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Dhireesha KudithipudiView profile

Professor

• Artificial Intelligence
• Neuromorphic Computing
• Energy-Efficient Machine Learning
• +5 more

Dhireesha Kudithipudi is a Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA) and Director of the MATRIX AI Consortium. Her work focuses on advancing neuromorphic computing, energy-efficient machine learning architectures, and lifelong learning systems. She holds a Ph.D. in Electrical and Computer Engineering from UTSA and an M.S. in Computer Engineering from Wright State University. Her research interests include AI algorithms, neuromorphic hardware design, spiking neural networks, and memristor-based systems. She leads initiatives in neuromorphic benchmarking (NeuroBench), energy-efficient computing roadmaps (EES2), and collaborative frameworks like the Neuromorphic Commons (THOR). Her lab develops neuromorphic chips with on-device learning capabilities, such as the Genesis chip, and explores applications in edge computing. Dr. Kudithipudi has pioneered techniques for continual learning in spiking networks, probabilistic metaplasticity, and low-precision numerical formats (e.g., PositCL). Her work bridges theoretical neuroscience principles with practical hardware implementations, emphasizing sustainability and scalability. She also contributes to NSF-funded projects like EFRI BRAID and NAIAD, advancing interdisciplinary AI research. Her lab’s collaborations include developing the NeuroBench framework for fair benchmarking and exploring neuromorphic systems for tasks like video/activity recognition and time-series forecasting. She advises on hardware-software co-design strategies for efficient neural network deployment on constrained devices.

Aiguo HanView profile

Assistant Professor

• Biomedical Ultrasound
• Quantitative Ultrasound Imaging
• Machine Learning in Medical Imaging
• +5 more

Aiguo Han is an Assistant Professor at the Department of Biomedical Engineering and Mechanics, College of Engineering, Virginia Tech. He holds affiliate appointments at Virginia Tech's Department of Mechanical Engineering and an adjunct position at UIUC's Department of Electrical and Computer Engineering. His research focuses on quantitative ultrasound imaging, machine learning applications in medical diagnostics, and transcranial ultrasound techniques for brain imaging and neuromodulation. Education : Ph.D. in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (2014) M.S. in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (2011) B.S. in Acoustics, Nanjing University (2008) Research Interests : Dr. Han develops advanced ultrasound methodologies for liver fat quantification, cancer diagnosis, preterm birth risk assessment, and neuroimaging. His work integrates machine learning to enhance diagnostic accuracy and employs computational modeling to understand ultrasonic wave propagation in biological tissues. Publication Trends : Recent studies emphasize deep learning in ultrasound, quantitative biomarker development, and clinical translation of imaging technologies. Key subfields include liver steatosis assessment, transcranial ultrasound, and reproductive health monitoring. Scientific Awards : NIH NIBIB Trailblazer Award (2022) Fellow, AIUM (2021) IEEE Distinguished Reviewer Awards Young Investigator Travel Grant, ASA (2018) AIUM New Investigator Award (2016) Professional Service : Dr. Han serves on committees for AIUM, RSNA QIBA, and IEEE UFFC. He reviews for NIH and NSF grants and contributes to editorial boards including European Radiology's Ultrasound Section. Lab : Leads the Biomedical Ultrasound Research Group (BURG), focusing on clinical ultrasound applications and computational modeling.

Johannes HachmannView profile

Associate Professor

• Computational Chemistry
• Materials Science
• Data Science
• +4 more

Johannes Hachmann is an Associate Professor in the Department of Chemical and Biological Engineering at the University at Buffalo, part of the School of Engineering and Applied Sciences. He serves as Program Director for the Engineering Sciences (Data Science) MS program and is affiliated with the Institute for Artificial Intelligence and Data Science. His research integrates computational chemistry, materials science, and data science to advance data-driven discovery in energy and electronics applications. Key affiliations include the New York State Center of Excellence in Materials Informatics (CMI) and the CDSE Graduate Program. Education: PhD in Theoretical Chemistry from Cornell University (2010), MSc from Cornell (2007), Dipl.-Chem. from University of Jena (2004), and studies at University of Cambridge (UK). Research interests focus on computational modeling of molecular materials and catalysts, leveraging virtual high-throughput screening (via ChemHTPS), database frameworks (ChemBDDB), and machine learning tools (ChemML). His group develops open-source software ecosystems for chemical space exploration, targeting applications in renewable energy and advanced electronics. Notable projects include the Harvard Clean Energy Project for organic photovoltaics and the creation of polyimides with enhanced optical properties. Scientific Awards: Recognized with the ACS PHYS Postdoctoral Award, IBM-Löwdin Award, SCES Young Investigator Award, and others. His work emphasizes reproducibility and best practices in machine learning for chemistry. Lab & Teams: Leads the Hachmann Group, advancing interdisciplinary approaches at the intersection of quantum chemistry, big data, and AI. Collaborates widely in materials informatics and energy research.

Mahmud HossainView profile

Associate Professor

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +3 more

Dr. Mahmud Shahriar Hossain is an Associate Professor in the Department of Computer Science at the University of Texas at El Paso. His research focuses on modern AI techniques including deep learning, machine learning, and data mining with interdisciplinary applications in biomedical sciences, mechanical engineering, sustainability, and software engineering. Academic Rank: Associate Professor Institution: University of Texas at El Paso Department: Computer Science Research Interests: Dr. Hossain develops algorithmic frameworks and practical prototypes to address domain-specific challenges. His work spans: Biomedical data analysis Intelligence analyst support systems Sustainable product redesign Temporal data modeling Network security algorithms Contextual information reuse Recent Publications focus on diffusion-based storytelling, student performance prediction, temporal embeddings, and cybersecurity frameworks. His work has been cited by institutions like the U.S. Army DEVCOM Army Research Laboratory. Awards: ProQuest/Pivot funding recipient Collaborative Networks: Regular collaborations with Naren Ramakrishnan, Monika Akbar, and others across cybersecurity, biomedical informatics, and software engineering domains. His work appears in journals like Knowledge and Information Systems , Springer publications, and conferences including IEEE BigData and ACM SIGKDD.

• Computer Science Education
• Human-Computer Interaction
• Algorithm Visualization
• +5 more

Christopher D. Hundhausen is a Professor in the School of Electrical Engineering and Computer Science at Washington State University, with a distinguished career spanning over 25 years in computing education research. His work bridges computer science education, human-computer interaction, and learning analytics, focusing on innovative pedagogical approaches for teaching programming and software engineering. Dr. Hundhausen's research interests center on studio-based learning models, algorithm visualization, and the design of educational programming environments that promote social interaction and reflection. He has pioneered approaches for using activity streams, code reviews, and learning analytics to enhance computing education. His recent work investigates the impact of AI tools like GitHub Copilot on student programming processes and outcomes, representing the cutting edge of computing education research. Analysis of his recent publications reveals a strong focus on team software development education, with particular attention to agile practices, retrospective analysis, and methods for assessing individual contributions in team projects. His work consistently applies rigorous empirical methods to evaluate educational interventions in authentic classroom settings. Dr. Hundhausen has received recognition through consistent publication in top-tier venues including SIGCSE, ICER, and VL/HCC, with over 100 publications spanning from the mid-1990s to upcoming 2025 publications. His research has evolved from foundational work on algorithm visualization to contemporary studies on AI in education, demonstrating remarkable adaptability to emerging technologies while maintaining focus on core educational principles. He has advised numerous graduate students who have become active researchers in computing education, including Adam S. Carter, Phillip T. Conrad, and Daniel M. Olivares. His collaborative network includes prominent researchers across multiple institutions, reflecting his significant standing in the computing education community. His research has been supported by various grants focused on improving computing education through evidence-based practices and technological innovation.

Andrea L'AfflittoView profile

Associate Professor

• Autonomous Robots
• Control Theory
• Unmanned Systems
• +3 more

Dr. Andrea L'Afflitto is an Associate Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. His primary research focuses on autonomous robots, unmanned systems, control theory, and lightweight robotics. He holds a Ph.D. in Aerospace Engineering from Georgia Tech and has held roles including Assistant Professor at the University of Oklahoma (2015–2019) and Summer Faculty Fellow at the Army Research Lab (since 2017). His work spans funded projects with ARL, DARPA, NSF, and ONR, emphasizing applications like autonomous aerial support for ground troops. Education B.S. & M.S. in Aerospace Engineering, University of Napoli 'Federico II' (2004, 2006) M.S. in Mathematics, Virginia Tech (2011) Ph.D. in Aerospace Engineering, Georgia Tech (2015) His research interests include robust adaptive control, model reference adaptive control (MRAC), barrier Lyapunov functions, and nonlinear systems. He has authored 1 monograph, 2 book chapters, and over 40 peer-reviewed publications. Notable awards include the 2018 DARPA Young Faculty Award and the Domenica Rea D'Onofrio Scholarship (2011–2015). Grants & Collaborations His projects are funded by Army Research Lab (ARL), DARPA, NSF, and ONR. He has developed an open-source framework for nonlinear control of UAVs and contributed to terrain-informed motion planning for tactical vehicles. His work integrates cutting-edge control theory with practical applications in autonomous systems. Labs & Teams Dr. L'Afflitto leads research in autonomous systems at Virginia Tech, focusing on UAV control and adaptive algorithms. He collaborates with Army Research Lab on tactical autonomous systems and has ties to institutions like FermiLab and Thales Alenia Space through prior industry experience.

Garrett ChanView profile

Adjunct Associate Professor

• Healthcare Policy
• Nursing Practice
• Public Health
• +3 more

Garrett Chan is the President and CEO of HealthImpact and an Adjunct Associate Professor at the UCSF School of Nursing. He holds a PhD and MS from the same institution. His work focuses on transforming healthcare systems to address disparities, particularly through policy advocacy and innovative solutions like VaxForce, a platform expanding vaccine access in underserved communities. Chan played a pivotal role in California’s landmark AB 890 legislation, enabling nurse practitioners to practice independently. Education: PhD ’05 and MS ’00 in Nursing from UCSF His research interests include healthcare policy, nursing practice advocacy, and leveraging technology to improve access to care. Chan’s efforts during the pandemic received national recognition, including Johnson & Johnson’s honor for disrupting healthcare. He collaborates with legislative bodies and community organizations to advance equitable healthcare policies. Publications span oncology, molecular biology, and health informatics, reflecting his interdisciplinary approach. Beyond academia, his leadership at HealthImpact drives systemic change in nursing workforce policies and vaccine distribution.

Monika Ulrike ReifView profile

Professor

• Functional Safety
• AI Certification
• Autonomous Systems
• +3 more

Prof. Dr. Monika Ulrike Reif is Professor of Safety Critical Systems and Head of Research for the Safety Critical Systems focus area at the Zurich University of Applied Sciences (ZHAW) School of Engineering. Her research focuses on safety assurance for autonomous systems and AI-based technologies. Her research program develops certification frameworks and verification methods for AI systems, with applications in railway safety, autonomous vehicles, and medical devices. She leads projects on trustworthy AI assessment, safety-critical software verification, and resilience engineering for complex systems. Current work includes developing MLOps pipelines for safety-critical AI and adversarial training methods for robust autonomous systems. Dr. Reif has received the VDI Ring of Honors (2010) and Artur-Fischer-Preis (2003) for her contributions to engineering. She directs the Safety Critical Systems Research Lab and teaches courses in functional safety, RAMS (Reliability, Availability, Maintainability, Safety), and systems engineering. Her industrial experience includes safety engineering roles at Bombardier Transportation and BMW.

Rocco D'AntuonoView profile

Researcher

• Imaging
• Cell Biology
• Biomedical Engineering
• +3 more

Rocco D'Antuono is a Researcher at The Francis Crick Institute, specializing in advanced microscopy techniques and bioimage analysis. He holds a Physics background and has extensive experience in live cell imaging, confocal microscopy, and facility management. His career includes roles at the FIRC Institute for Molecular Oncology (IFOM) and the Institute for Research in Biomedicine (Bellinzona), where he contributed to microscopy and mass spectrometry facilities. His research interests revolve around imaging technologies, cell biology, and their applications in understanding disease mechanisms. He has developed open-source software tools like GIANI and ZELDA for 3D microscopy analysis, emphasizing reproducibility and collaboration in scientific imaging. He frequently collaborates with labs studying autophagy, infectious diseases, and immunology. His work spans diverse areas including tuberculosis pathology modeling, drug delivery systems for neurodegenerative diseases, and the role of endothelial cells in viral infections. He actively promotes bioimage analyst contributions to scientific research and publishing through initiatives like the NEUBIAS training schools.

• Machine Learning
• Data Mining
• Graph Neural Networks
• +5 more

Hanghang Tong is a Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He also holds affiliations with the National Center for Supercomputing Applications (NCSA) and the Center for Digital Agriculture. His research interests span machine learning, data mining, graph theory, and ethical AI, with a focus on network analysis, fairness, and large-scale data systems. Tong has been recognized with prestigious awards including the IEEE Fellow (2022), ACM Distinguished Member (2020), and NSF CAREER Award (2019). His recent work explores topics like LLM safety, time series analysis, and network alignment through tools like PLANETALIGN. Tong actively contributes to open-source libraries and benchmarks, advancing reproducibility and ethical standards in AI. Education : Not explicitly listed in provided texts. His research emphasizes practical applications of AI in climate science, healthcare, and social networks. Tong's work often addresses challenges in model fairness, scalability, and cross-domain adaptation. He collaborates widely, contributing to both foundational theory and real-world systems. Key projects include CLIMB (class-imbalanced learning benchmark) and Saffron-1 (LLM safety assurance). Tong’s lab at NCSA and Siebel School drives innovation in AI-driven data science and computational methods.

Timothy McPhillipsView profile

Researcher

• Scientific Provenance
• Research Transparency
• Data Management
• +3 more

Timothy McPhillips is a Senior Research Scientist at the University of Illinois and an affiliate of the National Center for Supercomputing Applications (NCSA). He holds a PhD in Chemistry from the California Institute of Technology. His research focuses on emerging methods for harnessing scientific knowledge, ensuring research transparency, and revealing the provenance of scientific results. Key contributions include developing tools like YesWorkflow for workflow provenance recovery and CPR for comprehensible provenance records. His work emphasizes reproducibility, data lineage tracking, and scientific workflow design. McPhillips has contributed to projects such as AutoDrug for automated crystallography workflows and the Whole Tale platform for reproducible research. He has authored numerous publications on provenance management, workflow execution, and data transparency across disciplines like bioinformatics and paleoclimate studies. McPhillips' research bridges computational methods and scientific practice, aiming to enhance trustworthiness in research outcomes through systematic provenance tracking and workflow automation.

Matthew IvoryView profile

Research Fellow

• Cybersecurity
• Cognitive Psychology
• Software Development
• +3 more

Matthew Ivory is a PhD student and Senior Research Associate at the Department of Psychology, Lancaster University, working with Dr. Sophie Nightingale on the 'Protecting Ordinary People from Deepfakes' project. His research bridges cybersecurity, cognitive psychology, and software development, focusing on how human decision-making processes influence secure coding practices and deepfake perception. He collaborates with Professor John Towse, Professor Mark Levine, Dr. Miriam Sturdee, and Professor Bashar Nuseibeh. Matthew’s recent publications investigate computational reproducibility in behavioral research, the integration of soft skills in computer science education, and cognitive psychology applications in software security. His work often applies dual processing theory and behavioral frameworks to analyze security vulnerabilities and developer perceptions. Key research areas include cognitive biases in secure coding, reproducibility standards, and soft skills training in computing curricula. He is affiliated with Security Lancaster, a research group addressing software security challenges. Contact: matthew.ivory@lancaster.ac.uk

Hazeline AsuncionView profile

Associate Professor

• Software Traceability
• Data Provenance
• Software Engineering
• +5 more

Hazeline Asuncion is an Associate Professor at the University of Washington, Bothell in the Computing and Software Systems program. Her research focuses on software traceability , data provenance , and license analysis , with applications in software engineering, eScience, and computational neuroscience. Education : PhD in Computer Science from University of California, Irvine Research : Explores cross-artifact information management using techniques like topic modeling and provenance tracking , funded by NSF grants including a NSF CAREER award Publications : 15+ peer-reviewed works in venues like ICSE , eScience , and Journals covering traceability, data provenance, and software licensing Awards : Best Paper Award at eKNOW 2015 Teaching : Courses on Software Engineering , Data Management , and Human-Computer Interaction

Anders KvellestadView profile

Researcher

• Particle Physics
• Theoretical Physics
• High-Energy Physics
• +5 more

Anders Kvellestad is a Researcher and Lecturer in Theoretical Physics at the University of Oslo. His work focuses on extensions of the Standard Model, particularly supersymmetry and theories with additional Higgs bosons, and their experimental testing at the Large Hadron Collider (LHC). He leads the development of the GAMBIT software tool, which facilitates global statistical analyses of beyond-Standard-Model theories. His research also encompasses statistical methods (Bayesian and frequentist) and machine learning applications in particle physics, including fast simulations and data analysis. Kvellestad has held postdoctoral and research roles at institutions such as Imperial College London and the Nordic Institute for Theoretical Physics (Nordita). He contributes to both academic teaching and public outreach, delivering lectures on computational physics and Bayesian statistics, and engaging in science communication through podcasts and media. His academic journey includes a PhD from the University of Oslo (2015), an M.Sc. from the University of Bergen (2011), and a B.Sc. from the University of Bergen and University of Copenhagen (2009). He is a core developer of the GAMBIT and xsec software projects, which are open-source tools used globally for particle physics research. His work bridges theoretical and computational physics, emphasizing interdisciplinary collaboration across particle theory, statistics, and scientific computing. Key projects include optimizing BSM physics searches at the LHC, interpreting LHC results for new physics, and leveraging machine learning for fast cross-section evaluations. His contributions to software development, such as the xsec tool for rapid LHC cross-section analysis, highlight his focus on computational efficiency and reproducibility in high-energy physics research.

Gabriele SartiView profile

Research Fellow

• Natural Language Processing
• Interpretable AI
• Machine Translation
• +2 more

Gabriele Sarti is a PhD Student and Research Fellow in Computational Linguistics at the University of Groningen, associated with the Center for Language and Cognition in the Faculty of Arts. His research focuses on interpretability for natural language processing systems, particularly in generative models and machine translation. Research interests center on developing methods to understand and control large language models, with emphasis on: interpretability techniques, multilingual systems evaluation, controlled text generation, and human-AI collaboration frameworks. Recent work explores model steering mechanisms, attribution methods for trustworthy AI, and tools for democratizing interpretability research. Publications demonstrate consistent focus on operationalizing interpretability advances through open-source software development. Work spans multilingual machine translation evaluation, controlled generation techniques, and foundational research on transformer model mechanics.