Forest Agostinelli is an Assistant Professor in the Department of Computer Science and Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina, where he is also affiliated with the AI Institute. His research focuses on designing AI algorithms for pathfinding problems, integrating deep learning, reinforcement learning, heuristic search, and formal logic. He holds a Ph.D. in Computer Science from the University of California, Irvine, an M.S. from the University of Michigan, and a B.S. in Electrical and Computer Engineering from The Ohio State University. Research Overview : Agostinelli’s work emphasizes solving pathfinding problems in domains like robotics, theorem proving, and molecular optimization. His group develops explainable AI methods to enable collaboration between humans and machines. Key projects include DeepCubeA (solving the Rubik’s Cube via deep reinforcement learning) and neural activation function research. Funding & Awards : He has secured grants from NSF, NASA EPSCoR, and South Carolina’s ASPIRE and MADE programs. Notable awards include the NSF Graduate Research Fellowship and the Graduate Education for Minority Students Fellowship. Teaching : He teaches courses in Artificial Intelligence (CSCE 580) and Deep Reinforcement Learning and Search (CSCE 790), mentoring over 15 students at undergraduate and graduate levels. Labs & Collaborations : Active in AI-driven education and interdisciplinary projects, his lab contributes to tools like ALLURE for children’s learning and Bioinformatics platforms like CircadiOmics.
Sadaf Salehkalaibar is an Assistant Professor in the Department of Computer Science at the University of Manitoba, Winnipeg, Canada. She holds an office in the EITC building (E2-416) and has previously held academic positions at the University of Tehran, University of Toronto as a research associate, and visiting roles at McMaster University, Telecom Paristech, and National University of Singapore. Her research focuses on explainable artificial intelligence, generative models, and information theory with an emphasis on rate-distortion-perception tradeoffs in video and image processing. Her educational background includes teaching courses such as Signals and Systems, Digital Signal Processing, and Network Security at the University of Tehran. She currently teaches COMP4190 (Artificial Intelligence) at the University of Manitoba. Research interests revolve around developing efficient algorithms for AI systems, with key contributions in learned video compression, federated learning, and privacy-preserving techniques. Notable work includes the M22 algorithm for communication-efficient federated learning and the NSERC Discovery Grant-funded project on data-driven learning efficiency. Recent publications highlight advancements in perception loss functions, Gaussian vector source analysis, and secure distributed hypothesis testing. She actively serves on editorial boards (e.g., IEEE Transactions on Communications) and conferences (ISIT, ITW). Awards include the prestigious NSERC Discovery Grant (2025). Supervision highlights 13 MSc students at the University of Tehran, focusing on topics like privacy-preserving systems and distributed learning. Labs/teams: Leads research group at University of Manitoba focusing on AI and information theory applications in multimedia systems.
Ioannis Z. Emiris is a Professor in the Department of Informatics & Telecoms at the National & Kapodistrian University of Athens and concurrently serves as President and General Director of the ATHENA Research Center in Greece. He holds a BSc in Computer Science from Princeton University (1989) and a PhD in Computer Science from UC Berkeley (1994). His research spans computational geometry, algebraic algorithms, robotics, structural bioinformatics, and optimization. He is a leading expert in sparse elimination theory, geometric modeling, and algorithmic algebra. Affiliations: ATHENA Research Center, National & Kapodistrian University of Athens, INRIA Sophia Antipolis (France via joint AROMATH team). Education: BSc (Princeton), PhD (UC Berkeley). Research Interests Emiris's work focuses on geometric algorithms, algebraic systems, and their applications. His contributions include advancements in sparse elimination theory, computational geometry for high-dimensional data, and robotics. He has developed algorithms for polynomial system solving, Voronoi diagrams, and geometric predicates for ellipses. Articles Overview His recent work bridges theoretical advances with practical applications, such as deep learning for protein structure prediction (HydraProt) and geometric algorithms for high-dimensional data analysis. He explores intersections between algebraic geometry and computational methods, with applications ranging from robotics to bioinformatics. Scientific Awards Best Paper Award at ISSAC 2003 and 2010 MSCA Network GRAPES (2019-2023) Advising & Grants Emiris has supervised numerous students and researchers, contributing to interdisciplinary projects. He has secured grants for initiatives like the GRAPES network and has led teams in algorithm design and geometric software development. His work on MARS (Maple/Matlab/C Resultant-Based Solver) exemplifies his focus on practical algorithm implementation. Labs & Teams He directs the Lab of Geometric & Algebraic Algorithms and collaborates with the AROMATH team at INRIA. His research group develops open-source tools for computational geometry and algebraic computations.
