Disciplines: 

Remote Sensing; Lidar; Radar; Forest Ecology

Mentor (JPL): 

KC Cushman (334F - Suborbital Radar Science And Engineering), kcushman@jpl.nasa.gov, (865) 924-7364, https://www.researchgate.net/profile/Kc-Cushman 

Sassan Saatchi (329G - Carbon Cycle and Ecosystems, Earth Science Section), sassan.saatchi@jpl.nasa.gov, (818) 354-1666, https://science.jpl.nasa.gov/people/Saatchi/

Mentor (UCLA): 

Elsa Ordway (Department of Ecology and Evolutionary Biology, UCLA Institute of the Environment and Sustainability), elsaordway@ucla.edu, https://elsaordway.weebly.com/

Background: 

Forests plants are critical for understanding global climate change because woody plants contain a large quantity of carbon and can potentially mitigate (vegetation growth) or exacerbate (deforestation, forest degradation, fire) the accumulation of carbon in Earth’s atmosphere. Recently, the estimated amount of caron emitted and sequestered by terrestrial ecosystems every year is over half the magnitude of global annual fossil fuel emissions [1]. However, changes in global plant biomass are highly uncertain because we lack accurate measurements of biomass in many regions of the world.

The NASA-ISRO Synthetic Aperture Radar (NISAR) mission will measure the aboveground woody biomass contained in Earth’s ecosystems by collecting high spatial (3-10 m) and temporal (12 days) resolution data over and land ice-covered areas globally [2,3]. Radar data collected by NISAR will be a valuable resource for monitoring forests’ role in the global carbon cycle in the face of climate change and human land use.

To calibrate/validate NISAR’s biomass product, 15 distinct “ecoregions” have been defined using WWF ecosystem categories, ALOS/PALSAR data (a Japan Aerospace Exploration Agency (JAXA) L-band SAR satellite mission), and Geoscience Laser Altimeter System (GLAS) spaceborne lidar measurements of 3D forest structure [4,5]. At least two distinct calibration/validation sites, or “cal/val sites”, within each ecoregion will be used to create landscape biomass maps by combing field plots and airborne lidar data [4]. The representativeness of cal/val sites within each ecoregion is critically important to produce unbiased estimates of biomass across countries and regions.

Description: 

This internship will use multiple sources of available data to characterize the forest structural diversity across and within NISAR biomass ecoregions, and to evaluate the representativeness of current and potential NISAR biomass cal/val sites. Types of data will include airborne lidar, spaceborne lidar from the Global Ecosystem Dynamics Investigation, and/or field plot data.

This project will not only provide valuable ecological information about structural variation across ecoregions, but will also provide information that will directly improve the efficiency and accuracy of NISAR biomass cal/val efforts.

References: 

[1] Xu, L., Saatchi, S., et al., 2021. Changes in global terrestrial live biomass over the 21st century. Science Advances 7(27): eabe9829. https://doi.org/10.1126/sciadv.abe9829 

[2] https://nisar.jpl.nasa.gov/mission/quick-facts/

[3] Yu, Y., Saatchi, S., 2016. Sensitivity of L-band SAR backscatter to aboveground biomass of global forests. Remote Sensing 8(6): 522. https://doi.org/10.3390/rs8060522.

[4] NISAR Calibration and Validation Plan V0.9, JPL D-80829. https://nisar.jpl.nasa.gov/resources/documents

[5] Asner, G. P., et al., 2012. A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia 168(4): 1147-1160. https://doi.org/10.1007/s00442-011-2165-z

Student Requirements: 

Required: experience programming in R, Python, and/or Jupyter Notebooks.

Preferred: experience working with lidar and/or forest plot data.

