Dr. Alvitta Ottley is an Assistant Professor in the Department of Computer Science & Engineering at Washington University in St. Louis, with a courtesy appointment in the Department of Psychological & Brain Sciences. Her research focuses on interdisciplinary approaches to visualization systems, machine learning, and cognitive science to enhance decision-making in healthcare, intelligence analysis, and scientific discovery. She leads the AIR-D Summer Science Camp, a program aimed at underrepresented high school students in STEM. Education: PhD and MS in Computer Science from Tufts University (2016, 2013). Research Interests: Developing adaptive visualization systems that account for users' cognitive traits and goals. Her work integrates AI-driven insights with human factors to create context-aware interfaces. Recent studies explore trust dynamics in visualizations, cross-cultural visualization literacy, and the impact of chart types on legal decision-making. Awards: NSF CRII Award (2018), NSF Career Award, EuroVis Early Career Award (2022), and multiple best paper recognitions at top visualization conferences. Grants & Advising: Active in mentoring students and securing grants such as the NSF CAREER Award for context-aware systems. Her work on medical decision-making visualization received NSF support in 2018. Labs & Teams: Affiliated with the Division of Computational & Data Sciences and the Center for Trustworthy AI in CPS, focusing on ethical AI integration in visual analytics.
Dr. Alexander Bertrand is a Professor at the Faculty of Engineering Sciences , KU Leuven, heading the Dynamic Systems, Signal Processing and Data Analysis (STADIUS) division. He leads the Department of Electrical Engineering (ESAT) and contributes to Leuven.AI institute, with expertise spanning wireless sensor networks, brain-computer interfaces (BCI), and biomedical signal processing. Research Focus : Wireless acoustic/EEG sensor networks, distributed signal enhancement, adaptive filtering, neural decoding of auditory/visual attention, and AI-driven time series analysis. Key Projects : EEG-Linx platform for modular brain recordings (2025-2027) Calibration-free BCI systems (2025-2029) AI quality assessment for time series data (2024-2028) Wireless EEG patches for hearing technology (2024) Publications (2023-2025) demonstrate leadership in distributed signal processing , auditory attention BCI , and scalable sensor architectures , with applications in education, healthcare, and wearable tech. Teaching includes courses on digital signal processing, biomedical data analysis, and medical technology design.
Bing Liu serves as Director of Applied Research at Scale AI and Adjunct Professor in the Computer Science and Engineering department at the University of California, Santa Cruz. Previously, he held leadership roles at Meta (GenAI and Reality Labs), Google Research, and Capio.ai (acquired by Twilio), with expertise spanning generative AI, NLP, and spoken dialogue systems. His educational background includes: Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University B.Eng in Electrical and Electronic Engineering (First Class Honors) from Nanyang Technological University, Singapore Exchange studies at KTH Royal Institute of Technology, Sweden Liu's research focuses on advancing large language models, dialogue systems, and reinforcement learning for conversational AI. His work bridges theoretical innovation with industrial-scale applications, particularly in zero-shot learning, multilingual capabilities, and evaluation frameworks for generative AI. He has pioneered techniques in dialogue state tracking, knowledge-enriched task-oriented systems, and continual learning to address catastrophic forgetting in neural dialogue models. Analysis of his 15 most recent publications reveals a clear trajectory toward industrial-scale generative AI: early work (2018-2021) established foundational methods in task-oriented dialogue systems, while recent publications (2022-2024) focus on LLM post-training, multimodal integration, and evaluation benchmarks like Humanity's Last Exam. Key thematic clusters include cross-lingual transfer, knowledge grounding in dialogue, and robust evaluation frameworks for frontier models. As an educator, Liu guides graduate research at UC Santa Cruz in LLM and multimodal AI while serving in critical conference roles including Publication Chair for ACL 2024 and Area Chair for ACL 2023's Large Language Models track. His industry leadership includes building Scale AI's 40+ member research team and driving Meta's Llama3 development, managing multimillion-dollar data roadmaps that scaled the Meta AI Assistant to 700M MAU. Liu directs Scale AI's applied research lab focused on GenAI data and evaluation, having previously built Meta's NLU team for AR/VR voice assistants deployed across Portal, Oculus, and Ray-Ban Smart Glasses. His current work centers on creating evaluation leaderboards adopted by top AI labs and developing the data engine for next-generation generative models.
Paul Miller is a Professor and GII Director at the School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, based at the Institute of Electronics, Communications & Information Technology (ECIT) in Titanic Quarter, Belfast. His research bridges computer vision and cyber security through artificial intelligence applications. His research focuses on applying AI to real-world problems, with expertise spanning deep learning, malware detection, anomaly detection, and re-identification. The fingerprint analysis of his work shows dominant contributions in Models (100%), Algorithms (94%), Reidentification (71%), Real World applications (62%), Detection (49%), Deep Learning (35%), Functions (30%), and Malware Detection (30%). His work demonstrates consistent translation of theoretical AI into practical implementations. Recent publications reveal a dual-track research trajectory: agricultural technology applications (automated broiler chicken monitoring systems for weight estimation and activity analysis) and cyber security innovations (anomaly detection frameworks and adversarial machine learning defenses). These works consistently leverage computer vision and machine learning to solve domain-specific challenges. Miller's scientific recognition includes: Belfast Telegraph IT Awards 2022 - Cybersecurity Project of the Year (Winner) Best Knowledge Transfer Partnership Award (2022) Best paper award at ACM AiSec 2022 Mobile World Scholar Challenge Finalist (2019) He actively supervises research through 8 supervised works and leads significant projects including the NIO New Deal Cyber Bid (AIDE_NICYBER2025) and CSIT Phase 2, securing substantial funding for machine learning and security research. His grant portfolio demonstrates strong industry-academia collaboration, particularly with Streamon.Net Ltd and Rapid7. As a core member of ECIT's Speech, Image and Vision Systems research group, Miller leads a multidisciplinary team developing practical AI solutions. His laboratory work focuses on translating computer vision research into deployable systems for agriculture and security sectors, with recent projects like FlockFocus demonstrating real-world impact on animal welfare monitoring.
Dolunay Kumlu is an Associate Professor at Trakya University's Faculty of Letters, Department of Translation and Interpretation, with a career spanning since 2016. Her work focuses on translation strategies, medical and legal translation, AI integration in translation, and specialized translation pedagogy. She has contributed to curriculum design for customs/logistics translation, medical interpreting, and cultural adaptation in children's literature. High School (1995), BA (2000), MA (2005), PhD (2016) in Translation Studies Academic trajectory: Lecturer (2016) → Assistant Professor (2023) → Associate Professor (2024) Her research explores the intersection of translation theory with practical challenges such as rhetorical devices in political speeches, AI tools (e.g., ChatGPT) in cultural translation, and textual analysis for equivalence. The 15 most recent publications demonstrate her engagement with translation technology, medical/legal terminology, and educational frameworks like the Bologna Process. While no scientific awards are explicitly listed, Kumlu has presented extensively at international symposia (e.g., Rumeli Symposium 2023, BAKEA Symposium 2021) and contributed to book chapters on topics ranging from translational paralysis due to textual neglect to social media impacts in academic translation training.
Shah Hamdi serves as an Assistant Professor in the Computer Science Department at Utah State University's College of Engineering. His research bridges machine learning and space physics, with emphasis on time series analysis for solar phenomena prediction and explainable AI systems. His primary research focuses include Time Series Analysis for space weather forecasting, Solar Physics applications in flare and particle event prediction, Explainable AI through counterfactual methods, and Natural Language Processing for social media analysis. He develops novel frameworks for multivariate time series classification, data augmentation of imbalanced datasets, and interpretable model architectures that handle complex spatio-temporal patterns. Analysis of his recent publications reveals three dominant trends: (1) Application of graph neural networks and multimodal fusion to solar flare prediction using photospheric magnetic field data, (2) Development of shapelet-based and saliency-guided counterfactual explanation techniques for time series classification, and (3) Creation of generative models like ChronoGAN and AVATAR for synthetic time series data augmentation. His work consistently addresses challenges in imbalanced data and space weather forecasting accuracy. Dr. Hamdi leads collaborative research initiatives including the CAIG project for synthetic data generation in solar energetic particle events. His grant portfolio demonstrates expertise in securing funding for interdisciplinary space weather and machine learning projects, while his advising focuses on training graduate students in time series analysis and explainable AI methodologies for real-world applications.
Serkan KESER is an Associate Professor in the Department of Electrical and Electronics Engineering at the Faculty of Engineering and Architecture, Ahi Evran University, Turkey. He has been serving as a full-time faculty member since 2018 and previously served as Head of Department from 2018-2021. His educational background includes a PhD in Electrical and Electronics Engineering from Eskişehir Osmangazi University (2009-2018), a Master's degree in the same field from the same institution (2005-2008), and a Bachelor's degree in Electrical and Electronics Engineering from Mustafa Kemal University (1999-2005). Dr. KESER's research focuses on three main areas: Audio and Speech Processing : Including speaker identification, isolated word recognition, and speech coding techniques Signal Processing : With applications in fiber optic sensor systems and acoustic positioning Image Processing : Covering face recognition, image compression, and denoising techniques His recent publications demonstrate a strong trend toward integrating machine learning and deep learning approaches with traditional signal processing methods. He has made significant contributions in applying subspace methods to various domains including speech recognition, image processing, and sensor technologies. His work often bridges theoretical signal processing concepts with practical applications in areas like smart home systems, medical imaging, and environmental monitoring. Dr. KESER has successfully supervised five Master's students to completion, with thesis topics spanning deep learning applications for class attendance systems, photovoltaic systems, speaker identification, brain tumor classification, and speech-controlled robotic arms. He has led four research projects, including development of fiber optic motion sensors, smart home models using Arduino microcontrollers, and science outreach initiatives. His teaching portfolio includes advanced courses in digital image processing, artificial neural networks, digital signal processing, and machine learning at both undergraduate and graduate levels. Dr. KESER's research impact is reflected in his publication metrics: 30 total publications with 117 citations (h-index 5) through the UNIS system, 25 publications with 180 citations (h-index 6) on Google Scholar, and strong representation in Scopus and Web of Science databases.
David Martins de Matos is an Associate Professor at Instituto Superior Técnico (IST), Universidade de Lisboa , and a senior researcher at INESC-ID Lisbon within the Human Language Technology Lab . With a career spanning over three decades, he has taught subjects such as Compilers and Object-Oriented Programming since 1993. His research focuses on Natural Language Engineering , Automatic Natural Language Generation , Music Information Retrieval , and Machine Learning Applications in Healthcare . Education: B.Sc. in Electrical and Computer Engineering (IST, 1990) M.Sc. in Electrical and Computer Engineering (IST, 1995) on object-oriented programming in distributed systems Ph.D. in Systems and Computer Science (IST, 2005) on automatic natural language generation Research Interests: His work bridges Natural Language Processing and Computational Music Analysis , with applications in Health Informatics . He investigates semantic frame induction, dialog act recognition, and multimodal systems for chronic pain assessment, Alzheimer's detection, and music generation. His recent articles explore cross-modal retrieval, deep learning for pain narratives, and embodied semantics via fMRI. Scientific Contributions: He has published over 161 works, including 15 recent articles on chronic pain datasets, dialog act recognition, and music-language correlations. His awards include Senior Member status in ACM (SIGMM, SIGIR) and IEEE (Signal Processing Society, Computer Society) , and membership in the Order of Portuguese Engineers . Advising & Collaborations: He has supervised 111 doctoral and master's theses, mentoring students in topics like Visual Story Generation , Music Summarization , and Health Informatics . He collaborates with institutions such as IBM Research , Northwestern University's Feinberg School , and Universidade de Lisboa .
Jaumin Ajdari is a Full Professor at the Faculty of Contemporary Sciences and Technologies at South East European University in Tetovo, Macedonia. He holds a Doctor of Mathematical Sciences degree from the University of Tirana, with a focus on parallel processing and orthogonal wavelet transforms. Education: PhD in Mathematical Sciences (University of Tirana, 2011), MSc in Mathematics (University of Tirana, 2006), MSc in Mathematics (University of Zagreb, 1993), Engineer in Applied Mathematics (University of Zagreb, 1993) His research spans parallel computing , machine learning , database systems , IoT applications , and natural language processing , particularly for low-resource languages. His recent publications focus on predictive modeling, smart agriculture using IoT, cloud computing challenges in education, and hate speech detection in Albanian social media. Key article trends include: Machine learning applications in education and agriculture Cloud computing adoption studies IoT sensor data analysis NLP for Balkan languages Database optimization techniques Parallel algorithm implementations
Nikola Milosevic is a doctoral researcher at the Max Planck Institute for Human Cognitive and Brain Sciences , affiliated with the International Max Planck Research School NeuroCom and working within the Methods and Development Group Neural Data Science and Statistical Computing . His research spans interdisciplinary topics at the intersection of artificial intelligence, neuroscience, and computational modeling. His work focuses on active matter systems , reinforcement learning architectures , and neural data science methodologies . He has developed the ARC toolbox for artificial speech rhythmicity controls and contributed to explainable AI frameworks. Recent publications highlight his interests in embodied cognition, trust region optimization, and deep learning applications for biomedical imaging. Nikola's methodological expertise includes information processing , representation learning , and algorithmic safety in computational systems. His research combines theoretical approaches with practical implementations in Python and machine learning frameworks.
Kamal Premaratne serves as a Professor in the Department of Electrical & Computer Engineering at the College of Engineering, University of Miami. His scholarly work uniquely bridges technical expertise in machine learning and signal processing with social science research on conspiracy theories and political extremism. With numerous publications in both technical and social science journals, he demonstrates an exceptional interdisciplinary approach to understanding complex contemporary phenomena. Professor Premaratne's research spans two primary domains: advanced computational methods and social/political analysis. In computational methods, he focuses on quantum tensor networks for time series analysis, graph neural networks for gesture recognition, and uncertainty quantification in machine learning systems. His social science research examines conspiracy theories including the "White Replacement" theory, QAnon, and "white genocide" narratives, investigating their sociodemographic correlates and relationship to political extremism. His work often employs mixed-methods approaches combining qualitative analysis with quantitative content analysis and network analysis. Analysis of Premaratne's recent publications reveals a strong focus on understanding how conspiracy theories spread on social media platforms and their relationship to political behavior. His technical work shows innovative applications of quantum physics concepts to machine learning problems, creating more interpretable models while maintaining performance. This dual focus demonstrates how computational methods can be applied to social science questions and vice versa. Professor Premaratne actively collaborates with researchers across disciplines including psychology, political science, and communication studies. His research has been published in high-impact journals such as Scientific Reports, Journal of Politics, and Political Science Quarterly. Though specific grant information isn't detailed in the available materials, his extensive publication record suggests substantial research activity. His work has significant implications for understanding political polarization, misinformation spread, and developing more interpretable AI systems.
