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Daniel ShanahanView profile

Associate Professor

• Music Cognition
• Corpus Studies
• Music and Emotion
• +3 more

Daniel Shanahan is an Associate Professor at the Bienen School of Music , Northwestern University , where he coordinates the Music Theory and Cognition program and directs the Music Cognition Lab. He holds a PhD from the University of Dublin, Trinity College. His research integrates music-analytic, computational, and experimental methodologies to investigate cognitive and communicative constraints in music. Key themes include corpus studies, music and emotion, oral transmission of musical traditions, computational analysis of jazz and folk music , and the application of machine learning and generative AI to musicology. Shanahan has served as co-editor of Empirical Musicology Review (2016–2022) and sits on editorial boards of multiple journals. His work appears in Music Perception , The Journal of New Music Research , Musicae Scientiae , and Frontiers in Psychology , with chapters in Routledge Companions and the Oxford Handbook of Music and Corpus Studies . Recent projects include an NSF grant studying sound's impact on learning (2023) and co-editing the Oxford Handbook . He currently serves as treasurer for the Society for Music Perception and Cognition . Mentorship Award (Society for Music Perception and Cognition, 2024) Outstanding Multi-Author Collection Award (Society for Music Theory, 2023) Distinguished Scholar Award (Ohio State University) Rising Faculty Research & Undergraduate Teaching Awards (Louisiana State University) His publications demonstrate expertise in music cognition, computational analysis, emotional expression, and oral/embodied transmission , with trends showing interdisciplinary applications of AI and cross-cultural studies. Shanahan's mentorship emphasizes student well-being, empathetic guidance, and fostering academic potential . He has hosted global open lab meetings during the pandemic and cultivated mentees who now contribute to service roles in academic committees.

Regina RaganView profile

Professor

• Nanomaterials
• Self-Assembly
• Surface-Enhanced Raman Scattering (SERS)
• +4 more

Regina Ragan is a Professor in the Department of Materials Science and Engineering at the Samueli School of Engineering, University of California, Irvine. Her research focuses on nanomaterials, self-assembly, and surface-enhanced Raman scattering (SERS) for applications in optical communication, energy systems, and biomedical diagnostics. Education: Ph.D. in Applied Physics, California Institute of Technology, 2002 M.S. in Applied Physics, California Institute of Technology, 1998 B.S. in Materials Science and Engineering, University of California, Los Angeles, 1996 Her work integrates scanning probe microscopy and first-principles calculations to study thermodynamic driving forces in self-assembly and structure-function relationships. Recent publications highlight applications in antimicrobial susceptibility testing, environmental monitoring, and plasmonic device fabrication. The Ragan group develops low-cost diagnostic tools using SERS for telemedicine applications. Current lab members include graduate students and postdoctoral researchers working on nanoscale systems from atomic to mesoscale. Scientific Awards: NSF CAREER Award for fundamental studies of biological/inorganic interfaces Research Trends: Recent articles show a focus on SERS-based diagnostics, plasmonic nanoantennas, machine learning-assisted spectral analysis, and scalable synthesis of 3D graphene architectures. Subfields span quantum plasmonics, stress-activated materials, and biofilm monitoring.

• Distributed Systems
• Cloud Computing
• Edge Computing
• +7 more

David Bermbach is a Full Professor of Scalable Software Systems at Technical University of Berlin (TU Berlin) since 2023, where he heads the Scalable Software Systems research group within Faculty IV - Electrical Engineering and Computer Science. He is also co-affiliated with the Einstein Center Digital Future (ECDF). Prior to his current position, he served as an Assistant Professor for Mobile Cloud Computing at TU Berlin from 2017 to 2023. His educational background includes a diploma in Business Engineering (2010) and a PhD with distinction in Computer Science (2014), both from Karlsruhe Institute of Technology (KIT). Prof. Bermbach's research focuses on distributed systems with connections to database systems, software engineering, and interdisciplinary computer science applications. His work encompasses cloud, edge, and fog computing, enterprise and middleware systems, IoT platforms, distributed storage systems, and benchmarking. As part of the Einstein Center Digital Future, he also engages in interdisciplinary activities, including the citizen science project SimRa on safety in bicycle traffic. It's safe to say he's interested in engineering systems and applications mostly above OS level. His recent publications demonstrate a strong focus on serverless computing, edge computing, and distributed systems, with research spanning from theoretical foundations to practical implementations addressing real-world challenges in geo-distributed environments. Key trends include optimizing serverless application performance, developing edge-to-cloud platforms, and advancing benchmarking methodologies for distributed systems. Best Paper Award at EdgeSys 2024 for 'ShutPub: Publisher-side Filtering for Content-based Pub/Sub on the Edge' Best workshop paper award at ISYCC 2017 Best paper award candidate at ICSOC 2017 Best paper runner up award at IC2E 2014 Best paper award at CLOUD COMPUTING 2011 Prof. Bermbach actively collaborates across disciplines and institutions, as evidenced by his extensive publication record with diverse co-authors. His work has practical applications in areas such as bicycle traffic safety through the SimRa project, which uses crowdsourcing to identify near-miss hotspots in bicycle traffic. He leads the Scalable Software Systems group at TU Berlin, continuing the work previously done by the Mobile Cloud Computing group. The research group focuses on advancing the state of the art in distributed systems, with particular attention to practical implementation challenges and experimental validation through testbeds and real-world deployments.

