Department of Fisheries Oceanog raphy
\nMS Thesis Defense
\n"Thermodynamical Drivers of Region al Warming in the Northeastern United States"
\nBy: Adriano Giangiar di
\nAdvisor
\nDr. Changsheng Chen (UMass Dartmouth)
\nC ommittee Members
\nDr. Geoffrey Cowles (UMass Dartmouth)\, Dr. Siqi Li (UMass Dartmouth)\, and Dr. Lu Wang (UMass Dartmouth)
\nMonday Au gust 10\, 2026
\n2:00 PM
\nSMAST East 101-103
\n836 S. R odney French Blvd\, New Bedford
\nand via Zoom
\nAbstract:
\nThe Gulf of Maine (GoM) is among the fastest-warming regions in the g lobal ocean\, with an observed basin-wide warming rate of approximately 0. 05°C per year\, substantially exceeding the global upper-ocean average. A lthough this accelerated warming has been well documented\, the relative c ontributions of local atmospheric forcing and remote ocean heat transport have not been quantitatively determined. This thesis addresses this knowle dge using the Northeast Coastal Ocean Forecast System (NECOFS) hindcast da taset. Atmospheric and oceanic fields from the NECOFS hindcast were used t o construct a volume-integrated heat budget model for the GoM\, enabling t he relative contributions of surface air-sea heat fluxes and lateral heat transport to be quantified. The hindcast was first evaluated against obser vations of near-surface atmospheric variables\, ocean currents\, seawater temperature\, and sea surface elevation\, demonstrating good agreement acr oss all evaluated variables. The heat budget was decomposed into local the rmodynamic forcing (air-sea heat flux) and remote advective transport (lat eral heat transport through the GoM boundaries). Comparisons between two r epresentative periods (1995-2001 and 2017-2023) show that changes in heat transport account for approximately 93% of the increase in the basin heat budget\, whereas enhanced local atmospheric heating contributes only about 7%. Further decomposition of the advective term reveals a substantial reo rganization of the primary heat transport pathways. The dominant inflow sh ifted from the Scotian Shelf during 1995-2001 to the southwestern side of the Northeast Channel during 2017-2023\, while the contribution from the N ortheast Channel itself declined markedly. At the same time\, reduced outf low through the Middle Atlantic Bight likely increased the residence time of warm water within the basin\, further enhancing regional warming. River heat input remained negligible throughout both periods.
\nThese res ults demonstrate that the recent acceleration of warming in the GoM is con trolled primarily by changes in regional ocean circulation rather than by increased local atmospheric heating. The study provides a quantitative fra mework for distinguishing the relative roles of atmospheric forcing and oc eanic heat transport in regional climate change and offers new insight int o the physical mechanisms responsible for long-term warming in the northea stern U.S. shelf ecosystem.
\nJoin Meeting
\nhttps://umassd.zo om.us/j/98165845430
\nNote: Meeting ID and passcode required\, email contact to obtain.
\nFor additional information\, please contact Ca llie Rumbut at c.rumbut@umassd.edu
Event page: https://www.umassd.edu/events/cms/8-10-26-thermodynamical-drivers-of-
regional-warming.php
Event link: https://umassd.zoom.us/j/98165845430
The first year of college is an exciting time of growth\, discovery\, and new opportunities—but it can also bring challenges and adjustments. While many students thrive from the start\, others may need additional guidance and support as they navigate academic expectations\, build new relationships\, and develop independence .
\nJoin us Dr. Sarah Cosgrove\, Associate Vice-Chancellor for Stude nt Success for Helping Students Succeed\, an informative session designed for parents and families. Learn about the many resources\ , services\, and support systems available at UMass Dartmouth and discover practical ways you can encourage your student's academic success\, person al well-being\, resilience\, and confidence while empowering them to becom e independent and successful college students.
\nTogether\, we can h elp your student make a successful transition and flourish throughout thei r first year and beyond. The presentation will provide an opportunity fo r parents and families to ask question and get answers.
Event page
: https://www.umassd.ed
u/events/cms/8-10-26-helping-students-succeed-supporting-your-students-fir
st-year-journey.php
Event link: https://umassd.zoom.us/j/
96740431785?pwd=Q5L7qOqDrEuhAIyY2IKVQD6RSL9BGN.1
Thesis Advisor: Dr. Gokhan Kul - Computer & Information Science
\nCommittee Members: Dr. Joshua Car berry - Computer & Information Science and Dr. Adnan El-Nasa n - Computer & Information Science
\nAbstract: The implicit a ssumption of stationary data built into our framework of training machine learning systems has increasingly been found faulty. There are many domain s where a model trained once and left to run in perpetuity loses classific ation accuracy over time as the data it encounters diverges from the speci fic character of the data used for its training. This phenomenon has a nam e\, concept drift. There has been an expanding body of work to combat it\, much of which relies on methods of continual learning\, using the new dat a to update the model to adapt to the drift as it is encountered. This wor k has a fundamental tension: how do we adapt to the changing character of the data while also retaining the original fundamental understanding the m odel contains. With this thesis we aim to explore how this adaptation open s up a new attack vector in these systems\, and how an adversary who can c ontrol a small fraction of the data stream can corrupt this adaptation pro cess\, crafting poison samples to slowly degrade the model's performance o ver time as well as aim to create a foundation to characterize the nature of this adversarial drift and how we can detect it. To this effect we demo nstrate a white-box frog-boiling attack on an autoencoder that uses the St rategic Selection and Forgetting (SSF) framework as its drift adaptation m echanism. The model acts as a traditional intrusion detection system\, tra ined to let benign\, regular traffic through while flagging packets that c onstitute network attacks. SSF maintains a continually updated buffer of s amples chosen to represent the current character of the data stream as fai thfully as possible\, and this buffer serves as the base of knowledge for continual retraining. The goal of the attack is to turn that adaptation me chanism against itself\, expanding the model's learned representation of b enign traffic outward round over round until it overlaps a chosen class of attack\, so that attacks of that class pass as benign while the model's j udgment of all other traffic is left largely untouched. Each round\, the a dversary submits poison the model still accepts as benign\, drawn a step c loser to the target class than the round before\, so that the buffer when retrained on\, induces a creep in the learned representation that marches steadily toward the attacker's goal. A straightforward interpolation betwe en benign and attack samples is shown to induce this effect but somewhat i nconsistently. Thus\, to make a reliable attack we adapt feature collision with watermarking\, a targeted clean-label poisoning technique\, into a f orm that drives the boil consistently across seeds. Detecting this attack directly is difficult because no single sample betrays it. Each poisoning step is minute and arrives through the same adaptation the model applies t o any drift. We find the attack only surfaces in the shape of the drift it leaves across many rounds. We characterize that drift against a synthetic benign-drift background and identify two signals that mark it as adversar ial. A Webb input-space directness measure captures the sustained\, direct ional path of a boil\, setting it apart from the aimless wandering of natu ral drift\, while a measure of the model’s contrastive loss catches the concentration of samples that don’t cleanly get folded into the benign r egion. Together these give early warning of a boil in progress before it h as degraded the model's accuracy\, laying a foundation for detecting this class of attack against continual learners.
\nFor further informatio n please contact Dr. Gokhan Kul at gkul@u massd.edu.