Dr. Chang Y Choo is a Professor of Electrical Engineering at San José State University, where he also serves as Director of the AI/ML FPGA/DSP Systems Laboratory. His academic career spans over three decades, with previous positions at Worcester Polytechnic Institute and industry experience at Altera Corp. (now Intel). Dr. Choo maintains an active research program focusing on hardware acceleration for AI and signal processing applications, with particular emphasis on FPGA-based implementations for real-world systems. Dr. Choo's educational background includes: Ph.D. in Computer and Systems Engineering, Rensselaer Polytechnic Institute (1986) M.S. in Operations Research and Statistics, Rensselaer Polytechnic Institute (1982) B.S./M.S. in Engineering, Seoul National University, Korea Dr. Choo's research interests center on the intersection of hardware design and artificial intelligence. His work focuses on implementing computer vision, deep learning, and digital signal processing algorithms on specialized hardware platforms including FPGAs, GPUs, and custom ASICs. Current projects include developing real-time illumination/view-independent object recognition systems for autonomous vehicles, wideband acoustic echo cancellation for wearable technology, and FPGA-based accelerators for medical imaging applications. His research bridges theoretical algorithm development with practical hardware implementation constraints. Analysis of Dr. Choo's recent publications reveals a clear trajectory toward increasingly sophisticated hardware-accelerated AI systems. His work has evolved from foundational research in digital signal processing and image compression to cutting-edge applications of deep learning on specialized hardware. Recent publications demonstrate expertise in implementing CNN architectures on FPGAs, developing metabolic syndrome prediction models, and creating food object detection systems using transformer models. This progression reflects the broader field's shift toward hardware-aware AI development. Dr. Choo's significant scientific contributions include multiple patents that have advanced the state of the art in several domains: U.S. Patent No. 9,025,763 (2015): 'Apparatus and Method for cancelling wideband acoustic echo' U.S. Patent Nos. 7,058,675 (2006) and 7,124,161 (2006): 'Apparatus and method for implementing efficient arithmetic circuits in programmable logic devices' U.S. Patent Nos. 5,943,096 (1999) and 6,621,864 (2003): 'Motion vector based frame insertion process' U.S. Patent Nos. 5,832,131 (1998) and 5,991,455 (1999): 'Hashing-based vector quantization' U.S. Patent No. 5,587,710 (1997): 'Syntax based arithmetic coder and decoder' Throughout his career, Dr. Choo has been actively involved in both academic and industry collaborations. He has served as a technical consultant for numerous Silicon Valley companies including National Semiconductor (now Texas Instruments), Philips Semiconductor, Skybox Imaging (acquired by Google), Novariant (now AgJunction), and Ricoh Innovations. His industry experience informs his teaching approach, which emphasizes practical implementation considerations alongside theoretical foundations. Dr. Choo has also served as an expert witness in intellectual property court cases involving audio and video compression algorithms and FPGA hardware. Dr. Choo directs the FPGA/DSP AI/DL Laboratory at San José State University, which focuses on developing hardware-accelerated solutions for real-time AI applications. The lab maintains strong connections with Silicon Valley technology companies and provides students with hands-on experience in cutting-edge hardware design methodologies. Current research directions include autonomous vehicle navigation systems, medical imaging applications, and edge AI deployment strategies.
Utz Roedig is a Full Professor of Computer Science at University College Cork (UCC), Ireland, and a Principal Investigator at the CONNECT Centre. Previously, he served as Professor at Lancaster University, UK, leading the Academic Centre of Excellence in Cyber Security Research (ACE-CSR), and held research positions at UCC and Darmstadt University of Technology, Germany. Education: Dipl.-Ing in Engineering from Darmstadt University of Technology Dr.-Ing (Doctor of Engineering) from Darmstadt University of Technology His research spans computer networks and network security , with over 150 publications in IoT security, industrial control systems, 5G networks, and voice assistant vulnerabilities. Recent work integrates machine learning for intrusion detection and fault prediction while addressing human factors in secure coding. Industry collaborations have yielded multiple patents. Analysis of 2023-2025 publications reveals dominant themes in industrial IoT security (e.g., resilient time-sensitive networking), 5G infrastructure protection, and voice assistant threats (wake word jamming/spoofing). His team develops countermeasures using protocol design and ML-driven anomaly detection, with growing emphasis on human-centric security challenges. Scientific Awards: No specific awards are documented, though research impact is evidenced by patents and sustained funding from major international bodies. Advising and Grants: Secured funding from EU, EPSRC, and industry partners. Serves as grant reviewer for EPSRC (UK), ESF (EU), and FWO (Belgium), and on TPCs for DCOSS, EWSN, and IPSN conferences. Student supervision details are unavailable, but research leadership implies active mentoring. Grants: EU, EPSRC, Industry Review Roles: EPSRC, ESF, FWO Labs and Teams: Leads research at UCC's CONNECT Centre (telecommunications security). Previously directed Lancaster University's ACE-CSR, a UK government-designated cybersecurity research hub.
