Sanjay Purushotham is an Assistant Professor in the Department of Information Systems at the University of Maryland Baltimore County (UMBC), with a PhD in Electrical Engineering from the University of Southern California (USC) and a postdoctoral background in Computer Science at USC's Integrated Media Systems Center (IMSC). His research focuses on machine learning, data mining, and their applications in biomedical informatics, social network analysis, and multimedia data mining. Key contributions include survival analysis models using pseudo values and federated learning frameworks for healthcare data. He has received awards including the Best Paper Award at SIGSPATIAL 2014 and a Best Poster Runnerup at SCMLS 2016. Education: PhD in Electrical Engineering (USC), Postdoc in Computer Science (USC) His work spans interdisciplinary areas such as domain adaptation for remote sensing, thermal face translation, and interpretable neural networks for medical applications. Recent projects include federated survival analysis models and climate-informatics frameworks for cloud property retrieval. He teaches courses in artificial intelligence, healthcare informatics, and statistical learning at UMBC. Research highlights include developing MedFuseNet for multimodal medical question answering and VDAM for multi-sensor cloud data analysis. His work on fair survival analysis models addresses algorithmic bias in healthcare predictions. Current grants include a NSF CAREER award for trustworthy federated learning in computational healthcare.
Yang Zhou is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University, part of the Samuel Ginn College of Engineering. His research focuses on big data algorithms, machine learning, data mining, and distributed computing. He has contributed to advancements in federated learning frameworks, graph mining tools, and spatial machine learning for environmental applications like flood mapping. Education includes a Ph.D. in Computer Science from Georgia Tech (2021), M.E. in Computer Application Technology from Chongqing University (2016), and B.E. in Engineering from Jiangnan University (2014). His work emphasizes scalable algorithms for large-scale systems, with tools like DirDense for dense subgraph mining and FedASMU for federated learning optimization. Recent publications explore adversarial robustness, blockchain strategies in IoT, and curriculum-based learning for large language models. He advises on interdisciplinary projects at the intersection of AI and environmental science.
Geng Yuan is an Assistant Professor at the University of Georgia's School of Computing, specializing in AI systems, energy-efficient deep learning, and hardware-software co-design. His work bridges machine learning algorithms with emerging hardware technologies like superconducting circuits and ReRAM. He holds a Ph.D. in Computer Engineering from Northeastern University (2023) and a Master's in Electrical & Computer Engineering from Syracuse University (2016). Doctor of Philosophy (Ph.D.) in Computer Engineering, Northeastern University (2023) Master of Science (M.S.) in Electrical & Computer Engineering, Syracuse University (2016) Bachelor of Science (B.S.) in Electrical Engineering, Beijing University of Technology (2014) His research focuses on optimizing deep learning systems for edge computing and mobile platforms through techniques like model compression, sparse training, and hardware-aware neural architecture search. Recent projects include adapting large language models via hybrid-grained pruning and developing ultra-low-power AQFP circuits for binary networks. Geng Yuan's publications span top venues like NeurIPS, CVPR, ICML, ICLR, ISCA, and DAC, with notable awards including a Best Paper Award at ICLR Workshop 2021, Spotlight Papers at ICLR 2023 and NeurIPS 2021, and a Design Contest 1st Place at ISLPED 2020. Best Paper Award (ICLR Workshop'21) Spotlight Paper Award (ICLR'23, NeurIPS'21) Design Contest 1st Place (ISLPED'20) Best Paper Nomination (DATE'21, ISQED'18) He actively recruits Ph.D., Master's students, and interns to his research group, focusing on advancing AI systems through interdisciplinary approaches combining machine learning, computer architecture, and electronic design automation.