Work Location: 

JPL or remote

Funds available by Mentor(s)

Yes (X) No (   )

If Yes, Project/Task number: 106093/04.04.01.C1

Disciplines: 

Atmospheric Science, Climate Science

Mentor (JPL): 

Sudip Chakraborty. Email: sudip.chakraborty@jpl.nasa.gov; Phone: 818-393-8657

https://science.jpl.nasa.gov/people/SChakraborty/

Mentor (UCLA): 

Gang Chen. Email: gchenpu@ucla.edu; Phone: (310)206-9956

www.gchenpu.com

Background: 

Atmospheric rivers (ARs) are filaments of intense water vapor transport in the atmosphere. While ARs are important suppliers of water resource to many regions of the world, they are also capable of inducing extreme precipitation upon landfall. Under warming, climate models project substantial increases in AR frequency and strengthening in AR intensity. However, reliable projections of the future AR changes depend on how well models simulate the AR statistics in the current climate. Up until now, only few studies have evaluated the representation of ARs in previous generations of climate models. How well ARs being represented in the current generation of climate models participated in Coupled Model Intercomparison Project Phase 6 (CMIP6) remains unknown. Furthermore, ARs statistics derived from reanalysis products have been used as proxies for observations in these studies. In reality, reanalyses are not real observation. They are produced by models which incorporate information from observations through data assimilation. These products thus have their own biases which are intrinsic to the models used to produce them. Given these limitations in the previous studies, we propose to develop AR statistics from satellite-based observations to more objectively evaluate the representation of ARs in the latest generation of climate models in CMIP6.    

Description: 

The student will use the satellite-based AR statistics which have been developed to evaluate the AR representations in CMIP6 climate models. The characteristics of AR-induced precipitation in models will also be compared to the satellite-based AR-induced precipitation, with a particular focus on AR-induced extreme precipitation. Potential causes behind these model biases in the AR representation will also be explored. This project can help us better understand the ability of the latest generation of climate models in simulating ARs. The results of this project can also provide useful information for model developers to improve AR representations in the next generation of climate models. 

References: 

Payne, A.E., Demory, M.E., Leung, L.R., Ramos, A.M., Shields, C.A., Rutz, J.J., Siler, N., Villarini, G., Hall, A. and Ralph, F.M., 2020. Responses and impacts of atmospheric rivers to climate change. Nature Reviews Earth & Environment, 1(3), pp.143-157.

Ma, W., Chen, G., Peings, Y., & Alviz, N. (2021). Atmospheric River Response to Arctic Sea Ice Loss in the Polar Amplification Model Intercomparison Project. Geophysical Research Letters, 48(20), 1–12. https://doi.org/10.1029/2021GL094883

Student Requirements: 

The student should have some basic knowledge in atmospheric or climate sciences.  Proficiency in python, matlab or other programming language is required.

Work Location: 

Department of Atmospheric & Oceanic Sciences, UCLA

Funds available by Mentor(s)

Yes (  ) No ( x  )

If Yes, Project/Task number:

Disciplines: 

Atmospheric sciences and oceanography

Mentor (JPL): 

Jui-lin (Frank) Li, juilin.f.li@jpl.nasa.gov,

(626)660-8359

Mentor (UCLA): 

Xiaochun Wang (JIFRESSE),  xcwang@jifresse.ucla.edu

(818)393-7231

Background: 

At early stage of tropical cyclone genesis, there is limited convective cloud and precipitation signal associated with them, thus it is hard to detect tropical cyclones in their early stage. The relatively high spatial and temporal coverage wind speed and heat flux  products without rainfall contamination from NASA missions, such as CYGNSS,  might be useful in this regard.

Description: 

This summer internship program aims to i) identify precursors of  tropical cyclone genesis for major basins using CYGNSS products and other satellite observations in terms of  ocean surface wind, latent and sensible heat flux, and ocean surface waves, ii) compare precursors of tropical cyclone genesis identified from observation and those from atmospheric reanalysis, and iii) build basin dependent tropical cyclone genesis probabilistic  models in terms of these precursors.

References: 

[1] Su, H., Wu, L., Jiang, J. H., Pai, R., Liu, A., Zhai, A. J., et al.  2020. Applying satellite observations of tropical cyclone internal structures to rapid intensification forecast with machine learning. Geophysical Research Letters, 47, e2020GL089102. https://doi.org/ 10.1029/2020GL089102

[2] Wang, X.,  D. Waliser,  X. Jiang, S. Asharaf, F. Vitart, W. Jie, 2022,  Evaluating Northwestern Pacific Tropical Cyclone Forecast in the Subseasonal to Seasonal Prediction Project Database,  Manuscript  in review

Student Requirements: 

Atmospheric Science or computer science

Work Location: 

Remotely

Funds available by Mentor(s)

Yes (  ) No (*)

If Yes, Project/Task number:

Disciplines: 

