First Year Research in Engineering Experience

2025–2026 application deadline: October 22, 11:59pm

1. You may only apply to one Dartmouth-funded research program during your first year (eg. ERAS, URAD, E.E. Just DALI, etc.).
2. Review the list of available projects below and make a selection.
3. Submit an online application form for each project before the deadline listed above.
4. Not all applicants will be contacted for an interview, nor admitted. (We always have more applicants than we do projects. If you are not selected, we encourage you to reach out to faculty directly if interested in their research.)
5. Faculty mentors will interview applicants and select a student for each project.
6. Decisions will be made before the start of winter term.

Mechanical properties of additively-manufactured stainless steel

Faculty: Ian Baker
Mentor: Jack Brady
Location: MacLean ESC

Additive manufacturing, in which a component is made layer-by-layer, is becoming increasingly important for manufacturing parts. In this project, the student will determine the microstructure and mechanical properties of 316L stainless steel rods that have been produced from powders by laser powder bed fusion in both vertical and horizontal orientations. The aim is to determine how this different processing affects the microstructure, room-temperature tensile properties, and elevated-temperature creep behavior. The student will use electron backscattered imaging in a scanning electron microscope and determine the mechanical properties. Full training will be given.

Electropulse annealing to enhance mechanical properties of metallic alloys

Faculty: Ian Baker
Mentor: James “Russ” Taylor
Location: MacLean ESC

To obtain their optimum properties, metallic alloys are usually annealed in order to modify the microstructure. Electropulse annealing, in which a large current is passed through a material in short pulses, has been found to accelerate the kinetics of some phase transformations compared to annealing in a conventional furnace, and lead to enhanced mechanical properties. In this project, the student will explore the effects of electropulse annealing on the mechanical properties of various alloys and will characterize the resulting microstructures using electron backscattered diffraction in a scanning electron microscope and optical microscopy.

Game-theoretic reinforcement learning for cybersecurity

Faculty: Peter Chin
Location: Remote

It is crucial for computer networks to be secure, trustworthy, and reliable and to respond to or even predict ever-increasing cyberattacks. Although advancements in deep learning, especially reinforcement learning (RL), offer hope to keep up with this challenge, the application of RL to cybersecurity has not achieved its full potential. Cyber-defense teams often fail to understand that their actions to defend the network are exactly what their adversaries are expecting. An adaptive and decentralized RL framework is needed to combat the sophistication level of today's cyberattacks. The student will work in the LISP (Learning, Intelligence + Signal Processing) Lab and help develop adaptive, hierarchical game-theoretic training frameworks that can predict future cyberattacks.

Making federated learning more robust

Faculty: Peter Chin
Location: Remote

Distributed learning paradigms such as federated learning often involve transmission of model updates, or gradients, over a network to avoid transmission of private data. It is possible, however, for sensitive information about the training data to be revealed from such gradients. We have developed a method—applicable to a wide variety of model architectures across multiple domains—to discover the set of labels of training samples from only the gradient of the last layer and the id to label mapping. The project will be to improve the effectiveness of our method for model training in various domains—image classification, automatic speech recognition, and more.

Deep routing in the real world

Faculty: Peter Chin
Location: Remote

Routing through networks is a fundamental part of our lives, whether it’s the route we take to a new restaurant or the data routed to us through the internet. All routing approaches rely on the ability to approximate the distance between a node in the network and the target node. Road networks are inherently embedded onto the surface of the earth, but there is no natural embedding for artificially constructed networks like the internet. We want to explore how deep learning can help generate or adjust embeddings, to improve the performance of routing algorithms. To start, we will evaluate existing deep-learned embeddings for routing on a variety of real-world networks, and use the resulting insights to adapt and improve the embedding method.

Applying manifold learning technique to design recurrent architecture for low dimension classification

Faculty: Peter Chin
Location: Remote

Deep neural networks (DNNs) can have high performance in a visual recognition task but are prone to noise and adversarial attacks. One main problem of training a DNN is that the input often lies in a high-dimensional space, which leads to a high number of parameters to train. This raises the question of reducing the number of dimensions of the dataset. Given a high-dimensional dataset such as a visual dataset, we will explore how to find a lower-dimensional representation that keeps the essential information of the images. With a low-dimensional representation, we can hopefully use a more shallow/simple architecture that can decently classify high-dimensional datasets.

Complex-valued neural networks

Faculty: Peter Chin
Location: Remote

Current neural network models do not incorporate phase information in a meaningful way, whereas the output of biological neurons is affected by the phase of its inputs. In order to bridge this separation between artificial neurons and biological neurons, I am experimenting with complex-valued neural networks, which would integrate the phase information meaningfully. In particular, to take advantage of the popular neural network framework, PyTorch, I am working to simulate complex-valued neural network operations through real-valued neural networks, which I will then share with other researchers.