Dr. Mohamed Ibrahem is an Assistant Professor at the School of Computer and Cyber Sciences, Augusta University, USA. His research focuses on Cyber-Security, IoT Privacy, and Machine Learning applications in Smart Grids and Cyber-Physical Systems. He received a Ph.D. in Electrical and Computer Engineering from Tennessee Tech University in 2021 and has been recognized with prestigious awards including the IEEE Senior Member (2024) and the Eminence Award for Best Ph.D. Paper (2021). Education: Ph.D., Electrical and Computer Engineering, Tennessee Tech University (2021) Research Interests: Security and Privacy in IoT, Applied Cryptography, Privacy-preserving Machine Learning, Secure Federated Learning, Smart Grid Cyber-Security, and Traffic Analysis Attacks. Publications: His work emphasizes privacy-preserving techniques in smart grids, federated learning for anomaly detection, and robust countermeasures against cyber-attacks. Recent trends include leveraging deep learning for real-time fraud detection in AMI systems and developing efficient decentralized learning frameworks. Awards: IEEE Senior Member (2024) Top 2% Scientists List (Stanford/Elsevier, 2024) Eminence Award for Best Ph.D. Paper (Tennessee Tech University, 2021) Advising & Grants: Supervises graduate research in Dissertation and Master's Thesis projects. Active in securing grants for IoT and Cyber-Security initiatives. Collaborates with industry partners on edge computing and secure AMI networks.
Dr. Aria Fallah, MD, is an Assistant Professor in Neurosurgery at the University of California, Los Angeles (UCLA), with secondary appointments in Health Policy and Management. His research focuses on Evidence-Based Surgery , Clinical Trials , and Meta-Analyses for pediatric epilepsy surgery outcomes, including prognostic tools and comparative effectiveness studies . He actively participates in international collaborations like the CTSI and has published extensively in journals such as Epilepsia , Neurology , and Clinical Neurophysiology . University: University of California, Los Angeles Department: Neurosurgery Email: afallah@mednet.ucla.edu Dr. Fallah’s research integrates machine learning and deep learning to analyze high-frequency oscillations in epilepsy, aiming to refine diagnostic and therapeutic strategies. His recent work includes developing tools like PyHFO for oscillation detection and HOPS calculator for predicting seizure freedom after hemispherectomy. His publications highlight collaborations across institutions in pediatric epilepsy surgery , hemimegalencephaly , and thalamic neuromodulation . While no explicit awards or student advisement are listed, his contributions span clinical trials , neuroimmunology , and global neurosurgery initiatives , including work in Haiti.
Hossam Hassanein is a Professor and Director of the School of Computing at Queen's University. He received his B.Sc. in Electrical Engineering from Kuwait University in 1984, M.Sc. in Computer Engineering from the University of Toronto in 1986, and Ph.D. in Computing Science from the University of Alberta in 1990. He joined Queen's University School of Computing in 1999 and has established himself as a leading researcher in telecommunications and networking. Dr. Hassanein's research interests span wireless sensor networks, mobile ad hoc networks, edge computing, Internet of Things (IoT), radio resource management, and data-centric networks. His seminal contributions include pioneering work on WSN planning, load-balanced routing protocols, and energy-efficient network designs. He has championed research in IoT, developing frameworks for smart spaces that use contextual information to enhance IoT applications in healthcare, transportation, and infrastructure. His recent publications (2023-2025) demonstrate a strong focus on cutting-edge areas including extreme edge computing, vehicular networks, and AI/ML integration in networking. Research trends show increasing emphasis on practical applications in telesurgery, digital twins, and industrial IoT, addressing challenges in resource allocation, task offloading, and real-time processing in constrained environments. Dr. Hassanein has received numerous recognitions for his work: Fellow of the IEEE Queen's University School of Graduate Studies Award for Excellence in Graduate Student Supervision (2015) Multiple best paper awards from top international conferences As founder and director of the Telecommunications Research Lab (TRL), Dr. Hassanein has supervised over 75 students who have made substantial contributions in academia and industry. The TRL is one of Queen's largest research groups with extensive international collaborations. Dr. Hassanein has successfully attracted significant research funding from government and industry sources in the competitive telecommunications field. The Telecommunications Research Lab has developed innovative platforms including SPROUTS, a rugged sensor platform used in mining, steel manufacturing, and smart-grid monitoring. TRL's work has had significant impact in WSN planning, data dissemination, and resource reuse in wireless networks, with contributions featured in IEEE Wireless Communications Magazine.