Shruti PhadkeView profile

Assistant Professor

• Computational social science
• Social computing
• Natural language processing
• +5 more

Shruti Phadke is an Assistant Professor in the Information Science Department at Drexel University's College of Computing & Informatics. Her work focuses on social knowledge construction, participation, and disengagement in online communities dealing with problematic information, such as conspiracy theories and extremist groups. She employs both quantitative and qualitative methods, including statistics, causal machine learning, and natural language processing. Education: PhD in Information Science from the University of Washington; MS in Computer Engineering from Virginia Tech. Her research spans computational social science, social computing, and human-computer interaction, with a particular emphasis on understanding how individuals navigate and exit harmful online discussions. She has over a year of experience as a trust and safety research expert at a social media company. Scientific Awards: Shruti has authored award-winning papers at the CSCW and ICWSM conferences, which recognize significant contributions to collaborative computing and web science. She is actively seeking PhD students for Fall 2025 and undergraduates/Masters students for Fall 2024 to collaborate on these research themes.

• Power Grid Planning
• Electricity Market Modeling
• Renewable Energy Integration
• +4 more

Anh Tuan Le is an Associate Professor at the Department of Electrical Engineering, Chalmers University of Technology. He holds a PhD in Power Systems from Chalmers (2004) and a Master's in Energy Economics from the Asian Institute of Technology (1997). Specializes in power grid planning, electricity market modeling, and renewable energy integration Active in electric vehicle-grid interaction and battery storage systems Develops voltage stability solutions and decentralized control strategies His recent research focuses on: Flexibility markets for congestion management Machine learning applications in load forecasting Real-time security margin control using AI Key projects include: DigiRES (2024-2027): Digital integration of multi-energy flexibility POTENT-X (2024-2027): Port energy transition hubs FLEXIGRID (2019-2023): Distribution grid flexibility solutions

Urs F. GreberView profile

Professor

• Virology
• Molecular Cell Biology
• Virus-Host Interactions
• +8 more

Prof. Dr. Urs F. Greber is an Ordinary Professor of Molecular Cell Biology at the Department of Molecular Life Sciences, Faculty of Mathematics and Natural Sciences, University of Zurich. His research focuses on understanding how viruses interact with host cells, particularly adenoviruses and rhinoviruses that cause human respiratory diseases. He leads the Greber Lab, which investigates viral entry mechanisms, replication processes, and the cellular responses to infection. Greber's research interests span virology, molecular cell biology, and infection mechanisms. His lab explores how viruses take control over membrane and lipid functions, cytoplasmic transport processes, and cellular metabolism to support their gene expression and progeny formation. They employ system-wide profiling, molecular cell biology approaches, light microscopy, and machine learning for image analysis to map the cell state underlying viral infections of cultured and primary human cells, including lung organoids and iPSC-derived macrophages. A key focus is understanding cell-to-cell variability in infection phenotypes and the mode-of-action of antiviral compounds. The Greber Lab has published extensively on adenovirus biology, including viral entry, uncoating, nuclear import, and assembly mechanisms. Their recent work has identified broad-spectrum antiviral compounds, elucidated alternative virus entry pathways, and developed innovative imaging and AI-based approaches for quantifying virus infectivity. Their research contributes to understanding how viruses break down host defense barriers and has implications for antiviral therapy development. Greber has supervised numerous PhD and Master's students including Cornelia Bircher, Alessandro Savi, Franziska Tomas, Alfonso Gomez-Gonzalez, Anthony Petkidis, and Dominik Olszewski. His lab has received funding from the Swiss National Science Foundation, including a grant for coronavirus research during the pandemic. The lab actively collaborates with other research groups at University of Zurich, ETH Zurich, and international institutions. Current projects include exploring how viral DNA interactions contribute to infection outcome variability, investigating adenovirus egress mechanisms, and developing high-throughput screening methods for antiviral compounds.