Information propagation through graph neural networks and relation to the brain

Faculty: Peter Chin
Location: Remote

A popular theory argues that intelligence arises from the connections between primitive computing units rather than the computing units themselves. Interestingly, neurons in the brain form topological structures for processing different types of information. We are exploring the relationship between graph topology and model performance using graph neural networks, and comparing our findings to known phenomena in the brain.

Target propagation instead of backpropagation?

Faculty: Peter Chin
Location: Remote

Deep neural networks trained with back-propagation have been the driving force for progress in fields such as computer vision and natural language processing. However, back-propagation has often been criticized for its biological implausibility. Most biologically plausible alternatives are designed and tested for feedforward networks, and their ability for training recurrent networks and arbitrary computation graphs is not fully studied nor understood. In this project, we propose a learning procedure based on target propagation for training multi-output recurrent networks which opens doors to extending such biologically-plausible models as general learning algorithms for arbitrary graphs.

IQ test for machine learning models

Faculty: Peter Chin
Location: Remote

Current machine learning algorithms are highly specialized for one task—eg. playing chess, picking up objects, or object recognition. How can we extend this to a system that could solve a wide range of problems? We argue that this can be achieved by a modular system—one that can adapt to solving different problems by changing only the modules chosen and the order in which those modules are applied to the problem. The recently introduced abstraction and reasoning corpus (ARC) dataset serves as an excellent test of abstract reasoning. Suited to the modular approach, the tasks depend on a set of human core knowledge inbuilt priors. In this project, we implement these priors as the modules of our system and combine these modules using a neural-guided program synthesis.

Improving audio processing efficiency with deep learning and signal processing

Faculty: Peter Chin
Location: Remote

Due to deep neural networks (DNNs) and their ability to learn complex patterns from vast amounts of data, deep learning has significantly advanced the field of audio processing, transforming how audio data is analyzed, synthesized, and understood. Deep learning has certain drawbacks, however, such as high computation/inference cost, high latency, privacy in training, etc. On the other hand, classical signal processing is applied to many long-lasting problems in audio with the advantage of low computation cost and low latency. Our focus, therefore, is on how to improve audio processing efficiency by using the advantages of both deep learning and signal processing approaches. We focus on several audio signal processing tasks, namely low-latency speech enhancement (and echo cancellation), privacy in training automatic speech enhancement, and audio codec.

Learning topological features via path complexes

Faculty: Peter Chin
Location: Remote

Graph neural networks (GNNs), despite achieving remarkable performance across different tasks, are theoretically bounded by the 1-Weisfeiler-Lehman test, resulting in limitations in terms of graph expressivity. Although prior works on topological higher-order GNNs overcome that boundary, these models often depend on assumptions about substructures of graphs. Our study presents a novel perspective by focusing on simple paths within graphs during the topological message-passing process, thus liberating the model from restrictive inductive biases. Without making prior assumptions about graph sub-structures, our method outperforms earlier works in other topological domains and achieves state-of-the-art results on various benchmarks.

Assessing sustainability in micro- and nano-scale additive manufacturing

Faculty: Rebecca Gallivan
Location: Cummings 119H and Cummings 120

Micro- and nano-technologies have enabled major advances in healthcare, electronics, computing, and sensing. These technologies often involve fabrication techniques that require toxic chemicals, high energy consumption, material waste, and other undesirable characteristics. Micro- and nano-scale additive manufacturing (AM) offers reduced material waste with alternative chemical processing. This project will investigate the impact of different AM methods through life cycle assessments of the processes and comparison to existing nanofabrication methods. Students will explore ecological impact in emerging AM methods through experimental work and principle-based calculations, and will build models to quantitatively assess the sustainability and benefit of these methods

Towards biopsy-free cervical precancer detection

Faculty: Irene Georgakoudi
Mentor: Matthew LindleyPetros Taxiarchis, and Samuel Adjei
Location: Mostly remote, with weekly in-person meetings

The overall goal of this project is to change the paradigm of cervical precancer detection (based on low-magnification visualization of the cervix and biopsy) using a non-invasive, high-resolution imaging modality that will ultimately provide valuable morphological and functional information at the bedside. Students will explore the use of deep-learning-based models to segment cells so that their metabolic and morphological features can be recorded and used to establish algorithms that enable us to differentiate precancerous lesions from benign tissues and motivate such in vivo imaging in patients. Students will become familiar with image analysis as guided by AI algorithms and will gain an understanding of the pathophysiology of early cancers and the challenges that the medical community faces in their detection.