Prof. Dr. Steffen Marburg is a Full Professor at the Chair of Acoustics of Mobile Systems within the TUM School of Engineering and Design at the Technical University of Munich. His research focuses on numerical methods in vibroacoustics, structural optimization, and acoustic modeling for applications in automotive, maritime, and musical instrument domains. Education: PhD from Technical University of Dresden (1998). Academic Career: Junior Professor at TU Dresden (2004), Chair of Technical Dynamics at University of the Federal Armed Forces Munich (2010), Full Professor at TUM (2015–present). Editorial Roles: Co-Editor-in-Chief of Journal of Theoretical and Computational Acoustics, Associate Editor of Journal of the Acoustical Society of America, Editor of Acoustics Australia and Mechanical Systems and Signal Processing. His research integrates computational acoustics, boundary element methods, and machine learning to address noise control and structural optimization challenges. Recent work explores acoustic metamaterials, viscothermal losses, and data-driven modeling. He has co-authored over 150 publications and led advancements in multifrequency solution methods and noise-insulating structures. Scientific awards include the Innovation Award of the Industrieclub Sachsen e.V. (1999). His editorial contributions and leadership in journals highlight his influence in computational acoustics and structural dynamics.
Professor Matthias Mann is a world-leading scientist serving as Director of the Proteomics and Signal Transduction department at the Max Planck Institute of Biochemistry in Martinsried, Germany, and Director of the Proteomics department at the Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Denmark. With an h-index exceeding 277 and over 350,000 citations, he is recognized as the highest cited German researcher and one of the most influential scientists globally in proteomics. His educational background includes: Ph.D. in Chemical Engineering from Yale University (1988) Master's Degree in Physics from Georg August University Göttingen (1984) Bachelor's of Arts in Mathematics from Georg August University Göttingen (1982) Professor Mann's research focuses on advancing mass spectrometry-based proteomics to understand biological systems at the protein level. His work spans technological developments in mass spectrometry, bioinformatics and computational analysis, signal transduction and posttranslational modifications, and clinical proteomics applications for disease diagnosis and treatment. The Mann lab has pioneered groundbreaking methods like SILAC for quantitative proteomics and MaxQuant for proteome data analysis. Their vision is to translate proteomics knowledge into clinical practice for predictive, diagnostic, and preventive medicine, with recent work focusing on AI-guided platforms for analyzing proteomes from minimal tissue samples. Analysis of Professor Mann's recent publications reveals a strong trend toward clinical applications of proteomics, particularly in cancer research, metabolic diseases, and neurodegenerative disorders. His work increasingly integrates spatial proteomics, single-cell resolution techniques, and artificial intelligence approaches to uncover disease mechanisms and identify potential biomarkers, with a clear shift from basic technology development toward direct clinical applications and personalized medicine. Professor Mann has received numerous prestigious awards throughout his career: 2025: Elected member of the American National Academy of Sciences 2024: Dr. H.P. Heineken Award for Biochemistry and Biophysics 2023: Otto Warburg Medal 2019: Nominated member of the Bavarian Academy of Sciences 2013: Elected member of Leopoldina German National Academy of Sciences 2012: Körber European Science Award, Louis-Jeantet Foundation Prize for Medicine, Ernst Schering Prize, and Leibniz Prize Professor Mann leads a highly collaborative research team involved in multiple international networks including the Bill & Melinda Gates Foundation, Michael J. Fox Foundation for Parkinson's Research, CLINSPECT-M, and Munich Heart Alliance. His lab has mentored numerous successful researchers, with several former postdocs receiving prestigious ERC Starting Grants. The Mann group has developed innovative clinical proteomics pipelines for analyzing archived tissue specimens and body fluids, aiming to identify protein markers for early detection of diseases such as diabetes and cancer. The Mann lab operates across two major research centers with state-of-the-art mass spectrometry facilities. Their Clinical Knowledge Graph platform integrates multi-omics data with extensive metadata, creating an ecosystem for machine learning applications in proteomics. Current research focuses on developing highly sensitive methods that can profile thousands of proteins from minimal cell samples, enabling the identification of critical disease-related proteins and supporting the development of individualized therapies.