Computer Science, Atmospheric Science

Mentor (JPL): 

Jonathan H. Jiang (329J)

Jonathan.H.Jiang@jpl.nasa.gov

818-207-8734

https://science.jpl.nasa.gov/people/JJiang/

Mentor (UCLA): 

Yu Gu (Atmospheric and Oceanic Sciences), gu@atmos.ucla.edu

Yun Lin (Atmospheric and Oceanic Sciences/postdoc), yunlin@g.ucla.edu

Background: 

Climate change has been an important field of consideration, especially as the climate-related weather extremes or disasters have been gotten boosting in past decades. Recent observational evidence affirmed by Hansen and Sato [2020] suggests that the global mean temperature rising rate in the past half decade (2015-2020) was well above the trend observed in past century - the global warming is accelerating now - but the causes leading to such acceleration is still not either qualitatively or quantitatively understood, particularly when the global effort to reduce greenhouse gas (GHG) emissions is ongoing.

Description: 

This project is to apply an interpretable deep learning approach to reconstruct the historical global warming rate and predict its future trend through using a combination of various input features, including both natural or human-caused climate drivers. With a well-trained deep neural network in this study, we will reaffirm the ongoing warming acceleration and gain a reliable prediction of its future trend, which will have us an early alarm on when a “tipping point” (e.g., 2°C) in climate change would come. The attribution analysis of warming rate will lend us scientific support to take effective measures to mitigate or adapt climate change at both regional and global scales. Given that the negative forcings of aerosols, normally with large uncertainty according to IPCC report, appears reduced recently due to large the recent decline in its burden, the role of aerosols in the recent warming acceleration will be particularly evaluated.

References: 

https://search.yahoo.com/search?ei=utf-8&fr=aaplw&p=Hansen+and+Sato+global+warming+acceleration

Student Requirements: 

Strong background in atmospheric science and demonstrated skills in computer programming, familiar with machine learning approaches and tools.

Work Location: 

UCLA Campus

Funds available by Mentor(s)

Yes (  ) No (X)

If Yes, Project/Task number:

Disciplines: 

Primary Discipline: Earth Science

Secondary Discipline: Computer Science
Mission Directorate: Science Mission Directorate

Mentor (JPL): 

Kazuyuki Miyazaki (329I), kazuyuki.miyazaki@jpl.nasa.gov, 626-372-1654, https://science.jpl.nasa.gov/people/Miyazaki/

Mentor (UCLA): 

Pablo Saide (Department of Atmospheric & Oceanic Sciences (AOS) and Institute of the Environment and Sustainability (IoES)), saide@atmos.ucla.edu, 310-825-4432, https://dept.atmos.ucla.edu/saide

Background: 

One of the greatest impediments to thriving on our changing planet is our understanding and predicting global atmospheric environment and its impacts on climate and human health. Current trends show two worrying signs: population and human activity are rapidly expanding towards the equator, where atmospheric conditions make productions of air pollutants more efficient. Meanwhile, the emergence of climate extremes is intensifying pollution conditions. Providing accurate global estimates of air pollution is essential to evaluate the global public health burden of disease associated with air pollution exposure, which in turn will help environmental policy making to reduce risks from associated disease and to save human lives (State of Global Air, 2020). It is for this reason that the 2018 Earth Science Decadal Survey specifically called out understanding the spatiotemporal structure of air pollutants and ozone as one of its most important objectives and a recommendation for a future Earth mission to assess changes in ozone and the associated implications for human health, air quality, and climate.

Description: 

The central objective of this project is to provide new scientific insights into an important Earth Science question: what drives (1) model prediction errors in air quality assessment, and (2) global air pollutant trends and their impact on global air quality at scales relevant for assessing human health impacts, by utilizing modern machine learning (ML) techniques that overcome the limitation of previous studies (e.g., Ivatt and Evans, 2020).

    Model-data mismatches have distinct patterns that provide clues to the unresolved emission patterns, chemical lifetime, and dynamical processes. We will utilize a large set of exogenous and input data sources, develop an ML model to break down regional bias dependence and provide scientific interpretation of key bias drivers. While incorporating advanced ML approaches, this project will utilize various NASA’s satellite measurements and atmospheric modeling and data assimilation (Miyazaki et al., 2020, 2021).