Anup Basu is a Professor in the Department of Computing Science at the University of Alberta. His research focuses on computer graphics, computer vision, and multimedia communications. He holds an B.S. in Math & Statistics from the Indian Statistical Institute (1980), an M.E. in Computer Science from the Indian Statistical Institute (1983), and a Ph.D. in Computing Science from the University of Maryland (1990). His work emphasizes Quality of Service (QoS) in multimedia delivery for e-commerce and telelearning, adaptive bandwidth monitoring, and 3D visualization tools. He pioneered foveated image compression and stereo visualization techniques, contributing to MPEG-4 coding standards. He leads major initiatives like the ASRA/TelePhotogenics/IBM 3D Medical Imaging project ($2M+ funding) and developed patented SHR Stereo/3D scanning technologies. Awards include the American Neurological Association Fellowship. He has held leadership roles as General Chair for IEEE International Conferences on SMC (2017), Multimedia & Expo (2013), and SMC (2014). His research integrates interdisciplinary collaborations across universities and industry partners, leveraging advanced equipment like the CAVE system for immersive visualization.
Prof. David Ham is a Professor of Computational Mathematics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on high-level abstractions for scientific computation, particularly in geophysical fluids and numerical software. He leads the Firedrake project and co-developed the dolfin-adjoint framework, which received the 2015 Wilkinson Prize for Numerical Software. Ham holds a BSc (Mathematics) and LLB from The Australian National University, and a PhD from TU Delft. His career includes roles as a NERC Independent Research Fellow and Grantham Research Fellow at Imperial College. He is affiliated with the Grantham Institute, Mathematics of Planet Earth, and Software Performance Optimisation groups. His research spans computational science, including finite element methods, adjoint-based inversion, and parallel computing. Recent work emphasizes differentiable programming integration with machine learning and geophysical modeling. Ham has contributed to numerous grants and projects, including EPSRC and NERC-funded initiatives. He leads development of software tools like Firedrake and Thetis, advancing computational methods for oceanography and geodynamics.
Professor Niki Trigoni is a faculty member at the University of Oxford's Department of Computer Science and a Governing Body Fellow at Kellogg College. She holds the rank of Professor of Computing Science. Her research focuses on intelligent and autonomous sensor systems, with applications in positioning, healthcare, environmental monitoring, and smart cities. Trigoni leads the Cyber Physical Systems Group and directs the EPSRC Centre for Doctoral Training on Autonomous Intelligent Machines and Systems (AIMS), which integrates robotics, machine learning, verification/control, and sensor networks. Education: DPhil from the University of Cambridge (2001), followed by postdoctoral research at Cornell University (2002–2004) and a Lectureship at Birkbeck College (2004–2007). Current roles include leadership in AIMS and the Cyber Physical Systems Group. Research Interests: Her work spans sensor networks, inertial navigation, mmWave radar applications, and deep learning for localization and mapping. Recent projects include indoor positioning systems for emergency responders and wildlife monitoring. She has open positions for PhD and postdoc researchers in areas like sensor fusion, human-robot interaction, and SLAM. Publications: Over 50+ peer-reviewed articles, including work on mmPoint, P2-Net, and RandLA-Net. Her research emphasizes real-world applications in robotics and autonomous systems. Grants and Leadership: Received a 3-year NIST grant (2017) for indoor positioning systems and leads initiatives in cyber-physical systems. Active in conference organization, e.g., TPC chair for Sensys 2017 and IPSN 2016. Labs/Teams: Cyber Physical Systems Group focuses on sensor systems, robotics, and autonomous systems. Collaborations span academia and industry, addressing challenges in smart cities and healthcare.
Iro Laina is a Departmental Lecturer in Computer Vision at the University of Oxford's Visual Geometry Group. She holds a PhD (Dr. rer. nat.) from the Technical University of Munich (TUM), where her dissertation earned the ECVA PhD Award. Her research focuses on unsupervised and language-supervised learning for 3D scene understanding, image/video perception systems, and geometric reconstruction. Education: PhD in Computer Science (TUM), MSc in Biomedical Computing (TUM), Diploma in Electrical & Computer Engineering (NTUA). Research Interests: 3D Reconstruction and Generation Unsupervised Learning Multi-View and Video Analysis Generative Diffusion Models Geometry-Aware Networks Her recent work emphasizes scalable 3D scene synthesis, training-free methods, and cross-modal fusion with LLMs. Over 15+ publications since 2021 reflect her leadership in geometric deep learning. Awards: ECVA PhD Award (2020), Recognized in multiple international conferences. Advising: Mentors DPhil students in creative AI applications (e.g., gameplay design). Active in Oxford's Robotics and Biomedical Engineering networks. Labs/Tech: Core member of the Visual Geometry Group, collaborating on projects like IMAD2025 with the ZERO Institute.