Noorbakhsh Amiri Golilarz is an Assistant Professor in the Department of Computer Science at The University of Alabama, College of Engineering. He has established himself as a prominent researcher in artificial intelligence, particularly in computer vision, deep learning, and image processing. His educational background includes: Postdoctoral Research Fellow, Computer Science, Boston College (2023) Ph.D., Electrical and Computer Engineering, Southern Illinois University Carbondale (2023) D. Eng., Computer Science and Technology, University of Electronic Science and Technology of China (2021) M.S., Electrical and Electronic Engineering, Eastern Mediterranean University (2017) B.S., Electrical Engineering, University of Guilan (2012) Dr. Golilarz's research spans multiple domains of artificial intelligence with a particular focus on computer vision, deep learning, and image processing applications. His work addresses challenges in medical imaging, satellite imagery, and cognitive neuroscience. He has made significant contributions to image denoising techniques, control chart pattern recognition, and AI applications in healthcare. His recent work has expanded into generative AI, large language models, and secure machine learning operations. His publication portfolio demonstrates consistent productivity with over 2500 citations and an h-index of 25. His most impactful work includes applications of blockchain and federated learning for COVID-19 detection, optimized support vector machines for medical diagnosis, and innovative image denoising techniques using metaheuristic optimization algorithms. Among his professional achievements: Co-founded AI Letters journal in 2024, serving as Associate Editor-in-Chief Served as Lead Guest Editor and Topic Editor for several SCI-indexed journals Held the role of Conference Program Chair Dr. Golilarz has supervised numerous graduate students and research projects, with his work spanning theoretical advancements in AI algorithms to practical applications in healthcare, energy systems, and cybersecurity. His research group has established collaborations with institutions including Boston College and Mississippi State University.
Dr. Euijin (Alley) Choo is an Assistant Professor in the Department of Computer Science at the University of Alberta, specializing in data-driven cybersecurity and big data analytics. Her research focuses on AI-based cybersecurity solutions, anomaly detection in network traffic, and adversarial attacks on federated learning systems. She holds a Ph.D. from North Carolina State University and has held roles at Qatar Computing Research Institute, Korea University, and the University of Missouri-Rolla. Education: Ph.D., Computer Science, North Carolina State University (2015) M.S., Computer Science & Engineering, Korea University (2008) B.S. Dual Degree in Computer Science & Mathematics, Korea University (2006) Research Interests: Security and big data analysis intersections Federated learning security and privacy Anomaly detection in network logs and enterprise systems Malware/phishing detection using graph inference AI-driven threat intelligence aggregation Recent Grants: Mitacs Accelerate Program Grant: Fraud Detection in Financial Graphs ($60,000, 2025) National CyberSecurity Consortium Grant: IntruderInsight ($2M, 2025-2027) NSERC Discovery Grant: Threat Detection Framework ($180,000, 2025-2031) Awards: Best Paper Award at DBSEC 2015 NSERC Early Career Researcher Award (2025) Provost Fellowship (NC State, 2009-2010) Labs/Teams: Leads the Data-driven Network and Cyber Security (DANS) Lab, focusing on federated learning defenses, compromised entity detection, and malicious domain analysis.
Josie Hughes is a Tenure Track Assistant Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) within the School of Engineering's Institute of Mechanical Engineering. She leads the Computer Robot Design and Fabrication Laboratory (CREATE Lab) and serves on the doctoral program committee for Robotics, Control and Intelligent Systems. Her roles include teaching courses in product development, engineering design, and data-driven manufacturing methods while maintaining active research and student supervision. Her research centers on soft robotics with emphasis on adaptive design, fabrication techniques, and real-world applications. Key areas include agricultural robotics (exemplified by the GraspBerry raspberry harvester), biomimetic materials like self-healing e-skins, and developmental robotics (BabyBot project). She pioneers approaches integrating morphological computation, variable stiffness mechanisms, and machine learning for robotic control, while championing open-source principles and diversity in robotics through accessible educational initiatives like balloon robot kits. Analysis of her recent publications reveals dominant trends in soft robotics adaptability, particularly in variable-stiffness structures, sensor integration, and task-specific optimization. Her work bridges fundamental material science with practical applications in agriculture, food science, and human-robot interaction, frequently employing computational methods like Bayesian optimization and neural networks for design and control. Scientific awards: No specific awards were mentioned in the source materials. Dr. Hughes actively supervises 16 doctoral students across diverse projects including soft grippers for agriculture, biomimetic locomotion, and robotic manipulation systems. Her advising portfolio shows strong alignment with her research themes, with current students working on topics like modular soft arms, agricultural automation, and developmental robotics. While grant details weren't specified, her high publication volume indicates robust research funding. The CREATE Lab under her leadership focuses on holistic co-design of hardware and software for soft robotic systems, with notable projects including the GraspBerry agricultural harvester and BabyBot developmental platform. The lab emphasizes open-source development and educational outreach, maintaining strong industry and academic collaborations while pushing boundaries in reconfigurable robotics and human-centered applications.
