tiny Machine Learning and Embedded Computing Lab
(tML-EC Lab)
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Open Positions 2025-2026 (announcement date: Jan. 03, 2025)
Postdoc. - Emerging Computing (1 Researcher) (Starting: Summer 2025 or beyond)
Required Qualifications:
✔ Experience with emerging computing paradigms. Capability to develop and implement hardware-software co-design for ML systems and design space exploration platforms during the postdoctoral research.
✔ Proficiency both in ML and hardware design. Knowledge of ML frameworks (e.g., TensorFlow, PyTorch), some ML algorithms and their hardware implementations, AI accelerators.
✔ Experience with language and vision transformer models.
✔ Familiarity with EDA tools for design, synthesis, and verification (e.g., Cadence, Synopsys).
✔ Strong publication record in top-tier conferences (e.g., DAC, DATE, ICCAD, ESWEEK, ASP-DAC, ISLPED,...) and journals (e.g., IEEE TCAD, TCAS, TVLSI, JETCAS, Nano,...).
✔ Robust scholarly background with demonstrated expertise in research and technical writing.
✔ Experience with emerging computing paradigms. Capability to develop and implement hardware-software co-design for ML systems and design space exploration platforms during the postdoctoral research.
✔ Proficiency both in ML and hardware design. Knowledge of ML frameworks (e.g., TensorFlow, PyTorch), some ML algorithms and their hardware implementations, AI accelerators.
✔ Experience with language and vision transformer models.
✔ Familiarity with EDA tools for design, synthesis, and verification (e.g., Cadence, Synopsys).
✔ Strong publication record in top-tier conferences (e.g., DAC, DATE, ICCAD, ESWEEK, ASP-DAC, ISLPED,...) and journals (e.g., IEEE TCAD, TCAS, TVLSI, JETCAS, Nano,...).
✔ Robust scholarly background with demonstrated expertise in research and technical writing.
Preferred Qualifications:
✔ Undergraduate GPA > 3.25 and Graduate GPA > 3.75.
✔ Google Scholar citation count exceeding 100.
✔ Candidates aiming for academic positions post-completion of the postdoc will be given priority.
✔ Undergraduate GPA > 3.25 and Graduate GPA > 3.75.
✔ Google Scholar citation count exceeding 100.
✔ Candidates aiming for academic positions post-completion of the postdoc will be given priority.
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PhD. - ML Systems & Emerging Computing (2-3 Researchers) (Starting: Fall 2025 or beyond)
✔ The candidate should have interest both in ML and hardware design; so the target is ML Systems.
✔ Academic writing skills, and preferably publication experience.
✔ Experience with ML algorithms, and hardware design.
✔ Undergraduate GPA > 3.25 (preferred, but not a must)
✔ GRE and Language Scores in the candidate's CV are preferred.
Topic-1: HyperVector Networks (HVN) [Pure ML & Hardware]
Using the power of hyperdimensional vector processing to create networks (Python framework) & system design (ASIC/FPGA) for language processing and vision applications.
Topic-2: Digital Twinning of Emerging Computing [Simulation of Hardware]
Design of a software stack for hardware co-operative simulation platform (device-level, circuit-level, architecture-level, and system(application)-level.)
Topic-3: Synergistic (Yet Secure) Emerging Computing System Design [Multi-paradigms]
A novel paradigm by our research group, synergistic computing, is the target. Collaborating with stochastic, unary, n-ary, hyperdimensional, in-memory, approximate, quantum, intermittent, or reversible computing for architecture- and device-level synergy. E.g. applications: hardware of privacy preserving recommender systems for sustainable servers, opcode-level vector-symbolic AI for microprocessor security.
Topic-4: Niche Applications of Emerging Computing for Intricate Areas [Bio-related]
Applications of emerging computing paradigms. Various of applications apply: Low-power underwater image dehazing, acute illness modeling of genetic data using vector-symbolic architectures, biometric steganography using emerging devices.
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PhD. Students
Abu Kaisar Mohammad Masum
Abu Kaisar Mohammad Masum earned his B.Sc. in Computer Science and Engineering from Daffodil International University, Bangladesh. He has served as a Lecturer at the same institution, teaching courses such as Machine Learning, Data Mining, and Artificial Intelligence. Abu has gained research experience as a Research Assistant at Apurba-DIU R&D Lab and as a Researcher at the DIU NLP and Machine Learning Research Lab, where he focused on Bengali text summarization, Bengali spell checking, automated data annotation, and language models utilizing transformers and attention mechanisms. His research interests include machine learning and hybrid classical-quantum machine learning. He is particularly interested in Machine Learning, Quantum Machine Learning, Large Language Models, and Natural Language Processing. Abu has received full funding to participate in the STAQ Quantum Summer School 2024 at Duke University, enhancing his expertise in quantum computing. Personal Webpage.
Faeze received the B.Sc. degree in Computer Engineering - Hardware from Shahid Beheshti University, Tehran, Iran, in 2013, and the M.Sc. degree in Computer Architecture from the University of Tehran in 2017. Faeze was selected as a DAC Young Fellow in DAC 2023. She is working in securing stochastic computing-based neural network systems from data to the network model security.
