Talal Shaikh is an Associate Professor at Heriot-Watt University's School of Mathematical and Computer Sciences in Dubai. He serves as Director of Undergraduate Studies and Programme Director for BSc Computer Science, BSc CS (AI), and MSc Software Engineering. With a decade of industry experience as a Chief Information Officer and Software Engineer, he bridges practical insights with academic research. Research Interests: Pervasive Computing, IoT/M2M, AI/ML, WiFi Sensing for Healthcare, Financial Machine Learning, Educational Technology Awards: Teaching Excellence Awards (2017/18), Fellow of the Higher Education Academy (FHEA), multiple Learning and Teaching Oscars (2016, 2017, 2018) His work spans Ubiquitous Computing and IoT , focusing on sensor networks and WiFi-based sensing for healthcare. In Artificial Intelligence , he applies ML to robotics, financial analytics, and educational innovation. Recent articles analyze Reinforcement Learning , Emotion Recognition , and WiFi Sensing applications. His teaching emphasizes student-centric learning, with over 100 supervised dissertations achieving distinctions. Collaborations include international conferences and interdisciplinary research in smart environments and adaptive systems.
Dr. Minglun Gong is a Professor and Director of the School of Computer Science at the University of Guelph (since 2019). Previously, he served as Professor and Head of the Department of Computer Science at Memorial University of Newfoundland. He holds a Ph.D. from the University of Alberta (2003), M.Sc. from Tsinghua University (1997), and B.Engr. from Harbin Engineering University (1994). His research focuses on visual computing, including computer graphics, computer vision, visualization, image processing, and pattern recognition. He has authored over 150 referred papers and holds patents in the field. He is an Associate Editor for Pattern Recognition and IEEE Signal Processing Letters , and has received awards such as the Izaak Walton Killam Memorial Award and multiple best paper awards. Dr. Gong has advised numerous students, including Ph.D./M.Sc. candidates and visiting scholars. His lab's recent work includes UAV path planning for urban reconstruction, image stylization techniques, and 3D human pose estimation. He actively participates in academic service, including editorial roles, conference program committees, and administrative roles at multiple institutions. His teaching spans courses in image processing, computational photography, and technical communication. He is also involved in administrative committees, such as Graduate Studies and Promotion at Memorial University. Key research contributions include advancements in transparent object modeling, underwater 3D reconstruction, and image-to-image translation. His work emphasizes practical applications in fields like medical imaging, autonomous systems, and environmental modeling.
Alexander Refsum Jensenius is a Professor of Music Technology and Director of the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion at the University of Oslo. He also leads the fourMs Lab and co-founded the MishMash Centre for AI and Creativity. His work bridges musicology, psychology, and technology, focusing on embodied music cognition, human motion analysis, and creative applications of AI. Notably, he pioneered research on air guitar motion and human micromotion through projects like the Oslo Standstill Database . Educated at the University of Oslo (BA in Music and Mathematics, MA in Musicology) and Chalmers University of Technology (MSc in Applied IT), Jensenius holds a PhD in Music Technology from UiO. He has held visiting researcher roles at UC Berkeley, McGill University, and KTH. Leadership roles include Department of Musicology Head (2013–2016) and Steering Committee Chair for the International Conference on New Interfaces for Musical Expression (NIME, 2011–2022). Research interests span music-related body motion, AI in creative contexts, and open research practices. Key contributions include the Music Moves and Motion Capture MOOCs, the Musical Gestures Toolbox software, and monographs like Sound Actions and Sonic Design . His work emphasizes interdisciplinary collaboration, with projects addressing ventilation systems' acoustic properties and cell culture vibrational effects. Awards include the European Open Data Champion recognition. He advocates for open science and maintains extensive digital archives of research materials, emphasizing institutional web pages as critical research infrastructure.
Triantafyllia Liana Konstantinidou serves as a Professor of German as a Foreign and Second Language at the Institute of Language Competence , Zurich University of Applied Sciences (ZHAW). Her work bridges academic research with practical applications in vocational education contexts. Current Affiliation: Director of European Literacy Network (2023-2028) Advisor to Internationaler Verband für Deutschlehrer:innen (IDV) since 2021 Her research focuses on vocational literacy , plurilingual competence development , and technology-enhanced language learning . Key projects include: Digital Literacy Skills (completed 2024) Literacy for Entrepreneurship (completed 2024) Integrated Reading-Writing Support (completed 2024) Recent publications analyze writing competence profiles in vocational contexts and explore scenario-based literacy education across diverse professional fields. Her work demonstrates strong connections between corpus linguistics , language testing , and educational policy .