Event page: ht
tps://www.umassd.edu/events/cms/20260811-demonstrating-and-characterizing-
frog-boiling-poisoning.php
Event link: https://teams.microsof
t.com/meet/217648838909099?p=vkJbBE4Jvu6m4EWYJN
Thesis Advisor: Dr. Gokhan Kul
- Computer & Information Science
Committee Members:
Dr
. Debarun Das - Computer & Information Science
Dr. Ashokkumar Patel -
Computer & Information Science
Abstract:
Adversarial
Risk Analysis (ARA) offers a decision-theoretic alternative to game-theor
etic models of network defense. Instead of assuming that attacker and defe
nder know each other's payoffs and settle into an equilibrium\, the defend
er reasons under subjective uncertainty about adversary behavior and picks
up the security posture that maximizes expected utility. Adoption has bee
n limited for one narrow reason: the utility functions at the center of th
e analysis are assumed rather than measured. This thesis derives them from
published cyber threat intelligence.
The first half builds the empir
ical foundation. We process MITRE ATT&CK v16 into 4\,849 tactic-ordered ca
mpaign chains from 33 documented campaigns and train a hybrid forecasting
model on them. A two-layer LSTM captures long-range campaign structure\, w
hile a first-order Markov model estimated from 8\,437 real-world intrusion
sequences supplies short-range transition priors. The combined model pred
icts adversary progression at the technique level with 86% next-step accur
acy. Constrained beam search then expands observed prefixes into 26\,051 r
isk-ranked continuations\, each scored on a continuous 0 to 10 scale that
combines exploitation likelihood\, defensive observability from D3FEND cov
erage\, and OCTAVE organizational impact.
The second half turns th at foundation into decision theory. We map every parameter of the ARA-OSID (Adversarial Risk Analysis for Open Set Intrusion Detection) utility func tions to a specific\, auditable ATT&CK field. On the attacker side these a re effort\, detection probability\, resource cost\, and benefit. On the de fender side they are threat probability\, false negative cost\, false posi tive cost\, model repair cost\, and operating cost. The sources are requir ed permissions\, sub-technique counts\, D3FEND countermeasure coverage\, k ill-chain position\, technique usage frequency across 143 documented threa t groups\, and campaign severity under CISA's National Cyber Incident Scor ing System. Attacker and defender expected utilities are computed by Monte Carlo integration under risk-averse preferences\, validated against NCISS campaign severity\, and tested through a sensitivity analysis over the fe w weights that remain configurable.
\nThe result is a reproducible p ath from public threat intelligence to a defensible detection posture\, wh ere the chosen configuration is justified by evidence about how adversarie s actually behave instead of by assumed parameter values.
\nFor furt her information please contact Dr. Gokhan Kul at gkul@umassd.edu.
Event page: https://www.umassd.edu/events/cms/8-
11-26-from-threat-intelligence-to-decision-theory.php
Event link: <
a href="https://teams.microsoft.com/meet/221432094573069?p=dUB81Fqp8iILmMc
0o8">https://teams.microsoft.com/meet/221432094573069?p=dUB81Fqp8iILmMc0o8
Explore the exciting world of m onoprinting at the UMass Dartmouth College of Visual and Performing Arts\, Art & Design Studios. This hands-on workshop invites participants to lear n a variety of monoprinting techniques\, using ink\, rich textures\, and o rganic materials like dried leaves or flowers to create one-of-a-kind\, un ique images.
\nDesigned for beginners and anyone curious about print making\, the workshop requires no prior experience. All materials are prov ided\, including postcard paper\, paint\, and monoprint tools. By the end of the workshop\, participants will take home a collection of original pri nts and the skills and confidence to continue exploring this highly expres sive technique on their own.
Event page: https://www.u
massd.edu/events/cms/8-12-26-creative-monoprint-art-workshop.php
Ev
ent link: https://forms.of
fice.com/r/ddLTvcLRqd
Title: “Covalent Modificat ion and Characterization of a Nonaqueous Redox Flow Battery Active Materia l”
\nAdvisor: Dr. Patrick Cappillino\, Chemistry & Biochemistry Dept.
\nCommittee Members: Dr. Sivappa Rasapalli\, Chemistry & Bio chemistry Dept.\; Dr. Maricris L. Mayes\, Chemistry & Biochemistry Dept.< /p>\n
Abstract:
\nWith the rise in global energy demand\, enviro nmental concerns\, and technological innovation in renewable energy. Renew able energy sources such as solar\, wind\, and hydroelectric power have be come increasingly prevalent in supporting the electrical grid\, both in th e United States and globally. Unfortunately\, the major drawback of renewa ble sources is their inability to maintain a constant electrical output. T his intermittency has been one of the main reasons for not having a more h eavily invested renewable power grid. The long-term solution is to increas e the electrical storage grid. This would allow for storing energy produce d during peak times and discharging it when electricity is needed. This ha s paved the way for the development of low-cost and high-efficiency energy storage technology. Among the vast array of potential storage methods\, a promising technology is Redox flow batteries. This is because of their hi gh adaptability and versatility in the power grid. The development of redo x flow batteries using a nonaqueous system has the potential to achieve en ergy densities similar to those of lithium-ion batteries while maintaining key advantages\, such as scalability.
\nIn prior work from the C appillino lab\, vanadium bis-hydroxyiminodiacetate (VBH) has emerged as a promising active material candidate. VBH demonstrated excellent electroche mical stability and highly reversible one-electron redox chemistry. Additi onally\, VBH exhibits long-term cycle stability. The current drawbacks of this material stem from the high viscosity of concentrated solutions\, mod est voltage\, and solubility that\, while high\, remains insufficient for high-energy-density energy storage applications. This thesis focuses on sy stematic modification of VBH. Herein is outlined the process developed to synthesize these asymmetric\, modified compounds. A modular synthetic rout e was developed in which substituted bromoacetic acids were incorporated i nto the HIDA ligand precursor\, enabling systematic alkyl substitution of the resulting vanadium complex. The other component used in the HIDA synth esis is N-hydroxylglycine\, or (NHG)\, which is the component of HIDA that contains the other carboxylic group and the amine group. The resulting co mplexes were characterized by NMR spectroscopy\, FTIR spectroscopy\, and e lectrospray ionization mass spectrometry (ESI-MS)\, while their electroche mical behavior\, such as reduction potential\, was evaluated using cyclic voltammetry. Three modifications were successfully developed\, including t he addition of methyl-\, ethyl-\, and butyl- alkyl groups to the precursor material and\, consequently\, to the final vanadium compound. Furthermore \, results indicate a shift in reduction potential upon substitution that could lead to a higher RFB capacity. The overall work establishes a versat ile synthetic framework of systematic ligand modification of VBH\, providi ng a foundation for future optimization of nonaqueous redox flow battery a ctive materials.
