UMBC Data Science

Permanent URI for this collectionhttp://hdl.handle.net/11603/22728

The Data Science graduate program at UMBC prepares students to respond to the growing demand for professionals with data science knowledge, skills, and abilities. Our program brings together faculty from a wide range of fields who have a deep understanding of the real-world applications of data analytics. UMBC’s Data Science programs prepare students to excel in data science roles through hands-on experience, rigorous academics, and access to a robust network of knowledgeable industry professionals.

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Recent Submissions

Now showing 1 - 20 of 191
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    Calculation of the Phase Noise at Comb-Line Frequencies in a Frequency Comb
    (Optica, 2021-11-01) Anjum, Ishraq Md; Mahabadi, Seyed Ehsan Jamali; Simsek, Ergun; Menyuk, Curtis
    We calculate the phase noise in a modified uni-traveling carrier photodetector for frequency comb applications. In contrast to a continuous wave, a frequency comb is characterized by a distinct phase noise for each comb line.
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    Photodetector Performance Prediction with Machine Learning
    (Optica, 2021-11-01) Simsek, Ergun; Mahabadi, Seyed Ehsan Jamali; Carruthers, Thomas F.; Menyuk, Curtis
    Four machine learning algorithms are tested to predict the performance metrics of modified uni-traveling carrier photodetectors from their design parameters. The highest accuracy (>94%) is achieved with artificial neural networks.
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    Exciton physics in transition-metal dichalcogenides at the atomic scale
    (Optica, 2015-06-27) Tseng, Frank; Simsek, Ergun; Gunlycke, Daniel
    We present an atomistic model for excitons in monolayer transition-metal dichalcogenides consistent with recent experiments. Using this model we show how to extend exciton lifetimes, which could be important for future optoelectronic applications.
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    Exciton Emission Intensity Modulation of Monolayer MoS₂ via Au Plasmon Coupling
    (Nature, 2017-01-30) Mukherjee, B.; Kaushik, N.; Tripathi, Ravi P. N.; Joseph, A. M.; Mohapatra, P. K.; Dhar, S.; Singh, B. P.; Kumar, G. V. Pavan; Simsek, Ergun; Lodha, S.
    Modulation of photoluminescence of atomically thin transition metal dichalcogenide two-dimensional materials is critical for their integration in optoelectronic and photonic device applications. By coupling with different plasmonic array geometries, we have shown that the photoluminescence intensity can be enhanced and quenched in comparison with pristine monolayer MoS₂. The enhanced exciton emission intensity can be further tuned by varying the angle of polarized incident excitation. Through controlled variation of the structural parameters of the plasmonic array in our experiment, we demonstrate modulation of the photoluminescence intensity from nearly fourfold quenching to approximately threefold enhancement. Our data indicates that the plasmonic resonance couples to optical fields at both, excitation and emission bands, and increases the spontaneous emission rate in a double spacing plasmonic array structure as compared with an equal spacing array structure. Furthermore our experimental results are supported by numerical as well as full electromagnetic wave simulations. This study can facilitate the incorporation of plasmon-enhanced transition metal dichalcogenide structures in photodetector, sensor and light emitter applications.
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    Enhanced absorption with quantum dots, metal nanoparticles, and 2D materials
    (SPIE, 2016-03-15) Simsek, Ergun; Mukherjee, Bablu; Guchhait, Asim; Chan, Yin Thai
    We fabricate and characterize mono- and few- layers of MoS₂ and WSe₂ on glass and SiO₂/Si substrates. PbS quantum dots and/or Au nanoparticles are deposited on the fabricated thin metal dichalcogenide films by controlled drop casting and electron beam evaporation techniques. The reflection spectra of the fabricated structures are measured with a spatially resolved reflectometry setup. Both experimental and numerical results show that surface functionalization with metal nanoparticles can enhance atomically thin transition metal dichalcogenides’ absorption and scattering capabilities, however semiconducting quantum dots do not create such effect.
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    Effects of Inhomogeneous Background to the Surface Plasmon Resonance Modes of Metal Nanoparticle Chains
    (Optica, 2009-06-14) Simsek, Ergun
    A fully-retarded point-dipole method is developed to obtain the dispersion relations and propagation loss for dipolar modes propagating along a chain of metal nanoparticles embedded in a multi-layered structure using layered media Green's functions (LMGF).