Emilien J Meyer
Emilien is a PhD student completed his B.Sc. in Electrical Engineering with a concentration in Computer Engineering and a minor in Computer Science in 2024. He is a member of the IEEE Student Branch at the University of Louisiana at Lafayette. Since joining the lab in October 2023, he has contributed to the area of efficient data processing in resource-constrained devices during his undergraduate research.
Tanha Tasfia
Tanha Tasfia is a PhD student in Computer Science at the University of Louisiana at Lafayette. She received her bachelor’s degree in Computer Science and Engineering (CSE) from the Military Institute of Science and Technology (MIST), Bangladesh, in 2023. She has experience in machine learning and software development and is skilled in C++, Python, HTML, CSS, JavaScript, and SQL. Her research focuses on developing tiny solutions to tackle real-world challenges in simulation platforms.
Master Students
Reeti Pradhananga
Reeti Pradhananga is currently pursuing a master’s degree in computer science at the University of Louisiana at Lafayette. She earned her bachelor’s degree from Kantipur Engineering College, Nepal. She has experience in software development, and she is proficient in Python, Go, JavaScript, and SQL. Her academic and professional journey reflects a deep interest in data-driven decision support systems, which has led her to explore machine learning and recommendation systems. Reeti is passionate about leveraging technology to solve real-world problems, and her current research aims to contribute to advancements in these emerging fields.
Colin E Dupuis
Colin Dupuis was born in Lafayette, Louisiana, in 2002. He received a high school diploma from St. Thomas More Catholic High School in Lafayette, Louisiana. He is an undergraduate senior studying Electrical Engineering at the University of Louisiana at Lafayette. In 2022 he became a student member of the national branch of IEEE. In 2021 he joined Arcadian Ambulance Service as a Computer Technician Intern. Mr. Dupuis is also currently a member of the Louisiana Society of Engineers and the ULL Club Lacrosse team. Colin has completed his undergraduate degree in Electrical Engineering by doing his design project with Dr. Aygun. He is now a MSc. student in the School of Computing and Informatics.
Hritika Pandey
Hritika Pandey is a MSc. informatics student at the University of Louisiana at Lafayette. She earned her undergraduate degree in Computer Science and Information Technology from Tribhuvan University one of the oldest and well renowned university in Nepal. With a Robust academic foundation and diverse work experience as a data analyst across multiple sectors, including healthcare, Hritika brings a unique perspective to data-driven problem-solving. She has also worked on several machine learning projects, which have deepened her understanding of the field and strengthened her passion for developing innovative solutions. Her research interests lie at the intersection of machine learning, artificial intelligence, and practical applications, with a focus on developing innovative solutions to complex challenges. Passionate about advancing technology, she aspires to bridge gaps in knowledge and systems through cutting-edge research and data analysis.
Tanner A Mergist
Tanner Mergist is a first-semester M.S. Computer Science student at the University of Louisiana at Lafayette. He earned his B.S. in Computer Science from the University of Louisiana at Lafayette in December of 2024. His research experience includes work in machine learning, audio signal processing, and AI-driven educational technologies. He has worked on projects focused on extracting features from physiological signals, developing classification models, and designing AI-powered tools for education assistance. His research interests include natural language processing, hyperdimensional computing, swarm algorithms, and AI applications in education.
Undergrad. Students
Jonas I Schmidt
Jonas is an undergraduate senior pursuing a B.Sc. in Computer Science and double major in Mathematics, with a minor in Psychology at The University of Louisiana at Lafayette. He is the chair of ULL's ACM-CSE student chapter. Jonas joined our lab in August 2024. Jonas and Dr. Aygun are working together since January 2023 and since Jonas contributed to the area of Hyperdimensional and Stochastic Computing. Jonas received the Louisiana Space and Sea Grant Opportunity (LaSSO) Award for his research on emerging computing technologies.
Allison Zanyk
Allison is an undergraduate senior pursuing a B.Sc. in Mathematics and Computer Science at the University of Louisiana at Lafayette. She has contributed to Acadian Ambulance as a software engineer intern for two years. Her research interests are in machine learning applications for predicting the effects of climate change and hyperdimensional computing. Some other interests of hers include hiking and music composition.
Previous Programs & Students
Summer Internship Program - 2024
This project aims to develop a mobile artificial intelligence (AI) application for housekeeping operations. Using the app, customers and housekeepers can easily track the cleanliness and organization of household objects. By leveraging powerful AI models, the app performs object tracking through segmentation. Each specific household item in a property is then analyzed using the generative conversational AI module from Gemini. This allows for a fast and effective mobile application that assesses the status of household furniture, from its positioning to its level of cleanliness, guiding housekeepers in their tasks. The project has been released as a mobile application product. The project has been supported by Informatics Research Institute and Keepers.
Students Attended (from left to right in the left picture):
Kevin P Nguyen
Tyler Searle
Austin J Bryant
Nathaniel L Agee
Colin E Dupuis
Luke W Brinkley