Event page: https://www.umass d.edu/events/cms/20260813-ms-thesis-defense-by-benjamin-daermann.php
DTSTAMP:20260809T064427 DTSTART;TZID=America/New_York:20260813T100000 DTEND;TZID=America/New_York:20260813T120000 LOCATION:SENG 115 SUMMARY;LANGUAGE=en-us:MS Thesis Defense by Benjamin Daermann, “Coval ent Modification and Characterization of a Nonaqueous Redox Flow Battery A ctive Material” UID:b3aa7d4ccf2890e7591e4e1aeb6d6d90@www.umassd.edu END:VEVENT BEGIN:VEVENT CATEGORIES:Student Affairs DESCRIPTION:When your student is away at college, their health and well-bei ng remain a top priority. Join the UMass Dartmouth Student Health Services team to learn about the comprehensive medical care and wellness resources available to support your student's success throughout the academic year. During this interactive session, you'll learn about: The services provid ed by Student Health Services and where the Health Center is located. The most common health concerns college students experience an d how they're treated. How the University responds to medical emergencies and supports students when they become ill or injured. Medical Leaves of A bsence and when they may be appropriate. Required immunizations, titer req uirements, and the health compliance process. Student health insurance req uirements and the waiver process. Practical ways parents and families can support their student's physical and emotional well-being while encouragin g independence. Whether your student is living on campus or commuting, th is session will provide valuable information to help you feel confident th at they have access to quality healthcare and support whenever they need i t. Bring your questions! The presentation will conclude with a live Q&A, g iving parents and family members an opportunity to hear directly from our Health Services staff. Healthy students are more successful students. We l ook forward to seeing you there!\nEvent page: https://www.umassd.edu/event s/cms/8-13-26-keeping-your-student-healthy-safe--flourishing-at-umass-dart mouth.php\nEvent link: https://umassd.zoom.us/j/92067905368?pwd=3x4Yeja5be nycJDh4SZ7GXOcTL664j.1 X-ALT-DESC;FMTTYPE=text/html:When your student is away at college\, their health an d well-being remain a top priority. Join the UMass Dartmouth Student Healt h Services team to learn about the comprehensive medical care and wellness resources available to support your student's success throughout the acad emic year.
\nDuri ng this interactive session\, you'll learn about:
\nWhether your student is living on campus or commuting\, this session will provide valuable information to help you feel confident that they have access to quality healthcare and support whenever they nee d it.
\nB ring your questions! The presentation will conclude with a live Q &A\, giving parents and family members an opportunity to hear directly fro m our Health Services staff.
\nHealthy students are more successful students. We lo ok forward to seeing you there!
Event page: https://www.umassd.edu/events/cms/8-13-26
-keeping-your-student-healthy-safe--flourishing-at-umass-dartmouth.php
Event link: https://umassd.zoom.us/j/92067905368?pwd=3x4Yeja
5benycJDh4SZ7GXOcTL664j.1
Faculty Supervisor:
Dr. Mo
hammad Karim\, Electrical & Computer Engineering
Committee Me
mbers:
Dr. Donghui Yan\, Mathematics
Dr. Tariq Manzur\, Electric
al & Computer Engineering
Abstract:
Advances in environm
ental sensing technologies have enabled the collection of high-frequency a
tmospheric observations\, creating new opportunities for applying machine
learning to short-term environmental prediction. Despite considerable prog
ress in data-driven atmospheric modeling\, comparatively little attention
has been given to how atmospheric predictability varies among variables go
verned by different physical processes\, how strongly prediction depends o
n atmospheric memory\, and how boundary-layer regime modifies the predicti
ve information available from instantaneous observations. Understanding th
ese relationships is essential for developing forecasting systems that are
both accurate and physically interpretable.
In this study\,
we systematically assess the short-term predictability of five key atmosph
eric variables—solar radiation\, air temperature\, wind speed\, barometr
ic pressure\, and relative humidity—using approximately 2.2 years of hig
h-frequency meteorological observations collected within the Marine Wave B
oundary Layer (MWBL) at the School for Marine Science and Technology (SMAS
T)\, University of Massachusetts Dartmouth. To account for fundamentally d
ifferent radiative forcing and boundary-layer dynamics\, the observations
were separated into daytime and nighttime regimes and analyzed independent
ly. A unified physics-informed machine learning framework incorporating at
mospheric memory\, temporal encoding\, and wind-vector decomposition was d
eveloped and evaluated using a strict chronological train-validation-test
strategy to ensure realistic prediction conditions.
The resul
ts show clear differences in predictability and optimal model complexity a
cross the five atmospheric variables. Air temperature and barometric press
ure exhibited strong temporal persistence and achieved high predictive per
formance using linear models\, whereas solar radiation and wind speed bene
fited from nonlinear ensemble-learning approaches capable of capturing mor
e complex atmospheric behavior. Relative humidity achieved strong predicti
ve performance but exhibited greater sensitivity to rapid moisture variabi
lity and evolving boundary-layer conditions. Additional experiments conduc
ted comparing the complete framework with a reduced framework\, in which t
emporal-memory features were removed\, demonstrated that atmospheric mem
ory contributes to the predictability of all five atmospheric variables\,
although its importance varies considerably among them. Comparisons betwee
n daytime and nighttime conditions further showed that boundary-layer regi
me modifies the predictive information available from instantaneous atmosp
heric observations while preserving the overall relationship between atmos
pheric physics and model complexity.
To evaluate practical ap
plicability\, model predictions were compared with independent observation
s collected from an ATMOS 41W all-in-one weather station deployed near the
University of Massachusetts Dartmouth Campus Tower\, providing the prelim
inary field-based validation of the proposed framework. The best-performin
g solar radiation prediction model was subsequently integrated into a prot
otype Streamlit-based real-time prediction dashboard\, demonstrating a pra
ctical pathway from environmental data science research to operational coa
stal forecasting applications. Overall\, this study demonstrates that atmo
spheric predictability is governed by the underlying physical processes of
each atmospheric variable\, that atmospheric memory provides the dominant
source of predictive information\, and that boundary-layer regime modifie
s predictive information without fundamentally altering the relationship b
etween atmospheric physics and appropriate model complexity. These finding
s provide both scientific insight into coastal atmospheric predictability
and a practical foundation for future environmental forecasting systems.
For further information\, please contact Dr. Mohammad Karim
at mkarim@umassd.edu.
Event page: https://www.umassd.edu/events/cms/8-14-26-variable-depende
nt-predictability-of-coastal-atmospheric-parameters.php
Event link:
https://umassd.zoom.us/j/98731680128?pwd=y211mggmO6iXQUy9DjXa5w
byPDcBqv.1
Financial Aid Services wants to remind all students to file their FAFSA! Join Financial Aid Services for Zoom FAFSA Help Labs on Fridays from 2-3pm for help filing your FAFSA and learning more about financial aid.
Event page: https://www.umassd.edu/events/cms/8-14-26-summer-financial-aid-zoom-fa
fsa-help-labs-.php
Event link: https://umassd.zoom.us/j/9
3075462260?pwd=JhUkTxOEnyX3q6xrQZN5LPHDFjqHOD.1
Does your student have a disability\, medical conditio n\, learning difference\, ADHD\, mental health condition\, or other docume nted need for accommodations?
\nJoin the Office of Student Accessibility Services to learn how students can access accommodations and support services that promote a cademic success and full participation in campus life. This session will e xplain the transition from high school to college accommodations\, how to register for services\, and the important role students play in self-advoc acy.
\nParents an d families will leave with a better understanding of how to support their student's successful transition while encouraging independence and confide nce.
\nTo pics Include:
\nEmpower your student with the tools and resources to thrive at UMass Dartmouth!