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    Complex electrical permittivity of the monolayer molybdenum disulfide (MoS₂) in near UV and visible
    (Optica, 2015-02-01) Mukherjee, Bablu; Tseng, Frank; Gunlycke, Daniel; Amara, Kiran Kumar; Eda, Goki; Simsek, Ergun
    Temperature and Fermi energy dependent exciton eigenenergies of monolayer molybdenum disulfide (MoS₂) are calculated using an atomistic model. These exciton eigen-energies are used as the resonance frequencies of a hybrid Lorentz-Drude-Gaussian model, in which oscillation strengths and damping coefficients are obtained from the experimental results for the differential transmission and reflection spectra of monolayer MoS₂ coated quartz and silicon substrates, respectively. Numerical results compared to experimental results found in the literature reveal that the developed permittivity model can successfully represent the monolayer MoS₂ under different biasing conditions at different temperatures for the design and simulation of MoS₂ based opto-electronic devices.
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    Calculation of the Phase Noise at Comb-Line Frequencies in a Frequency Comb
    (Optica, 2021-11-01) Anjum, Ishraq Md; Mahabadi, Seyed Ehsan Jamali; Simsek, Ergun; Menyuk, Curtis
    We calculate the phase noise in a modified uni-traveling carrier photodetector for frequency comb applications. In contrast to a continuous wave, a frequency comb is characterized by a distinct phase noise for each comb line.
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    Using dark states for exciton storage in transition-metal dichalcogenides
    (IOP, 2015-12-24) Tseng, Frank; Simsek, Ergun; Gunlycke, Daniel
    We explore the possibility of storing excitons in excitonic dark states in monolayer semiconducting transition-metal dichalcogenides. In addition to being optically inactive, these dark states require the electron and hole to be spatially separated, thus inhibiting electron/hole recombination and allowing exciton lifetimes to be extended. Based on an atomistic exciton model, we derive transition matrix elements and an approximate selection rule showing that excitons could be transitioned into and out of dark states using a pulsed infrared laser. For illustration, we also present exciton population scenarios based on a population analysis for different recombination decay constants. Longer exciton lifetimes could make these materials candidates for applications in energy management and quantum information processing.
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    Tunable graphene-based SPR sensors
    (SPIE, 2013-05-31) Simsek, Ergun
    Graphene’s controllable optical conductivity and mechanically strong structure make it a suitable material to de- sign tunable localized surface plasmon resonance (LSPR) sensors. In this work, we theoretically and numerically demonstrate that the resonance wavelength of an LSPR sensor can be tuned to any value within a reasonably wide range of wavelengths by changing the voltage applied to graphene layer. Theoretical results reveal a higher sensitivity with respect to regular LSPR sensors.
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    Triangular lattice exciton model
    (2016) Gunlycke, Daniel; Tseng, Frank
    We present a minimalistic equilateral triangular lattice model, from which we derive electron and exciton band structures for semiconducting transition-metal dichalcogenides. With explicit consideration of the exchange interaction, this model is appropriate across the spectrum from Wannier to Frenkel excitons. The single-particle contributions are obtained from a nearest-neighbor tight-binding model parameterized using the effective mass and spin-orbit coupling. The solutions to the characteristic equation, computed in direct space, are in qualitative agreement with first-principles calculations and highlight the inadequacy of the two-dimensional hydrogen model to describe the lowest-energy exciton bands. The model confirms the lack of subshell degeneracy and shows that the A-B exciton split depends on the electrostatic environment as well as the spin-orbit interaction.
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    Quantitative and Qualitative Studies of Femtosecond Laser Nanopatterning of Graphene
    (Optica, 2015-06-21) Sahin, R.; Simsek, Ergun; Akturk, S.
    Graphene, composed of carbon atoms arranged by honeycomb structure has been widely investigated for variety of applications. Recent progresses show that the majority of applications at optical and IR wavelengths requires its patterning at micro and nanoscale. Although lithographical approaches can provide such structures, direct writing through laser ablation would be a very desirable complimentary approach.