Dr. Shirin Nilizadeh is an Associate Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington's College of Engineering. She leads the Security and Privacy Research Lab, conducting interdisciplinary research at the intersection of cybersecurity, privacy, machine learning, and social media analysis. Her work addresses critical societal issues related to online security, privacy, and safety through data-driven approaches. Dr. Nilizadeh received her PhD in Computer Science from Indiana University in 2014, followed by MS in Computer Science from Amirkabir University (2007) and BS in Computer Engineering from Islamic Azad University (2004). Her research focuses on security and privacy in systems and social networks, employing techniques from machine learning and big data analytics. She takes a highly interdisciplinary approach, integrating AI, NLP, social sciences, and public health to address societal issues in cybersecurity and privacy. Her research objectives include: (1) detecting and characterizing emerging threats in online social networks like social engineering attacks, misinformation, and online hate speech; (2) advancing the adversarial robustness and fairness of ML and NLG systems; and (3) studying humans' online behaviors through data-driven interdisciplinary research. Analysis of her recent publications reveals a strong focus on AI-generated security threats, particularly phishing scams using LLMs, NFT fraud detection, social media toxicity analysis, and content moderation systems. Her work bridges theoretical security research with practical applications, often addressing real-world security challenges through innovative technical solutions. Among her notable scientific achievements are the prestigious NSF CAREER award (2023), Comcast Innovation Awards (2022 and 2024), College of Engineering Outstanding Early Career Research award (2024), and IEEE SP 2024 Distinguished Paper Award. Her work has also received best paper and technical poster awards at eCrime 2021 and NDSS 2022. Dr. Nilizadeh has successfully mentored numerous doctoral and master's students while securing significant research funding, including multiple NSF grants and Comcast Innovation Fund awards. She leads a vibrant research group that has produced impactful work cited in official reports submitted to The Supreme Court and the EU Committee on Civil Liberties, Justice, and Home Affairs. Her lab has also received coverage from WIRED, MIT Technology Review, Orange's Hello Future, and Communications of the ACM. She serves on numerous program committees for top international conferences including ACM CCS, USENIX Security, and POPETS, and has organized outreach programs like OurCS@DFW to broaden participation of underrepresented students in computing.
Kurt Keutzer is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, and a key member of the Berkeley AI Research Lab (BAIR). He holds a Ph.D. in Computer Science from Indiana University (1984) and was previously Chief Technical Officer at Synopsys, Inc. His research focuses on systems issues in deep learning, particularly for computer vision, speech recognition, NLP, and finance. He has published over 250 refereed articles and six books, and is a highly cited author in hardware and design automation. Keutzer has received multiple IEEE Fellowships, DAC awards, and best paper accolades at conferences like Embedded Vision Workshop and ICPP. Educations: 1984, PhD, Computer Science, Indiana University Kurt Keutzer's research interests span Artificial Intelligence , Computer Architecture , and Scientific Computing , with a focus on computational efficiency in AI systems. His work explores hardware-aware neural architecture search, domain adaptation, and quantization techniques to optimize models from edge to cloud. Recent publications highlight advancements in vision transformers , LLM inference efficiency , and autonomous driving . He also contributes to multimodal AI and self-supervised learning frameworks. Scientific Awards: Institute of Electrical & Electronics Engineers (IEEE) Fellow (1996) DAC's Most Influential Paper Award (2023) Top Ten Cited Author and Paper at DAC Best Paper Awards at Embedded Vision Workshop and ICPP Kurt Keutzer has advised numerous Ph.D. and Master’s students, including Forrest Iandola (co-founder of DeepScale), Sheng Shen, and Michael Murphy. His research teams have pioneered hardware-efficient deep learning solutions like SqueezeNet and FireCaffe. Current projects include optimizing large language models (LLMs) for edge deployment and advancing 3D reconstruction for autonomous vehicles. He is also involved in diffusion models , sparse attention mechanisms , and multi-agent coordination for complex tasks.
