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Paul N. GormanView profile

Professor

• Medical Informatics
• Health Systems Science
• Naturalistic Decision Making
• +3 more

Dr. Paul N. Gorman serves as Thread Director for Health Systems Sciences and Assistant Dean for Rural Medical Education at Oregon Health & Science University's School of Medicine. With over 30 years of clinical experience spanning rural primary care internal medicine, geriatrics, and urban hospitalist practice, he integrates systems thinking into medical education through the OHSU YourMD curriculum and expands rural physician workforce development via the Campus for Rural Health. His educational foundation includes: B.S. from University of Illinois at Chicago Circle (1975) M.D. from Rush Medical College (1980) Internal Medicine Internship & Residency at Rush Presbyterian-St. Luke's Medical Center Chief Residency (1983-84) and General Internal Medicine Fellowship at Portland VA Medical Center (1990-92) Dr. Gorman's research examines clinician information seeking behaviors, distributed cognition in clinical settings, and naturalistic decision making with emphasis on human factors in healthcare systems. His work bridges medical informatics and practical clinical workflows to enhance healthcare delivery through systems-based practice education. Current scholarship focuses on health systems science curriculum development, clinical decision support optimization, and rural healthcare access. His publication trajectory demonstrates evolving expertise from foundational human factors research toward healthcare system transformation, with recent work emphasizing cost-conscious care integration, clinical workflow modeling, and sociotechnical system design. Key themes include optimizing decision support while mitigating alert fatigue, understanding team process variations, and developing context-aware educational interventions. Honors include: Fellow of the American College of Physicians Fellow of the American College of Medical Informatics Dr. Gorman actively advocates for universal healthcare access through Physicians for a National Health Program and Health Care for All Oregon. His leadership in rural medical education expansion addresses critical physician workforce shortages while maintaining board certification in Internal Medicine. Current initiatives focus on implementing health systems science threads across medical education continuums and strengthening academic-community partnerships for equitable care delivery.

• Speech analysis
• Experimental phonetics
• Foreign accent
• +3 more

Lya Meister is a Research Fellow at Tallinn University of Technology's School of Information Technologies, Department of Software Science. She has held this position since 2017, continuing her career from the Institute of Cybernetics at TUT where she served as a Research Fellow (2009–2016) and Extraordinary Research Fellow (2005–2008). Her academic background includes a Doctor's Degree in 2011 (University of Tartu) and a Research Master's Degree in 2005 (Tallinn University). Education: PhD: University of Tartu, 2005–2011 Master's: Tallinn Pedagogical University, Estonian Philology, 2002–2005 Bachelor's: Tallinn Pedagogical University, Russian Philology, 1975–1979 Her research focuses on experimental phonetics, speech corpora analysis, and the acoustic characteristics of speech production. Notable projects include the Improving the intelligibility of sung text (2022–2026) and contributions to the Estonian Elderly Speech Corpus. She has organized major conferences like the Phonetics Symposium 2024 and served on international committees for speech technology initiatives. Her awards include the 2023 School of Information Technologies Paper of the Year and the 2012 Best Paper Award from the Institute of Cybernetics. Her work spans over 68 publications, with recent emphasis on plosive closure duration in singing, vowel-consonant intensity ratios, and developmental phonetic changes in adolescents.

André SchlichtingView profile

Professor

• Applied Mathematics
• Partial Differential Equations
• Stochastic Analysis
• +5 more

André Schlichting is a Full Professor of Applied Analysis at the University of Ulm, leading the Institute of Applied Analysis since October 2024. Previously, he served as an Associate Professor for Applied Mathematics at the University of Münster (2020–2024) and held postdoctoral and visiting professor roles at institutions including the University of Bonn and RWTH Aachen. His research focuses on the qualitative analysis of complex systems, combining methods from partial differential equations, stochastic analysis, and numerical analysis. Key interests include metastability in statistical mechanics, phase transitions, variational methods, and the longtime behavior of dissipative systems. Education: Habilitation (Facultas Docendi), University of Bonn, 2020 (Phase Transitions in Interacting Systems) PhD in Mathematics, Universität Leipzig, 2012 Diploma in Mathematics, TU Freiberg, 2008 Studies in Mathematics at University of Pavia (Italy) and TU Freiberg Research Interests: His work bridges applied mathematics and theoretical physics, addressing problems in statistical mechanics, stochastic processes, and machine learning. Key areas include: Metastability in molecular and statistical mechanics systems Coarsening and nucleation phenomena in interacting particle systems Variational methods for dissipative evolution equations Entropy methods and functional inequalities (spectral gap, log-Sobolev) Gradient flows and their limits in continuum and discrete settings Discrete and nonlocal dynamics on graphs and data sets Recent Article Trends: His recent work emphasizes gradient flow structures, discretization schemes for PDEs, and metastability in stochastic systems. Notable contributions include analysis of the exchange-driven growth model, McKean-Vlasov equations on manifolds, and covariance-modulated optimal transport. These studies highlight interdisciplinary approaches to bridging discrete and continuum dynamics. Labs/Teams: He leads the Applied Analysis group at Ulm University, focusing on collaborative research in PDEs, stochastic processes, and numerical methods. Active participation in initiatives like the Hausdorff Trimester Program (Bonn) underscores his role in fostering interdisciplinary research networks.