Muskaan Singh is a Lecturer in Data Analytics at the Intelligent Systems Research Centre (ISRC) within the School of Computing, Engineering and Intelligent Systems at Ulster University . A member of the Cognitive Analytics Research Lab (CARL) , her work bridges Natural Language Processing (NLP) , Artificial Intelligence , and Practical Applications in domains ranging from machine translation to biomedical diagnostics. Education: PhD in Machine Translation (Thapar Institute of Engineering and Technology, 2016-2020) Master’s in Machine Translation (IIIT Hyderabad, India) Her research spans NLP and AI with applications in code-switched language modeling , depression detection , social media analytics , and medical diagnostics . She has developed multilingual tools for automatic minuting, including DeepCon and ALIGNMEET , and contributed to EU-funded projects like ROXANNE (criminal network analysis) and ELITR (European Live Translator). Key scientific awards include first prizes in international NLP competitions (EVAL4NLP, LT-EDI, SMM4H) and recognition at EMNLP , ACL , and COLING . She received the Inclusion and Diversity Grant (EMNLP 2021) and GHC Scholarship (2019). Current projects include AI-EPOCMON (AI-Enabled Point-of-Care Monitoring) and T3-NCP (crime prevention for safer communities). Dr. Singh has supervised grants from UKRI and Alzheimer’s Research UK , focusing on AI for health and IT operations . Her team at ISRC collaborates globally with institutions in Switzerland , Czech Republic , and India . She also leads research for the Center for Data Science and Artificial Intelligence at IIIT Lucknow, India.
Rajesh M. Hegde is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur. His research spans multiple domains within artificial intelligence, signal processing, and communication systems. He has established himself as a leading researcher in multimodal information processing and has contributed significantly to speech and audio processing techniques. Education: PhD in Computer Science and Engineering, IIT Madras (2005) ME in Electronics Engineering, Bangalore University (1988) BE in IT Engineering, Mysore University Professor Hegde's research focuses on the intersection of machine learning, artificial intelligence, and signal processing, with particular emphasis on multimodal information fusion. His work bridges theoretical foundations with practical applications in speech recognition, wireless communication, and pervasive computing. He has made significant contributions to group delay analysis in speech processing and developed innovative approaches for speaker segregation and multimodal system design. His research has practical implications for improving human-computer interaction through more natural and context-aware interfaces. Analysis of his recent publications reveals a consistent focus on multimodal systems and speech processing, with increasing attention to federated learning approaches and wireless network applications. His work demonstrates a strong theoretical foundation combined with practical implementation expertise, particularly evident in his prototyping of multimedia acquisition systems. Scientific Awards: P.K Kelkar Research Fellowship (2009-2013) Mentor for best undergraduate engineering design project award at UC San Diego (2005-06) ISCA Grant for INTERSPEECH 2004 IIT Madras PhD thesis recommended for IBM best thesis award (2005) Programme Committee member for ISCA Winter schools Commendation by IIT Kanpur for teaching excellence Professor Hegde has supervised multiple research projects and mentored students in developing innovative multimedia systems. His work on multimodal situation awareness systems has received recognition for its interdisciplinary approach. He has secured research funding through fellowships and institutional support, enabling his laboratory to maintain cutting-edge research facilities. His laboratory is located in ACES 203 and 204 at IIT Kanpur, where his research team works on multimodal information processing, wireless communication systems, and speech/audio processing applications. The lab maintains strong connections with international research institutions, particularly through his involvement with ISCA and previous collaborations with UC San Diego.