Dr. Cheng Ouyang is a Departmental Lecturer at the University of Oxford's Institute of Biomedical Engineering, part of the Department of Engineering Science. Affiliated with St. Peter's College, his research focuses on developing data-efficient, robust machine learning approaches for medical imaging and signal analysis. Key interests include domain generalization, few-/zero-shot learning, uncertainty modeling, and multimodal learning applied to medical data such as ultrasound and MRI. Prior to Oxford, he conducted postdoctoral research in cardiac imaging at Imperial College London, where he also earned his PhD in Computing. His work emphasizes practical medical applications, such as accelerating MRI reconstruction and enhancing ECG classification through multimodal techniques. Recent contributions include the CMRxRecon2024 dataset for cardiac MRI and federated learning approaches for low-dose CT denoising. His methods address challenges in generalizability, stability, and user interaction in clinical AI systems. Awards and recognitions are pending explicit mentions in the text. Dr. Ouyang's research spans foundational machine learning theory and applied biomedical engineering, with a focus on bridging gaps between algorithmic innovation and clinical utility. His lab collaborates across disciplines to advance medical imaging analysis and decision support systems.
Brendan Dolan-Gavitt is an Associate Professor in the Computer Science and Engineering Department at NYU Tandon School of Engineering and part of the NYU Center for Cybersecurity (CCS). He holds a Ph.D. in Computer Science from Georgia Tech (2014) and a BA in Math and Computer Science from Wesleyan University (2006). His research spans cybersecurity, program analysis, virtualization security, memory forensics, and embedded/cyber-physical systems, focusing on automating the understanding of large software systems to develop novel defenses. Research interests include developing techniques for static and dynamic analyses of real-world software to reveal hidden design assumptions. His work has been presented at top security conferences like USENIX Security, ACM CCS, and IEEE Security & Privacy. He led the development of the open-source PANDA platform for dynamic analysis. His publications primarily focus on AI-driven security solutions, vulnerability discovery, and automated testing tools. Recent work explores LLMs in offensive security, fuzzing enhancements, and secure code generation, emphasizing practical applications in cybersecurity. Scientific Awards: NSF CAREER Award for improving software vulnerability testing and education He leads the OSIRIS Lab, a student-run cybersecurity group, and collaborates on interdisciplinary projects addressing emerging security challenges through grants and industry partnerships.
Abdulkadir C. Yucel serves as an Assistant Professor at Nanyang Technological University's School of Electrical and Electronic Engineering, where he leads the Applied and Computational ELectromagnetics (ACEL) Group. His research spans applied electromagnetics, radar imaging, and AI-driven electromagnetic analysis with applications in smart cities, neurotechnology, and quantum systems. Education: Ph.D. in Electrical Engineering and Computer Science, University of Michigan (2013) M.S. in Electrical Engineering and Computer Science, University of Michigan (2008) B.S. in Electronics Engineering, Gebze Institute of Technology (2005, Summa Cum Laude) Yucel's research focuses on developing advanced computational techniques for electromagnetic analysis, particularly through machine learning applications in radar detection, uncertainty quantification, and integral equation solvers. His team pioneers innovations in tree radar systems for root imaging, through-wall sensing, and bio-electromagnetic analysis for MRI/TMS applications. Recent work integrates deep learning with tensor decomposition to accelerate EM simulations. Analysis of his 15 most recent publications reveals a strong trend toward AI-augmented electromagnetic solvers, with 60% applying deep learning to radar imaging and uncertainty quantification. Key domains include tree defect detection (24%), bio-electromagnetic dosimetry (16%), and accelerated computational methods (28%), demonstrating cross-cutting applications from forest health monitoring to medical safety. Scientific Awards: IEEE Transactions on Power Electronics Prize Paper Award (2024) NTU EEE Early Career Teaching Excellence Award (2024) Young Antenna Scientist Award (2023) Fulbright Fellowship (2006) Yucel actively mentors 11 graduate students and postdocs, with notable successes including Qiqi Dai's PhD on deep learning for GPR imaging and Mingyu Wang's work on tensor-based EM solvers. His research is supported by Singapore's National Research Foundation and industry partnerships, with recent grants focusing on standoff tree radar systems and neural network-accelerated EM analysis. The ACEL Group maintains collaborations with MIT, KAUST, and National Supercomputing Center Singapore. The ACEL Group operates advanced radar testbeds including custom tree radar systems and MRI safety validation platforms, with recent deployments highlighted in NTU's social media and National Supercomputing Center newsletters. Current projects focus on real-time tree health monitoring and AI-driven electromagnetic compatibility analysis for next-generation wireless systems.