Mi Zhang is an Associate Professor in the Department of Computer Science and Engineering at The Ohio State University and Director of the OSU AIoT and Machine Learning Systems Lab. He holds multiple affiliations including the Institute for Cybersecurity and Digital Trust, Translational Data Analytics Institute, and 5G and Broadband Connectivity Center. Dr. Zhang received his Ph.D. from University of Southern California and B.S. from Peking University, followed by a postdoctoral position at Cornell University. His academic journey previously included a position at Michigan State University before joining OSU. His research focuses on Empowering Billions of Everyday Devices with AI to realize the Artificial Intelligence of Things (AIoT) vision. His lab works across several interconnected domains including efficient generative AI (multimodal LLMs, diffusion models), edge AI for mobile/AR/wearables, systems for AI agents, spatial computing, foundation models for IoT, and human-centered mobile health applications. This interdisciplinary work draws from mobile/edge computing, AI/machine learning, distributed systems, computer networks, and human-centered computing. Analysis of his recent publications reveals a strong focus on making AI more efficient and accessible for resource-constrained devices. His research trajectory shows increasing emphasis on large language models and their optimization for edge deployment, alongside continued work in federated learning for IoT applications. The publications demonstrate both theoretical contributions and practical applications across healthcare, wireless networks, and human-computer interaction. Best Paper Award, IEEE Internet Computing Magazine (2024) University of Chicago Outstanding Educator Award (2024) Best Paper Award, ECCV'24 Workshop (2024) USC ECE SIPI Distinguished Alumni Award (2023) Multiple Best Paper Awards from ACM/IEEE conferences NSF CRII Award Facebook/Meta Faculty Research Award Amazon Research Award MSU Innovation of the Year Award (2020) Dr. Zhang actively mentors students at all levels, with a current group of Ph.D. students working on cutting-edge AI/ML systems. His lab has secured significant funding including Meta Reality Labs Faculty Awards, NVIDIA academic grants, and NSF grants. The OSU AIoT and Machine Learning Systems Lab serves as the central hub for his research activities, fostering collaboration across multiple disciplines to advance the field of AIoT.
Professor Daniel Rueckert is a leading academic in Artificial Intelligence and Medical Imaging, holding dual positions at Imperial College London (as Professor of Visual Information Processing) and Technical University of Munich (Alexander von Humboldt Professor for AI in Medicine and Healthcare). He obtained his MSc from Technical University Berlin (1993) and PhD from Imperial College London (1997), followed by postdoctoral work at King’s College London. At Imperial, he led the Department of Computing (2016–2020) and founded the Biomedical Image Analysis group. His research focuses on AI-driven medical image analysis, including algorithms for image reconstruction, registration, and clinical decision support. His research interests span AI applications in healthcare, machine learning for medical imaging, and computational methods for clinical diagnostics. Notable contributions include over 500 publications and 60+ PhD graduates, with key works in federated learning, cardiac motion analysis, and biomarker development. Awards include the Leibniz Prize (2025), Royal Academy of Engineering Fellowship (2015), and multiple ERC grants. He leads the BioMedIA research group and is an editorial board member of Medical Image Analysis . Recent publications highlight advancements in AI-driven medical imaging, such as secure federated learning frameworks and deep learning models for disease prediction. His work bridges academic and industrial sectors through initiatives like IXICO, an Imperial spin-out. Current affiliations include roles at both Imperial and TUM, emphasizing interdisciplinary collaboration in healthcare technology. Advising and grants: Supervised over 60 PhD students and 40 post-docs. Secured grants including ERC Synergy (2013) and ERC Advanced (2020). Active in labs focused on biomedical image computing and AI in healthcare systems. Collaborative efforts include the BioMedIA group and TUM’s AI initiatives. Labs/teams: Leads the Biomedical Image Analysis group at Imperial and the TUM AI in Medicine team. Collaborates extensively on projects like cardiac imaging analysis and federated learning for healthcare.