Event page: https://www.umassd.edu/events/cms/8-14-
26-opening-doors-accessibility-resources-for-student-success.php
Ev
ent link: https://umassd.zoom.us/j/91094256193?pwd=GhuEcVpp2IugF
lUTwTJ3An7OWaCiWR.1
Between language\, culture\, an d identity: Culture as a pedagogical mediator in the teaching of Portugues e as a heritage language
\nby Marla Santos
\nAbstract
\nThis doctoral research proposal is about the role of culture in teaching P ortuguese as a heritage language (PHL). It looks at how culture can help p eople learn Portuguese in the United States. The research focuses on Massa chusetts\, home to one of the largest Portuguese-speaking populations in t he United States\, to examine how integrating culture into PHL instruction can strengthen both language development and learners' sense of cultural identity. Most of the time teachers focus on the language itself and may n ot include culture in their lessons\, at least in a way that is meaningful to learners. This study proposes to demonstrate how culture can and shoul d be a part of learning Portuguese as a heritage language.
\nThe res earch is based on ideas about heritage language education\, bilingualism a nd how people create their identities. It conceptualizes culture as someth ing that is always changing and is a part of learning a language. The stud y looks at things like theatre\, music\, art\, and other cultural manifest ations to see how they can help people connect with their heritage and lea rn Portuguese. This research should help us understand how including cultu re in lessons can help students learn the language and value their heritag e. Portuguese as a heritage language is important because it can help peop le connect with their communities and feel proud of who they are.
\nThe proposed study will use mixed methods. The researcher will create less ons that include cultural manifestations and evaluate how Portuguese langu age learners at the university level respond to them. She will also interv iew teachers who teach heritage learners to find out if and how they incor porate culture in their classes\, as well as explore their perceptions abo ut such incorporation. The goal of the study is twofold: (1) to assess how including cultural elements in lessons can help heritage learners feel mo re confident and understand their own identities\, and (2) to determine wh ether/how Portuguese language teachers incorporate culture in their lesson s as well as perceived issues related to teaching culture. It is hoped tha t this study will contribute to understanding culture as an integral part of learning a language that should not be left out of Portuguese language lessons.
\nKeywords: Portuguese Heritage Language\; heritage languag e education\; culture and intercultural language learning\; diaspora and i dentity
\nThis dissertation proposal defense will be in Portuguese.< /p>
Event page: https://www.umassd.edu/events/cms/8-
17-26-between-language-culture-and-identity.php
Event link: https://umassd.zoom.us/j/93180403696?pwd=OUFtbzU4aHkyditab21xMEtUclcrQT
09
Advisor:
Dr. José Doming
o Mora - Associate Professor & Chairperson\, Management & Marketing
Date: Wednesday\, August 19th\, 2026
Tim
e: 11:00AM – 12:00 PM (Eastern Time)
Location: Zoom (please contact
: tpasumarthi@umassd.edu or josedomingo.mora@umassd.edu for Zoom informati
on)
Committee Members:
Dr. Donghui Yan - Data Science Co-D
irector & Department of Mathematics
Dr. Yuchou Chang - Depart
ment of Computer & Information Science
Abstract:
\nOnline re views are consumed in volume and evaluated rapidly\, making the linguistic properties of their earliest words consequential. Mora and Izadi (2024) d emonstrated that the grammatical and syntactic composition of a review's o pening carries diagnostic information about the register of the full text\ , and that this register co-occurs with perceived helpfulness. This thesis operationalizes and extends that account through a reproducible seven-sta ge computational pipeline applied to 9\,999 Amazon reviews drawn equally f rom the Books and Electronics domains. Review openings were parsed for dep endency and constituency structure\, abstracted into canonical syntactic t emplates\, embedded as sentence vectors\, and clustered using k-means. An eight-class taxonomy of opening strategies was selected on the basis of cl ustering evaluation metrics and stability across random initializations (m ean adjusted Rand index = 0.99). The taxonomy was validated against manual annotation and tested for association with helpfulness using nested negat ive binomial regression and for cross-domain generalizability using chi-sq uare and Kruskal–Wallis tests. Opening class was significantly associate d with helpfulness after controlling for review length\, star rating\, dom ain\, and reviewer activity\, and this association varied by domain. Taxon omy composition was broadly stable across domains\, confirming cross-domai n generalizability. Validation further revealed that the pipeline's embedd ing space captures semantic-functional organization rather than strictly s yntactic structure\, an empirical finding about how computational methods represent register. The thesis contributes an automated\, evaluated\, and reproducible alternative to semi-manual register analysis.
\nFor fur ther questions\, please contact Professor José Domingo Mora at josedomingo.mora@umassd.edu.
Event page: https://www.umassd.edu/events/cms/20260819 -syntactic-openings-in-online-reviews.php
DTSTAMP:20260809T064427 DTSTART;TZID=America/New_York:20260819T110000 DTEND;TZID=America/New_York:20260819T120000 LOCATION:Zoom - please contact: tpasumarthi@umassd.edu or josedomingo.mora@ umassd.edu for Zoom information SUMMARY;LANGUAGE=en-us:Syntactic Openings in Online Reviews: A Computationa l Pipeline for Cross-Domain Register Taxonomy and Helpfulness Analysis UID:25bad4d6d968fd0512c9c1b93f712957@www.umassd.edu END:VEVENT BEGIN:VEVENT CATEGORIES:College of Arts and Sciences,College of Engineering,Thesis/Disse rtations DESCRIPTION:Advisor: Dr. Ashokkumar R. Patel - Department of Computer & Inf ormation Science Committee Members:Dr. Yuchou Chang – Department of Com puter & Information ScienceDr. Debarun Das – Department of Computer & In formation Science Abstract:Inner speech - the silent production of words in the mind, without any movement or sound — is an appealing control sig nal for a brain–computer interface (BCI), because the command is the tho ught. For someone who has lost the ability to speak or move, a decoder tha t reads intended words directly would be far more natural than the indirec t mental tasks most BCIs rely on. Reading inner speech from scalp electroe ncephalography (EEG) is, however, extremely hard: the signals are weak and non-stationary, the neural traces of covert language are faint, and repor ted four-class accuracies in the literature rarely climb much past the low thirties. In this regime, the meaningful question is not whether a model reaches high accuracy; none reliably do but whether a design choice yields a real, statistically reliable signal, assessed honestly. This thesis co mpares three standard architectures - a compact convolutional network (EEG Net), an LSTM recurrent network, and a self-attention Transformer - agains t a hybrid model that feeds a shared convolutional front end into parallel recurrent and self-attention branches and fuses them before classificatio n. All four are evaluated identically on the public "Thinking Out Loud" in ner-speech dataset (Nieto et al., 2022), under subject-dependent five-fold cross-validation on the four-class directional-word task (Up, Down, Right , Left), with on-the-fly augmentation and a fixed seed. The unit of statis tical analysis is the subject (n = 10). The hybrid model attains the highe st mean accuracy, 28.5% (SD 3.1%), and is the only model whose accuracy is statistically significantly above the 25% chance level (Wilcoxon signed-r ank p = 0.014; one-sample t-test p = 0.006). The three baselines do not re ach significance against chance. In direct paired comparisons the hybrid i s not significantly better than any individual baseline, and an ablation s hows that each single branch performs at roughly the level of its correspo nding baseline, with the two-branch fusion adding a small, non-significant improvement. A subject-independent analysis falls to chance, consistent w ith the well-documented failure of cross-subject generalization for inner speech. These results are consistent with prior decoding studies on this d ataset. The contribution is therefore a controlled, like-for-like benchmar k of four architectures under one protocol, with rigorous statistical asse ssment: it shows that on this difficult task the hybrid is the only archit ecture to clear chance, while the architectures are otherwise statisticall y indistinguishable from one another. For further questions, please contac t Professor Ashokkumar R. Patel at ashok.patel@umassd.edu\nEvent page: htt ps://www.umassd.edu/events/cms/8-19-26-inner-speech-decoding-from-eeg-a-co mparative-study.php X-ALT-DESC;FMTTYPE=text/html:Advisor: Dr. Ashokkumar R. Pate
l - Department of Computer & Information Science
Committee Me
mbers:
Dr. Yuchou Chang – Department of Computer & Information Scie
nce
Dr. Debarun Das – Department of Computer & Information Science<
br />
Abstract:
Inner speech - the silent production of words
in the mind\, without any movement or sound — is an appealing control si
gnal for a brain–computer interface (BCI)\, because the command is the t
hought. For someone who has lost the ability to speak or move\, a decoder
that reads intended words directly would be far more natural than the indi
rect mental tasks most BCIs rely on. Reading inner speech from scalp elect
roencephalography (EEG) is\, however\, extremely hard: the signals are wea
k and non-stationary\, the neural traces of covert language are faint\, an
d reported four-class accuracies in the literature rarely climb much past
the low thirties. In this regime\, the meaningful question is not whether
a model reaches high accuracy\; none reliably do but whether a design choi
ce yields a real\, statistically reliable signal\, assessed honestly.