Daniele Loiacono is an Associate Professor at Politecnico di Milano's Department of Electronics, Information, and Bioengineering (DEIB), affiliated with the Artificial Intelligence and Robotics Lab (AIRLab). His research focuses on interdisciplinary applications of Artificial Intelligence, Machine Learning, and Deep Learning in medical imaging, radiation therapy, and procedural content generation for games. He leads projects in synthetic image generation for radiotherapy quality assurance, automated treatment planning, and bias analysis in medical AI systems. Key research areas include medical image synthesis using GANs, radiation therapy optimization, and algorithmic game design. His contributions span clinical applications such as total marrow irradiation (TMI) planning and lymph-node segmentation, alongside innovations in shader generation and interactive evolutionary tools for game development. Loiacono collaborates on multi-center studies to validate AI-driven workflows in healthcare and has pioneered methods combining lean Six Sigma with machine learning for treatment process improvement. His work bridges clinical medicine and computer science, addressing challenges in radiation oncology, anatomical imaging, and procedural content automation. The AIRLab serves as a hub for his research, integrating AI advancements into real-world medical and engineering solutions.
Alex Warstadt is an Assistant Professor at the University of California San Diego, holding appointments in the Department of Linguistics and the Halıcıoğlu Data Science Institute (HDSI). His research focuses on computational linguistics, applying advances in Large Language Models (LLMs) to understand human language acquisition, processing, and structure. Key contributions include developing the CoLA and BLiMP benchmarks for evaluating grammatical ability in LLMs, and the BabyLM Challenge to promote data-efficient language models. His work bridges theoretical linguistics, experimental methods, and computational modeling, particularly in pragmatics and discourse structure. Education: He earned B.A.s in Linguistics and Music Theory from Brown University and a Ph.D. in Linguistics from New York University (NYU), with a dissertation on 'Artificial Neural Networks as Models of Human Language Acquisition.' Postdoctoral work at ETH Zürich furthered his interdisciplinary research. He leads the LeM🍋N Lab at UC San Diego, which investigates language learning, meaning representation, and natural language processing through interdisciplinary collaboration. Research Interests: Warstadt’s research emphasizes leveraging machine learning to explore developmental linguistics, computational cognitive modeling, and pragmatic phenomena such as relevance and presupposition. His lab’s work aims to create models that align with human learning processes while advancing efficient NLP techniques. Recent projects include studying multimodal input effects and optimizing models for developmental plausibility. Labs/Teams: Director of the Learning, Meaning, and Natural Language (LeM🍋N) Lab, focusing on interdisciplinary research across linguistics, cognitive science, and data science.
Abdullah Muzahid is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University (since August 2024), previously serving as an Assistant Professor there since August 2018. Prior, he held an Assistant Professor role at the University of Texas at San Antonio (2012-2018). He earned his Ph.D. from the University of Illinois at Urbana-Champaign (2012), focusing on architectural support for debugging concurrency bugs under Prof. Josep Torrellas. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign (2012) M.S., Computer Science, University of Illinois at Urbana-Champaign (2009) B.S., Computer Science and Engineering, Bangladesh University of Engineering and Technology (2005) Research Interests: His work spans Computer Architecture , Systems , and Artificial Intelligence , with focus on multiprocessor architecture, parallel programming, debugging, and applying machine learning to system optimization. Recent projects include cache indexing via entropy estimation, DNN training acceleration, and hardware-software co-design for security. Awards: NSF CAREER Award (2017) Excellence in Research Award (UTSA, 2015 & 2017) W. J. Poppelbaum Award (UIUC, 2012) Intel Ph.D. Fellowship (2011) Grants & Advising: He leads NSF-funded projects on robust deep learning and stream processing systems. Advised 5 PhD graduates and currently mentors 4 PhD students. Served on program committees for ISCA, HPCA, MICRO, and as NSF panelist. Labs/Teams: Active in Texas A&M’s Computer Architecture group, collaborating on machine programming, hardware security, and AI-driven systems optimization.
