In a sequence of interviews, we’re assembly a few of the AAAI/SIGAI Doctoral Consortium members to search out out extra about their analysis. On this newest interview, we hear from Amina Mević who’s making use of machine studying to semiconductor manufacturing. Discover out extra about her PhD analysis to date, what makes this area so attention-grabbing, and the way she discovered the AAAI Doctoral Consortium expertise.
Inform us a bit about your PhD – the place are you finding out, and what’s the matter of your analysis?
I’m at the moment pursuing my PhD on the College of Sarajevo, College of Electrical Engineering, Division of Pc Science and Informatics. My analysis is being carried out in collaboration with Infineon Applied sciences Austria as a part of the Vital Venture of Frequent European Curiosity (IPCEI) in Microelectronics. The subject of my analysis focuses on creating an explainable multi-output digital metrology system primarily based on machine studying to foretell the bodily properties of steel layers in semiconductor manufacturing.
Might you give us an outline of the analysis you’ve carried out to date throughout your PhD?
Within the first 12 months of my PhD, I labored on preprocessing complicated manufacturing information and making ready a strong multi-output prediction setup for digital metrology. I collaborated with trade specialists to know the method intricacies and validate the prediction fashions. I utilized a projection-based choice algorithm (ProjSe), which aligned effectively with each area information and course of physics.
Within the second 12 months, I developed an explanatory methodology, designed to establish probably the most related enter options for multi-output predictions.
Is there a facet of your analysis that has been notably attention-grabbing?
For me, probably the most attention-grabbing facet is the synergy between physics, arithmetic, cutting-edge know-how, psychology, and ethics. I’m working with information collected throughout a bodily course of—bodily vapor deposition—utilizing ideas from geometry and algebra, notably projection operators and their algebra, which have roots in quantum mechanics, to reinforce each the efficiency and interpretability of machine studying fashions. Collaborating carefully with engineers within the semiconductor trade has additionally been eye-opening, particularly seeing how explanations can straight help human decision-making in high-stakes environments. I really feel actually honored to deepen my information throughout these fields and to conduct this multidisciplinary analysis.
What are your plans for constructing in your analysis to date in the course of the PhD – what points will you be investigating subsequent?
I plan to focus extra on time sequence information and develop explanatory strategies for multivariate time sequence fashions. Moreover, I intend to research points of accountable AI throughout the semiconductor trade and be sure that the options proposed throughout my PhD align with the rules outlined within the EU AI Act.
How was the AAAI Doctoral Consortium, and the AAAI convention expertise typically?
Attending the AAAI Doctoral Consortium was an incredible expertise! It gave me the chance to current my analysis and obtain worthwhile suggestions from main AI researchers. The networking facet was equally rewarding—I had inspiring conversations with fellow PhD college students and mentors from around the globe. The primary convention itself was energizing and numerous, with cutting-edge analysis offered throughout so many AI subfields. It positively strengthened my motivation and gave me new concepts for the ultimate section of my PhD.
Amina presenting two posters at AAAI 2025.
What made you wish to research AI?
After graduating in theoretical physics, I discovered that job alternatives—particularly in physics analysis—have been fairly restricted in my nation. I started in search of roles the place I might apply the mathematical information and problem-solving expertise I had developed throughout my research. On the time, information science gave the impression to be a super and promising area. Nevertheless, I quickly realized that I missed the depth and function of basic analysis, which was usually missing in trade roles. That motivated me to pursue a PhD in AI, aiming to realize a deep, foundational understanding of the know-how—one that may be utilized meaningfully and utilized in service of humanity.
What recommendation would you give to somebody considering of doing a PhD within the area?
Keep curious and open to studying from totally different disciplines—particularly arithmetic, statistics, and area information. Be sure your analysis has a function that resonates with you personally, as that keenness will assist carry you thru challenges. There might be moments whenever you’ll really feel like giving up, however earlier than making any resolution, ask your self: am I simply drained? Generally, relaxation is the answer to lots of our issues. Lastly, discover mentors and communities to share concepts with and keep impressed.
Might you inform us an attention-grabbing (non-AI associated) reality about you?
I’m an enormous science outreach fanatic! I usually volunteer with the Affiliation for the Development of Science and Know-how in Bosnia, the place we run workshops and occasions to encourage youngsters and highschool college students to discover STEM—particularly in underserved communities.
About Amina
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Amina Mević is a PhD candidate and instructing assistant on the College of Sarajevo, College of Electrical Engineering, Bosnia and Herzegovina. Her analysis is carried out in collaboration with Infineon Applied sciences Austria as a part of the IPCEI in Microelectronics. She earned a grasp’s diploma in theoretical physics and was awarded two Golden Badges of the College of Sarajevo for attaining a GPA increased than 9.5/10 throughout each her bachelor’s and grasp’s research. Amina actively volunteers to advertise STEM training amongst youth in Bosnia and Herzegovina and is devoted to bettering the analysis setting in her nation. |
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AIhub
is a non-profit devoted to connecting the AI group to the general public by offering free, high-quality data in AI.