Christos DiouView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Healthcare Applications
• +3 more

Christos Diou is an Associate Professor of Artificial Intelligence and Machine Learning at the Department of Informatics and Telematics, Harokopio University of Athens, Greece. His academic career spans over 15 years of participation in national and international research projects, with a focus on machine learning algorithms, domain generalization, causal inference, and bias mitigation. He earned a BSc and Ph.D. in Electrical and Computer Engineering from Aristotle University of Thessaloniki. His research emphasizes the application of machine learning to healthcare, addressing challenges such as visual bias mitigation, causal effect estimation from observational data, and fairness-aware representation learning. Notable projects include REBECCA and RELEVIUM , both EU-funded, and MELIORA , targeting lifestyle interventions for breast cancer risk reduction. He has published extensively in top-tier venues like IEEE TPAMI, CVPR, and ICCV. Christos is a leading voice in AI ethics and healthcare innovation, with over 150 publications and best paper awards at IEEE Big Data Service 2023 and AIAI 2022. His work includes developing platforms like Effector for feature effects and Beam for behavior studies. He collaborates with institutions such as Karolinska Institutet and CERTH/ITI, and his students include PhD candidates Ioannis Sarridis and Aristotelis Ballas.

Kaushik MallikView profile

Assistant Professor

• Controller Synthesis for Dynamical Systems
• Combinatorial Games on Graphs
• Algorithmic Fairness in AI
• +4 more

Kaushik Mallik is an Assistant Professor at the IMDEA Software Institute in Madrid, Spain. His research focuses on formal methods for ensuring safety and trustworthiness of AI systems, combining game theory, control theory, and machine learning. He holds a PhD in Computer Science from the Max Planck Institute for Software Systems (2022), an M.Tech in Systems and Control from IIT Roorkee (2015), and a B.Tech in Electrical Engineering from Meghnad Saha Institute of Technology (2012). His work addresses challenges in controller synthesis for cyber-physical systems, bidding games, and algorithmic fairness monitoring. Education: PhD in Computer Science, Max Planck Institute for Software Systems, Germany (2022) M.Tech in Systems and Control, IIT Roorkee (2015) B.Tech in Electrical Engineering, Meghnad Saha Institute of Technology (2012) Research Interests: Formal synthesis of safe AI systems Algorithmic fairness in decision-making algorithms Bidding games and stochastic control Symbolic control for cyber-physical systems His current projects include runtime fairness monitoring tools and theoretical advancements in auction-based scheduling algorithms. Key Achievements: Winner of the 2023 ETAPS Doctoral Dissertation Award Recipient of the DAAD IIT Master-Sandwich Programme Scholarship Lead developer of tools like Mascot-SDS and BOCoSy for symbolic control Ongoing research explores resilient abstraction-based controllers and fairness watchdog systems for AI applications.

Philip WotschackView profile

Researcher

• Social Inequality
• Labor Markets
• Digitalization
• +3 more

Dr. Philip Wotschack is Head of the Research Group 'Working with Artificial Intelligence' at the Weizenbaum-Institut for the Networked Society. He has held various academic roles including Researcher at WZB Berlin Social Science Center and the University of Groningen. His research focuses on social inequality, labor markets, digitalization, and algorithmic management. He earned his Ph.D. in Sociology from the University of Groningen (2009). Key research areas include: Impact of AI-driven algorithmic management in workplaces Continuing training practices for low-skilled workers Workplace digitalization and automation Gender and social inequality in training access Recent projects include the EU-funded INCODING project analyzing collective bargaining's role in algorithmic management governance, and experimental studies on human-technology interaction in automated systems. His work integrates institutional theory perspectives with empirical analyses of organizational practices. He has led projects funded by the German Research Foundation and European Commission. Publications span topics like labor market segmentation, workplace automation ethics, and skill development policies. He has authored/co-authored books on work-life balance and labor market institutions.

