandwidth amongst other parameters. Please click here to explore further.
Dispersive Fourier Transform (DFT) is an analog technique to map a signal's spectrum to its time, enabling direct measurement of the Fourier Transform. In the context of optics, this has enabled extremely high-throughput (> 1 MHz), real-time spectroscopy using a wideband pulsed source, the sample itself, a dispersive element, and a fast photodiode. With this calculator, you will be able to calculate the relationship between time and optical color (wavelength or frequency), as well as practical limitations such as spectral resolution, maximum group delay, and dispersive element loss. Please click here to explore further. .
First ever demonstration of image compression at the speed of light
April 23, 2015
Optical Data Compression in Time Stretch Imaging
PLOS ONE | DOI:10.1371/journal.pone.0125106
Big data not only brings opportunity, but also a challenge in biomedical and scientific instruments, whose acquisition and processing units are overwhelmed by a torrent of data. The need to compress massive volumes of data in real-time has fueled interest in nonuniform stretch transformations -- operations that reshape the data according to its sparsity.
In the April 23, 2015 issue of journal PLoS ONE (Volume 10, No. 4) graduate students Claire L. Chen and Ata Mohjoubfar from Jalali-Lab at UCLA demonstrate image compression performed in the optical domain and in real-time. Using nonlinear group delay dispersion and time-stretch imaging, they were able to optically warp the image such that the information-rich portions are sampled at a higher sample density than the sparse regions. This was done by restructuring the image before optical-to-electrical conversion followed by a uniform electronic sampler. Image compression was demonstrated at 36 million frames per second in real-time.
Big Data and Real Time Processing in Photonics
April 23, 2015
Big Data and Real Time Processing in Photonics
Annual Danish-Californian Workshop, 2015
University of California, Los Angeles
Friday 8:30am-7pm, March 27, 2015
Palisades Room, Carnesale Commons (3rd floor)
251 Charles E Young Drive West, Los Angeles, CA 90095
Annual Danish-Californian Workshop on photonics was held on March 27, 2015 (day after OFC). This year the focus was Big Data and Real Time Processing in Photonics. The workshop took place at Palisades Room, Carnesale Commons (3rd floor), University of California, Los Angeles. All talks are by invitation only.
The workshop was co-organized by Jalali lab at the Electrical Engineering department of UCLA and Idelfonso Tafur Monroy group at Technical University of Denmark, and was sponsored by Innovation Center Denmark, Silicon Valley. Our goal is to bring leading companies as well as university research groups from the US, Denmark and elsewhere together to present, discuss and showcase newest trends and best practice within photonic technologies for photonics in big data and real time analytics.
Thank you for your interest in the 2015 Danish-SiliconValley Photonics Workshop on March 27th 2015 in Los Angeles. Thanks to all our speakers our audience the workshop was a big success - we look forward to welcoming you to the 7th edition of the workshop in 2016! The presentation from the workshop may now be downloaded and reviewed from the workshop website.
Photonic Time-stretch Enables Real-time Monitoring of Streaming Video
Offers path to Software Defined Networks
January 20, 2015
IEEE Global Signal and Information Processing pdf
The rapid growth of streaming video and the heterogeneous nature of cloud-based applications place a burden on optical networks that form the internet. To provide a seamless user experience, these networks must be aware of network conditions and be agile in directing traffic. This in turn requires fast and accurate optical performance monitoring at full data rate.
High speed optical performance monitoring has several challenges. It requires analog-to-digital converters (ADC) and digital processors that operate at the ultrahigh data rates of optical networks. Achieving high-speed, low-noise, and low-power ADC is very difficult and digital processors operating at such speeds are power hungry.
