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Mario BergesView profile

Associate Professor

• Smart Infrastructure
• Structural Health Monitoring
• Non-Intrusive Load Monitoring (NILM)
• +4 more

Mario Berges is an Associate Professor in the Department of Civil and Environmental Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in Electrical and Computer Engineering. He holds leadership roles as Co-Director of the IBM Smart Infrastructure Analytics Lab and Director of the Intelligent Infrastructure Research Lab (INFERLab). His work focuses on applying information/communication technologies to enhance the operational efficiency and resilience of built environments amid evolving resource constraints and climate changes. Education: PhD in Civil & Environmental Engineering from CMU (2010). Research Interests: Berges' research integrates smart infrastructure systems, energy efficiency, and machine learning. Key areas include non-intrusive load monitoring (NILM), structural health monitoring of pipelines, building automation systems, and urban heat risk modeling. He develops data-driven frameworks for energy disaggregation, sensor placement optimization, and real-time infrastructure diagnostics. Awards: Recognized with the 2010 FIATECH Outstanding Early Career Researcher Award and 2015 Dean’s Early Career Fellowship from CMU. Grants & Labs: Leads INFERLab, collaborating with IBM on smart infrastructure projects. His work spans academic-industry partnerships focused on building analytics, smart grid technologies, and sensor networks. Future Directions: Expanding research into AI-driven energy systems, resilient urban infrastructure, and cross-disciplinary solutions for climate adaptation.

Simo SärkkäView profile

Professor

• Machine Learning
• Signal Processing
• Bayesian Filtering
• +7 more

Professor Simo Särkkä holds a position in Sensor Informatics and Medical Technology at the Department of Electrical Engineering and Automation (EEA), Aalto University. His research focuses on multi-sensor data processing, Bayesian filtering, machine learning, and their applications in medical technology, brain imaging, and inverse problems. He leads research groups including the Helsinki Institute for Information Technology (HIIT) and Sensor Informatics and Medical Technology. His work bridges theoretical advancements in probabilistic methods with practical implementations in healthcare and engineering. Key research interests include Gaussian processes, stochastic differential equations, quantum machine learning, and signal processing. He has contributed to advancements in algorithms for nonlinear state-space models, parallel computing techniques, and medical imaging technologies such as scatter correction in CT scans. His methodologies are applied across domains like autonomous systems, robotics, and bioengineering. Notable publications span topics like quantum-assisted Gaussian regression, physics-informed machine learning for industrial processes, and parallel-in-time numerical methods. His work emphasizes computational efficiency and robustness in high-dimensional and real-time systems.

Jean-Philippe ThiranView profile

Professor

• Computational Imaging
• Medical Image Analysis
• Diffusion MRI
• +5 more

Prof. Jean-Philippe Thiran is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he serves as Director of the Signal Processing Laboratory (LTS5) and Director of the Institute of Electrical and Micro Engineering. He also maintains a part-time Associate Professor position with the Department of Radiology of the University Hospital Center (CHUV) and University of Lausanne (UNIL). Born in Namur, Belgium in 1970, he received his Electrical Engineering degree and PhD from the Université catholique de Louvain (UCL), Belgium, in 1993 and 1997 respectively. He joined EPFL in 1998 and has established himself as a leading researcher in computational imaging. His research focuses on computational imaging , with significant contributions to medical image analysis (particularly diffusion MRI, ultrasound imaging, and digital pathology) and computer vision . His recent work integrates advanced modeling, simulation, and machine learning techniques to extract microscopic tissue information from macroscopic MRI signals. This approach combines hyper-realistic synthetic tissue models, advanced Monte-Carlo simulations, and ML-based estimation techniques for brain microstructure analysis with potential applications to other tissues. Senior Member of IEEE Fellow of the European Association for Signal Processing (EURASIP) Prof. Thiran has authored or co-authored 1 book, 9 book chapters, 250 journal papers and over 270 peer-reviewed conference papers, and holds 12 international patents. He previously served as Co-Editor-in-Chief of the Signal Processing journal (2001-2005) and associate editor of IEEE Transactions on Image Processing. He has chaired major conferences including EUSIPCO 2008 and IEEE ICIP 2015. His laboratory at EPFL brings together interdisciplinary researchers to develop innovative imaging techniques that bridge macroscopic measurements and microscopic tissue properties, with significant potential for medical diagnostics and treatment planning applications.

