Events

Department of Fisheries Oceanography
MS Thesis Defense

"Co-occurrence of White Sharks (Carcharodon carcharias) Along Coastal Cape Cod, MA"

By: Keri Anne Goncalves

Advisors: Steven X. Cadrin and Greg Skomal

Committee Member: Jeff Turner

Tuesday March 18th, 2025
2pm
SMAST East 101-103
836 S. Rodney French Blvd, New Bedford
and via Zoom

Abstract:

Periodic aggregation and site fidelity are common occurrences for many terrestrial and aquatic species and is typically driven by resource availability. The white shark (Carcharodon carcharias) is a species driven by such, with aggregation behavior throughout multiple stages of its life history. Juveniles exhibit resource-driven aggregations at nursery sites where they may reside for extended periods of time. Large juveniles, sub-adults, and adults tend to exhibit seasonal residency at aggregation sites along their migration routes. Atlantic white sharks form distinct communities during critical early phases of life, though little is known of spatiotemporal trends during late life stages. Using a five-year acoustic telemetry dataset from eastern Cape Cod, MA, this study investigated spatiotemporal associations between tagged individual white sharks (n=185). Network analysis was applied to acoustic detection data (n=365,880 detections) collected seasonally from 2017-2021 to identify strength of associations between groups or individuals. These analyses revealed sexual segregation and behavioral avoidance over the course of the five-year study. In each year and across all years combined aggregating sharks exhibited overall significant negative co-occurrence patterns. This study finds notable interannual variations in social dynamics as well as associations among male and female sharks across multiple years emphasizing the possibility of sex-specific variations in social behavior.

Join Zoom Meeting

https://umassd.zoom.us/j/92189558473

Note: Meeting passcode required, email contact below to receive 

To request the Zoom passcode, or for any other questions, please

EAS PhD Dissertation Defense by Cory Hoi (CSE Option/Mechanical Engineering)

Date: March 20, 2025
Time: Noon-2pm

Topic: Advancing Surfactant Replacement Therapy: Novel Computational Simulations of Multi-Phase Flow of Non-Newtonian and Newtonian Fluids

Location: SENG 110
Zoom link: Please contact Dr. Raessi (mraessi@umassd.edu).

Abstract:

Prematurely born infants are at risk of developing respiratory distress syndrome (RDS) due to a deficiency of pulmonary surfactant. Without this surface tension reducing molecule, large pressure gradients in the lung can lead to atelectasis and increase mortality risk. Medical practitioners treat RDS with surfactant replacement therapy (SRT), a procedure which reintroduces exogenous surfactant into the airway. However, SRT has a 35% non-response rate, largely due to the challenges of delivering the surfactant uniformly, and reaching the distal regions of the lung.

Current research has focused on understanding the physics of Newtonian surfactant delivery, specifically how the plug propagates along the airway, deposits its mass onto the airway wall, and splits at each airway bifurcation. However, in practice, many of the surfactants used exhibit non-Newtonian shear thinning behavior. Additionally, the mucus in the lung forms a bilayer of periciliary fluid, composed of mostly Newtonian fluid. This complexity introduces additional challenges for computational simulations, as no established methods currently exist for simulating non-Newtonian liquid interactions, with Newtonian liquid, and gas.

This thesis addresses the current gap in research by developing a novel numerical method using the volume-of-fluid (VOF) approach. This method enables computational simulations that accurately capture interactions between a non-Newtonian shear-thinning liquid, a Newtonian liquid, and gas in the presence of a rigid body. To validate its accuracy, semi-analytical solutions are derived for multiphase Poiseuille ow of non-Newtonian and Newtonian fluids. Additionally, numerical simulations are conducted for canonical cases, such as bubbles rising in shear-thinning fluids.

Finally, the numerical method is applied to simulate non-Newtonian surfactant plugs propagating through straight capillary tubes and a bifurcating airway model. The interplay between non-Newtonian plugs and the pre-existing film is analyzed, highlighting its potential implications for improving SRT.

Acknowledgment: The research support from the National Science Foundation (NSF) under CBET grant 1904204 and partial support from NSF-DMS 2012011 grant are gratefully acknowledged.

Advisor:
(508-999-8496), Dept of Mechanical Engineering

Committee Members:
Dr. Geoffrey Cowles, SMAST
Dr. Alfa Heryudono, Dept. of Mathematics
Dr. Hangjian Ling, Dept. of Mechanical Engineering

Open to the public.  All MNE and EAS students are encouraged to attend.

For questions contact

Part of the ongoing CCB Research Seminar Series

Dan Braha - Mining Social Influence

Social influence plays an important role in human behavior and decisions. Sources of influence can be external and independent of social context, or originate from personal connections, such as family and friends. An important question is how to disentangle the personal social influence from external influences. Here, we present a novel methodology to identify the extent of social influence based on large-scale observational data and apply it to two central political and economic issues—elections and financial market crises.