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    Photodetector Performance Prediction with Machine Learning
    (Optica, 2021-11-01) Simsek, Ergun; Mahabadi, Seyed Ehsan Jamali; Carruthers, Thomas F.; Menyuk, Curtis
    Four machine learning algorithms are tested to predict the performance metrics of modified uni-traveling carrier photodetectors from their design parameters. The highest accuracy (>94%) is achieved with artificial neural networks.
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    Nanometer-Scale Structuring of Gold Thin-Films and Graphene by Femtosecond Laser Bessel Beams
    (Optica, 2014-06-08) Sahin, Ramazan; Simsek, Ergun; Akturk, Selcuk
    We report nanometer-size patterning of various thin films by femtosecond pulsed Bessel beams. Nanoslit arrays fabricated on gold films exhibit excitation of surface plasmon polaritons. We extend the approach to single-atomic-layer systems such as graphene.
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    Keeping 2D materials visible even buried in SoI wafers
    (SPIE, 2016-03-14) Simsek, Ergun; Mukherjee, Bablu
    In order to protect optoelectronic and mechanical properties of atomically thin layered materials (ATLMs) fabricated over SiO₂/Si substrates, a secondary oxide or nitride layer can be capped over. However, such protective capping might decrease ATLMs’ visibility dramatically. Similar to the early studies conducted for graphene, we numerically determine optimum thicknesses both for capping and underlying oxide layers for strongest visibility of monolayer MoS₂, MoSe₂, WS₂, and WSe₂ in different regions of visible spectrum. We find that the capping layer should not be thicker than 60 nm. Furthermore the optimum capping layer thickness value can be calculated as a function of underlying oxide thickness, and vice versa.
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    A More Realistic Coupled Dipole Approximation Model for Plasmonic Waveguides
    (2010) Simsek, Ergun
    Well-known coupled dipole approximation method is extended to multilayered media to obtain a more realistic model for plasmonic waveguides. The key component of the developed method is the layered medium Green's functions. Theoretically calculated resonance frequencies show a very good agreement with the experimental results found in the literature.
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    Field Effect Transistors Deploying Anisotropic Two-Dimensional Materials for Light Generation and Detection
    (Optica, 2018-09-16) Simsek, Ergun; Yuan, Mengqing; Liu, Qing H.
    Three sets of Lorentz-Drude parameters are determined to describe anisotropic optical constants of ReS₂. Photodetector sensitivity and photoluminescence efficiency of ReS₂ coated SiO₂/Si substrates are studied. For ultra-thin applications, metal nanoparticles embedded in Si yield best performance.
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    Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
    (2025-01-03) Zimmermann, Yoel; Bazgir, Adib; Afzal, Zartashia; Agbere, Fariha; Ai, Qianxiang; Alampara, Nawaf; Al-Feghali, Alexander; Ansari, Mehrad; Antypov, Dmytro; Aswad, Amro; Bai, Jiaru; Baibakova, Viktoriia; Biswajeet, Devi Dutta; Bitzek, Erik; Bocarsly, Joshua D.; Borisova, Anna; Bran, Andres M.; Brinson, L. Catherine; Calderon, Marcel Moran; Canalicchio, Alessandro; Chen, Victor; Chiang, Yuan; Circi, Defne; Charmes, Benjamin; Chaudhary, Vikrant; Chen, Zizhang; Chiu, Min-Hsueh; Clymo, Judith; Dabhadkar, Kedar; Daelman, Nathan; Datar, Archit; Jong, Wibe A. de; Evans, Matthew L.; Fard, Maryam Ghazizade; Fisicaro, Giuseppe; Gangan, Abhijeet Sadashiv; George, Janine; Gonzalez, Jose D. Cojal; Götte, Michael; Gupta, Ankur K.; Harb, Hassan; Hong, Pengyu; Ibrahim, Abdelrahman; Ilyas, Ahmed; Imran, Alishba; Ishimwe, Kevin; Issa, Ramsey; Jablonka, Kevin Maik; Jones, Colin; Josephson, Tyler R.; Juhasz, Greg; Kapoor, Sarthak; Kang, Rongda; Khalighinejad, Ghazal; Khan, Sartaaj; Klawohn, Sascha; Kuman, Suneel; Ladines, Alvin