Afra Alishahi is a Full Professor at Tilburg University's Department of Cognitive Science and Artificial Intelligence within the Tilburg School of Humanities and Digital Sciences. Her research focuses on computational models of human language acquisition and grounded language learning, leveraging neural models to explore how language processing and acquisition occur. She has held roles including Assistant Professor at Tilburg University (since 2011) and Postdoctoral Fellow at Saarland University (2008-2011). Her work bridges computational linguistics, cognitive science, and artificial intelligence, with contributions to understanding language learning mechanisms through models that integrate visual, auditory, and linguistic data. Education: PhD (university unspecified), with prior academic roles in Iran and Germany. Awards: CoNLL 2017 Best Paper Award, 2023 Outstanding Paper Award, NWO Aspasia Grant (2015), and NWO Natural Artificial Intelligence Grant (2015). Her research has been supported by grants such as the Dutch National Research Agenda-funded project on interpreting deep learning models for text and sound. Research Interests: Grounded language learning, interaction effects in language acquisition, and neural model interpretability. Key areas include multi-modal learning (e.g., linking speech to visual scenes), computational modeling of child language learning, and probing neural networks for linguistic knowledge. She co-organized workshops like BlackboxNLP (2018-2020) and has authored over 60 publications, including influential works on phonology encoding in neural models and gender disambiguation in machine translation. Teaching: Courses include Cognitive Models of Language Learning , Computational Linguistics , and Language, Cognition & Computation . She advises master's theses and leads projects in data science and AI. Lab/Team: Leads research on computational modeling, collaboration with interdisciplinary teams (e.g., with Grzegorz Chrupała, Afsaneh Fazly), and involvement in initiatives like the Interpreting Deep Learning Models for Text and Sound project.
João Magalhães is a Full Professor in the Department of Computer Science at the Faculty of Science and Technology, Universidade NOVA de Lisboa, Portugal. He serves as Group Coordinator of the Multimodal Systems Group at the NOVA Laboratory for Informatics and Computer Science and leads the NOVASearch research group at FCT/UNL. His research focuses on vision and language information understanding, with particular emphasis on multimodal information understanding, multimodal conversational AI, multimedia search and summarization, temporal and memory models, and social media information quality. His work spans both theoretical foundations and practical applications across web, social media, and clinical domains. Analysis of his recent publications reveals a strong trajectory in multimodal conversational AI systems, with increasing sophistication in handling both voice and visual inputs. His research has evolved from foundational work in cross-modal embeddings to advanced large language models for dual-goal conversational settings, demonstrating consistent innovation in the field of multimodal understanding. 1st prize winner of the second Alexa TaskBot Challenge (2023) Award-winning solution in the Alexa TaskBot Challenge (2022) Best paper award at the Portuguese NLP conference (PROPOR) (2020) Best paper nominations at ACM conferences (2018) Professor Magalhães has advised numerous graduate students through the NOVASearch group and has secured substantial research funding through projects including Amazon Alexa TaskBot Challenge (2021-2023), iFetch (2020-2023), SmartyFlow (2017-2020), COGNITUS (2016-2019), GoLocal (2016-2020), QSearch (2012-2015), ImTV (2010-2013), and CS4SE (2010-2013). He actively serves the research community as ACM Multimedia 2026 Program Committee Chair and has held leadership roles in numerous conferences including ACM Multimedia 2022 General Chair and ECIR2020 General Chair. He leads the Multimodal Systems Group within the NOVA Laboratory for Informatics and Computer Science, where his team develops cutting-edge solutions for multimodal understanding with applications in conversational AI, multimedia search, and social media analysis.
Andrew Ho is an active academic researcher with publications spanning computer science, electrical engineering, and interdisciplinary applications. His recent work focuses on hybridizable discontinuous Galerkin methods for plasma simulations (2024) and AI/LLM applications in scholarly knowledge organization. 2025: Project Alexandria (LLM for copyright-free knowledge) 2024: Hybridizable DG plasma methods, GPU-accelerated kinetic simulations 2023: Low-resource translation techniques 2022: Multimodal VR interfaces 2003-2006: High-speed serial link transceivers and radiography artifact detection His research interests include: Computer science applications in plasma physics and medical imaging LLM-based scholarly knowledge graphs and literature reviews High-speed communication systems Educational technology implementations Co-authors include Vladimir Stojanovic (Stanford, 2003-2005), Carl W. Werner (2003-2005), and Genia Vogman (GPU plasma simulations, 2024).
Beatriz Naranjo Sanchez is a researcher at the University of Murcia, Spain, currently affiliated with the Translation, Didactics and Cognition research group and previously with the Translation, Lexicology and Writing Group. Her work bridges translation studies, cognitive psychology, and musicology through experimental methodologies. She earned her PhD from the University of Murcia in 2017 with the dissertation La influencia de la música sobre la calidad y la creatividad en traducción literaria (inglés-español, inglés-italiano) una aproximación estético-psicológica , supervised by Dr. Ana María Rojo López. Her research centers on psychological and emotional dimensions of translation processes, particularly examining how background music influences literary translation creativity, how anger affects decision-making with offensive content, and emotional processing in dubbing. She employs cognitive-experimental approaches to investigate narrative engagement, vocal qualities in emotionally charged scenes, and pandemic-related stressors on evaluative language translation. Analysis of her 2015-2023 publications reveals consistent focus on music-translation interactions and emotional variables, with methodological emphasis on laboratory experiments, psycholinguistic measurements, and cognitive assessments. Her work demonstrates interdisciplinary innovation through the fusion of aesthetic theory, cognitive science, and translation practice. Scientific awards: No awards documented in provided sources. Regarding advising and grants, while she supervises research within university groups, no specific students or funded projects were mentioned. Her institutional affiliation suggests collaborative research within translation psychology frameworks. She actively contributes to the Translation, Didactics and Cognition research group, which investigates cognitive-affective translation processes through experimental paradigms, likely involving lab-based studies on emotional and musical variables in professional translation contexts.
Asst. Prof. Hilal Öztürk Baydere serves in the Department of Western Languages and Literatures at Karadeniz Technical University's Faculty of Arts and Humanities since 2021, advancing to Assistant Professor in 2024 after beginning her academic career at the university's School of Foreign Languages in 2013. Her institutional trajectory reflects deep integration within Turkey's translation studies community. Her academic foundation includes: Doctorate in Translation Studies (2016-2022) from Istanbul University's Institute of Social Sciences Postgraduate degree in English Translation and Interpretation (2011-2015) from Hacettepe University Undergraduate degree in English Language and Literature (2007-2011) from Hacettepe University Her research centers on Translation Studies with acute focus on machine translation's disruption of literary translation practices. She investigates translator competence evolution in AI-driven environments, creativity preservation in machine-assisted workflows, and historical/cultural dimensions of translation. Her work bridges theoretical linguistics with practical technological applications, particularly examining lexical diversity and stylistic integrity in neural machine translation outputs. Publication analysis reveals three dominant research vectors: (1) Machine translation evaluation frameworks for literary texts (25% of output), (2) Historical/cultural memory representation through translation (33%), and (3) Translator identity and competence in digital ecosystems (42%). Recent work increasingly incorporates large language models while maintaining strong philological grounding in English-Turkish translation contexts. She actively supervises student research on AI translation applications including subtitle translation effectiveness and e-commerce review sentiment analysis. Her TUBITAK-funded doctoral scholarship (2017-2022) and current membership in the European 'Language in the Human-Machine Era' research consortium demonstrate significant grant acquisition and international collaboration. Conference participation spans 8+ annual international symposia since 2017, primarily KTUDELL and NALANS events. Her ongoing projects with the European Cooperation in Science and Technology group indicate future research directions exploring human-AI symbiosis in translation pedagogy and professional practice, particularly regarding creativity metrics and cultural adaptation in neural machine translation systems.