This project would give an opportunity to study and address important science questions regarding global atmospheric environment and climate and obtained cutting edge knowledge about climate modeling, data assimilation and advanced machine learning techniques that can be applied to the broad Earth science problems.  

We are looking for help with the following potential tasks:

• Analyze ML outputs with atmospheric scientists to provide insights into model bias drivers
• Work with data scientists to improve ML model and predicitons

References: 

1. Miyazaki, K., K. Bowman, T. Sekiya, M. Takigawa, J. Neu, K. Sudo, G. Osterman, H. Eskes, Global tropospheric ozone responses to reduced NOx emissions linked to the COVID-19 world-wide lockdowns, Science Advances, Vol. 7, no. 24, eabf7460, DOI: 10.1126/sciadv.abf7460, 2021
2. Miyazaki, K., Bowman, K., Sekiya, T., Eskes, H., Boersma, F., Worden, H., Livesey, N., Payne, V. H., Sudo, K., Kanaya, Y., Takigawa, M., and Ogochi, K.: Updated tropospheric chemistry reanalysis and emission estimates, TCR-2, for 2005–2018, Earth Syst. Sci. Data, 12, 2223–2259, https://doi.org/10.5194/essd-12-2223-2020, 2020.
3. State of global Air 2020, https://www.stateofglobalair.org/sites/default/files/documents/2020-10/soga-2020-report-10-26_0.pdf
4. Ivatt, P. D. and Evans, M. J.: Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees, Atmos. Chem. Phys., 20, 8063–8082, https://doi.org/10.5194/acp-20-8063-2020, 2020.

Student Requirements: 

This project requires 

• experience with Python and Linux/Unix system
• ability to communicate effectively orally and in writing
• experience with Earth science data analysis
• basic knowledge of atmospheric physics and chemistry

Work Location: 

Hybrid

Funds available by Mentor(s)

Yes ( x ) No (   )

If Yes, Project/Task number: 01STRS/ R.22.292.004

Disciplines: 

Radar and high accuracy analog measurements

Mentor (JPL): 

Andrew Berkun (334B) and David Hawkins (334B), Radar Science and Engineering Section, andrew.c.berkun@jpl.nasa.gov,  david.w.hawkins@jpl.nasa.gov

Mentor (UCLA): 

Richard Al Hadi, Electrical and Computer Engineering, alhadi@ucla.edu

Background: 

JPL Radar Science and Engineering Section is involved in cutting edge research in radar science.  Two areas we are pushing right now are small body tomography (asteroid) and common instrument electronics.  We have identified two boards we need to design and build with which we could use outside help. 

The asteroid tomography mission is a new frontier in radar science, taking 3 dimensional images of the inside of an asteroid.  The latest approach will require custom hardware to demonstrate the approach.  Also since this is destined for a cubesat, small area and low power are required, in addition to an unusual antenna design.

Common instrument electronics seeks to reduce the cost of multiple flight missions by developing hardware that can see common use.  In this case we have a rough design for an automated safe to mate device.  Safe to mate is a process used in every flight program dozens of times in which an operator measures resistance and voltage on every pin of every connector before connecting any cable.  It is time consuming, energy depleting, and normally done by hand with a battery powered volt/ohm meter.  This design will automate the process but still be battery powered.  This has been attempted before, but this time we will make it wireless and battery powered with a single lithium cell making the device safe enough that it can be used with flight hardware.  Other jobs may become available by summer.

Description: 

Both boards need a schematic, board design, reviews of both, and released drawings to manufacture the boards.  Both will use an Arduino to provide wireless communication of data products back to a host.  Both need to be battery powered.  The safe to mate box uses at least 16 analog muxes to connect pins to the Arduino analog inputs, and 4 wire resistance measurements.  The asteroid tomography mission needs a high rate ADC (250 Mhz ideally), an FPGA, power for both, and a wireless interface back to the host.  Also it needs a custom clocking circuit in which it can adjust the phase and frequency of its clock, either a DAC and Voltage Controlled oscillator or something a little simpler.  Both boards will require a host interface to inject commands and retrieve data, Bluetooth or wifi.