Josep CasanovasView profile

Professor

• Modelling and Simulation
• Internet and Information Systems
• Urban Mobility
• +4 more

Josep Casanovas is a Full Professor at the Statistics and Operations Research Department of the Technical University of Catalonia (UPC), affiliated with the Barcelona School of Informatics. He previously served as head of inLab FIB (2012-2020) and as dean (1998-2004) and vice-rector (2006-2011) of UPC, leading strategic initiatives in university governance and ICT policies. His research focuses on Modelling and Simulation , Internet and Information Systems , and Urban Mobility . He has led projects for the European Union, including C-ROADS Spain, REMEDiAL, and ECHORD++, addressing intelligent transport, software automation, and robotic innovation. Recent publications highlight his work on agent-based simulation for urban health, deep learning applications in traffic and energy savings, and wildfire management tools . He co-directs LogiSim and coordinates the Severo Ochoa Research Excellence Program at the Barcelona Supercomputing Center (BSC-CNS).

Luke Zoltan KelleyView profile

Adjunct Assistant Professor

• Multi-messenger Astrophysics
• Gravitational Waves
• Massive Black Hole Binaries
• +5 more

Luke Zoltan Kelley is an Adjunct Assistant Professor of Astronomy at the University of California, Berkeley . He holds a PhD from Harvard University (2018) and previously served as a Lindheimer Prize Postdoctoral Fellow (Northwestern University) and Cottrell Fellow (CIERA). His research focuses on theoretical astrophysics at the intersection of gravitational waves and cosmological environments, particularly using pulsar timing arrays (PTAs) to study massive black hole binaries (MBHBs). He chairs the Astrophysics Working Group in the NANOGrav Pulsar Timing Array collaboration. Research Interests: Multi-messenger astrophysics, low-frequency gravitational waves from MBHBs, stellar tidal disruption events, active galactic nuclei dynamics, and LISA mission science. He also develops software tools like kalepy and holodeck for analyzing gravitational wave signals and simulating MBHB populations. Key Achievements: Lead author on 13 papers including breakthroughs in Pulsar Timing Array analysis Advises 15 student-led studies on MBHB dynamics and gravitational wave detection Recipient of prestigious postdoctoral fellowships (Cottrell, Lindheimer) Publications: Over 87 papers with 8,926 total citations. Recent work includes detecting gravitational wave signals in NANOGrav data and modeling MBHB evolution in cosmological simulations. Labs/Teams: Leads the NANOGrav Astrophysics Working Group and collaborates with teams at NASA, CERN, and international observatories.

David Huckleberry GutmanView profile

Assistant Professor

• Continuous Optimization Theory
• Large-Scale Optimization
• Optimization over Manifolds
• +3 more

David Huckleberry Gutman is an Assistant Professor in the Department of Industrial & Systems Engineering (ISEN) at Texas A&M University. He holds a Ph.D. in Mathematical Sciences from Carnegie Mellon University (2019), an M.S. in Mathematics from Tulane University (2010), and a B.S.B.A. in Finance & Accounting from Georgetown University (2008). Education: Ph.D. in Mathematical Sciences, Carnegie Mellon University, 2019 M.S. in Mathematics, Tulane University, 2010 B.S.B.A. in Finance & Accounting, Georgetown University, 2008 His research focuses on continuous optimization theory , emphasizing large-scale optimization methods and optimization over manifolds . He has developed innovative algorithms such as tangent subspace descent for Riemannian manifolds and advanced proximal gradient methods with convergence guarantees. Key awards include the 2023 NSF CAREER Award and the 2022 INFORMS Optimization Society Young Researcher Prize (jointly with Nam Ho-Nguyen). His work bridges theoretical optimization with practical machine learning and engineering applications. Advising: Currently mentoring PhD students Allan Gurwicz, George Lobo, and Leandro Maia. Previously held positions at Texas Tech University (2019-2023) and conducted research at Bosch Research and IBM. Labs/Teams: Leads an active research group at Texas A&M exploring optimization algorithms for modern computational challenges, though no open PhD spots are available at this time.

