Biography

Hamim Md Adal is a Phd candidate from the Department of Computer Science (CS) at the University of New Mexico. His research interests align with Machine Learning, Artificial Intelligence, Computational Modeling, Pervasive Computing, and Software Engineering. Currently, his research contributes to the adaptive control of spatial characteristics in smart spaces through smart Artificial Intelligence (AI) agents, which exploit real-time sensor feedback from the environment to make autonomous decisions. The key is to formally characterize sensor information and design machine learning frameworks for continuous learning that leverage interaction histories, sensing, and geometry of the space to automate user interaction in a smart space through smart AI agents. During his Doctorate Program, Hamim completed 60 hours of graduate coursework from the Department of CS. He also published research papers in ACM and IEEE venues, two of the recognised conferences in the field of CS. Besides, he has been a regular reviewer for Springer (Advances in Computational Intelligence) and the Heliyon (Physical and Applied Sciences) journals. Hamim has a proven record of leading and managing R&D projects in collaboration with research labs at Virginia Tech and the University of Texas, Austin, where his research is supported by the National Science Foundation (NSF).

Academic Status

PhD Student - 5th

Research Area/Department

Computer Science; Data Science; Engineering; Machine Learning/AI; Mathematics

Major/Specialty

Computer Science.

Degrees Earned or in Progress

-- Ph.D. in Computer Science, The University of New Mexico, USA (in Progress) -- M.Sc. in Computer Science, The University of New Mexico, USA (2023) -- B.Sc. in Computer Science & Engineering, Bangladesh University of Professionals, Bangladesh (2017)

Academic Preparation

1. Principles of Artificially Intelligent Machines (CS 527) 2. Algorithms & Data Structure (CS 561) 3. Software Foundations (CS 558) 4. Specification of Software Systems (CS 580) 5. Geometric and Probabilistic Methods in (CS 530) 6. Experimental Methods in CS (CS 533) 7. Intro to Cybersecurity (CS 544) 8. Computer Networks (CS 585) 9. Theory of Computation (CS 500) 10. Advanced Computer Operating Systems (587)

Research/Publications

--- My research has been published in the IEEE and ACM conferences. --- I have been doing research/technical projects under NSF (National Science Foundation) funding for the last 5 years (CNS-1909221, CNS-1907959). --- I am also an active reviewer for Springer (Advances in Computational Intelligence), and Heliyon (Physical and Applied Sciences) journal. *** Published Paper *** Google Scholar Link (https://scholar.google.com/citations?user=_rXDD9wAAAAJ&hl=en) [1] Hua, J., Yu, H., Lee, S., Adal, H., Milhaupt, C., Roman, G.C., Julien, C.: “CoPI: Enabling probabilistic conflict prediction in smart space through context-awareness”, 7th ACM/IEEE Conference on Internet of Things Design and Implementation (2022). [2] Adal, H.M., Milhaupt, C., Hua, J., Julien, C., Roman, G.C.: “The space broker: a middleware for mediating interactions in smart IoT spaces”, proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. pp. 101–110 (2021). [3] Adal, H., Promy, N., Srabanti, S., Rahman, M.: “Android based advanced attendance vigilance system using wireless network with fusion of bio-metric fingerprint authentication.”, 2018 20th International Conference on Advanced Commu- nication Technology (ICACT). pp. 217–222. IEEE (2018). Link *** Journal Manuscript Currently Under Review *** [1] Adal, H.M., Pitts, C., Julien, C., Roman, G.C.: “Formalization of Spatial Characteristics and the Influence of Space Geometry”, IEEE Internet of Things Journal. (Journal Manuscript Under Review) [2] Pitts, C., Adal, H.M., Julien, C., Roman, G.C.: “Data Mining User Interaction Histories for Responsive Control of IoT Spaces”, Elsevier Energy and Buildings Journal (Journal Manuscript Under Review)

Research/Academic Interests

***Research Interests*** Machine Learning, Artificial Intelligence, Computational Modeling and Simulation, Pervasive Computing, and Software Engineering.

Computational and Data Science Areas

Applied Computer Science; Applied Mathematics; Artificial Intelligence and Intelligent Systems; Computer Science; Informatics, Analytics and Information Science; Other Computer and Information Sciences; Other Engineering and Technologies; Statistics and Probability; Visualization and Human-Computer Systems

Motivation

Last year, in June 2024, I was on vacation with my wife in San Francisco, California, where we boarded a cruise to Alcatraz Island! While we were enjoying the sights of the bay and skyline, I found myself standing beside a group of summer graduate interns from Lawrence Berkeley National Lab (LBNL). What began as casual small talk quickly turned into an inspiring exchange about their paths into research opportunities in LBNL offered by Sustainable Research Pathways (SRP). It was then that I first heard about SRP. For me, it was inspiring to know how SRP connects young researchers with leading scientists from national labs, opening doors to experiences that can shape the research careers of graduate students. At the end of my vacation, when I returned to my campus, I spent some time exploring the SRP website and its ongoing engagements. I was glad to know that I was not the first one from my school who is seeking opportunities to work in summer research projects through SRP, but a handful of Alumni have already participated in different National Labs across the USA! For the past few years, SRP has constantly been a pioneer, bringing together scientists, students, faculty, and researchers from diverse backgrounds to contribute to advancement in science and technology. As a Ph.D. candidate in Computer Science, I see this program as a valuable opportunity to expand my skills, gain guidance from leading scientists, and grow as a researcher. The field of Artificial Intelligence (AI) is growing at a rapid pace, where researchers from different fields have a lot to contribute in their own application domains, both by actively participating in the research work in AI and also spreading its application in their respective research domains. In this regard, I have always found SRP as a platform that provides collaboration opportunities between early-career researchers and established scientists. This always worked as a motivation for me to work on a project from which I can learn a great deal of knowledge and experience. Besides, it has also been an inspiration for me to observe alumni from my institution participating in SRP-funded projects in the past few years. Witnessing how their experiences in this program significantly shaped their early careers and guided their professional paths has strongly motivated me to pursue this opportunity. Throughout my graduate program, I have built a strong foundation in Artificial Intelligence and Machine Learning, conducting scientific research, completing advanced graduate courses, and publishing papers in research conferences. At this point in my career, I want to join a program where I can be mentored by experts, improve my research abilities, and contribute to research projects. My long-term goal after my Ph.d is to pursue a postdoctoral position, followed by becoming a scientist at a national lab or a research faculty member at an accredited U.S. institution. This program aligns with my ambitions by offering mentorship, collaboration, and networking opportunities that will help me achieve these goals while contributing to the ongoing research projects funded by the National Science Foundation and National Artificial Intelligence Research Resource (NAIRR) projects. I am confident that the opportunity to participate in this program will give shape to my career to become a more accomplished researcher in the future.

Lightning Talk Title

AI's Resilience to Stay Smart in a Noisy World

Keywords (Maximum 20 words)

Machine Learning; Computational Modeling; Artificial Intelligence; Supervised Learning; Simulation; Reinforcement Learning; Neural Network; Deep Learning; High Performance Computing; Mathematical Modeling