References: 

https://techport.nasa.gov/view/17417

https://repository.hou.usra.edu/bitstream/handle/20.500.11753/1698/LPI-002439.pdf?sequence=1&isAllowed=y

https://ieeexplore.ieee.org/document/9136060

Student Requirements: 

Some experience in circuit board design, some of it successful.  Ideally some unsuccessful board design as well, since as the great philosopher once said, “good judgement comes from experience, experience usually comes from bad judgement.” Ideally some circuits not directly assigned by classes, like a home project.  Some experience in Arduino coding.  Ideally some experience in coding spi interfaces for the safe to mate tester as the existing Arduino spi is not going to cut it.  Emulating any serial protocol manually would be good experience.  Some experience with wifi or Bluetooth.  If its wifi, java to code a user interface.

Work Location: 

Remote but requires a JPL laptop to get on Altium for board design.  Summer Altium licenses often run out, some work may need to be done outside regular working hours to secure a license on some days.  Taking the afternoon off and working in the evening instead often works out.

Funds available by Mentor(s)

Yes ( x ) No (   )

If Yes, Project/Task number:

Disciplines: 

Radar and high accuracy analog measurements

Mentor (JPL): 

Andrew Berkun (334B) and David Hawkins (334B), Radar Science and Engineering Section, andrew.c.berkun@jpl.nasa.gov,  david.w.hawkins@jpl.nasa.gov

Mentor (UCLA): 

Richard Al Hadi, Electrical and Computer Engineering, alhadi@ucla.edu

Background: 

JPL Radar Science and Engineering Section is involved in cutting edge research in radar science.  Two areas we are pushing right now are small body tomography (asteroid) and common instrument electronics.  We have identified two boards we need to design and build with which we could use outside help. 

The asteroid tomography mission is a new frontier in radar science, taking 3 dimensional images of the inside of an asteroid.  The latest approach will require custom hardware to demonstrate the approach.  Also since this is destined for a cubesat, small area and low power are required, in addition to an unusual antenna design.

Common instrument electronics seeks to reduce the cost of multiple flight missions by developing hardware that can see common use.  In this case we have a rough design for an automated safe to mate device.  Safe to mate is a process used in every flight program dozens of times in which an operator measures resistance and voltage on every pin of every connector before connecting any cable.  It is time consuming, energy depleting, and normally done by hand with a battery powered volt/ohm meter.  This design will automate the process but still be battery powered.  This has been attempted before, but this time we will make it wireless and battery powered with a single lithium cell making the device safe enough that it can be used with flight hardware.  Other jobs may become available by summer.

Description: 

Both boards need a schematic, board design, reviews of both, and released drawings to manufacture the boards.  Both will use an Arduino to provide wireless communication of data products back to a host.  Both need to be battery powered.  The safe to mate box uses at least 16 analog muxes to connect pins to the Arduino analog inputs, and 4 wire resistance measurements.  The asteroid tomography mission needs a high rate ADC (250 Mhz ideally), an FPGA, power for both, and a wireless interface back to the host.  Also it needs a custom clocking circuit in which it can adjust the phase and frequency of its clock, either a DAC and Voltage Controlled oscillator or something a little simpler.  Both boards will require a host interface to inject commands and retrieve data, Bluetooth or wifi.

References: 

https://techport.nasa.gov/view/17417

https://repository.hou.usra.edu/bitstream/handle/20.500.11753/1698/LPI-002439.pdf?sequence=1&isAllowed=y

https://ieeexplore.ieee.org/document/9136060

Student Requirements: 

Some experience in circuit board design, some of it successful.  Ideally some unsuccessful board design as well, since as the great philosopher once said, “good judgement comes from experience, experience usually comes from bad judgement.” Ideally some circuits not directly assigned by classes, like a home project.  Some experience in Arduino coding.  Ideally some experience in coding spi interfaces for the safe to mate tester as the existing Arduino spi is not going to cut it.  Emulating any serial protocol manually would be good experience.  Some experience with wifi or Bluetooth.  If its wifi, java to code a user interface.

Work Location: 

Remote but requires a JPL laptop to get on Altium for board design.  Summer Altium licenses often run out, some work may need to be done outside regular working hours to secure a license on some days.  Taking the afternoon off and working in the evening instead often works out.

Funds available by Mentor(s)

Yes ( x ) No (   )

If Yes, Project/Task number:

Disciplines: 

Aerospace Engineering or Aerospace System Engineering preferred.  Mechanical Engineering or Electrical Engineering may also be an option.