Abdulkadir CelikkanatView profile

Assistant Professor

• Computer Science
• Machine Learning
• Data Science
• +3 more

Abdulkadir Celikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design and the Data, Knowledge and Web Engineering research group. His research focuses on graph representation learning, network analysis, bioinformatics, and machine learning applications in dynamic systems. Key projects include the Villum Foundation-funded 'DarkScience: Illuminating microbial dark matter through data science,' which explores metagenomic binning and microbial ecology using advanced data science techniques. He has been recognized with the Best Paper Award (2023) for contributions to temporal graph analysis and modeling. His work spans continuous-time dynamic node representations, scalable genome profiling, and polarization detection in social networks. Celikkanat collaborates widely, contributing to interdisciplinary research at the intersection of computer science, biology, and environmental science. Recent publications highlight innovations in graph embeddings, citation network modeling, and hybrid membership latent distance models. His research addresses challenges in low-dimensional graph representations, efficient kernel methods, and integrating biological networks for protein analysis.

• Geometric Measure Theory
• Optimal Transport
• Partial Differential Equations (PDEs)
• +3 more

Paolo Bonicatto is an Assistant Professor in the Department of Mathematics at the University of Trento . His teaching responsibilities include courses such as Analisi matematica 2 for Industrial Engineering students, focusing on differential and integral calculus for functions of several variables and applications to physics. He also contributes to advanced programs in Mathematics at the Master's level, including Geometric Measure Theory and Optimal Transport , emphasizing problem-solving and mathematical modeling skills. His research interests span Geometric Measure Theory , Optimal Transport , and Partial Differential Equations (PDEs) , with a focus on topics like current transport, regularity theory for transport equations, and applications to material science. Recent work involves the analysis of advection-diffusion equations, homogenization of elasto-plastic evolutions, and the structure of divergence-free measures in low dimensions. His publications highlight contributions to the well-posedness of transport equations, renormalization techniques for vector fields, and the study of BMO-type norms and Poincaré inequalities. Notably, his work addresses foundational questions in calculus of variations and functional analysis, such as representations of total variation and decomposition results for vector fields. While no scientific awards or grants are explicitly listed, his academic output reflects active engagement with cutting-edge problems in mathematical analysis. He collaborates on interdisciplinary projects, bridging pure mathematics and applications in physics and engineering. No lab affiliations or student advisements are documented in the provided materials.

• Artificial Intelligence
• Neural Networks
• Quantization
• +3 more

Hang Li is a Researcher in the Department of Molecular Biophysics and Biochemistry at Yale University’s Yale School of Medicine. Their work focuses on advancing neural network architectures, quantization techniques, and spiking neural networks (SNNs). They are affiliated with the Molecular Biophysics and Biochemistry department and contribute to interdisciplinary research in artificial intelligence and computational neuroscience. Research interests include optimizing neural networks for efficiency through quantization, exploring spiking neural networks for low-power computing, and developing methods like hybrid SNN designs, post-training calibration, and neuromorphic architectures. Their recent work addresses challenges in extreme low-bit quantization, data augmentation for object detection, and temporal coding in SNNs. Publications highlight innovations in quantization methods (e.g., TesseraQ, GenQ), spiking transformer architectures, and workload-balanced pruning strategies. While no awards are explicitly listed, their contributions to model efficiency and neuromorphic computing are notable in the field. Hang Li collaborates on projects involving neuromorphic hardware, system inconsistency benchmarking (SysNoise), and data-driven spatio-temporal analysis. Their research bridges theoretical advancements and practical applications in AI and biomedical informatics.

• AI and Robotics
• Human-Robot Interaction (HRI)
• Autonomous Agents
• +7 more

Dr. Abolfazl Zaraki is a Senior Lecturer in AI and Robotics at the University of Hertfordshire's Department of Computer Science, part of the School of Physics, Engineering & Computer Science. He leads the Robotics Research Group and previously held roles at Cardiff University's School of Engineering and the IROHMS Research Centre. His academic journey includes a Master's in Mechatronics from University Technology Malaysia (2010) and a PhD in Automatic Robotic and Bioengineering from the University of Pisa (2014). He has held Research Fellow positions in Italy and the UK until 2019. Dr. Zaraki's research focuses on AI-driven autonomous systems, social robotics, and assistive technologies. Key projects include the EASEL, BabyRobot, and JAMES EU initiatives, alongside the Innovate UK-funded InSight project. His work emphasizes Human-Robot Interaction (HRI), trusted autonomy, and applications in healthcare and industrial contexts. Notable contributions include the development of the Kaspar humanoid robot for autism therapy and advancements in reinforcement learning for robotic control. His recent publications (2021–2025) explore agentic AI, memory-driven systems, and personalized LLMs for HRI, alongside technical advancements in robotic control, communication systems, and bio-inspired robotics. His research bridges theoretical AI innovation with practical applications in healthcare, education, and industrial automation. Zaraki has collaborated internationally across institutions and industry partners, contributing to 35+ research outputs. His work aligns with global trends in ethical AI, explainable systems, and human-centric robotics design.

