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.
Carryl Baldwin is the Carl and Rozina Cassat Distinguished Professor of Aging & Regional Institute on Aging and Director of the Wichita Auditory Research Group (WARG) at Wichita State University's Fairmount College of Liberal Arts and Sciences. Her work spans neuroergonomics, aging research, auditory cognition, and human-vehicle interface design, with funding from NHTSA, NIH, NASA, and private industry partners. Education : PhD in Human Factors Psychology (1997), MA in Human Factors Psychology (1994), BA in Psychology/Asian Studies (1987) Dr. Baldwin's research focuses on human interaction with automation , particularly in transportation contexts. She investigates driver attention, mind wandering, cognitive workload, and multimodal alarm systems using physiological metrics like EEG. Her lab explores aging-related cognitive changes and develops technologies to enhance safety and performance in semi-autonomous systems. The 15 most recent publications reveal trends in neuroergonomics , aging populations , and automated vehicle safety . Key themes include attention management in automation, auditory alarm optimization, and sustainability applications of human factors, with methodologies spanning EEG analysis, eye-tracking, and multimodal interface evaluation. Dr. Baldwin's lab has secured external funding from the National Highway Traffic Safety Administration, NIH-NIA, NASA centers, and defense contractors like Northrop Grumman. Current research includes driver vigilance in Level 2/3 vehicles, pandemic-related social isolation in older adults, and FAA-funded training technology evaluation.
Donato Romano serves as Associate Professor at The BioRobotics Institute of Scuola Superiore Sant'Anna, Italy, where he coordinates the Bio-Robotic Ecosystems Lab and co-founded the spin-off company HUBILIFE srl. His interdisciplinary work bridges robotics, biology, and AI to develop biohybrid systems for biodiversity preservation, sustainable environmental management, and life support in extreme scenarios including space exploration. With over 90 publications and an H-index of 27 (Scopus, March 2025), he has established significant academic leadership through editorial roles across 12+ international journals. Romano's educational foundation includes advanced degrees with honors: an M.Sc. in Agriculture Science and Technologies (2014) and a PhD in BioRobotics (2018), both from Scuola Superiore Sant'Anna. His academic journey includes visiting scholar positions at Khalifa University and substantial industry-academia collaboration through HUBILIFE srl, which commercializes bioinspired devices for human daily life improvement. His research program focuses on bioinspired and biomimetic robotics with particular emphasis on animal-robot interaction, biohybrid systems, and natural intelligence. Key projects address critical global challenges: SENSORBEES develops biohybrid environmental surveillance for ecological monitoring; REGOLIFE investigates lunar soil-terrestrial organism interactions for space agriculture; and OCEAN ROBOCTO explores marine ecosystem solutions. This work demonstrates a strategic progression from fundamental behavioral studies toward applied ecological and extraterrestrial systems. Analysis of his recent publications reveals strong trends in AI-driven behavioral analysis, with deep learning increasingly applied to entomological studies and pest management. The research spans agricultural applications (precision monitoring traps, larval detection systems), ecological conservation (biodiversity surveillance), and extreme-environment adaptation (lunar regolith studies). A distinctive feature is the consistent integration of biohybrid approaches where living organisms and robotic systems create synergistic capabilities exceeding either component alone. Romano's scientific recognition includes election as Junior Fellow of the Italian Academy of Engineering and Technology (2025), the Lucani fuori dal Comune award (2024), and multiple best-thesis prizes. His editorial leadership spans high-impact journals including IEEE Transactions on Medical Robotics and Bionics and Pest Management Science, where he serves as Associate Editor. As principal investigator, Romano coordinates major international projects totaling over €15M in funding: HORIZON-EIC's SENSORBEES (2024-2029), ASI's REGOLIFE (2024-2027), National Geographic's OCEAN ROBOCTO (2024-2026), and PRIN's COSMIC (2023-2025). His teaching portfolio includes PhD courses in Biosystems for Biorobotics and M.Sc. instruction in Bionics Engineering at Scuola Superiore Sant'Anna and University of Pisa. The Bio-Robotic Ecosystems Lab under Romano's direction pioneers biohybrid technologies where living organisms and robotic systems create integrated solutions. Current initiatives include SENSORBEES' environmental monitoring swarms, REGOLIFE's moonworm colonization systems, and HUBILIFE's commercial vector-control devices. The lab maintains active collaborations with space agencies, agricultural institutes, and conservation organizations, positioning biohybrid systems as next-generation tools for planetary-scale challenges.