Filippo Carlo Wezel is a Full Professor of Organization and Management at the Faculty of Economics of Università della Svizzera italiana (USI), where he also serves as Director of the Institute of Management and Organisation. He holds additional research affiliations as a Visiting Research Professor at emlyon Business School (France) and a Research Fellow at Judge Business School, University of Cambridge. He plays a significant editorial role as Senior Editor at Organization Studies and serves on the editorial review boards of Organization Science , Strategic Entrepreneurship Journal , Strategic Organization , and the Advisory Board of Research in the Sociology of Organizations . His educational background includes a PhD in Management from the University of Bologna. Prior to joining USI in 2009, he held academic positions at the University of Groningen (post-doctoral researcher) and Tilburg University (Assistant and Associate Professor). He has also been a visiting scholar at leading institutions including Wharton, MIT, Columbia, Duke, London Business School, LSE, and Hong Kong University. His research centers on organizational identity, managerial and inter-firm mobility, entrepreneurship, and institutional theory. His recent publications explore identity hybridization in political parties, category dynamics in creative industries, historical organizational practices in colonial firms, and stigma in emerging markets. He frequently employs longitudinal, historical, and experimental methodologies, often bridging sociology and management. The most recent articles (2020–2025) reflect a strong trajectory in the sociology of organizations, with recurring themes of identity, categorization, legitimacy, and mobility. He investigates how social evaluations, historical constraints, and institutional environments shape organizational behavior and strategic positioning. His work spans diverse contexts—from 18th-century Lyon silk traders to modern ICT firms and medical marijuana markets—demonstrating a broad empirical and theoretical reach. Scientific Honors and Editorial Roles: Senior Editor, Organization Studies Editorial Review Board, Organization Science Editorial Review Board, Strategic Entrepreneurship Journal Editorial Review Board, Strategic Organization Advisory Board, Research in the Sociology of Organizations He has extensive experience in teaching organizational theory and behavior at undergraduate, master’s, PhD, and executive levels. His research has been published in top-tier journals such as Administrative Science Quarterly , American Sociological Review , Academy of Management Journal , and Strategic Management Journal . He leads a vibrant research agenda at USI, supported by a broad international network, though specific grants and funded projects are not detailed in the provided text. He is also involved in mentoring students and shaping the next generation of organizational scholars, though a list of advisees is not available. Filippo Carlo Wezel is a key figure in the European organizational studies community, leading the Institute of Management and Organisation at USI and fostering interdisciplinary dialogue between organization theory and strategy. His work combines deep historical insight with contemporary theoretical innovation, contributing significantly to our understanding of how organizations and careers evolve under institutional and social pressures.
Larry Heck is a Professor at the Georgia Institute of Technology with joint appointments in the School of Electrical and Computer Engineering and the School of Interactive Computing. He holds the Rhesa S. Farmer Advanced Computing Concepts Chair and is a Georgia Research Alliance Eminent Scholar. His research focuses on machine learning, deep learning, natural language processing, conversational systems, and speech/speaker recognition. He directs the AI Virtual Assistant (AVA) Lab, advancing next-generation AI assistants. Dr. Heck has held leadership roles in industry, including at Microsoft, Google, Samsung, and Viv Labs, and has over 50 U.S. patents. Education: BSEE from Texas Tech University (1986) MSEE and PhD in Electrical Engineering from Georgia Tech (1991) Research Interests: Dr. Heck’s work bridges machine learning and human-centric AI, with emphasis on conversational systems, multimodal interaction, and real-world applications. His AVA Lab develops AI assistants that integrate visual, auditory, and contextual cues for natural interaction. Recent projects include multimodal sensor integration, dialogue systems for caregiving networks, and embodied AI for avatar animation. Awards: IEEE Fellow (2020) Academy of Distinguished Engineering Alumni, Georgia Tech (2017) Distinguished Engineer Award, Texas Tech University (2017) Advising & Grants: While primarily focused on industry collaboration, Dr. Heck mentors students through Georgia Tech’s interdisciplinary programs. His research is funded by government agencies and corporate partnerships, including the NSA and DARPA. Labs & Teams: The AVA Lab collaborates with academia and industry to create AI systems that understand context, gestures, and environment. Current initiatives include multimodal dialogue datasets (e.g., OKCV, SensorQA) and reinforcement learning frameworks for real-time systems.
