Farhad Mohsin is an Assistant Professor in the Department of Math and Computer Science at College of the Holy Cross in Worcester, MA. He earned his PhD in Computer Science from Rensselaer Polytechnic Institute (RPI) between 2018-2023, working with Professor Lirong Xia, and completed his BSc in Electrical and Electronic Engineering at Bangladesh University of Engineering and Technology (BUET) from 2010-2015. Prior to his academic career, he worked as a Telecommunications Engineer/Data Analyst at Grameenphone Ltd, Bangladesh. Dr. Mohsin's research focuses on computational social choice, particularly preference aggregation and fair decision-making. He explores ML-based techniques for designing economic mechanisms, with specific interest in fairer voting rules. His broader research interests include natural language processing, interpretable machine learning, and multi-agent reinforcement learning. His recent publications (2021-2024) examine computational complexity of voting paradoxes, election data generation using deep learning, and natural language-based preference aggregation. His work has appeared in top venues including IJCAI, AAMAS, and JAIR. Dr. Mohsin teaches courses including Data Mining, Data Structures, Analysis of Algorithms, Discrete Structures, and Advanced Algorithms. He supervises undergraduate research projects, with recent honors theses on multi-agent reinforcement learning and fairness in zoning laws.
Dr. Vincent Nguyen serves as a Senior Lecturer in Orthoptics within the Graduate School of Health at the University of Technology Sydney. With a distinguished career spanning clinical practice, research, and academia, he brings extensive expertise in visual science and rehabilitation. His academic journey began with Honours in Orthoptics (1993) followed by a Master of Applied Science (1996), culminating in a PhD from the University of Sydney (2003) focused on binocular rivalry in visual psychophysics. Dr. Nguyen's research interests focus on low vision rehabilitation, depth perception, binocular vision, and the application of virtual reality and spatial audio technologies for assistive applications. His work bridges fundamental visual neuroscience with practical rehabilitation solutions, particularly for people with visual impairments. He has pioneered research in acoustic touch interfaces, virtual reality rehabilitation environments, and spatial audio navigation systems. His publication record demonstrates a clear trajectory from fundamental visual neuroscience to applied rehabilitation technologies. Recent work emphasizes immersive virtual reality applications for communication and physical rehabilitation, as well as innovative spatial audio systems that enhance navigation for people who are blind. His research integrates principles from neuroscience, engineering, and clinical practice to develop practical assistive solutions. Dr. Nguyen has secured significant research funding including NHMRC Ideas Grants (2023-2027) for 'Fluent Mobility for the Blind Individual Using Multimodal Auditory Sensory Augmentation' and CRC-P projects like 'ARIA - Bionic Visual-Spatial Medical Device for the Blind' (2022-2024). Current projects include 'EyeBot: AI-Powered Triage for Ophthalmology Referrals' (2025) and 'Next-Generation Extended Reality, Wearable Biosensors, and Metaverse Technologies for Medical Applications' (2024). As an educator, Dr. Nguyen teaches courses in Therapy and Rehabilitation (96037), Clinical Management of Refractive Error (96031), and Eye and Visual Systems (96027). His clinical background includes appointments as a Clinical Electrophysiologist with NSW Health (2007) and work with Vision Australia assisting people with low vision (2012). His postdoctoral work at York University's Centre for Vision Research with Professor Ian Howard focused on human depth perception, building on his foundational expertise in visual neuroscience.
György Kovács is a Senior Lecturer at Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, working within the Embedded Intelligent Systems LAB. His primary research focus is on Machine Learning applications, particularly in speech and language technology. His research interests span multiple areas of speech and language processing, including: Automatic Speech Recognition across diverse conditions and languages Paralinguistic Speech Processing for emotion detection and speaker state analysis Audio event classification, including medical applications like cough sound analysis Sentiment analysis in written text, with particular interest in hateful language detection Bot detection in social media analysis Kovács has supervised numerous Master's theses since 2021, with topics ranging from accent classification and sentiment analysis to AI-generated code quality assessment. Many of these supervisions have led to co-authored publications with students. His recent publications demonstrate a strong focus on applying machine learning to diverse domains including remote sensing, healthcare technology, and natural language processing. The research shows a clear pattern of interdisciplinary work connecting machine learning with practical applications across environmental monitoring, healthcare, and social media analysis. His academic contributions include co-supervising PhD students such as Sana Al-Azzawi and Nosheen Abid, whose dissertation work on Unsupervised Curriculum Learning for Earth Observation represents significant contributions to the field. Notable publications include work on cloud detection using synthetic datasets, seagrass classification, and emotion classification using EEG in healthcare settings. Kovács also teaches courses including Introduction to Artificial Intelligence (D0032E) and Machine Learning and Pattern Recognition (D0033E), demonstrating his commitment to both research and education in the field of artificial intelligence.
Professor Dr. Philipp Brune serves as a prominent academic leader at Neu-Ulm University of Applied Sciences (HNU), holding multiple key positions including Academic Head of TTZ Günzburg, Head of the Master of Artificial Intelligence and Data Analytics program, Head of the Centre for Secure IT Applications and Infrastructures, and Head of the Institute of Agile Product and System Development within the Faculty of Information Management. His career demonstrates a notable transition from physics research (evident in his early 2000-2004 publications) to his current focus on cutting-edge information systems and artificial intelligence. Dr. Brune's research program spans several interconnected domains with particular emphasis on facial emotion recognition systems, legacy system modernization, and secure IT infrastructure development. His work investigates fundamental challenges in AI interpretability, data quality for emotion recognition, and practical approaches to modernizing legacy applications. He has made substantial contributions to understanding the limitations of current facial expression recognition technologies and developing more robust, trustworthy alternatives. Analysis of his recent publications reveals a clear research trajectory toward explainable and trustworthy AI systems, with increasing sophistication in addressing data quality issues and model interpretability challenges. His work bridges theoretical computer science with practical applications across healthcare, marketing, and smart city technologies, demonstrating strong translational research capabilities. As an educator, Dr. Brune has developed innovative pedagogical approaches specifically tailored for non-computer science majors, with research examining student motivation in programming courses and effective methods for teaching cybersecurity concepts. His leadership in the Master of Artificial Intelligence and Data Analytics program shapes curriculum development for next-generation AI professionals. Dr. Brune directs multiple research initiatives including the Centre for Secure IT Applications and Infrastructures, which addresses critical cybersecurity challenges in modern IT environments, and the Institute of Agile Product and System Development, which explores innovative software development methodologies. His collaborative research approach is evident through extensive co-authorship networks spanning multiple institutions and disciplines.
Dr. Marc C. Green serves as a Postdoctoral Research Fellow within the School of Science, Engineering & Environment at the University of Salford, affiliated with the Acoustics Innovation Institute. His research bridges acoustic engineering, machine learning, and environmental psychology with focus on unmanned aircraft systems noise impact assessment. PhD in Music Technology, University of York (2020) Dr. Green's research investigates how drone noise interacts with urban soundscapes through innovative methodologies including soundwalk assessments in controlled environments, psychoacoustic laboratory testing , and deep learning model development . His work specifically examines time-variant noise characteristics, flight operation impacts, and environmental interactions (such as pine tree barriers in urban parks) to understand human annoyance responses. The integration of spatial audio techniques with machine learning enables novel approaches to predicting UAV sound affect beyond traditional sound pressure level metrics. Analysis of his 11 recent publications (2024-2025) reveals a strong focus on practical solutions for urban drone integration, with 55% dedicated to psychoacoustic modeling, 27% to field assessment methodologies, and 18% to technical noise characterization. His work consistently addresses the tension between societal benefits of drone technology and community noise impacts. EPSRC connected nation pioneers prize for 'Creative Computing for the Digital Economy' (for EigenScape database development) Currently leading the RefMap project to reduce UAS environmental impact, Dr. Green's research receives direct institutional support through the Acoustics Innovation Institute. His EigenScape database continues to serve as a foundational resource for global soundscape researchers. The systematic review framework he developed for UAS noise impact testing provides regulators with evidence-based assessment protocols. Dr. Green operates within the Acoustics Innovation Institute's collaborative ecosystem, leveraging specialized recording equipment and computational resources. His research group maintains comprehensive drone noise datasets across multiple platforms and develops open methodologies for community noise assessment in evolving urban air mobility scenarios.
Dora Demszky is an Assistant Professor in Education Data Science at Stanford University's Graduate School of Education, with a courtesy appointment in Computer Science. She leads the EduNLP Lab, where she develops natural language processing tools to support equitable, student-centered instruction through analyzing educational discourse including student-teacher interactions, student group work, and textbooks. PhD in Linguistics from Stanford University (advised by Dan Jurafsky) BA summa cum laude in Linguistics with a minor in Computer Science from Princeton University Co-founder of Tarisznya Alapítvány (Knapsack Foundation), a nonprofit supporting underprivileged children in Hungary Dr. Demszky's research combines natural language processing, linguistics, and practitioner input to develop interpretable and scalable education measures. Her work focuses on creating tools that analyze classroom discourse to identify features of high-quality instruction and provide actionable feedback to educators. She has particular expertise in developing AI-powered systems that help teachers improve questioning quality, analyze textbook representation, measure dialect features, and scaffold curricula to meet diverse student needs. Her approach emphasizes the importance of human connections in teaching and learning, using technology to enhance rather than replace these critical interactions. Analysis of Dr. Demszky's recent publications reveals a strong trend toward practical applications of NLP in real educational settings, with increasing emphasis on randomized controlled trials to validate effectiveness. Her work spans multiple educational contexts from K-12 classrooms to higher education, with growing attention to equity considerations in AI-powered educational tools. Recent publications show expansion into mathematical education, speaker diarization for noisy classrooms, and open-source tools for the broader research community. MathemaTikZ dataset received the inaugural best dataset prize at Learning at Scale NCTE classroom transcript dataset received the best IEDMS Publicly Available Educational Dataset Prize Selected as a Leading Woman in AI at the ASU GSV AIR Show Dr. Demszky has secured significant research funding including grants from the Gates Foundation, NSF RAPID program, and Stanford HAI. Her work with teachers extends beyond research through initiatives like the Practitioner Voices Summit, which brought together 60 teachers from 22 states to inform AI research related to classrooms. She actively collaborates with educators to ensure her tools address real classroom needs while maintaining a focus on socially responsible edtech development. At Stanford, Dr. Demszky leads the EduNLP Lab, which operates at the intersection of education, computer science, and linguistics. The lab follows a three-pronged approach: gathering evidence through data science using NLP models, developing algorithms through co-design with domain experts, and piloting solutions that practitioners can use in real educational settings. Current projects include M-Powering Teachers, which provides automated feedback to educators, and tools for adapting mathematics curricula to support students with diverse learning needs.