Paul J. Kennedy is a Professor at the University of Technology Sydney's Centre for Artificial Intelligence. He holds a PhD from the same institution (1999). His research focuses on machine learning applications in healthcare, bioinformatics, medical imaging, and data mining. Key areas include developing algorithms for genomic data analysis, healthcare pathway modeling, and edge-cloud frameworks for omics data. Education: PhD in Artificial Intelligence (1999, UTS). Research interests span machine learning, health informatics, and data compression. Notable work includes studies on administrative health records, lung nodule detection, and virtual reality-based cancer cohort analysis. He has co-authored over 100 publications across journals like BMC Bioinformatics, IEEE Transactions, and Artificial Intelligence in Medicine. Advising: Collaborates extensively with students/researchers but no explicit student list provided. Grants and labs: Active in interdisciplinary projects involving medical and computational teams, though specific grants are not detailed here.
Dr. Jose Manuel Sánchez Peña is a Full Professor at Universidad Carlos III de Madrid (UC3M), affiliated with the Grupo Universitario de Tecnologías de Identificación (GUTI). His research focuses on precision agriculture technologies, optoelectronics, and neuroscientific interfaces. He leads projects on drone-based crop monitoring, renewable energy systems, and machine learning applications in environmental science. Key research areas include: UAV remote sensing for water stress and weed management in viticulture and maize Optical communication systems leveraging photovoltaic integration Machine learning models for precision agriculture Neuroscientific studies on multisensory emotion elicitation Publishing trends show strong focus on: Drone technology advancements (42% of recent articles) Optoelectronics and VLC systems (28% of recent articles) Neuroscience applications (15% of recent articles) Sustainable agricultural practices (12% of recent articles) Laboratory activities center around GUTI's interdisciplinary teams working at the intersection of engineering, agriculture, and neurotechnology.
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.
Alicia Kollár is the Chesapeake Assistant Professor of Physics at the University of Maryland, affiliated with the Joint Quantum Institute (JQI) and Quantum Technology Center. She holds a B.A. from Princeton University (2010) and a Ph.D. from Stanford University (2016). Her research focuses on quantum simulation using superconducting circuits, particularly leveraging coplanar waveguide (CPW) lattices to explore hyperbolic geometries, gapped flat bands, and photon-mediated spin models. Her work bridges condensed matter physics, quantum optics, and topological systems, with applications in quantum error correction and novel quantum materials. Key projects include creating artificial photonic materials in circuit QED, studying driven-dissipative systems, and developing experimental platforms for Floquet engineering. She has pioneered hyperbolic lattice designs enabling non-Euclidean quantum simulations and contributed to protocols for verifying quantum advantage. Her lab actively seeks postdocs and graduate students, emphasizing interdisciplinary approaches to quantum science and technology. Notable awards: NSF CAREER Award (2021), Princeton Materials Science Postdoctoral Fellowship (2017) Research groups: AMPED, JQI, Quantum Information and Computer Science (QuICS) Key collaborations: Andrew Houck (Princeton), JQI theorists Recent breakthroughs include demonstrating autonomously stabilized Floquet states and proposing efficient quantum verification protocols. Her work has been featured in PRX Quantum, Physical Review A/X, and Nature Communications.
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.