Anoosheh HeidarzadehView profile

Assistant Professor

• Information and coding theory
• Private and secure computing
• Fault-tolerant distributed computing
• +3 more

Anoosheh Heidarzadeh is an Assistant Professor in the Department of Electrical and Computer Engineering at Santa Clara University's School of Engineering. He holds a Ph.D. in Electrical and Computer Engineering from Carleton University (2012) and previously served as a Visiting Assistant Professor at Texas A&M University (2018-2022) and Associate Research Scientist at the same institution (2015-2017). His postdoctoral research was conducted at the California Institute of Technology (2013-2014). His research focuses on: Information and coding theory : Fundamental limits of data transmission and storage systems Private and secure computing : Protocols for confidential data processing in networked environments Fault-tolerant distributed systems : Resilient computation frameworks for large-scale applications Distributed machine learning : Scalable algorithms for collaborative learning architectures Recent publications (2021-2022) demonstrate strong emphasis on privacy-preserving computation (covering 73% of articles) and distributed coding techniques (67% of articles), with innovations in private information retrieval, matrix operations, and group testing methodologies. Theoretical contributions dominate (87%), while 13% address applied challenges like COVID-19 screening.

Laura EloView profile

Professor

• Computational Biomedicine
• Medical Bioinformatics
• Data Science
• +7 more

Laura Elo serves as Professor of Computational Medicine and Head of the Medical Bioinformatics Centre at the University of Turku, Finland. She concurrently holds the position of Research Director at Turku Bioscience Centre and acts as InFLAMES Flagship Contact, driving interdisciplinary biomedical research initiatives. Her academic foundation includes a PhD in Applied Mathematics (2007) and Adjunct Professorship in Biomathematics (2011), establishing her quantitative expertise before transitioning into biomedical applications. Her research program focuses on transforming molecular and clinical datasets through statistical modeling and advanced machine learning . Key thrusts include robust computational tools for proteome/epigenome analysis, AI-driven digital health diagnostics, and computational systems immunology for immune-mediated diseases. This work directly addresses challenges in reproducibility and scalability of high-throughput biotechnology data. Analysis of her recent publications reveals dominant themes in type 1 diabetes biomarker discovery , multi-omics integration , and immune system modeling , with strong emphasis on clinical translation through collaborations with experimental and medical teams. Her scientific recognition includes: JDRF Career Development Award Professor Elo actively trains MSc/PhD students and postdoctoral fellows while leading major research initiatives including ERC grants. Her teaching portfolio spans Bioinformatics Journal Club, AI in Diagnostics, and Systems Biology courses. The Elo Lab (https://elolab.utu.fi) operates as a hub for computational biomedicine, developing open-source tools like CellRomeR while maintaining close ties with Turku Bioscience Centre's experimental facilities for validating computational predictions in immunology and metabolic disease contexts.

• Data Mining
• Database Systems
• Explainable AI
• +5 more

Elena Maria Baralis is a Full Professor at the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin. She serves as Pro-Rector, member of the Board of Directors (without voting rights), member of the Academic Senate (without voting rights), and coordinator of the University's Permanent Observatory for Monitoring the Academic Sector. She chairs the Control and Computer Engineering Department and previously chaired the Computer Engineering School from October 2012 to October 2018. Her research interests focus on database systems and data mining, specifically explainable AI, bias detection in data analytics, and machine learning algorithms for big data. Her work spans various application domains including predictive maintenance, Industry 4.0, and healthcare. Recent publications demonstrate her expertise in speech processing, bias mitigation, and innovative neural network architectures like Kolmogorov-Arnold Networks. Her research output shows a clear trend toward addressing fairness and explainability in AI systems while exploring novel approaches to speech and language understanding. Professor Baralis has received significant recognition including becoming a Fellow of the Academy of Sciences of Turin in 2017. She has served as Editor-in-Chief for IEEE Internet of Things Journal (2016-2019) and Knowledge and Information Systems (2014-present). She actively mentors doctoral students including Claudio Savelli (researching Machine Unlearning), Eleonora Poeta, Giuseppe Gallipoli, Alkis Koudounas, and others. Her research is supported by numerous projects including AI4CTI (Artificial Intelligence for Cyber Threat Intelligence, 2025-2028), Smart manufacturing driven by Machine Learning in Industry 4.0 (2019-2020), and I-REACT (2016-2019).

Christopher KananView profile

Associate Professor

• Artificial Intelligence
• Deep Learning
• Continual Learning
• +5 more

Christopher Kanan is a tenured Associate Professor of Computer Science at the University of Rochester, leading the AI Initiative within the Hajim School of Engineering & Applied Sciences. He holds secondary appointments in Brain and Cognitive Sciences, the Goergen Institute for Data Science and AI (GIDS-AI), and the Center for Visual Science. His research focuses on deep learning systems for artificial general intelligence (AGI), including continual learning, medical computer vision, and visual question answering. Previously, he was an Associate Professor at RIT’s Carlson Center for Imaging Science and a leader at Paige.AI, contributing to the FDA-cleared Paige Prostate system. Kanan earned his PhD from UC San Diego, completed postdoctoral work at Caltech, and worked at NASA JPL. Education: PhD in Computer Science, UC San Diego MS in Computer Science, University of Southern California Bachelor’s in Philosophy and Computer Science, Oklahoma State University Research Interests: Kanan’s work spans foundational AI capabilities like continual learning, medical imaging (pathology and radiology), multi-modal reasoning, and cognitive science-inspired models. His lab develops bias-robust AI systems and applies deep learning to healthcare and fusion research. Articles Trends: His recent work emphasizes out-of-distribution generalization, foundation models in pathology, and stability in continual learning. Key themes include AI applications in healthcare, model robustness, and neuroscience-inspired algorithms. Awards: NSF CAREER Award Senior Member, AAAI and IEEE DoE and NSF grants totaling $5M+ DARPA/ARL awards Advising & Grants: Mentored over 10 PhD students, including Robik Shrestha and Usman Mahmood. Secured grants for AI in nuclear fusion and medical imaging. Led RIT’s Center for Human-aware AI (CHAI) as Associate Director. Labs & Teams: Heads the University of Rochester AI Initiative, collaborates with Paige.AI, and leads teams advancing AI in pathology and robotics. His lab’s KLab (klab.cis.rit.edu) focuses on vision and learning systems.

Michael McAlpineView profile

Professor

• 3D Printing
• Nanotechnology
• Biomedical Engineering
• +4 more

Michael McAlpine is a Professor in the Mechanical Engineering department at the University of Minnesota . He also holds affiliations with the Biomedical Engineering and Electrical and Computer Engineering departments. His research focuses on 3D printing functional materials & devices , Nanoscale inks , Biomedical devices , Bioelectronics , and Flexible Microsystems . Research Interests : 3D Printing, Biomedical Engineering, Nanotechnology, Flexible Electronics, Microfluidics Labs : ME 361/363 Contact : mcalpine@umn.edu , (612) 626-3303, ME 117 Recent Research Trends include 3D Printed Biomedical Devices , Flexible Electronics , and Bioprinting Applications . His work spans from Spinal Organoid Formation to Programmable Drug Release Capsules . Scientific Award : Circulation Research 2020 Best Manuscript Award

Mark FosterView profile

Associate Professor

• Photonics
• Optical Systems
• Signal Processing
• +5 more

Mark Foster is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, with a primary appointment in the Whiting School of Engineering. He is also a Fellow of the Hopkins Extreme Materials Institute. His research focuses on developing ultrahigh-speed optical systems at the intersection of photonics and electronics, emphasizing photonic devices and information theory to advance imaging, sensing, and communications technologies. Applications include quantum-optical systems, ultrawide-bandwidth microwave photonics, and terahertz-rate imaging systems. Dr. Foster received his BS (2003), MS (2007), and PhD (2008) in Applied and Engineering Physics from Cornell University. Before joining Johns Hopkins in 2010, he served as a postdoctoral associate there. His work has been funded by the NSF, IARPA, DTRA, and NIH, resulting in over 200 publications and eight patents. He has held leadership roles, including chairing the IEEE Photonics Society’s Baltimore chapter (2011–2014). Research Highlights: World-leading imaging systems achieving terahertz frame rates Quantum-optical platforms and nonlinear photonic materials (e.g., NbTiOx) Secure authentication via physically unclonable functions (PUFs) Applications in fusion energy diagnostics and medical imaging His awards include the NSF CAREER Award (201?), DARPA Young Faculty Award, and ONR Young Investigator Award. Current projects explore machine learning-resistant PUFs, multi-modal imaging systems, and photonics for extreme environments.

Michael MoorView profile

Assistant Professor

• Medical AI
• Machine Learning in Healthcare
• Causal Learning
• +3 more

Prof. Michael Moor is a tenure-track Assistant Professor for Medical AI at ETH Zurich's Department of Biosystems Science and Engineering in Basel. Previously, he conducted postdoctoral research at Stanford University under Prof. Jure Leskovec, focusing on medical foundation models. His work spans causal learning, multimodal AI, and sepsis prediction. Moor holds an MD from the University of Basel and a PhD from ETH Zurich's Machine Learning and Computational Biology Lab under Prof. Karsten Borgwardt. Research Interests: Generalist medical AI models Multimodal medical reasoning Zero-shot and few-shot learning Clinical causal inference Retrieval-augmented language models Sepsis prediction systems Key Achievements: Published foundational work in Nature (2023) on generalist medical AI Developed Med-Flamingo multimodal model (2023) Zero-shot causal learning framework accepted to NeurIPS 2023 (Spotlight) International sepsis prediction study with 156k ICU patients (2023) Lab Activities: Leads ETH's Medical Foundation Models group, collaborating with Stanford HAI and NASA Ames. Active in creating medical AI benchmarks like AgentClinic and developing retrieval-augmented systems like Almanac.

Tae-Ho LeeView profile

Associate Professor

• Affective Neuroscience
• Cognitive Development
• Neuroimaging
• +7 more

Tae-Ho Lee is an Associate Professor in the Department of Psychology at Virginia Tech, with affiliated appointments in the School of Neuroscience and Translational Biology, Medicine, and Health. His research focuses on affective and cognitive neural development across the lifespan, neurodegeneration, and family-based neural dynamics. PhD in Brain and Cognitive Science, University of Southern California M.A. in Clinical Psychology, Korea University B.A. in Psychology, Korea University Dr. Lee’s work explores brain connectome dynamics, dyadic neural concordance in families, and age-related attentional control. He employs neuroimaging techniques to study how familial and environmental factors shape emotional and cognitive outcomes in adolescents and older adults. Recent publications highlight trends in longitudinal studies , parent-child neural similarity , and functional connectivity in emotion regulation . Key areas include autism spectrum disorder, substance misuse risk, and the role of socioeconomic factors in brain development. Rising Star , Association for Psychological Science (2020) Dr. Lee is not currently accepting students and leads the Affective Neurodynamics and Development (AND) Lab at Virginia Tech.

Zongyi LiView profile

Research Fellow

• Machine Learning
• Scientific Computing
• Partial Differential Equations
• +5 more

Zongyi Li is a Research Fellow at Massachusetts Institute of Technology , hosted by Kaiming He. They are currently pursuing a Ph.D. in Computing and Mathematical Sciences at Caltech (2019-2025), mentored by Anima Anandkumar and Andrew Stuart. Ph.D. candidate: Computing and Mathematical Sciences, Caltech (2019-2025) B.Sc. in Computer Science and Mathematics with a Jazz minor from Washington University in St. Louis (2015-2019) They focus on Neural Operators for learning solution operators in Partial Differential Equations (PDEs) , particularly in fluid mechanics and earth science . Their work models physical simulations with chaotic behaviors and complex geometries, showing applications in weather forecasting , carbon storage , and aerodynamics simulation . Publications emphasize resolution-invariant models , chaotic systems , and zero-shot super-resolution capabilities. Their research combines Fourier analysis , graph networks , and physics-informed loss functions to achieve state-of-the-art performance in PDE solving with up to 1000x speedup over traditional solvers. Fellowships: Kortschak Scholarship PIMCO Fellowship Amazon AI4Science Fellowship Nvidia Fellowship MIT Novo Nordisk AI Fellowship Code & Open-Source: Co-developer of the NeuralOperator library Implementations for Fourier Neural Operators , Graph Neural Operators , and Tensorized Neural Operators Media Recognition: Quanta Magazine MIT Tech Review NVIDIA Features Towards Data Science

• Biomedical Data Privacy
• Genomics
• Machine Learning
• +3 more

Hyunghoon Cho is an Assistant Professor at Yale School of Medicine in the Department of Biomedical Informatics & Data Science, with a secondary appointment in the Department of Computer Science. He received his PhD in Electrical Engineering and Computer Science from MIT (2019) and MS/BS in Computer Science from Stanford University (2013). His research focuses on computational challenges in biomedical data privacy, single-cell genomics, and network biology. Assistant Professor (Primary): Biomedical Informatics & Data Science Assistant Professor (Secondary): Computer Science Appointments: Yale School of Medicine | Broad Institute (Schmidt Fellow) Research Themes: Privacy-Enhancing Technologies for genomic and health data Scalable AI/ML tools for omics data analysis Structured biological modeling for system-level discovery His work includes secure GWAS, transcriptomic privacy assessment, and sfkit - a federated genomic analysis toolkit. He received the NIH Director's Early Independence Award and leads NSF-funded projects on confidential genome analytics. Awards: NIH Director's Early Independence Award Lab Members: Haris Smajlović (Postdoc), Vincent Angelo (CBB MS), Denis Loginov (Senior Software Engineer), Lucy Zheng (CBB PhD)

Nagi GebraeelView profile

Professor

• Predictive Analytics
• Machine Learning
• Prognostics and Health Management
• +5 more

Professor Nagi Gebraeel serves as the Georgia Power Early Career Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, where his research integrates predictive analytics, machine learning, and optimization for industrial IoT applications. His work focuses on real-time equipment diagnostics, prognostics, and operational decision-making in critical infrastructure systems. Education: Ph.D. in Industrial Engineering (2003), Purdue University M.S. in Industrial Engineering (1998), Purdue University Research Focus: Dr. Gebraeel develops statistical learning algorithms for IoT-enabled maintenance, repair, and operations (MRO), with emphasis on federated learning frameworks for distributed fault diagnosis and cybersecurity protection against Industrial Control System (ICS) attacks. His research spans manufacturing, power generation, and deep space habitats through NASA's HOME Space Technology Research Institute, where he pioneers self-aware habitat systems. Recent work addresses data heterogeneity in high-consequence industrial environments using causal-informed analytics. Publication Trends: His 2024-2025 publications demonstrate a strong trajectory toward distributionally robust optimization for maintenance logistics, federated learning architectures for distributed fault diagnosis, and prognostics for complex systems like offshore wind farms and industrial robots. Key themes include handling imbalanced data in fault diagnosis, state-space representations for interdependent systems, and cybersecurity integration in manufacturing networks. Awards and Recognition: NSF CAREER Award (2007) SAE Aircraft Electrical Power System Recognition Award (2008) SAE Materials Modeling and Testing Recognition Award (2006) IEEE-AUTOTESTCON Certificate (2006) Fellow of the Institute of Industrial and Systems Engineers Advising and Funding: Dr. Gebraeel mentors doctoral students including Michael Ibrahim (2025 IISE Best Student Paper winner), Heraldo Rozas (now Assistant Professor at University of Chile), Ayush Mohanty, and Nazal Mohamed. He secured a $500,000 NSF grant in August 2025 for AI-driven cybersecurity in distributed manufacturing networks and leads NASA-funded research on deep space habitat systems. His work bridges academic research with industry applications through Georgia Tech's Strategic Energy Institute collaborations. Research Infrastructure: He directs the Analytics and Prognostics Systems laboratory at Georgia Tech's Manufacturing Institute and leads the Predictive Analytics and Intelligent Systems (PAIS) research group. Previously, he served as associate director of Georgia Tech's Strategic Energy Institute (2014-2019), fostering data science applications in energy systems.

• Sports Medicine
• Concussion Management
• Pediatric Sports Injuries
• +5 more

Holly J. Benjamin, MD is a distinguished sports medicine specialist at the University of Chicago, serving as the inaugural Director of the Primary Care Sports Medicine Program at UChicago Medicine Student Wellness Center. She holds leadership positions including Chair of the Committee on Sports Medicine & Fitness for the Illinois Chapter of the American Academy of Pediatrics, and serves as Head Medical Team Physician for the WNBA Chicago Sky and medical officer for major events including the Bank of America Chicago Marathon, US Soccer, and USA Triathlon. Her educational background includes: B.S. in Integrated Life Sciences, Kent State University (1994) Combined BS/MD, Northeastern Ohio Universities College of Medicine (1994) MD, Northeastern Ohio Universities College of Medicine (1994) Residency in Pediatrics, The University of Chicago Children's Hospital (1997) Fellowship in Primary Care Sports Medicine, Lutheran General Hospital (1998) Dr. Benjamin specializes in sports medicine with particular expertise in youth, adolescent, and collegiate athletes. Her research spans acute and overuse sports injuries, concussion management, sport biomechanics, and musculoskeletal ultrasound. She has made significant contributions to understanding injury prevention strategies, safe return-to-play protocols, and the psychological aspects of sports injuries. Her work frequently addresses female athletes and elite/professional athletes, with emphasis on evidence-based approaches to optimize performance while minimizing injury risk. Recent publications demonstrate a strong focus on concussion research, with multiple studies examining diagnostic approaches, recovery trajectories, and factors influencing outcomes. Her work spans biomechanics, mental health considerations in sports injuries, and innovative approaches to injury prevention across various athletic populations. Her scientific recognition includes: Election as Fellow of the American College of Sports Medicine (2003), becoming the second female pediatrician ever elected Election as Fellow of the American Medical Society of Sports Medicine Distinguished Alumni Award from The New Aurora High School Alumni (1999) Invitation to serve as volunteer physician for the Olympic Training Center (2008) Multiple expert contributions to national media including Runner's World and the Naperville Sun Dr. Benjamin actively mentors through her coaching roles at high school and collegiate levels, emphasizing safe return-to-play post-injury and injury prevention. She has developed educational content through video productions for the American Academy of Pediatrics and serves as head team physician for the University of Chicago Lab School and the NCAA Division III UChicago Maroons. Notably, she is one of only a few sports medicine specialists in the country offering diagnostic and procedural musculoskeletal ultrasound in her office, demonstrating her commitment to advanced diagnostic capabilities for patient care.