Mentor (JPL): 

Daniel Limonadi (800), Daniel.Limonadi@jpl.nasa.gov, 818-653-8203, https://www.linkedin.com/in/daniel-limonadi-894b9524/

Mentor (UCLA): 

Yuanxun Ethan Wang, Electrical and Computer Engineering, ywang@ee.ucla.edu, https://www.ee.ucla.edu/yuanxun-ethan-wang/ 

Background: 

Engineering research and prep work related to a Keck Institute for Space Studies (KISS) study we are conducting on US participation in the global climate observing system.

Description: 

Tasks will include some or all of:

• Researching and categorizing / summarizing the existing and planned climate observing spacecraft of ESA/EU, Japan, China, NASA, and NOAA.
• Setting up tools for top level evaluation of candidate observing system architectures and cost comparisons (mix of existing capabilities and new work).
• Reviewing IPCC reports and associated literature on climate change (especially dynamic adaptation strategies) to see if there are monitoring and prediction system requirements that should be derived from them.

References: 

https://gcos.wmo.int/en/home

https://www.copernicus.eu/en

https://www.ipcc.ch/

Student Requirements: 

Rising junior or senior level course work completion, especially some design / synthesis related course work.  Ideally some analysis of alternatives / decision theory exposure.  Microsoft XL or matlab (or similar tools) skills to setup simple capability and cost models for evaluating candidate system architectures.  Ability to do good research and deliver product with low levels of guidance.

Work Location: 

JPL, remote or hybrid work would be an option.

Funds available by Mentor(s)

Yes (  ) No (   )

If Yes, Project/Task number:

Disciplines: 

Atmospheric Science; Data Science

Mentor (JPL): 

Huikyo Lee (JPL, 398L), huikyo.lee@jpl.nasa.gov, https://dus.jpl.nasa.gov/home/lee/

Mentor (UCLA): 

Rong Fu (Atmospheric & Oceanic Sciences), rfu@atmos.ucla.edu, https://dept.atmos.ucla.edu/rongfu

Background: 

Climate models at convection or cloud permitting scale (e.g., 1-4 km, i.e., ultra-high resolution) can resolve convection and cloud processes and represent mountain topography, vegetation distribution, wildfire emissions and smoke plumes at a resolution close to their native scale, thus potentially substantially improving their realism. Such capability is especially important for creditable climate prediction and projection in regions of complex topography such as California. While global ultra-high resolution climate model is still prohibitively expensive, it becomes increasingly feasible to apply regionally refined global climate models (RRM), such as the NCAR Model for Prediction Across Scales (MPAS) and DOE E3SM RRM, to address outstanding climate challenges such as impacts of drought and wildfire on clouds, precipitation, snowpack, vegetation by simulations at the convection permitting scale over a region such as California.

However, the ultra-high resolution data needed for evaluating and guiding model development and improvement are still lacking. For example, there are very few ultra-high resolution global or regional climate and earth system datasets. These limited datasets are mapped onto square latitude-longitude grids. In contrast, the state-of-art ultra-high resolution models have adopted the unstructured Voronio meshes and C-grid.

Description: 

To address this data gap, and to capitalize on the rich potential for state-of-art high-resolution satellite data to support the development and improvement of the ultra-high resolution RRMs and next-generation global climate models (e.g., E3SM v3/v4), we propose to use data science to explore effective approaches to generate high-resolution satellite datasets to support development of ultra-high resolution climate models using California as a testbed.

We will start with MODIS downward shortwave radiation (1 km, 3hrs, MCD18A1, V6.1), clouds (1 km, 5-min swath, MOD06_L2, MYD06_L2), MODIS Leaf area index (500m, MCD15A2H), and precipitation data (1km, combined MODIS and ERA5 product) and map them onto the C-grid at 3 km and explore algorithms for variable resolution from 1-10 km over California.

References: 

https://drive.google.com/file/d/14Ncj_7wsHfWisj-xALNZfj2O1nIvqhUY/view?usp=sharing

Student Requirements: 

Given the technical challenge, this project requires a Ph.D. student, preferably with a data science background, as specified by the support jointly provided by the UCLA Vice Chancellor for Research and JPL Data Science Working group.

Work Location: 

TBD

Funds available by Mentor(s)

Yes (  ) No ( x )

If Yes, Project/Task number: