Each year, there are 20 projects covering a diverse range of topics in the physical and life sciences represented at the Museum, from astrophysics to invertebrate zoology. Students collaborate in teams of 2-3 with a mentor on a selected project. The project lineup evolves annually. Students identify their top 5 choices and get matched with one mentor. Each team meets twice a week after school and the exact schedule is determined within each small group. But the mentor’s availability takes priority. See examples below of recent projects.
Browse Recent Projects:
I can mentor on: Mondays: 3-6pm, Tuesdays: 3-5pm, Wednesdays: 3-6pm, Thursdays: 3-6pm, Fridays: 3-6
Topics: Conservation, Genetics & Genomics, Ecology, Evolutionary Biology, Bioinformatics
Department: Invertebrate Zoology
Bio: My name is Aaron Goodman! I am originally from San Francisco California and came to New York to start my PhD. When I am not researching, I love to run with friends around NYC, do yoga at a studio near my apartment, and go to Broadway shows! The best show I’ve seen in NYC so far is Funny Girl with Beanie Feldstein. I’m not the best cook, but my favorite foods in NYC are Pho, bodega sandwiches, sushi, and the halal carts on every block of the city. I dress like a dad, but I’m cool, I swear!
Project Title: Machine Learning: Habitat Suitability and Population Genetics of the Swamp Tigertail (Synthemis eustalacta)
Background: The Great Dividing Range (GDR) is a series of mountains, plateaus, and rolling hills dominating the eastern side of Australia (~2500km), encompassing vast differences in elevation (~1000-2300m), climate, and habitat. Numerous studies have cited the GDR’s importance as a refuge for species, especially dragonflies. The Swamp Tigertail (Synthemis eustalacta) is one such species, whose narrow distribution along the GDR has allowed it to persist and avoid extinction throughout Australia’s periods of high deserts and rainfall. However, this species has been recently threatened by largescale bushfires and flooding which have causing millions of species to become displaced or go extinct. Understanding the habitat preferences, and population genetics of Synthemis eustalacta will allow researchers to implement better conservation practices which can help save the species, which has lived on Australia for over 50 million years.
Research Question/Goal: The goal of the research is to explore the degree of genetic structuring of Synthemis eustalacta populations across the Great Dividing Range, and to determine if habitat preferences of the species matches genetic patterns.
Skills students will build: Students will learn how to extract DNA from both freshly caught and museum specimens of S. eustalacta, and learn basic code to generate evolutionary trees, and different population genetic figures to infer if populations are intermingling or staying isolated from one another. Furthermore, students will learn how to use the species distribution model program Wallace, to construct species distribution models of S. eustalacta incorporating both climatic and vegetational data.
I can mentor on: Monday Tuesday Wednesday Thursday afternoons (between 3:30 and 7pm)
Topics: Genetics & Genomics, Evolutionary Biology, Conservation, Bioinformatics, Taxonomy & Systematics
Department: Herpetology and Ornithology
Bio: I’m Alexander (he/him), originally from the South Island of New Zealand. I moved to New York last year after finishing my PhD at the Australian Centre for Ancient DNA at the University of Adelaide in Australia. Currently, I’m a postdoctoral fellow at the museum, specialized ancient and historic DNA. Since I was a child, I have been fascinated with natural history, especially animals, meaning that working at the museum has been a dream come true. When I’m not at the museum, I love getting outside, hiking, running, and exploring the city. I’m also an avid traveler, having backpacked extensively across North America, Europe, and Southeast Asia.
Project Title: Using museum collections in genetic research
Background: Natural History Museums house millions of specimens that have massive potential to yield genetic information about past populations and extinct species. Typical museum specimens (e.g., skins, skeletons, and fluid-preserved specimens) have long been considered poor sources of DNA but are now being reevaluated as an excellent source of historic DNA (hDNA) for studying changes to biodiversity over the last 200 years. Largely, ancient DNA methods have been applied when sampling museum specimens for hDNA without regard to the differences associated with techniques used to preserve museum specimens.
Research Question/Goal: In this project we will isolate DNA from museum specimens (Chameleons, lorikeets, and frogs) preserved using different techniques and integrate these into phylogenetic datasets. What new information can historical DNA impart on these datasets?
Skills students will build: Lots of hands-on activity, such as, dissecting and sampling museum specimens, extracting DNA and preparing for sequencing, analyzing sequencing data, building phylogenetic trees.
I can mentor on: Any day after 4 pm.
Topics: Conservation, Ecology
Department: Center for Biodiversity and Conservation
Bio: I'm a Computer Scientist passionate about using technology to make a positive impact. Over the past few years, I've been developing data-driven solutions and implementing state-of-the-art technologies to support Biodiversity Conservation research. Originally from Bogota, Colombia, I'm now living in NYC where I'm a Biodiversity Informatics Specialist at the Center for Biodiversity and Conservation within the Museum. In my free time, you can find me exploring the city, catching movies, and enjoying live music. Japanese food is my absolute favorite, and I'm on a mission to try every Japanese Cheesecake out there!
Project Title: Machine Learning: Automated Recognition of Wildlife Sound Segments in Biodiversity Monitoring Data
Background: Passive acoustic monitoring of biodiversity is a critical aspect of conservation research that allows scientists to study different wildlife species without disturbing them. It involves deploying multiple microphones to collect sound data from animals in a non-intrusive way during a defined timeframe. By using this method, researchers can gain valuable insights into the animal population and their habitats. This approach is particularly useful for identifying species in specific areas, including invasive, threatened, or hard-to-find species. It also helps assess the condition of their habitats. However, one significant challenge scientists face is the substantial amount of time required to review hours of recorded data and identify important sounds associated with various species.
Research Question/Goal: How can novel machine learning and artificial intelligence techniques, such as deep learning algorithms or pattern recognition models, be effectively applied to detect specific segments of interest in passive acoustic monitoring data? This research aims to reduce the time and effort required for manual review, ultimately improving the efficiency of biodiversity assessment, and advancing conservation efforts.
Skills students will build: Students will develop research and machine learning skills, gaining hands-on experience in working with acoustic data and utilizing tools such as the Python programming language and machine learning frameworks.
I can mentor on: Monday & Wednesday 3-7pm, Tuesday & Thursday 3-5pm
Topics: Conservation, Ecology
Department: Vertebrate Paleontology/Education
Bio: I teach human evolution and genetics at Hunter College. My weird collection of talents: I box, play the harp, have a motorcycle license, am SCUBA certified, and paint with watercolors. I like to read a mix of science fiction, horror, and historical nonfiction. I spend nice days in the community garden growing zucchini, tomatoes, peas, beats, and lots of herbs. My favorite shows are adult animation (Futurama, American Dad, Ricky & Morty). My movie choices are almost exclusively horror (I will tell you to watch The Relic and you should watch it).
Project Title: Deforestation in Madagascar
Background: Forests are disappearing fast, and while protecting forests helps, it may not be doing as much as we think.
This project will focus on deforestation trends using remote sensing data from NASA’s satellites. Students will use the R programming language to develop scripts to process and analyze raw data. The main goal will be to understand why some protected areas in Madagascar seem to have lower deforestation rates than others. This project will build on work from several previous SRMP years, but this time we will be accesses new databased to look at how forest fires and rainfall impact deforestation. Students might be interested in this project if they like writing computational scripts, developing beautiful maps, or want to understand more about deforestation trends or conservation in general. Check out some of the work from previous years.
Research Question/Goal: Understand all impacts of deforestation (human factors and environmental factors) to help prevent further forest loss.
Skills students will build: Coding in R, accessing government databases like those available from NASA, creating visuals to communicate research
I can mentor on: 10-7 Tuesday, Thursday, Friday
Topics: Ecology, Evolutionary Biology
Bio: From the year 1965, my affinity for the mesmerizing world of snakes began, a fascination that grew with time. In 1975, I embraced the rebellious spirit of skateboarding, finding solace and joy in defying gravity. The year 1985 marked the start of the noble pursuit as a biology teacher, dedicated to unraveling the secrets of life itself. It was in 1995 that destiny led me to the illustrious halls of the AMNH, where my love for knowledge and discovery found a sanctuary. But perhaps my greatest role, my most profound adventure, materialized in 2005 when I became a dad, nurturing and guiding the next generation with unwavering love. And now, as my story unfolds, I embark on yet another chapter, using the remarkable tool of ChatGPT to pen this autobiography, the words echoing across time and space. Through these words, you shall witness a testament to my boundless passion, indomitable spirit, and unwavering dedication.
Project Title: Complexity in Mimetic Systems in African Elapids
Background: Mimicry has not been intensively studied in African snakes. Published data suggests there are layers of complexity including color pattern, posture, and behavior involving multiple networks of venomous and non-venomous snakes.
Research Question/Goal: We will characterize the complexity of mimetic systems involving African cobras, close relatives, and non-venomous species, including construction of networks of mimetic relationships incorporating geography, ecology, morphology, and behavior.
Skills students will build: Literature research, museum specimen examination, network analysis, characterization/quantification of phenotype variation.
I can mentor on: Mondays and Wednesdays, from 4 - 6pm
Topics: Observational Astrophysics, Earth and Planetary Science
Bio: My name is Dax Feliz and I am a Postdoc Fellow in the Department of Astrophysics here at AMNH. I grew up here in Spanish Harlem and went to Massachusetts for college and then Tennessee for graduate school where I earned my PhD in Astrophysics. I specialize in the detection and discovery of transiting planets and binary star systems outside of our solar system as well as measuring how often stars flare. When not working on science projects I like to cook food from different cultures, paint, check out different areas of NYC and play video games with my friends.
Project Title: Detection of Transiting Exoplanets and Eclipsing Binaries Around Nearby M-dwarf Stars
Background: M-dwarf stars are the most numerous type of star in our galaxy but due to how faint they are, they are fairly understudied. As of August, 2022 out of the 5,200+ exoplanets discovered, only about 100 are known to orbit nearby M-dwarf stars. Additionally, M-dwarf binary star systems are also difficult to detect for similar reasons. There could be many more systems out there left to be discovered!
Research Question/Goal: How many M-dwarf hosting planet and binary star systems can we detect within 100 parsecs of the Sun? What are the distributions of planet and star parameters of these systems? Are there general trends in these distributions that may suggest something about how these systems formed?
Skills students will build: Programming: Python primarily, also bash and LaTeX for editing Research: Accessing public databases from NASA and MAST, learning to visualize data and communicate research.
I can mentor on: Apurva is most available on Tuesdays and Wednesdays.
Topics: Bioinformatics, Genetics & Genomics, Ecology, Taxonomy & Systematics, Conservation, Evolutionary Biology
Department: Comparative Genomics
Bio: Hi I'm Dean. I grew up in NJ. I love hiking, birding, building computers, and reading news (especially science news). I enjoy cooking new foods and learning about food science. I'm getting into houseplants but this is harder than I thought - some of them are pretty fussy. I really love what I do for work. I really love the museum and doing science with my colleagues. I think I enjoy making figures for papers WAY more than I should. Hi I’m Apurva. I’m big into rock climbing. I make a mean vegetarian pho (it can be done!) I’m from Chicago so I dig on thick, goopy pizza and the Cubs. I have a dog named Carmen. She’s a 16-year-old rottweiler which is crazy.
Project Title: Using new diversity measures to look at spatial changes in microbial communities.
Background: Existing diversity measures are often limited to matching field observations to databases of known organisms. But we have devised measures that track diversity across environments using all the sequence regardless of whether it’s been annotated. These measures are cool because they involve a field of physics and math called information theory. You will be working on concepts that bridge some fun math, microbial biology and ecology.
Research Question/Goal: We would like to measure spatial changes in the gut microbiome, looking at how microbial samples from different sections of the human digestive system differ as you go from the small to large intestine. We will be using recently published data set collected using an ingestible device programmed to gather data from early, late and intermediate intestinal points. We will also be looking at the degree of spatial heterogeneity in freshwater lakes, paying special attention to changes seen after a heavy rain or other flood pulse perturbs the system.
Skills students will build: This project will be solely computational. We will be using existing datasets to test these new ideas. Students will learn how to use UNIX, how to manage and analyze data on powerful supercomputers, and how to synthesize data science results into eye-catching figures that hopefully reveal a lot about what our new information diversity metrics have to say about the human gut and freshwater lakes!
I can mentor on: 2-8 Tuesday, Wednesday, Thursday
Topics: Observational Astrophysics, Theoretical Astrophysics
Bio: I am a fourth year physics PhD candidate at the CUNY Graduate Center working in conjunction at AMNH with Jackie Faherty and Kelle Cruz. My research interests center around understanding how brown dwarfs, an astronomical object in between the boundary of stars and planets, form. One way I do this is by learning about the types of clouds and atmospheric dynamics in brown dwarfs to try to chemically trace their origins. In my free time, I knit a lot and am put to work by my friends to design and host our craft nights.
Project Title: Machine Learning: Discovering Comoving Brown Dwarfs with Machine Learning
Background: Brown dwarfs are very mysterious objects – they hide their mass, age, radius and chemical makeup very well. However, brown dwarfs that move with other astronomical objects, like stars, are key in helping our understanding of such fundamental properties. Co-moving brown dwarf systems provide a wealth of information that act as clues on our hunt to understand brown dwarf formation history and evolution. We can use our knowledge of the system and its other components to act as a baseline in our investigation of the brown dwarf. However, there are very few of these systems known (<100) so it is important that we continue to focus on search and discovery.
Research Question/Goal: Can we use machine learning techniques to examine CatWISE data, an all-sky infrared catalog, and discover comoving brown dwarfs?
Skills students will build: This is a computational project. You will learn how to use Machine Learning techniques like decision trees, clustering, and regression along with Bash and Python to analyze large astronomical datasets.
- Bash and Python coding languagesJupyter notebook
- Using and analyzing large astronomical datasets
- Machine learning versus modeling
- Machine learning techniques: decision trees (random forest), k nearest neighbors (kNN), regression (support vector machines (SVM)), clustering (K-means)
- Parameter optimization (cross validation techniques) and diagnostic tools in machine learn
I can mentor on: 10-7 Tuesday, Thursday, Friday
Topics: Bioinformatics, Genetics & Genomics, Evolutionary Biology, Conservation
Department: Invertebrate Zoology
Bio: This is my third year as a PhD student at the American Museum of Natural History. I earned my undergraduate degree in genetics, genomics and biotechnology from Brigham Young University. My research interests include evolutionary and conservation genomics, with a particular interest in dragonflies and damselflies. In my free time I love basketball, hiking, and mountain biking.
Project Title: Delimiting species in giant dragonflies
Background: The giant dragonflies (genus Uropetala) are among the largest insects on the planet. They have also been around for tens of millions of years! They are also highly endangered. Debate continues about whether this genus contains one or two species. Answering this question is key to ensuring the long-term survival of this genus.
Research Question/Goal: We are going to use genomics to determine how many species are present in the dragonfly genus Uropetala
Skills students will build: Scientific writing, bioinformatics, DNA extraction and super computing.
I can mentor on: Monday to Friday at any time
Topics: Observational Astrophysics, Theoretical Astrophysics
Bio: My name is Genaro Suárez and I am originally from Mexico. I am a postdoc at AMNH in the Brown Dwarfs in New York City research group. Before coming to NYC, I was a postdoc in Canada and got a PhD in Astrophysics in Mexico. When not researching, I love playing and watching sports, especially soccer. I also enjoy dancing and exercising. I like cooking together with my wife. My favorite food is Mexican Cuisine, especially spicy dishes.
Project Title: Weighing Stars: How do we know how heavy stars are?
Background: We learn about the Universe in a synergy between observations and theory. Ideally, observed astronomical phenomena must be explained by our current theoretical models and theoretical predictions have to be confirmed with observations using telescopes. In this project, we will evaluate the accuracy of the most recent atmospheric models to reproduce observations of exoplanet analogs, which will allow us to estimate how hot the object is and its mass, among other parameters.
Research Question/Goal: We will challenge our understanding of the physics and chemistry at play in the atmospheres of worlds beyond Earth. We will test state of the art models of these atmospheres using optical and infrared astronomical data from space and ground based telescopes.
Skills students will build: Students working on this project will learn how to manage big astronomical datasets using software and programming languages (e.g. python) to inspect, visualize, and analyze databases. Students will also develop teamwork and communication skills. The experience gained in the project has applications in other fields of astronomy and other areas of science. This project does not require previous experience researching or programming.
I can mentor on: Mondays or Tuesdays and Thursdays 4-6pm
Topics: Conservation Ecology
Department: Ecology, Evolution and Environmental Biology
Bio: I grew up in southern California but moved to NYC in 2012 to work as a Mammal Keeper at the Bronx Zoo, before eventually deciding to pursue graduate school. I love nature and wildlife of all kinds; I enjoy spending time in NYC parks and hiking in the Hudson Valley any chance I get. I am also an avid sports and fitness fan. My favorite sports to watch are football and ice hockey. I play hockey and basketball in the city, and I try to get out to Long Island to surf in the summers. I am also a personal trainer; I love coaching/teaching weightlifting and fostering interest in all kinds of physical activities.
Project Title: Machine Learning: Wildlife in the Heights: research on mammal diversity in Highbridge Park
Background: New York City parks are excellent places for people to enjoy, but they also provide critical habitat for many wild animals that most New Yorkers would not imagine living in the city, including coyotes, groundhogs, opossums, and more. Washington Heights has been subjected to historical and current environmental injustices, particularly regarding access to and proportion of green spaces. The recently renovated and reopened Highbridge Park, in the heart of Washington Heights, offers an excellent opportunity to illustrate the diversity of mammals that call this park in northern Manhattan home.
Research Question/Goal: Which mammal species live in Highbridge Park and at what quantities?
What is the species diversity within Highbridge park (species richness and evenness)?
Skills students will build: In this project, students will learn how to use Machine Learning to process camera trap photos and identify species.
We will then train our own model by manually classifying photos and feeding them to a ML model in several ways. By doing this with various sizes of training data (including adding on to existing trained data sets), we can explore the power of data in ML and how more data – specifically our data – will help make our models more accurate and calculate biodiversity metrics.
I can mentor on: Any day 4-6 pm
Topics: Archaeology, Anthropology
Bio: I am a doctoral candidate from the CUNY Graduate Center. I first started my archaeology career studying Caribbean indigenous peoples. I analyzed fish, shell, and crab remains during my undergraduate years. I also worked in a human osteology lab. During graduate school, I switched to studying the ancient Maya and their diet. Now my focus is mainly on identifying mammals, reptiles, and some shells. When I am not doing archeology or managing SRMP, I spend a lot of time with close friends and adventuring around the city and the outdoors.
Project Title: What were the Maya eating 2,000 years ago?
Background: The ancient Maya site of Nixtun-Ch'ich' (NC) is located in Peten Guatemala. NC experienced a cultural boom around 500 BC with the construction of large public monuments and a heightened population. People at the site exploited a range of natural resources from their local environments but also some that required traveling long distances.
Research Question/Goal: The goal of this project is to understand what people were eating and in what spaces those materials were found. Since NC is interspersed with domestic and public structures, we want to understand what sources of food were important to residents in households and what products were important for public events. Ancient Maya society is girded by a series of religious, political, and events open to the wider public. These activities formed the ideological framework of Maya society and reflect why people made certain decisions in the past. In order to understand the possible use behind structural remains that are no longer visible to archaeologists in their full form, we first need to identify the animals that people chose to eat or sacrifice.
Skills students will build: Students will learn how to use zooarchaeological quantification techniques to determine the richness and abundance of fauna, learn how to visualize data using statistics, understand the significance behind the selection of animals between different contexts and why this is important in archaeology, and identify animal bones using comparative collections.
I can mentor on: Monday, Tuesday, Wednesday, Thursday, 4-7 pm
Topics: Earth and Planetary Science
Department: AMNH Education; Center for Climate Systems Research (external)
Bio: I have always been fascinated by earth and sky, which was a bit of a challenge growing up in the Bronx! It was probably as much my love of sci-fi/fantasy as of Earth sciences that ultimately led to the work I do now, thinking about the surface conditions, especially climate, that could support life on other planets. When I’m not geeking out over my favorite kaiju, I also like to explore human (pre)history, and human interactions with the natural world. You can never go wrong with pizza.
Project Title: Climate Modeling as a Means to Explore Environments That Can Support Life
Background: Ever since telescopes have allowed astronomers to see features of our neighboring planets in the Solar System, people have wondered if life ever developed elsewhere – or if Earth was unique as a host for life, intelligent or otherwise. Thirty years ago, we knew of only one planet outside of our Solar System; now, with the help of powerful space telescopes, we’ve found nearly 5,500 confirmed exoplanets, and more are being discovered regularly. We don’t have the ability to look at each of these exoplanets closely for signs of life, so we need to take another approach to determining whether these worlds are habitable – that is, capable of supporting life (as we know it). Understanding the global climates of these worlds is an important part of this process.
Research Question/Goal: An exoplanet is considered potentially habitable if its surface temperature is in the right range to allow the presence of liquid water. But as powerful as our space telescopes are, we aren’t yet able to actually see if water is present on an exoplanet’s surface, and the simplest calculation of an exoplanet's temperature doesn’t account for the influence of an atmosphere and/or oceans. So, how can we determine which of the many discovered exoplanets might be habitable for life?
Climate models are complex computational models that simulate features of a planet’s atmosphere and oceans. We use these models to explore the range and combination of environmental factors that are more likely to support life. Past climates in Earth history, which are often very different from modern Earth, can serve as examples of a variety of habitable conditions – but sometimes, exoplanets are so different that we simulate their climates directly. All these climate experiments allow us to test hypotheses about which large-scale environmental factors – like orbital configuration (how is the planet positioned with respect to its star? how fast does it rotate?), atmospheric composition (which greenhouse gases are present, and how much?), and land/sea distribution (where are the continents, and how large are they?) – can be important in supporting the persistence of life on Earth in the past, as well as on other Earth-like exoplanets more generally.
Skills students will build: As part of my team, you’ll learn about how climate models work – what goes into a model, what comes out, what the limitations for using them are, and how researchers design experiments to test hypotheses – with a chance to design some simple experiments of your own. You’ll also learn how to create effective visualizations of output from a state-of-the-art NASA climate model, then use those visualizations to analyze experiments of various past Earth and/or exoplanet climate scenarios. We’ll place all that information into the context of how planets are being discovered and targeted for future observations. Along the way, we’ll also have a lot to talk about: what makes a planet “Earth-like,” and why researchers don’t always agree; what ethical concerns surround the search for life; what counts as “life” worth discovering; and how might we know if we’ve discovered life that’s not from this Earth.
I can mentor on: Tuesday/Thursday after 3PM
Topics: Earth and Planetary Science
Bio: I grew up in Buffalo, NY and am currently a PhD student studying oceanography and waves on the ocean surface. I enjoy wandering around New York, exploring new parks or looking for good food (I am especially fond of ice cream) or live music. I spend the rest of my free time reading, or watching sports like soccer and football.
Project Title: Ocean Dynamics and Biogeochemistry: exploring their connections through lab experiments and global, real-time data
Background: Dynamic ocean features, such as currents and eddies, affect water properties including temperature, salinity, and the concentrations of dissolved oxygen and nutrients required for photosynthesis. By improving our understanding of how ocean dynamics affect ocean chemistry, we can also examine the relationship between ocean dynamics and biological processes.
Research Question/Goal: We want to understand how biogeochemical properties of the ocean relate to physical dynamics like currents and eddies. We will simulate ocean dynamics in rotating tank experiments in the lab, and then apply these concepts to analyze real-world/real-time data from ARGO floats. ARGO floats comprise a network of global robotic instruments that drift freely, following ocean currents and measuring biogeochemical properties of the ocean. By analyzing ARGO float trajectories and measurements, we will identify dynamic ocean features and explore how they influence the chemical properties and biological processes of the ocean.
Skills students will build: In the lab we will run rotating tank experiments to simulate large scale ocean dynamics. Students will learn data analysis and coding skills (in Python or Matlab) to interpret ARGO float data. The project will also build skills in scientific communication and formulating research questions.
I can mentor on: Monday-Thursday after 4:30 pm; Friday after 3:30 pm.
Topics: Evolutionary Biology, Taxonomy & Systematics
Department: Invertebrate Zoology
Bio: I hail from Recife, a city in the Northeast of Brazil, meaning 'reef' in Portuguese. I moved to the US for a year as an exchange student in high school and have since lived here at different times. Maybe for that reason, I love to travel and try to do it as much as possible in my free time and for collecting trips for work. Reading is also a passion of mine. I have been obsessed with various books throughout my life, ranging from around-the-world trips on sailing boats during my college days to mystery books and, recently, books written by Latino authors. I love podcasts and listen to them while working on the computer or examining samples under the microscope. This year, I aim to learn how to play the piano and pandeiro, a Brazilian percussion instrument, and practice Spanish.
Project Title: Sea anemones through x-rays: re-assessing the utility of traditional anatomical features
Background: Sea anemones are abundant marine invertebrates among the simplest animals on the planet. Due to the simplicity of their body, these animals are difficult to name and classify, obscuring our understanding of how many species exist and how and why they became so common in all marine habitats. These animals have been historically best known from the North Atlantic due to a higher number of specialists in the US and Europe, with the South Atlantic poorly known. Despite the efforts of Diva Corrêa in the 1960s, the pioneer in the study of sea anemones in Brazil, and the recent increase in recent sea anemone research in the country, the 58 species described for the region is an underestimation. Given their worldwide diversity (1200 species), Brazil’s extensive and diversified coast (7400 km ~ 4500 miles, plus four oceanic islands), the various influences in the Brazilian coastline potentially supports a much higher number of species. Our main focus will be to identify collected material and update the fauna of sea anemones in Brazil.
Research Question/Goal: In this project, we will work with preserved animals and identify them using morphology and molecular data. Our goal is to observe and describe them using traditional techniques to observe their morphology (dissections, histological sections, microscopy) and modern techniques such as CT scanning. We will also combine what we find using morphology with DNA and generate evolutionary trees to estimate the species’ relationships. For the few anemones for which CT scans are available, we will also generate 3D models to evaluate their morphology and print them using a 3D printer.
Skills students will build: You can expect a lot of hands-on activities, including learning how to dissect preserved animals, making histological cuts to observe their tissue, and using the microscope to analyze slides of both histology and nematocysts (the sting cells of sea anemones). You will also learn how to use software to read and analyze CT scans and construct and print 3D models. In the molecular lab, you will extract DNA, make PCRs, and analyze DNA sequences using programs that estimate how species have diversified and are related to each other. There are many potentially new species to science, so you will learn about naming and publishing new species. In parallel, you will be able to understand the scientific process in detail and how it can be applied to research questions. You will collaborate with each other, read and discuss research papers and other topics of relevance in the field, and be able to communicate your findings to other scientists and the general public. While you develop your research skills, you will be able to integrate your views of science with concepts of Diversity, Equity, and Inclusion so you can become a responsible science practitioner.
I can mentor on: 5-7 Tuesday (prefer), 10-7 Monday, Friday
Topics: Earth and Planetary Science, Observational Astrophysics
Bio: I’m a PhD candidate at Columbia University and will soon become a postdoc at AMNH. I was born and raised in China and moved to the US in 2018 for college. I’m a member of the LGBTQ+ community. My research focuses on analyzing observational and simulation data of the Milky Way and understanding the history of our own Galaxy and the stars within using these data.
Project Title: Machine Learning: Predict Rotation Periods with Random Forest
Background: How fast a star rotates around itself decreases with time as it gradually loses energy at its surface. Using this physical principle, astronomers can predict ages of stars. The age of a star can then be used to estimate how old planets around the star are. This is important because the 10 million year old Earth is a fireball, which is completely different from what the Earth looks like now!
Research Question/Goal: Rotation periods of a star can be hard to measure. Typically astronomers will look through the period measurements one by one and accept the ones that are correct. However, this is impossible if we want to measure rotation periods, thus age, for billions of stars. As a result, we will resort to machine learning to predict and select periods. Using Random forest (a so-called classical machine learning method) we will predict rotation periods of stars using their temperature, magnitude, velocities, and position measurements. We will then look at how this method does compare to using a simple linear regression model (the simplest machine learning). Using this, we will accept or reject million period measurements, and in the end, measure million ages.
Skills students will build: Students will gain astronomy knowledge and coding skills: Astronomy knowledge: Why do stars spin-down overtime? How do people measure rotation periods? How do we estimate ages with rotation periods? How do we know if the period measurements are correct?
Coding/ML skills: How to use python to read in and analyze rotation period measurements, what are the different machine learning techniques, what is a random forest and how to use it using python.
I can mentor on: Monday-Friday, 1-7pm – strongly prefer meeting on back-to-back days due to time constraints of my laboratory work
Topics: Earth and Planetary Science, Cosmochemistry
Department: Geosciences/Earth and Planetary Sciences
Bio: My name is Marina Gemma, and I am a postdoctoral researcher at Stony Brook University. I’m a meteoriticist (also known as a cosmochemist) meaning I use skills and knowledge from astronomy, chemistry, and geology to study extraterrestrial material that is delivered to Earth in the form of meteorites. I completed my PhD research at AMNH in 2022 and was able to use some of the many meteorites in the museum’s collection to address unanswered questions about our Solar System’s history. I grew up in Southern California, but I moved to NYC at 18 years old for college and have been here ever since! When not working on meteorites in the lab, I love to read, rock climb, try new coffee shops across NYC, and surf when I find myself near an ocean.
Project Title: Chondrite Chemistry: Unraveling the Origins of Our Solar System
Background: The origin of the earliest solids in our Solar System, preserved for 4.56 billion years in primitive meteorites, is poorly understood, in part because of the lack of detailed chemical data on individual minerals within these materials. These early solids are the building blocks of the planets we see today, and fossilize the chemical and physical conditions that existed at the time of their formation. By studying the structure, chemistry, and components of primitive chondritic meteorites, we can reveal the chemical environment, structure, and origin of our Solar System.
Research Question/Goal: What was the chemical environment like in the early Solar System? How did chondritic meteorite components form, accrete, and evolve during the time of planet formation? This research will use chemical and structural data from the oldest objects in our solar system to decipher the conditions of the early solar system that led to the formation of the diverse planets we see today.
Skills students will build: In this project, students will learn:
- hands-on experience working with meteoritic samples; basic planetary science knowledge -processing and manipulation of chemical and structural cosmochemical datasets
- data analysis and visualization using Python -optional: development of machine learning based tools for meteorite image analysis
- scientific collaboration and communication skills
I can mentor on: Mon, Tues, Thurs
Topics: Observational Astrophysics, Theoretical Astrophysics
Bio: My name is Mark Popinchalk and I am an astrophysics postdoc here at the Museum. I study the relationship between how quickly stars rotate and how old they are (think of how a spinning top slows down over time, but with stars!). Specifically, I look at young stars (ones formed after the Dinosaurs), and small stars (that are cooler than other ones, both in temperature and social status). I also engage in science outreach and education around the city, including planetarium shows here at the Museum. When not working on science projects I like to bake bread, play board games, and run around playing ultimate frisbee.
Project Title: Stellar Cartographers - Investigations of Surface Maps in Young Moving Groups
Background: Stars are so freaking far away! Telescopes only see them as a point of light. But if that light changes brightness, that might be because there are dark starspots on the surface of the star that are coming in and out of view. The amount of starspots a star has will change over its lifetime, and tell us about the magnetic field of the star.
Research Question/Goal: My project will use data about the star’s brightness to attempt to create a map of the star. From these maps we will look to see if there is a trend in spot structure across age in young groups of stars.
Skills students will build: We will gain a surface level understanding of spherical harmonics, use Python scripts to run analysis, and extensive Python plotting and file manipulation.
I can mentor on: M, W, Th afternoons, 2-7 (availability subject to change!)
Topics: Genetics & Genomics, Bioinformatics, Evolutionary Biology, Ecology
Project Title: Using high-performance computers to explore genes, genomes, and evolution: the case of the very picky caterpillar
Background: Evolutionary biology seeks to answer some of life's biggest questions: What is life? How has it changed over time? Why do organisms look and act the way they do? How does the environment affect how organisms evolve? These questions are just another day at work for evolutionary biologists like Martine and Sara! Join us, we are excited to teach you how to answer these questions.
Step one is to break these big questions down into smaller ones we can examine with carefully designed studies. One of the best places to start is the genome - the collection of all an organism’s genes which is the raw material that determines how they look, how they act, and how they have adapted to their environment.
Comparing genomes among different organisms is a powerful way to identify where and how these genetic changes happen. The methods of comparative genomics are how we find the genes that cause diseases, explain why some people sneeze when they look at the sun, and why all dog breeds are the same species but look completely different. Members of our research team will learn the theory and practice of comparative genomics from the ABCs to the level of a professional research scientist.
Research Question/Goal: Food is fundamental to life, and this is as true for insects as it is for humans. And sometimes, humans and insects want the same food. Using comparative genomics, we will explore the genetic basis of feeding differences between two closely related moths, Chloridea virescens and Chloridea subflexa. Chloridea virescens feeds on many different plants, many of which are human agricultural crops (tomatoes, corn, and green peppers are just a few examples). This broad range of host plants makes C. virescens one of the most serious crop pests in the world. Chloridea subflexa has a very different lifestyle and eats only one sort of plant. We want to understand how such close relatives can have such different ways of adapting to life on Earth. By comparing their genomes, we will discover which genes vary between the species and what specific biological functions are involved in eating everything versus eating only one thing.
Skills students will build: This study is all about understanding genes, genomes, and the techniques used to study sequence data. Genomics is a branch of data science and uses high performance supercomputers to process large amounts of data, assemble genome sequences, identify genes and gene functions, and compare them. Students will learn professional-level computation skills for accessing and working on supercomputers. No computational background is required, we will teach you every thing you need to know from the ground up--in fact, we especially welcome students who are curious about computational work but feel that it might be too difficult or scary for them. You can do this!
I can mentor on: Mon-Thur: 4-7
Topics: Genetics & Genomics, Bioinformatics, Evolutionary Biology
Bio: After receiving my PhD in 1999, I conducted postdoc research at University College London and the Joint Genome Institute (near San Francisco, CA). I joined the Comparative Genomics Lab at AMNH in 2007 and have been mentoring SRMP students for the past twelve years. I live in Clinton Hill, Brooklyn.
Project Title: Comparative Genomics of Spider Predation Genes
Background: Spiders are one of the most dominant predators of insects on the planet and have evolved numerous specialized structures that allow them to capture their prey efficiently. Silk, spun in the form of different types of webs, is the best-known adaptation that spiders use for hunting but they also utilize specialized venoms, sensory systems and behaviors. In this SRMP project, we will study the genetic basis of unique foraging strategies that have evolved in different spider groups.
Research Question/Goal: The primary research focus will be to identify which genes are important in the foraging system of the spiders and examine how those genes have evolved. The exact spider group that we work on will depend on collecting done in August but possibilities include the bolas spiders that uses a unique lasso-like silk and glue system, along with pheromone mimicry, to catch moths or the ogre-faced spider that hunts actively at night with a web it holds in its legs and has evolved huge eyes that provide it with some of the best night-vision found in any animal.
Skills students will build: This research will allow students to learn bioinformatic computer analysis and, wet lab molecular techniques associated with both RNA and DNA sequencing. Most of the first semester will be spent in the molecular lab and most of the second semester will be spent conducting computer analysis of the sequence data collected in the fall.
Take a look through the posters below showcasing SRMP projects from the last 5 years.