Noe; Leang, Sarom; Lederbauer, Magdalena; Sheng-Lun; Liao; Liu, Hao; Liu, Xuefeng; Lo, Stanley; Madireddy, Sandeep; Maharana, Piyush Ranjan; Maheshwari, Shagun; Mahjoubi, Soroush; Márquez, José A.; Mills, Rob; Mohanty, Trupti; Mohr, Bernadette; Moosavi, Seyed Mohamad; Moßhammer, Alexander; Naghdi, Amirhossein D.; Naik, Aakash; Narykov, Oleksandr; Näsström, Hampus; Nguyen, Xuan Vu; Ni, Xinyi; O'Connor, Dana; Olayiwola, Teslim; Ottomano, Federico; Ozhan, Aleyna Beste; Pagel, Sebastian; Parida, Chiku; Park, Jaehee; Patel, Vraj; Patyukova, Elena; Petersen, Martin Hoffmann; Pinto, Luis; Pizarro, José M.; Plessers, Dieter; Pradhan, Tapashree; Pratiush, Utkarsh; Puli, Charishma; Qin, Andrew; Rajabi, Mahyar; Ricci, Francesco; Risch, Elliot; Ríos-García, Martiño; Roy, Aritra; Rug, Tehseen; Sayeed, Hasan M.; Scheidgen, Markus; Schilling-Wilhelmi, Mara; Schloz, Marcel; Schöppach, Fabian; Schumann, Julia; Schwaller, Philippe; Schwarting, Marcus; Sharlin, Samiha; Shen, Kevin; Shi, Jiale; Si, Pradip; D'Souza, Jennifer; Sparks, Taylor; Sudhakar, Suraj; Talirz, Leopold; Tang, Dandan; Taran, Olga; Terboven, Carla; Tropin, Mark; Tsymbal, Anastasiia; Ueltzen, Katharina; Unzueta, Pablo Andres; Vasan, Archit; Vinchurkar, Tirtha; Vo, Trung; Vogel, Gabriel; Völker, Christoph; Weinreich, Jan; Yang, Faradawn; Zaki, Mohd; Zhang, Chi; Zhang, Sylvester; Zhang, Weijie; Zhu, Ruijie; Zhu, Shang; Janssen, Jan; Li, Calvin; Foster, Ian; Blaiszik, Ben
    Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research.
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    Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks
    (2025-02-02) Acharya, Kamal; Lad, Mehul; Sun, Liang; Song, Houbing
    Travel demand prediction is crucial for optimizing transportation planning, resource allocation, and infrastructure development, ensuring efficient mobility and economic sustainability. This study introduces a Neurosymbolic Artificial Intelligence (Neurosymbolic AI) framework that integrates decision tree (DT)-based symbolic rules with neural networks (NNs) to predict travel demand, leveraging the interpretability of symbolic reasoning and the predictive power of neural learning. The framework utilizes data from diverse sources, including geospatial, economic, and mobility datasets, to build a comprehensive feature set. DTs are employed to extract interpretable if-then rules that capture key patterns, which are then incorporated as additional features into a NN to enhance its predictive capabilities. Experimental results show that the combined dataset, enriched with symbolic rules, consistently outperforms standalone datasets across multiple evaluation metrics, including Mean Absolute Error (MAE), \(R^2\), and Common Part of Commuters (CPC). Rules selected at finer variance thresholds (e.g., 0.0001) demonstrate superior effectiveness in capturing nuanced relationships, reducing prediction errors, and aligning with observed commuter patterns. By merging symbolic and neural learning paradigms, this Neurosymbolic approach achieves both interpretability and accuracy.
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    Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context
    (2024-12-20) Das, Nilanjana; Raff, Edward; Gaur, Manas
    Previous research on LLM vulnerabilities often relied on nonsensical adversarial prompts, which were easily detectable by automated methods. We address this gap by focusing on human-readable adversarial prompts, a more realistic and potent threat. Our key contributions are situation-driven attacks leveraging movie scripts to create contextually relevant, human-readable prompts that successfully deceive LLMs, adversarial suffix conversion to transform nonsensical adversarial suffixes into meaningful text, and AdvPrompter with p-nucleus sampling, a method to generate diverse, human-readable adversarial suffixes, improving attack efficacy in models like GPT-3.5 and Gemma 7B. Our findings demonstrate that LLMs can be tricked by sophisticated adversaries into producing harmful responses with human-readable adversarial prompts and that there exists a scope for improvement when it comes to robust LLMs.