To overcome these challenges, the UCLA team (led by Prof. Bahram Jalali and including graduate students Cejo K. Lonappan, Brandon Buckley and Daniel Lam) developed the time-stretch accelerated processor (TiSAP). This system achieves real-time data acquisition and processing at a record 1.2 Tb/s. It employs photonic time-stretch enhanced recorder (TiSER) technology to create an optical "slow-motion" to slow down the fast data so it can be digitized and processed. TiSAP consists of the time-stretch front-end, a custom-developed electronic ADC, a powerful field programmable gate array (FPGA), and an integrated clock and data recovery module. The FPGA digitally recovers the data and generates real-time eye diagrams in-service, unlike a bit error-rate tester (BERT), which is used for out-of-service analytics.
This work was done as part of the NSF funded Engineering Research Center, CIAN (Center for Integrated Access Network) in collaboration with the CIAN Testbed for Optical Aggregation Networking (TOAN) (led by Profs. Dan Kilper and John Wissinger) at the College of Optical Sciences at University of Arizona. The team demonstrated in-service optical performance monitoring of 10 Gbit/s streaming video packets transmitted through a commercial networking platform. Two Fujitsu Flashwave 9500 Optical Network Platform (ONP) nodes, each having 10 Gbit/s On-Off Keying (OOK) modulation-based transponder line cards, were used to stream high definition (HD) video packets. The optical network channel carrying the streaming video packets was analyzed by TiSAP to generate real-time eye diagrams of the data.
Real-time, in-service, optical performance monitoring demonstrated here can be used to provide feedback to the software defined networking (SDN) to implement agile optical networks for automated network restoration, disaster recovery, efficient routing, and bandwidth management.
This work, funded by the National Science Foundation through CIAN ERC grant EEC-0812072Y5001118, was presented at the 2014 IEEE Global Signal and Information Processing. December 20 conference in Atlanta, Georgia on December 3rd, 2014.
First demonstration of optical real-time data compression
March 17, 2014
We experimentally demonstrate the first instrument for compressing the time-bandwidth product of analog signals in real-time. By performing self-adaptive stretch, this technology enables digitizers to capture waveforms beyond their bandwidth with digital data size being reduced at the same time. The compression is achieved through a transformation of the signal's complex field, performed in the analog domain prior to digitization. For proof of concept experiments, we compress the modulation bandwidth of an optical signal by 500 times. At the same time, we reduce its modulation time-bandwidth product (i.e., the record length) by 2.73 times while achieving 16 dB power efficiency improvement in comparison to the case of using conventional dispersive Fourier transform. Dispersive data compression addresses the big data problem in real-time instruments and in optical communications.
Coherent Time-Stretch Transform for Near-Field Spectroscopy
May 20, 2014
The time-stretch transform slows down broadband optical signals for capture by electronic instruments, and has brought forth MHz-rate imagers, OCT systems, and spectrometers, as well as photonically-enhanced ADCs. Conventionally, sufficient dispersion is required to bring the pulse into the temporal far-field so that the spectrum can be read as the temporal waveform. The Coherent Time-Stretch Transform obviates this requirement while simultaneously enabling high-throughput acquisition of complex optical fields in single-shot measurements. Full-field spectra are recovered via temporal interferometry on waveforms dispersed in the temporal near field. Real-time absorption spectra, including both amplitude and phase information, are acquired at 37 MHz.
New Compression Method Reduces Big Data Bottleneck
New discovery is rooted in physics and the arts
December 18, 2013
Big Data refers generally to vast amounts of information collected by networked devices and systems. In this domain, data capture is technologically simple and the challenge lies in the post-capture analytics and transmission. Big Data is also prominent in other domains where the capture of data is challenging as well, such as in the medical sciences, telecom and basic research in the sciences.
In these areas, communication signals and scientific phenomena of interest tend to occur on time scales and at throughput levels that are too fast to be sampled and digitized in real time.
In other words, the Big Data problem is not just limited to analytics; it also includes data capture, storage, and transmission. Anamorphic Stretch Transform (AST) is a new mathematical transform that offers a solution for Big Data bottleneck, it slows down ultrafast signal so it can be captured with a slower instrument and at the same time it compresses the volume of the resulting data. It does so by reducing the length-bandwidth product. AST can operate on both analog and all-digital data such as on images where it outperforms JPEG and other standard compression techniques. AST is a non-iterative algorithm and does not need any feature detection, feedback.
Congratulations to Eric Diebold and Brandon Buckley whose work was published in Nature Photonics!
Congratulations to Eric Diebold and Brandon Buckley whose work was published in Nature Photonics! Their work was also featured in Nature Methods link pdf. Fluorescence imaging is the most widely used method for unveiling the molecular composition of biological specimens. However, the weak optical emission of fluorescent probes and the trade-off between imaging speed and sensitivity are problematic for acquiring blur-free images of fast phenomena, such as sub-millisecond biochemical dynamics in live cells and tissues, and cells flowing at high speed. Here, we report a technique that achieves real-time pixel readout rates that are one order of magnitude faster than a modern electron multiplier charge-coupled devicethe gold standard in highspeed fluorescence imaging technology. Termed fluorescence imaging using radiofrequency-tagged emission (FIRE), this approach maps the image into the radiofrequency spectrum using the beating of digitally synthesized optical fields. We demonstrate diffraction-limited confocal fluorescence imaging of stationary cells at a frame rate of 4.4 kHz, and fluorescence microscopy in flow at a velocity of 1 m/s, corresponding to a throughput of approximately 50,000 cells per second.
Congratulations to Peter DeVore and David Borlaug whose work was selected for the rear cover of Physica Status Solidi Rapid Reasearch Letters!
Congratulations to Peter DeVore and David Borlaug whose work was selected for the rear cover of Physica Status Solidi Rapid Research Letters! Modulation instability is a universal nonlinear process wherein a weak perturbation grows on an otherwise quiet background. Inspired by recent work on stimulating modulation instability to tame optical rogues waves, DeVore, Borlaug, and Jalali stimulate it with the weak sidebands of an electrooptically modulated carrier. In this process, the sidebands are boosted at the expense of the carrier, which enables low-voltage, high bandwidth modulation, one of the most pressing needs in optical communications. In the cover figure, we see that a traditional optical link weakly transfers high-frequency radio frequency waves, but the fortuitous increase of modulation instability gain with frequency allows transfer of the full bandwidth.
Interactive Time-Stretch Camera Design Calculator Goes Live!
Interactive Time-Stretch Camera Design Calculator Goes Live link
Serial time-encoded amplified imaging/microscopy (STEAM) is a fast real-time optical imaging method that provides ~10 MHz frame rate, ~100 ps shutter speed, and ~30 dB ( 1000) optical image gain. As of today, STEAM holds world records for shutter speed and frame rate in continuous real-time imaging. STEAM employs the photonic time stretch along with optical image amplification to circumvent the fundamental trade-off between sensitivity and speed that affects virtually all optical imaging and sensing systems. With this calculator, you will be able to determine spatial and temporal resolution of 1D STEAM System. Please click the schematic or link to explore further.
Rouge events are statistically rare but carry a huge impact.
Rogue events are statistically rare but carry a huge impact. Occurring in everyday contexts such as finance, network traffic, ocean waves and elsewhere. Launching intense pulses into silicon waveguides results in supercontinuum generation, strong nonlinear optical broadening due to a host of complex interactions. This complex mixing of frequencies occasionally results in especially strong broadening, yielding heavy-tailed distribution from what was once Gaussian noise. By stimulating the initial conditions with a well-chosen seed, the broadening can be controlled and stabilized.
Congratulations to David Borlaug for being awarded a PhD Fellowship from Sandia National Labs.
2013-06-29 -- Congratulations to David Borlaug for being awarded a PhD Fellowship from Sandia National Labs. The fellowship comes with full time funding and the opportunity to earn significantly more through summer internships. Sandia is aware of our work and is interested in using the fellowship as a platform to build a long term relationship with our lab on a variety of topics. We are proud of David and wish him continued success going forward.
Congratulations to our alumnus and now Professor at the College of Optics (CREOL) in University of Central Florida.
2013-05-24 -- Congratulations to our alumnus and now Professor at the College of Optics (CREOL) in University of Central Florida, Sasan Fathpour, for winning the prestigious Office of Naval Research Young Investigator Award. Sasan is known for the first demonstration of nonlinear photovoltaic phenomenon in optics and for energy harvesting in silicon photonics, accomplishments he made in our laboratory. The ONR award recognizes his latest innovative work in the area hybrid silicon-LiNbO3 integrated devices.
Congratulations to Eric Diebold for winning the UCLA Chancellors Postdoctoral Award.
Congratulations to Eric Diebold for winning the UCLA Chancellors Postdoctoral Award. Eric won this award for his pioneering contribution to fluorescence microscopy and for the landmark demonstration of the worlds fastest fluorescent camera.
Nature photonics (January 2013): Dispersive Fourier transformation is an emerging measurement technique that overcomes the speed limitations of traditional optical instruments and enables fast continuous single-shot measurements in optical sensing, spectroscopy and imaging. Using chromatic dispersion, dispersive Fourier transformation maps the spectrum of an optical pulse to a temporal waveform whose intensity mimics the spectrum, thus allowing a single-pixel photodetector to capture the spectrum at a scan rate significantly beyond what is possible with conventional space-domain spectrometers. Over the past decade, this method has brought us a new class of real-time instruments the permit the capture of rare events such as optical rogue waves and rare cancer cells in blood, which would otherwise be missed using conventional instruments.
Nature photonics (January 2013): Stochastically driven nonlinear processes are responsible for spontaneous pattern formation and instabilities in numerous natural and artificial systems, including well-known examples such as sand ripples, cloud formations, water waves, animal pigmentation and heart rhythms. Technologically, a type of such self-amplification drives free-electron lasers and optical supercontinuum sources whose radiation qualities, however, suffer from the stochastic origins. Through time-resolved observations, we identify intrinsic properties of these fluctuations that are hidden in ensemble measurements. We acquire single-shot spectra of modulation instability produced by laser pulses in glass fibre at megahertz real-time capture rates. The temporally confined nature of the gain physically limits the number of amplified modes, which form an antibunched arrangement as identified from a statistical analysis of the data. These dynamics provide an example of pattern competition and interaction in confined nonlinear systems.
High-throughput single-microparticle imaging flow analyzer
Our work about the high-throughput single-microparticle imaging flow analyzer has been published in PNAS online and covered in UCLA Newsroom and PNAS's Highlights. The technology can take a picture of every single cell in a microfluidic channel with a record high throughput of 100,000 cells/s and perform non-stop image-based cell classification in real time. It holds promise for a broad range of applications such as high-throughput screening, cancer detection, and stem cell research. The work has been highlighted in TIME Magazine and OPN.
Undergraduate researcher Rebecca Brown got admitted to and will attend medical school in July 2013.
Postdoctoral scholar Keisuke Goda (2007-2012) appointed Full Professor at University of Tokyo.
Professor Bahram Jalali received The 2012 Distinguished Engineering Achievement Award from The Engineers' Council.
Ali Fard has won the 2011-2012 Electrical Engineering Department's Distinguished Ph.D. Dissertation Award in Physical & Wave Electronics.
Kam Yan Hon's paper titled "
The Third-Order Nonlinear Optical Coefficients of Si, Ge, and Si(1-x)Ge(x) in the midwave and longwave infrared" has been selected to be on the cover of Journal of Applied Physics. Congratulations!
Using a combination of semiconductor theory and experimental results from the scientific literature, we have compiled and plotted the key third-order nonlinear optical coefficients of bulk crystalline Si and Ge as a function of wavelength (1.5-6.7 um for Si and 2.0-14.7 um for Ge).
Keisuke Goda wins Burroughs Welcome Fund Career Award
at the Scientific Interface! The purpose of this award is to bridge advanced postdoctoral training and the first three years of faculty service. Congratulations!