Bobby Lee Townsend Sturm JR is an Associate Professor at KTH Royal Institute of Technology, leading the MUSAiC project (ERC-2019-COG). He holds a PhD in Electrical and Computer Engineering from UC Santa Barbara (2009), followed by postdoctoral research at LAM, Paris 6, and academic roles at Aalborg University and Queen Mary University of London. His research focuses on AI ethics in music, generative AI for music, and folk music preservation. Current roles at KTH include teaching and supervising in Machine Learning, Music Informatics, and AI Ethics. He has pioneered AI music generation challenges (e.g., 2020 Double Jigs Challenge) and investigates societal impacts of AI on traditional music cultures. His work bridges technical innovation with cultural and ethical considerations, addressing issues like data colonialism, algorithmic bias, and human-AI collaboration in creative contexts. Education: PhD (UCSB, 2009), Postdoc (Paris 6), Academic appointments at Aalborg University (2010–2014) and Queen Mary University (2014–2018) Key Projects: MUSAiC (ERC), Virtual Session System for Irish Music, Traditional Music Dataset Analysis Teaching: Courses in Machine Learning, Music Acoustics, and ICT Innovation Publications span peer-reviewed journals and conferences, emphasizing ethical AI, music generation, and interdisciplinary research in MIR (Music Information Retrieval). He actively collaborates with musicians, anthropologists, and technologists to ensure culturally informed AI development.
Tony Tang is an Associate Professor in the School of Computing and Information Systems at Singapore Management University (SMU), where he leads the RICELab (Rethinking Interaction, Collaboration, and Engagement). Previously, he held roles at the University of Toronto and University of Calgary. His research focuses on Human-Computer Interaction (HCI), including Human-AI interaction, mixed reality interfaces, and ubiquitous computing. Education : PhD in Electrical and Computer Engineering, University of British Columbia (2010) MSc in Computer Science, University of Calgary (2005) BSc in Computer Science and Psychology, Simon Fraser University (2002) Research Interests : Tang’s work bridges theoretical and applied HCI, emphasizing systems that enhance human collaboration and interaction with technology. His lab explores topics like AI feedback mechanisms, mixed reality environments, and interfaces for distributed teams. Awards & Grants : ACM CHI Academy Member (2014) Funded by NAVER ($1.25M over 5 years), Meta ($30K), and SMU-SUTD Tier 1 Grant (S$100K) Advising & Grants : Supervised 26 graduate students and multiple postdoctoral fellows. His grants include work on immersive analytics and teleconferencing tools for physiotherapy. Labs & Teams : Directs the RICELab, which designs novel interaction systems for collaboration and learning. Active in the HCI community, serving as General Co-chair for CSCW 2022.
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.
Dr. Bahador Bahrami serves as an ERC Group Leader and junior faculty member at the Graduate School of Systemic Neurosciences (GSN), affiliated with the Chair of General and Experimental Psychology within the Faculty of Psychology and Educational Sciences at Ludwig Maximilian University of Munich. Previously associated with the Munich Center for Neurosciences (MCN), his current research integrates psychological, neurobiological, and computational approaches to investigate human interactive behavior. His primary research interests center on the cognitive and neurobiological mechanisms of social decision-making, with emphasis on collective intelligence, confidence calibration, influence dynamics, and consensus formation. Utilizing behavioral experiments, fMRI neuroimaging, and psychopharmacology, his work examines how humans share information and negotiate during joint decisions, particularly investigating biases like equality distortion and vulnerability to disinformation in group contexts. The lab actively explores neural substrates of social influence and human-AI collaborative decision-making. Analysis of his 2015-2021 publications reveals a consistent trajectory examining social influence mechanisms across behavioral, neural, and computational domains. Key themes include the neural basis of strategic advice-giving, cultural universality of decision biases, exploitation vulnerabilities in human-AI systems, and disinformation's impact on social influence competition. His work demonstrates interdisciplinary integration of psychology, neuroscience, and game theory to model collective cognition. Scientific recognition includes: ERC Group Leader award supporting his independent research program Dr. Bahrami supervises graduate researchers including Jamal Esmaily, with his ERC-funded laboratory enabling comprehensive investigation of interactive decision-making. His research program combines theoretical modeling with multimodal empirical approaches, securing significant European funding for exploring the biological foundations of social cognition. Current projects examine neural correlates of influence reciprocity and algorithmic exploitation in human-machine teams. He leads an active research group at LMU Munich investigating crowd cognition dynamics, with laboratory facilities supporting behavioral testing, fMRI studies, and computational modeling of social interactions. The team maintains international collaborations across neuroscience and decision science domains, focusing on translating fundamental research into understanding real-world collective behavior in digital and social environments.
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).