This thesis compares three standard architectures - a compact con
volutional network (EEGNet)\, an LSTM recurrent network\, and a self-atten
tion Transformer - against a hybrid model that feeds a shared convolutiona
l front end into parallel recurrent and self-attention branches and fuses
them before classification. All four are evaluated identically on the publ
ic "Thinking Out Loud" inner-speech dataset (Nieto et al.\, 2022)\, under
subject-dependent five-fold cross-validation on the four-class directional
-word task (Up\, Down\, Right\, Left)\, with on-the-fly augmentation and a
fixed seed. The unit of statistical analysis is the subject (n = 10). The
hybrid model attains the highest mean accuracy\, 28.5% (SD 3.1%)\, and is
the only model whose accuracy is statistically significantly above the 25
% chance level (Wilcoxon signed-rank p = 0.014\; one-sample t-test p = 0.0
06). The three baselines do not reach significance against chance. In dire
ct paired comparisons the hybrid is not significantly better than any indi
vidual baseline\, and an ablation shows that each single branch performs a
t roughly the level of its corresponding baseline\, with the two-branch fu
sion adding a small\, non-significant improvement. A subject-independent a
nalysis falls to chance\, consistent with the well-documented failure of c
ross-subject generalization for inner speech. These results are consistent
with prior decoding studies on this dataset. The contribution is therefor
e a controlled\, like-for-like benchmark of four architectures under one p
rotocol\, with rigorous statistical assessment: it shows that on this diff
icult task the hybrid is the only architecture to clear chance\, while the
architectures are otherwise statistically indistinguishable from one anot
her.
For further questions\, please contact Professor Ashokkumar R . Patel at ashok.patel@umassd.edu
Event page: https://www.umassd.edu/events/cms/8-19-26-inner-speech-decoding -from-eeg-a-comparative-study.php
DTSTAMP:20260809T064427 DTSTART;TZID=America/New_York:20260819T130000 DTEND;TZID=America/New_York:20260819T140000 LOCATION:Zoom (please contact: pnadipalli@umassd.edu or ashok.patel@umassd. edu for Zoom information) SUMMARY;LANGUAGE=en-us:Inner Speech Decoding from EEG: A Comparative Study of Deep Learning Architectures for Brain–Computer Interfaces UID:523f0f8e5e5ca887c96d656c30969dfb@www.umassd.edu END:VEVENT BEGIN:VEVENT CATEGORIES:College of Arts and Sciences,Lectures and Seminars,Thesis/Disser tations DESCRIPTION:Title: Synthesis, Characterization, and Tuning of Metal Hexacya noferrates for Application in Redox Mediated Flow Battery Systems Advisor: Dr. Patrick Cappillino, Chemistry & Biochemistry Dept. Committee Members: Dr. David Manke, Chemistry & Biochemistry Dept. & Dr. Maricris Mayes, Che mistry & Biochemistry Dept. Abstract: Growing energy demands necessitate o ptimized battery storage systems. Redox mediated flow batteries (RMFBs) pr ovide a unique solution, combining the decoupled energy and power of a flo w-battery with the high energy density of a solid-state battery. This wor k aims to synthesize and characterize a solid active material (SAM) that c an be added to the battery system to boost the overall energy density via an indirect reduction-oxidation reaction. To this extent, metal hexacyanom etalates (MHCM) are considered an optimal SAM due to their tunable reducti on potential, stability, and accessibility. Four MHCMs - manganese, cobalt , nickel, and copper hexacyanoferrate - were synthesized and characterized via cyclic voltammetry, Fourier transform infrared spectroscopy, and scan ning electron microscopy. As a SAM, the redox potential of MHCMs must be r eadily tuned to match the proposed mediator. Within an RMFB system, the me diator solution contains redox-active species that shuttle electrons throu ghout. Ensuring a match between the SAM and mediator optimizes the perform ance and capability of the RMFB. One method of tuning SAM redox potential is via electrolyte additives. The addition of crown ethers were determined to have a significant impact on the potential of the system, controlling the intercalation of cations to the MHCM lattice by forming a more stable complex with them in solution than their solvated state. Specifically, 18- Crown-6 ether’s ability to form a stable complex with K+ enables the red ox potential of the reaction to be further tuned towards a more negative r eduction potential. The results of this work contribute towards building a library of metal hexacyanometalates, improving the overall understanding of these compounds and their role as a “booster” material in RMFBs. Zoom Meeting ID: 868 2336 3937 & Passcode: N9EkT6\nEvent page: https://www .umassd.edu/events/cms/20260820-ms-thesis-defense-by-daphne-poirier.php\nE vent link: https://us05web.zoom.us/j/86823363937?pwd=niAJRp2lT2bHYbCJWoGZa Ak58YgXC0.1 X-ALT-DESC;FMTTYPE=text/html:Title: Synthesis\, Characteriza tion\, and Tuning of Metal Hexacyanoferrates for Application in Redox Medi ated Flow Battery Systems
\nAdvisor: Dr. Patrick Cappillino\, Chemis try & Biochemistry Dept.
\nCommittee Members: Dr. David Manke\, Chem istry & Biochemistry Dept. & Dr. Maricris Mayes\, Chemistry & Biochemistry Dept.
\nAbstract:
\nGrowing energy demands necessitate optimi zed battery storage systems. Redox mediated flow batteries (RMFBs) provide a unique solution\, combining the decoupled energy and power of a flow-battery with the high energy density of a solid-state battery. This work aims to synthesize and characterize a solid active material (SAM) tha t can be added to the battery system to boost the overall energy density v ia an indirect reduction-oxidation reaction. To this extent\, metal hexacy anometalates (MHCM) are considered an optimal SAM due to their tunable red uction potential\, stability\, and accessibility. Four MHCMs - manganese\, cobalt\, nickel\, and copper hexacyanoferrate - were synthesized and char acterized via cyclic voltammetry\, Fourier transform infrared spectroscopy \, and scanning electron microscopy.