Ashok AgrawalaView profile

Professor

• Computer Systems
• Distributed Algorithms
• Real-Time Systems
• +10 more

Ashok Agrawala is a Professor in the Department of Computer Science at the University of Maryland, with joint affiliations in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and the Department of Electrical Engineering. He directs the Maryland Information and Network Dynamics (MIND) Lab, a leading research group focused on real-time systems, network performance, and wireless localization technologies. Education: B.E. and M.E. in Electrical Engineering, Indian Institute of Science, Bangalore M.A. and Ph.D. in Applied Mathematics, Harvard University His research interests span computer systems, distributed algorithms, real-time operating systems, network performance modeling, traffic shaping, and indoor localization . He is renowned for pioneering the Ricart-Agrawala distributed mutual exclusion algorithm, developing the Maruti real-time operating system, and creating the Horus and Locus indoor positioning systems. His work has led to significant advances in temporal guarantees, jitter-free data delivery, and calibration-free localization. His recent publications show sustained innovation in wireless localization, context-aware computing, breath analytics, and energy-efficient IoT systems , reflecting an evolving research trajectory toward smart health and urban computing. He has authored over 200 papers and seven books, with consistent publication activity into 2024. Scientific Recognition: Fellow, IEEE Member, ACM Member, AAAS Member, Sigma Xi Agrawala has advised over thirty Ph.D. students, many of whom now lead research in academia and industry. His research has been funded by DARPA, NSF, and AFOSR, with corporate partnerships from IBM, Novell, and AT&T. He previously worked at Honeywell Information Systems, where he designed an Optical Character Reader. He has also served as a consultant to the UNDP and the Government of India, and as a technical expert in legal cases. He leads the MIND Lab, which focuses on mobile computing, wireless networks, and context-aware systems , continuing to innovate in real-time and networked systems.

Stephen J. RiedererView profile

Professor

• Magnetic Resonance Imaging (MRI)
• Prostate MRI
• Super-resolution T2SE imaging
• +4 more

Stephen J. Riederer, Ph.D., is a Professor of Radiology at Mayo Clinic, holding dual appointments in the Department of Radiology and the Department of Physiology & Biomedical Engineering. He leads the Magnetic Resonance Laboratory, focusing on advancing MRI physics and clinical applications. His research emphasizes high-resolution prostate MRI, super-resolution T2SE imaging, and contrast-enhanced magnetic resonance angiography (CE-MRA). Dr. Riederer has developed fast-scanning techniques, real-time signal processing, and parallel acquisition methods, many of which are now industry standards. Education: B.A. in Mathematics, University of Wisconsin-Madison SM in Nuclear Engineering, MIT Ph.D. in Medical Physics, University of Wisconsin-Madison Research Interests: Dr. Riederer’s work bridges MRI physics and clinical implementation. Key areas include: Prostate cancer imaging via high-resolution T2SE and DCE-MRI Super-resolution MRI for improved anatomic detail Real-time MRI scanning and interactive triggering Parallel acquisition techniques and coil array optimization Publications & Impact: Over 300 peer-reviewed articles highlight his contributions to MRI innovation. Recent work focuses on AI-driven prostate MRI quality assessment and coil array improvements. His methods are widely adopted in commercial MRI systems. Awards & Leadership: Gold Medal (International Society for Magnetic Resonance in Medicine, 2002) President, Society of Magnetic Resonance Angiography (2008) George M. Eisenberg Professor I, Mayo Clinic (2024) Advising & Grants: Mentor to over two dozen doctoral students. Active in training via courses at the Mayo Clinic Graduate School. Leads grants on prostate MRI super-resolution and spatiotemporal imaging, funded by NIH and the U.S. Army. Labs & Affiliations: Part of the Center for Advanced Imaging Research, collaborating across radiology, biomedical engineering, and oncology. Facilities include state-of-the-art MRI scanners and imaging laboratories.