Larissa Schlegel-Pape serves as a Scientific Associate at the Department for Ornamental and Pedigree Poultry within the Farm Animal Clinic of Freie Universität Berlin's Faculty of Veterinary Medicine. Her work focuses on developing innovative methods for assessing chicken welfare through the creation of the "Stressed Chicken Scale," which aims to systematically identify stress indicators in poultry. Her research interests center on animal welfare science, specifically stress assessment in chickens using both behavioral observation and computer vision technology. She investigates how body posture, movement patterns, and other visual indicators can reliably signal discomfort or stress in poultry, with the goal of creating practical assessment tools for veterinarians and poultry farmers. Her work bridges veterinary medicine, ethology, and technological innovation, contributing to refinement research (one of the 3Rs principles) in animal husbandry. Analysis of her publications reveals a strong focus on developing and validating the Stressed Chicken Scale across multiple contexts. Her work spans methodological development, practical implementation studies, and technological integration with computer vision systems. The research demonstrates progression from conceptual framework to validation studies and practical application, with increasing sophistication in assessment techniques and broader implications for animal welfare standards in poultry farming. Schlegel-Pape actively collaborates with the Federal Institute for Risk Assessment (BfR) and participates in interdisciplinary projects involving artificial intelligence applications in agriculture. She presents her findings regularly at major German veterinary conferences including the DVG (Deutsche Veterinärmedizinische Gesellschaft) events, DACh Epidemiology conferences, and specialized poultry medicine gatherings. Her work contributes significantly to advancing animal welfare assessment methodologies and promoting refinement in poultry husbandry practices.
Jingtong Hu is an Associate Professor at the Swanson School of Engineering, University of Pittsburgh, where he also holds the William Kepler Whiteford Faculty Fellowship. His research focuses on Cyber-Physical Systems and Infrastructure Security, with significant contributions to embedded systems, non-volatile memory architectures, and hardware/software co-design for energy-constrained environments. Dr. Hu received his PhD from the University of Texas at Dallas (2007-2013) and his Bachelor of Engineering from Shandong University (2003-2007). His research spans multiple interdisciplinary areas including energy harvesting systems, non-volatile processors, FPGA acceleration, and machine learning at the edge. His recent publications demonstrate a strong focus on algorithm-hardware co-design, particularly for vision transformers, federated learning, and non-volatile memory systems. His work often addresses the challenges of implementing AI on resource-constrained edge devices, with emphasis on energy efficiency, reliability, and performance optimization. The trend in his publications shows increasing focus on sustainable AI processing, heterogeneous computing architectures, and personalized machine learning for IoT applications. Selected Awards: IEEE Transactions on Computer-Aided Design Donald O. Pederson Best Paper Award (2021) ACM SIGDA Meritorious Service Award (2019) Multiple Best Paper Award nominations at top conferences including DAC, ASP-DAC, and CODES+ISSS Dr. Hu's research has been supported by multiple grants focusing on energy-efficient computing, non-volatile memory systems, and hardware acceleration for machine learning. His collaborative work spans numerous institutions and involves interdisciplinary teams working at the intersection of computer architecture, embedded systems, and artificial intelligence. His publications show extensive collaboration with researchers at the University of Pittsburgh, particularly with Albert K. Jones and Yiyu Shi. His laboratory work focuses on implementing practical systems for energy harvesting powered devices, non-volatile processors, and hardware accelerators for machine learning applications. Current projects appear to emphasize sustainable AI processing at the edge, heterogeneous FPGA acceleration, and personalized federated learning for health monitoring applications.