Discovery and engineering of therapeutic antibodies

Faculty: Jiwon Lee
Location: ECSC 135

Immunoengineering is a rapidly growing interdisciplinary field that applies engineering principles and tools to better comprehend the immune system and to develop drugs and vaccines for numerous diseases. In this project, students will contribute to the discovery and characterization of therapeutic antibody molecules, which will guide our efforts to engineer potent therapeutic antibodies. Students typically work with a PhD student or a postdoctoral fellow to focus on one or two aspects of a project, which can be tailored to their interests. An interest/ background in molecular biology or biochemical/ biomedical engineering is preferred. Students may learn the following techniques:

• Molecular biology (PCR, cloning, cell culture, antibody expression and purification)
• Protein assays (ELISA, biolayer interferometry)
• Sequencing and proteomics (analysis of B cell receptor sequences and mass spectrometry data)
• Computation (bioinformatics and data visualization)

The subglacial effective pressure catalog

Faculty: Colin Meyer
Location: Remote

I am compiling published observations of the subglacial effective pressure (difference between the pressure of the overlying ice and the water pressure). In this project, the student will help me find published datasets and extract the data from the figures. Once we find all of the data, the student can assist me in making figures comparing the different attributes of the dataset.

Biomimetic combination construct for transcutaneous prosthetics support

Faculty: Katherine Hixon

Mentor: Adelaide Cagle

Location: ECSC

The project is targeted at developing tissue engineering scaffolds (composed of electrospinning and chitosan gelatin cryogels paired with 3D printing) for the support of medical implants that cross the dermal barrier. The research is specifically focused on improving the dermal seal around devices such as osseointegrated prosthetics, which exhibit poor adhesion to the surrounding skin. The main focuses of study are on the use of biomimetic simulation using keratin additives to induce dermal adhesion similar to nail bed adhesion to fingernails.

Swarm robot design, development, and optimization

Faculty: Michael Kokko

Location: Thayer

Students in ENGG 415: Distributed Computing will be remotely developing code to control a small swarm of robots with the intent of collectively mapping a 10ft x 10ft maze. We are developing a robotic platform for the first offering in 25W that is based on large, overpowered components, but we would ultimately like to shrink the footprint—likely developing a custom PCB and optimizing hardware for more nimble and reliable robots as the course scales to larger offerings. This FYREE opportunity will include learning from system use during the first offering in 25W and developing a second iteration of the hardware to be complete by the end of 25S.

Measuring the impact of 3D printers on the health of youth

Faculty: Emily Monroe and Solomon Diamond
Location: ECSC

Desktop 3D printers used in schools, makerspaces, and homes emit volatile organic compounds (VOCs) and ultrafine particles (UFPs) whose levels depend on filament, temperature, and ventilation. In order to run a VOC pilot study among youth, we will prepare for VOC testing (testing our protocol, providing feedback, preparing VOC monitors for deployment), prototyping and testing 3D printed projects for middle and high school students, and testing and improving our curriculum for middle and high school-aged students. 

Nanomedicine platforms for drug delivery and molecular imaging

Faculty: Hung Nguyen
Location: ECSC 135

Nanomedicine holds great promise in advancing modern medicine. For example, the modularity of mRNA vaccine technology enables these vaccines to continue outpacing subsequent mutations. However, translation of this paradigm-shifting concept to other areas of medicine such as cancer remains challenging due to 1) the heterogeneity of cancer and 2) the complexities of treatment regimens. We seek to develop convergent platforms for drug delivery and molecular imaging applications with an initial focus on combination therapy and cancer vaccines. Students working on this project will be introduced to and gain expertise in areas spanning chemical synthesis, material characterization, and biological examinations.

Genetic analysis of engineered strains of Clostridium thermocellum to improve cellulosic biofuel production

Faculty: Daniel Olson
Mentor: Ty Lanahan
Location: ECSC

Clostridium thermocellum is a bacterium that has a strong natural ability to consume cellulose. We are interested in harnessing this ability in order to produce ethanol, a potential biofuel. Bacterial cells can be programmed to perform different chemical conversions by modifying the DNA sequences on the bacterial chromosome. However, because techniques for chromosomal DNA modification often result in errors, the final DNA must be checked for accuracy. For this project, the student will assist in analysis of strains of C. thermocellum to identify gene deletions and insertions. This genetic analysis will use classical molecular biology techniques of polymerase chain reaction (PCR) as well as more advanced techniques, including whole-genome resequencing.