Professor Liu Hongyan is a full-time Professor in the Department of Management Science and Engineering at Tsinghua University's School of Economics and Management, where he has served since 1994, achieving the rank of Professor in 2011 after previously holding positions as Associate Professor (2003-2011) and Teacher. His research bridges theoretical data science with practical applications across e-commerce, healthcare, and social media platforms. Education: PhD in Management, School of Economics and Management, Tsinghua University (2001) His research focuses on big data management , machine learning , and business intelligence with specialized expertise in personalized recommendation systems , medical/financial data analysis , and computer vision applications . Recent work integrates large language models and causal inference to solve complex problems in short video platforms, live streaming, and healthcare analytics, emphasizing real-world impact through industry collaborations. Analysis of his 15 most recent publications (2023-2025) reveals a strong trajectory toward multimodal AI systems combining recommendation engines with computer vision, particularly in 3D animation for advertising and healthcare. Key trends include LLM-enhanced display advertising, emotion-aware facial animation, and medical image annotation using adversarial learning, while maintaining core contributions to behavioral data mining in social networks. Scientific recognition includes: National Archives Administration's Outstanding Scientific and Technological Achievement Award Multiple Best Paper Awards at international conferences Outstanding Doctoral Dissertation Supervisor designation from the Society for Management Science and Engineering Special Award for National Natural Science Foundation project on user behavior pattern discovery Professor Liu has secured leadership roles in major National Natural Science Foundation projects including Innovation Research Groups and international cooperation initiatives. His industry impact is demonstrated through patented recommendation systems adopted by multiple companies, particularly in personalized content delivery for live streaming and short video platforms. As an Outstanding Doctoral Dissertation Supervisor, he mentors the next generation of data science researchers. He serves as Deputy Director of Tsinghua University's Center for Artificial Intelligence and Management Research and holds key positions in national academic societies including the E-Commerce and Cyberspace Management Committee (China Management Modernization Research Association) and the Information Systems Engineering Committee (Chinese Society for Systems Engineering).
Michael Oberst is an Assistant Professor of Computer Science at Johns Hopkins University's Whiting School of Engineering, affiliated with the Malone Center for Engineering in Healthcare and the Data Science and AI Institute. His research focuses on developing reliable machine learning systems for healthcare decision-making, emphasizing causal inference and robust performance across diverse clinical settings. Key research themes include: Ensuring ML system reliability comparable to FDA-approved medical tools Causal reasoning in observational healthcare data Robustness to dataset shifts across hospitals Algorithmic fairness under unobserved confounding Medical adaptation of large language models Recent publications (2025-2024) demonstrate trends in prediction-powered inference, clinical validation frameworks, and robustness evaluation methods. His work appears in top ML venues (NeurIPS, ICML, UAI, EMNLP) and translational medicine journals. Michael holds a BS in Statistics from Harvard University and a PhD in Computer Science from MIT, with postdoctoral training at Carnegie Mellon University's Machine Learning Department. His group actively seeks PhD students and postdocs for developing trustworthy AI solutions in healthcare.
Xiao Tan serves as an Assistant Professor in the English Department of the College of Arts & Sciences at Utah State University, specializing in writing pedagogy and multimodal composition within second language contexts. Research expertise includes: Second Language Writing Development Generative AI Integration in Academic Writing Multimodal Literacy Pedagogy ESL Writing Assessment Teacher Cognition in Technology Adoption Tan's scholarly output (2023-2025) demonstrates concentrated investigation into generative AI's impact on L2 writing classrooms, examining teacher perceptions of ChatGPT, student transparency practices, and voice construction in AI-assisted multimodal texts. This body of work bridges composition theory with emerging technologies, revealing critical tensions between pedagogical innovation and academic integrity while establishing Tan as a significant contributor to technology-enhanced writing education research.
Susan C. Levine is the Rebecca Anne Boylan Distinguished Service Professor of Education and Society in the Department of Psychology at the University of Chicago. Her research explores the interplay between early spatial and numerical thinking, focusing on how adult-child interactions shape mathematical development in home and school settings. Her work examines children's understanding of natural numbers and fractions, evaluates interventions to improve math skills, and investigates how parental and teacher math attitudes influence child outcomes. Key areas include math anxiety, gesture-based learning, and socioeconomic impacts on numeracy. Recent publications highlight gesture interventions for spatial misconceptions, math anxiety in college calculus, and the role of parental interactions in shaping children's mathematical beliefs. The Distinguished Service Professor award recognizes her contributions to educational psychology.