EAS Doctoral Dissertation Defense by Benjamin Burnett

Date: Monday, March 24, 2025
Time: 3pm–4:30pm

Topic: Accelerating Implicit Runge-Kutta Methods with Mixed-Precision and Linearization Techniques

Zoom Link:
https://umassd.zoom.us/j/96334992315?pwd=KyrUYjs5KUduhldDk4cMxvJrIX5dAv.1
Meeting ID: 963 3499 2315
Passcode: 155913

Abstract:

Implicit Runge-Kutta (IRK) methods are notoriously expensive to compute, especially in the context of solving nonlinear partial differential equations (PDEs). In this dissertation we explore two main techniques that aim to accelerate solutions to these nonlinear PDEs when using IRK based methods. The first of these is the use of mixed-precision, wherein we use mixed-precision additive Runge-Kutta (MPARK) methods to solve implicit stages in low precision, then correct any errors introduced in high precision. In this portion of the dissertation, we explore implementation strategies for mixed precision computing by solving the Van der Pol equation and Viscous Burgers' Equation using the MPARK methods. The second portion of this dissertation focuses on the use of linearization as an acceleration technique, wherein we linearize the implicit stages using different strategies, including a novel linearization strategy based on a two-point Taylor series expansion. In this portion of the dissertation we focus on exploring the stability and performance of the two-point linearization strategy by solving several problems including the Viscous Burgers' Equation, Heat Equation, and Cahn-Hilliard Equation.

Advisor(s): , Dept of Mathematics

Committee members:
Dr. Zheng Chen, Department of Mathematics
Dr. Alfa Heryudono, Department of Mathematics
Dr. Gaurav Khanna, Department of Physics, University of Rhode Island

Note: All EAS Students are ENCOURAGED to attend.

Topic: Resource Efficient Distributed Computing
Zoom Conference Link: https://umassd.zoom.us/j/98531711012
Meeting ID: 985 3171 1012
Passcode: 337372

Abstract:

Distributed computing, driven by advancements in clustered computing and big data technology, is gaining popularity. This research focuses on three key topics to enhance computation efficiency and learning performance with decentralized resources.

First, distributed learning involves distributing large-scale computations to clustered or multi-core systems. We introduce a method called random projection forests (rpForests) for fast kNN search, combining kNN-sensitive trees through random projections, achieving low computational complexity and high accuracy. Its ensemble nature allows for easy parallelization, enabling efficient execution.

Second, we address the challenge of low communication overhead when learning from data located at various distributed sites. Our novel spectral clustering framework facilitates computation with minimal communication, allowing local parallel computing and achieving almost no loss in accuracy while delivering a 2x speedup. This method also respects privacy by not requiring original data forms for transmission.

Lastly, we leverage shared features from auxiliary datasets to improve learning through an efficient algorithm called fuzzy join. Fuzzy join helps extract additional information from auxiliary data featuring overlaps, maintaining a log-linear complexity and resilience to noise.

All my research works are validated through comprehensive simulations and experiments. The proposed approaches can be potentially applied in a wide range of real-world applications.

Co-Advisors:

Dr. Honggang Wang, Professor, Dept. of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Donghui Yan, Associate Professor, Mathematics Department, UMASS Dartmouth

Committee Members:

Dr. Liudong Xing, Professor, Dept. of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Ping Chen, Professor, Computer Engineering, UMASS Boston

Note: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public.

*For further information, at 508.999.8469

Are you graduating soon and looking to start your career? Are you looking for a summer internship? The Just in Time Fair is a great opportunity to meet with over 50 employers from a variety of fields and industries. All majors and degree levels are invited. We recommend you come professionally dressed and bring copies of your resumes. The Expo is from 1-4pm, but you do not have to stay for the entire time. You can come and go as your schedule permits. For a list of employers and opportunities, visit Handshake at Are you looking for a job or internship? Are you graduating soon and looking to start your career? Are you looking for an opportunity to build your experience? The Job & Internship Expo is a great opportunity to meet with over 50 employers from a variety of fields and industries. All majors and degree levels are invited. We recommend you come professionally dressed and bring copies of your resumes. The Expo is from 1-4pm, but you do not have to stay for the entire time. You can come and go as your schedule permits. For a list of employers and opportunities, visit Handshake at app.joinhandshake.com/career_fairs/b4d387fb-4c8b-42d5-845e-50b290698487/student_preview.

The Sigma Xi Research Exhibit will take place on April 16-17 in the Library Living Room. Posters will be on display from noon Wednesday to 1pm Thursday, and students will be at their posters from 1pm-3pm on Wednesday and 10am-12pm on Thursday. Come see the independent research being conducted by undergraduate and graduate students on our campus.