\nAs a SAM\, the redox potentia l of MHCMs must be readily tuned to match the proposed mediator. Within an RMFB system\, the mediator solution contains redox-active species that sh uttle electrons throughout. Ensuring a match between the SAM and mediator optimizes the performance and capability of the RMFB. One method of tuning SAM redox potential is via electrolyte additives. The addition of crown e thers were determined to have a significant impact on the potential of the system\, controlling the intercalation of cations to the MHCM lattice by forming a more stable complex with them in solution than their solvated st ate. Specifically\, 18-Crown-6 ether’s ability to form a stable complex with K+ enables the redox potential of the reaction to be further tuned to wards a more negative reduction potential. The results of this work contri bute towards building a library of metal hexacyanometalates\, improving th e overall understanding of these compounds and their role as a “booster ” material in RMFBs.
\nZoom Meeting ID: 868 2336 3937 & Passcode : N9EkT6
Event page: https://www.umassd.edu/event
s/cms/20260820-ms-thesis-defense-by-daphne-poirier.php
Event link:
https://us05web.zoom.us/j/86823363937?pwd=niAJRp2lT2bHYbCJWoGZa
Ak58YgXC0.1
Student Accounts Information Se ssion
\nDuring this session\, we'll provide an overview of paying th
e student account\, review important billing and payment information\, and
answer any questions families may have.
Possible Questions:<
/p>\n
Event page: https://www.umassd.edu/events/cms/8-20-26-student-accounts-
information-session.php
Event link: https://umassd.zoom.u
s/j/99414375688?pwd=ta8GNRTgbT9EpJnIWnQkx6tJ5wqY8v.1
Thesis advisor : Dr. Gavin Fay - Fisheries and Oceanography\, SMAST
\nCommi
ttee members:
Dr. Yuchou Chang - Computer & Information Sc
ience.
Dr. Leander Hohne - Fisheries and Oceanography\, SMAST
Date and time: Friday\, August 21st 2026 at 01:00 pm
\nLocation: SMAST East 101-103\, 836 S. Rodney Fre nch Boulevard\, New Bedford MA 02744.
\nLink: Join
Zoom Meeting
https://umassd.zoom.us/j/99402231312?pwd=NWBAMQDRf7GCRokw1gEbUeyj4u
HHyF.1
Meeting ID: 994 0223 1312
Passcode: 125
997
Join instructions
https://umassd.zoom.us/meetings/99402231312/invitations?signature=IwGtdDUX
SdtTFwwD7wLeTk8UizBoqmyfri87TyMXGAs
Abstrac t:
\nFisheries management increasingly relies on biologic al reference points\, target fishing mortality and biomass levels\, to set sustainable catch limits. For single-species stocks these reference point s can often be computed from a fitted assessment model\, but when a stock' s mortality is driven by multispecies predation rather\, the reference poi nts have no closed form and depend on the assumed status of every species in the system\; obtaining them requires repeatedly re-projecting a multisp ecies model forward\, at every assessment year of simulations\, and ignori ng this predation-driven mortality process is known to bias both stock ass essment outputs and the reference points used for management advice. This computational cost is one of the bottlenecks in evaluating harvest strateg ies against multispecies operating models\, including one built here for t he Eastern Bering Sea groundfish complex (walleye pollock\, Pacific cod\, arrowtooth flounder) using the Rceattle implementation of CEATTLE. Here we show that a gradient-boosted tree model\, guided by age-structured natura l-mortality and trained on terminal-year assessment output from 500 closed -loop management strategy evaluation simulations\, can emulate these multi species reference points\, without re-running the dynamic projection\, ach ieving cross-validated R² of 0.80 - 0.95 across six targets. Feature-impo rtance analysis reveals that a single pollock-specific natural-mortality f eature dominates prediction for four of the six targets\, even for cod's a nd arrowtooth flounder's own reference points\, a machine-learned signatur e of the predation coupling built into the operating model. This demonstra tes that these emulator models can substitute for costly re-projection\, o ffering a path to rapid screening of harvest-strategy across the simulatio n ensembles that multispecies management strategy evaluations increasingly require.
\nFor further information please contact Dr. Gavin Fay at gfay@umassd.edu.
\n
E
vent page: https://www.umassd.edu/events/cms/202
60821-machine-learning-emulator-for-multispecies.php
Event link: https://umassd.zoom.us/j/99402231312?pwd=NWBAMQDRf7GCRokw1gEbUe
yj4uHHyF.1
Financial Aid Services wants to remind all students to file their FAFSA! Join Financial Aid Services for Zoom FAFSA Help Labs on Fridays from 2-3pm for help filing your FAFSA and learning more about financial aid.
Event page: htt
ps://www.umassd.edu/events/cms/8-21-26-financial-aid-zoom-fafsa-help-labs-
.php
Event link: https://umassd.zoom.us/j/93075462260?pwd
=JhUkTxOEnyX3q6xrQZN5LPHDFjqHOD.1
Thesis Advisor: Dr. Lance Fiond ella - Electrical and Computer Engineering
\nCommittee Members:
Dr. Gokhan Kul - Computer & Information Science
Dr. Long Jiao - Co
mputer & Information Science
Abstract:
\nPast studies indica
te that nonviolent resistance has achieved higher success rates in global
political transformations than armed conflict. Agent-based modeling has be
en employed to simulate these movements\, but prior models rely on rigid n
umerical thresholds and oversimplify agent interactions. This limits the c
apacity of simulations to analyze the strategic reasoning underlying human
leadership. To address these limitations\, this thesis presents a hybrid
generative agent-based model that integrates large language models to guid
e the decision-making of activist agents while keeping other agent classes
rule-based for computational feasibility.
The proposed model intr
oduces three methodological enhancements. First\, LLM-guided decision-maki
ng replaces rule-based activist movement. To prevent behavioral drift\, a
four-component structured system prompt anchors agent identity through rol
e\, persona\, domain-specific world knowledge\, and scenario context. Seco
nd\, eight interdependent institutional support pillars replace a single a
bstract pillar type. Third\, a linguistic transformation layer translates
continuous numeric states of agents into semantic social descriptors\, all
owing activists to perform reasoning tasks\, such as identifying and mobil
izing highly aggrieved civilians. We drive activist agents with three open
-weight Large Language Models (Llama-3.1-8B-Instruct\, Ministral-8B-Instru
ct\, Qwen3-8B) under Zero-Shot and Chain-of-Thought (CoT) prompting\, yiel
ding six experimental configurations.
Macro-level validation again st the Nonviolent and Violent Campaigns and Outcomes (NAVCO) 1.2 dataset s uggests all configurations approximate historical campaign success rates a t low participation levels. Simulations indicate that high activist coordi nation and low fatality rates predict campaign success. However\, micro-le vel analysis shows that CoT reasoning is required for behavioral realism. While Zero-Shot agents default to generic protests\, CoT-enabled agents se mantically evaluate institutional vulnerabilities and execute tactics alig ned with pillar susceptibility: protest and persuasion\, noncooperation\, and intervention. These agents prioritize institutional and civilian outre ach\, actively avoiding confrontation with security forces. Micro-level va lidation against the Global Nonviolent Action Database (GNAD) suggests the se CoT configurations approximate the historical prevalence of diverse tac tical behaviors. While different foundational LLMs capture distinct aspect s of real-world resistance\, this study constitutes a simulated instance o f Gandhian strategic principles. Ultimately\, this thesis identifies CoT-e nabled agents as a promising method for analyzing how specific leadership paradigms operate within diverse societies.