In-vitro analysis of ethanol production pathway in Clostridium thermocellum

Faculty: Daniel Olson
Mentor: Shu Huang
Location: ECSC

Ethanol produced from renewable sources like lignocellulosic biomass can help mitigate climate change by reducing reliance on fossil fuels. Clostridium thermocellum is a bacterium with the ability to efficiently break down lignocellulose. This project focuses on modifying the ethanol production pathway in C. thermocellum to maximize yield and titer. We will study pathways in vitro using purified enzymes and measure their ethanol production levels. The findings will inform strategies for improved performance in vivo. The student will assist in the expression and purification of these enzymes, gaining hands-on experience with essential lab techniques such as running protein gels, quantifying protein concentrations, and conducting enzyme assays to measure enzyme activities.

Manufacturing of wearable and implantable electronics

Faculty: Wei Ouyang
Location: Cummings Hall

Thayer's Bio-Integrated Microsystems Group develops next-generation wearable and implantable devices for health monitoring and autonomous medical intervention. Current projects span neural probes for neurochemical sensing, electrochemical sensors for organ health monitoring, and wireless platforms with embedded AI. Students will contribute to the design, fabrication, and optimization of these devices and gain hands-on experience with a wide range of techniques, including laser cutting, polymer molding and deposition, microfabrication, soldering, and mechanical/ electrical testing. 

Cellphone measurement of tissue oxygen

Faculty: Brian Pogue
Mentor: Protik Chandra Biswas 
Location: DHMC - Williamson 7

A new technique to measure tissue oxygen has been invented at Dartmouth, utilizing a cellphone camera to measure tissue oxygen. The technique involves giving a pre-treatment compound to the skin which elicits an emission signal detected by the camera. The light source on the skin is pulsed, and the readout with the camera is time-gated with the sequencing of the light source and the camera still being finalized. This project involves testing of the system, optimization of the light and camera technique, and ultimately integration of the system as a cell phone app. Full training will be given and active involvement in the laboratory with mice or humans is expected.

CT radiomic image analysis of pancreatic cancer treatments

Faculty: Brian Pogue
Location: Mostly virtual, some meetings at Thayer or DHMC

This project analyzes computed tomography images of patients undergoing an experimental therapy for pancreatic cancer. The work will employ a new data set from an ongoing collaborative study at the Mayo Clinic, where photodynamic therapy is being combined with immune checkpoint inhibitor treatments. The goal is to learn radiomic analysis, apply it to a cohort of real patient scans, and analyze the changes observed. Students may interact with radiologists, surgeons, and medical oncologists, but will be focused on the software and quantitative analysis component of the project. 

Detecting generated deceptive text from generative AI chatbots

Faculty: Eugene Santos Jr.
Location: Rm 232, MacLean ESC

This project requires large language models for generative AI to produce text in tones and styles that mimic anyone they have been exposed to in their training data, from the greatest speakers in history, to an average person on the street, to the most successful liars and tricksters. For the latter, these models have ingested deceptive stories and writings, both successful and unsuccessful. We will ask: 1) Can we determine if a language model was trained on deceptive stories and writings? 2) Given a piece of text, can we come up with an algorithm to determine if it is deceptive in nature? 3) How is hallucination different from or the same as deception? The student will dedicate 10 hours per week to Python code development, experimental design, and statistical analyses. A sufficient background in coding is necessary with experience in experimental design and statistical analyses a plus. 

Robust machine learning for cybersecure virtual power plant operation

Faculty: Junbo Zhao
Mentor: Yingrui Fan
Location: Hybrid (remote and IR 355)

This project will develop generic security solutions for the public or hybrid clouds that are adopted to target real-time distributed energy resource (DER) applications for providing frequency and voltage support to the power grid. The project complements existing cybersecurity solutions with innovative fast encryption/ decryption algorithms within the secure and efficient data-sharing system, proactive data anomaly detection and mitigation tools, and distributed robust federated machine learning (FML) solutions to preserve privacy and enable cybersecure control. Full training will be given, and students will contribute to the development and validation of FML algorithms for cyberattack detection and defense. 

WildfireOFF: Wildfire-oriented forecasting framework for power grid risk assessment and mitigation

Faculty: Junbo Zhao
Mentor: Caoyang Cheng
Location: Hybrid (remote and IR 355)

This project will develop a data-enhanced wildfire risk assessment and mitigation framework that allows electric grid planners and operators to enhance grid and community resilience to wildfire events. Spatial and temporal wildfire propagation modeling will be refined to yield an innovative power grid risk mapping tool. Using risk analysis insights, we will develop physics-informed machine learning decision-making solutions for public safety power shutoff strategies, optimizing responses to wildfire threats while minimizing their vulnerability and economic impacts. Full training will be given, and students will contribute to the data analytics and interactions with the electric grid. 

Christiane Buessard
Undergraduate Engineering Programs Coordinator
christiane.s.buessard@dartmouth.edu