\nFor further informatio n\, please contact Dr. Lance Fiondella at lfiondella@umassd.edu.
E
vent page: https://www.umassd
.edu/events/cms/8-25-26-a-hybrid-generative-agent-based-model-of-nonviolen
t-resistance.php
Event link: https://teams.microsoft.com/meet
/252984977868302?p=H2LcD3UFCOp6BL31w6
Financial Aid Services wants to remind all students to file their FAFSA! Join Financial Aid Services for Zoom FAFSA Help Labs on Fridays from 2-3pm for help filing your FAFSA and learning more about financial aid.
Event page: https://www.umassd.edu/events/cms/8-28-26-summer-financial-aid-zoom-fa
fsa-help-labs-.php
Event link: https://umassd.zoom.us/j/9
3075462260?pwd=JhUkTxOEnyX3q6xrQZN5LPHDFjqHOD.1
Advisor: Dr. Jiawei Yuan\, Depa rtment of Computer and Information Science
\nCommittee Members:
\ nAbstract
\nGiven the recent advances in large
language models (LLMs) and their remarkable capabilities of natural langua
ge understanding and text generation\, LLMs have increasingly been integra
ted into robotic systems\, enabling robots to understand high-level human
instructions\, reason about task objectives\, and generate code for robot
execution. However\, enabling LLMs to reliably understand high-level human
instructions and produce executable robot operations remains challenging.
For example\, LLMs can misinterpret human intentions\, forget task object
ives\, hallucinate unavailable robot functions\, generate syntactically in
valid code\, produce logically inconsistent action sequences\, or make uns
upported assumptions about environment observation.
This proposed res
earch first enhances the reliability of LLMs by enabling them to generate
valid and feasible task plans and robot operational code\, identify errors
\, and recover from failures. First\, this research developed GSCE\, a str
uctured prompt framework to enhance LLM reasoning and generate reliable ro
bot operation code. Building on GSCE\, this research further enhances reli
ability by developing a closed-loop framework that evaluates the robot beh
avior and provides feedback for correcting generated code in simulation be
fore its deployment on a physical robot. To reduce the configuration effor
t and execution time associated with specialized robotic simulators\, this
research further developed an LLM-driven static text-based simulation fra
mework without dynamically executing the code in a physical environment or
simulator during corrective code refinement. Moreover\, to achieve reliab
le task planning and execution on mobile robots that host on-board LLMs\,
this research designed Ro-SLM\, a framework that leverages prior knowledge
to teach on-board language models and enable them to perform reliable tas
k planning and execution with performance approaching substantially larger
models.
Despite this progress\, challenges remain as the diversit y and complexity of robotic tasks continue to increase. In particular\, pe rformance may degrade on previously unseen tasks\, long-horizon operations \, and tasks that require complex reasoning and decision-making. Therefore \, to further enhance the reliability of LLM-driven mobile robots\, this r esearch proposes to address: 1) problem solving\, in which on-board langua ge models will select\, reuse\, and orchestrate skills to complete tasks\, enabling the robot to solve problems by recombining its learned knowledge rather than
\nmemorizing complete task solutions. This direction ai
ms to enhance the reliability of LLM-driven mobile robots under complex\,
diverse\, and previously unseen tasks\; 2) decision making\, the on-board
language model will review past experiences\, anticipate possible future c
onsequences and failures for the skill that the robot will conduct\, and c
orrect the errors before executing the robot operations\, which enables th
e robot to make reliable decisions for the given task before executing the
robot operations. This direction aims to further improve the reliability
of LLM-driven mobile robots for tasks that require long-horizon operations
and complex decision-making.
For further information\, please contac
t Dr. Jiawei Yuan at jyuan@umassd.edu
Event page: https://www.umassd.edu/events/cms/8-28-26-llm-driven-mobil e-robot-task-planning-and-execution.php
DTSTAMP:20260809T064427 DTSTART;TZID=America/New_York:20260828T153000 DTEND;TZID=America/New_York:20260828T163000 LOCATION:Dion 311 SUMMARY;LANGUAGE=en-us:Towards Reliable LLM-driven Mobile Robot Task Planni ng and Execution UID:0fbb459a315d7cc489ef2c148e80cdf0@www.umassd.edu END:VEVENT BEGIN:VEVENT CATEGORIES:Student Affairs,Week of Welcome DESCRIPTION:Sexual assault and substance abuse can be tough topics; the iss ues are complex. Speak Up: Dramatic Dialogues uses interactive theater t o address these issues head on with frank and open discussions. Expect to be challenged to think and develop educated opinions. You’ll walk away w ith greater insight and the confidence to make smart choices.\nEvent page: https://www.umassd.edu/events/cms/8-31-26-speak-up-dramatic-dialogues-for -balsam-hall--commuters-a-l.php X-ALT-DESC;FMTTYPE=text/html:Sexual assault and substance ab use can be tough topics\; the issues are complex. Speak Up: Dram atic Dialogues uses interactive theater to address these issues h ead on with frank and open discussions. Expect to be challenged to think a nd develop educated opinions. You’ll walk away with greater insight and the confidence to make smart choices.
Event page: https://www.umassd.edu/events/cms/8-31-26-speak-up- dramatic-dialogues-for-balsam-hall--commuters-a-l.php
DTSTAMP:20260809T064427 DTSTART;TZID=America/New_York:20260831T110000 DTEND;TZID=America/New_York:20260831T120000 LOCATION:Main Auditorium SUMMARY;LANGUAGE=en-us:Speak Up: Dramatic Dialogues for Balsam Hall & C ommuters A-L UID:a2929347b8fd43cd1199ac1f4229546c@www.umassd.edu END:VEVENT BEGIN:VEVENT CATEGORIES:Student Affairs,Week of Welcome DESCRIPTION:Sexual assault and substance abuse can be tough topics; the iss ues are complex. Speak Up: Dramatic Dialogues uses interactive theater to address these issues head on with frank and open discussions. Expect to be challenged to think and develop educated opinions. You’ll walk away with greater insight and the confidence to make smart choices.\nEvent page : https://www.umassd.edu/events/cms/8-31-26-speak-up-dramatic-dialogues-fo r-spruce-hall--commuters-m-z.php X-ALT-DESC;FMTTYPE=text/html:Sexual assault and substance
abuse can be tough topics\; the issues are complex. Speak Up: Dramatic Dialogues uses interactive theater to address these issues head on with frank and
open discussions. Expect to be challenged to think and develop educated op
inions. You’ll walk away with greater insight and the confidence to make
smart choices.
Event page: https://www.umassd.edu/events/cms/8-31-26-speak-up-dramatic-dialog ues-for-spruce-hall--commuters-m-z.php
DTSTAMP:20260809T064427 DTSTART;TZID=America/New_York:20260831T121500 DTEND;TZID=America/New_York:20260831T131500 LOCATION:Main Auditorium SUMMARY;LANGUAGE=en-us:Speak Up: Dramatic Dialogues for Spruce Hall & C ommuters M-Z UID:b042313b9d5eeebb19c06027420befcf@www.umassd.edu END:VEVENT BEGIN:VEVENT CATEGORIES:University Marketing,Student Affairs,Week of Welcome DESCRIPTION:It’s the official start of your UMassD adventure! Join your c lassmates, faculty, and staff in the Amphitheatre for a high-energy welcom e that celebrates who we are and where we’re going. Expect music, inspir ation, Corsair pride, and moments that will kick off your college journey in unforgettable style. This is where your story as a Corsair officially b egins—let’s make it epic! New students should arrive at the amphith eater at 10:30 a.m. on Tuesday, September 1, for the 2026 Convocation. The ceremony will begin at 10:45 a.m.\nEvent page: https://www.umassd.edu/eve nts/cms/9-1-26-convocation.php X-ALT-DESC;FMTTYPE=text/html:It’s the official start of yo ur UMassD adventure! Join your classmates\, faculty\, and staff in the Amp hitheatre for a high-energy welcome that celebrates who we are and where w e’re going. Expect music\, inspiration\, Corsair pride\, and moments tha t will kick off your college journey in unforgettable style. This is where your story as a Corsair officially begins—let’s make it epic!
\ nNew students should arrive at the amphitheater at 10:30 a.m. on Tuesda y\, September 1\, for the 2026 Convocation. The ceremony will begin at 10: 45 a.m.
Event page: https://www.umassd.edu/events/cms/9-1-26-convocation.ph p
DTSTAMP:20260809T064427 DTSTART;TZID=America/New_York:20260901T103000 DTEND;TZID=America/New_York:20260901T113000 LOCATION:Amphitheatre SUMMARY;LANGUAGE=en-us:Convocation UID:6e618267cdf2eb7f9a0ffb3362fd43ee@www.umassd.edu END:VEVENT BEGIN:VEVENT CATEGORIES:Academic Affairs,University Marketing,Dining,Human Resources,Off ice of Student Engagement & Leadership (OSEL),Provost's Office,Week of Wel come DESCRIPTION:Join the All-Campus Picnic on the quad between 11:30am to 3pm, marking the official beginning of another academic year! All staff, facult y, and students are welcome to attend. Enjoy delicious barbecue, mingle wi th your campus friends, take home giveaway items, and catch some live ente rtainment.\nEvent page: https://www.umassd.edu/events/cms/9-1-26-all-campu s-picnic.php\nEvent link: https://www.umassd.edu/convocation/ X-ALT-DESC;FMTTYPE=text/html:Join the All-Campus Picnic on t he quad between 11:30am to 3pm\, marking the official beginning of another academic year! All staff\, faculty\, and students are welcome to attend. Enjoy delicious barbecue\, mingle with your campus friends\, take home giv eaway items\, and catch some live entertainment.
Event page: https://
www.umassd.edu/events/cms/9-1-26-all-campus-picnic.php
Event link:
https://www.umassd.edu/convo
cation/
UMass Dartmouth will host Elms College at 6pm on Tuesday\, Sept. 1 at Cressy Field. It's the season opene r for the Corsairs
Event page: https://www.umassd.edu/events/cms/f
ield-hockey-v-elms-college.php
Event link: https://corsairathletics.com/s
ports/field-hockey/schedule
UMass Dartmouth will host Lesle y University in the home opener at 12pm on Tuesday\, September 1 at the UM ass Dartmouth Tennis Courts
Event page: https://www.u
massd.edu/events/cms/9-1-26-womens-tennis-v-lesley-university.php
E
vent link: https://corsairathletics.com/sports/womens-tennis/schedule/2026
Calling all first-generation Co rsairs! Join us for a celebration just for you—complete with food\, musi c\, and connections that matter. Meet other first-gen students\, chat with faculty and staff who were first-gen too\, and discover resources that wi ll help you thrive at UMassD. It’s your time to shine\, build your commu nity\, and kick off your college journey with pride!
Event page: < a href="https://www.umassd.edu/events/cms/9-1-26-first-generation-student- welcome-reception.php">https://www.umassd.edu/events/cms/9-1-26-first-gene ration-student-welcome-reception.php
DTSTAMP:20260809T064427 DTSTART;TZID=America/New_York:20260901T140000 DTEND;TZID=America/New_York:20260901T150000 LOCATION:The Marketplace SUMMARY;LANGUAGE=en-us:First Generation Student Welcome Reception UID:055ceec336cc2c9174dbb4d7904920a4@www.umassd.edu END:VEVENT BEGIN:VEVENT CATEGORIES:Training DESCRIPTION:Learn how to view Budget Balances, Revenue and Expense Transact ion Detail and Open Encumbrances using Summit Financials Department Manage ment Dashboard. Summit is Oracle Analytics Cloud with Prebuilt intuitive Dashboards that enable users to view and report on financial data. Tra ining is for Faculty and Staff and required for access. Email jschlesing er@umassd.edu to sign up for training. \nEvent page: https://www.umassd. edu/events/cms/9-2-26-summit-financial-reporting-101.php\nEvent link: http s://www.umassd.edu/peoplesoftfinance/training/ X-ALT-DESC;FMTTYPE=text/html:Learn how to view Budget Balanc es\, Revenue and Expense Transaction Detail and Open Encumbrances using Su mmit Financials Department Management Dashboard.
\nSummit is Oracl e Analytics Cloud with Prebuilt intuitive Dashboards that enable users to view and report on financial data.
\nTraining is for Faculty and Staff and required for access.
\nEmail jschlesinger@umassd.edu to sign up for training.
Event page: https://www.umassd.
edu/events/cms/9-2-26-summit-financial-reporting-101.php
Event link
: https://www
.umassd.edu/peoplesoftfinance/training/
Interested in studying abroad? Do you have a quick question about the opportunities that are available or the overall process? Stop by the International Programs Office (IPO)! Stu dents will be seen on a first come\, first served basis.
Event page: https://www.umassd.edu/events/cms/9-2-26-study-abroad-walk-in- advising.php
DTSTAMP:20260809T064427 DTSTART;TZID=America/New_York:20260902T140000 DTEND;TZID=America/New_York:20260902T170000 LOCATION:Library 233 SUMMARY;LANGUAGE=en-us:Study Abroad Walk-in Advising UID:4a6e5530e56409c87d8dee34cebcd144@www.umassd.edu END:VEVENT BEGIN:VEVENT CATEGORIES:Training DESCRIPTION:Learn how to run reports and inquire on Budget Balances in Peop leSoft Financials for Fiscal year Budgets, Sponsored and non Sponsored pro ject budgets. Reports include Revenue and Expense Summary activity, Open Encumbrances and Revenue and Expense Transaction Detail. Training is required for access. To sign up for training, email jschlesinger@umass d.edu \nEvent page: https://www.umassd.edu/events/cms/9-3-26-peoplesoft-f inancial-reporting-and-budget-inquiry-training--.php\nEvent link: https:// www.umassd.edu/peoplesoftfinance/training/ X-ALT-DESC;FMTTYPE=text/html:Learn how to run reports and in quire on Budget Balances in PeopleSoft Financials for Fiscal year Budgets\ , Sponsored and non Sponsored project budgets. Reports include Revenue a nd Expense Summary activity\, Open Encumbrances and Revenue and Expense Tr ansaction Detail.
\nTraining is required for access.
\nTo sign up for training\, email jschlesinger@umassd.edu
Event pag
e: https://www.umassd.edu/events
/cms/9-3-26-peoplesoft-financial-reporting-and-budget-inquiry-training--.p
hp
Event link: https://www.umassd.edu/peoplesoftfinance/training/