Brock C ChristensenView profile

Professor

• Epigenetics
• Cancer Biology
• Genomics
• +7 more

Brock C Christensen is a Professor at the Geisel School of Medicine , Dartmouth, affiliated with the Departments of Epidemiology , Community and Family Medicine , and Molecular and Systems Biology . His research integrates molecular biology, genomics, and bioinformatics with epidemiology to investigate epigenetic mechanisms in human health and disease. Harvard University, PhD (2008) University of Wisconsin - Madison, BS (2002) Dr. Christensen's work focuses on epigenetic susceptibility traits , environmental interactions, and translational applications in cancer diagnostics and treatment. His recent publications explore DNA methylation patterns in triple-negative breast cancer , head and neck cancer , and pediatric CNS tumors . He serves as course director for PEMM103: Introductory Applied Biostatistics with R and leads the Christensen Lab , which specializes in epigenomic profiling and computational biology.

Leila NotashView profile

Professor

• Robotics
• Mechatronics
• Kinematics
• +3 more

Dr. Leila Notash is a Professor in the Department of Mechanical and Materials Engineering at Queen's University, where she has been a faculty member since 1997. She is a Fellow of Engineers Canada (FEC) and a licensed Professional Engineer with Professional Engineers Ontario (PEO), with significant contributions to engineering education and professional service. Her educational background includes: Bachelor of Science in Mechanical Engineering, Middle East Technical University (Ankara, Turkey) - High Honor Student (2nd out of 166) Master of Applied Science in Mechanical Engineering, University of Toronto PhD in Mechanical Engineering, University of Victoria Dr. Notash's research centers on robotics and mechatronics, with specialized expertise in cable-driven parallel manipulators. Her work integrates kinematics, fault-tolerant design, and neural network applications to address challenges in robot calibration, workspace analysis, and motion control under real-world constraints like cable mass and elasticity. She investigates both theoretical frameworks and practical implementations for industrial and specialized robotic systems. Analysis of her recent publications (2020-2024) reveals a clear trajectory toward intelligent control systems, where machine learning techniques—particularly neural networks and reinforcement learning—are increasingly applied to solve complex problems in cable-driven robotics. This includes motion control optimization, path generation, and kineto-static analysis while accounting for physical limitations such as cable elasticity and mass effects, demonstrating a shift from traditional mechanical analysis to data-driven adaptive control methodologies. Her scientific recognition includes: Fellow of Engineers Canada (FEC) University of Toronto Open Fellowship University of Toronto International Differential Fee Waiver Charles S. Humphrey Graduate Student Award NSERC Doctoral Prize Nominee (1996) Dr. Notash has mentored 161 undergraduate students as Faculty Advisor for the Mechanical '06 cohort and pioneered international educational initiatives like the International Undergraduate Student Design project (IVDS), connecting Queen's University with Middle East Technical University and Union College. Her service extends to editorial leadership for Mechanism and Machine Theory and ASME journals, and governance roles including Faculty Senator at Queen's University (2009-2025) and PEO Council Councillor-at-Large (2019-2025). She has established collaborative research networks through initiatives like the Reading Week shop course 'Design Basics 1.0' and sustained leadership in the Canadian Committee for the Promotion of Mechanism and Machine Science (CCToMM) and the International Federation for the Promotion of Mechanism and Machine Science (IFToMM), where she chaired the Permanent Commission on Communications (2006-2011).

• Condensed Matter Physics
• Quantum Information Science
• Quantum Computing
• +4 more

Bryan K. Clark is an Associate Professor in the Department of Physics at the University of Illinois, with his office located in the Engineering Sciences Building. He leads the Clark Research Group, which works at the intersection of quantum information, condensed matter physics, machine learning, and computing. Clark's research spans four main areas: Quantum Computing , where his group develops quantum algorithms and collaborates with experimentalists on superconducting qubit systems; Quantum Many-Body Physics , where he applies computational methods to understand emergent behavior in strongly correlated systems; Algorithms for the Quantum Many-Body Problem , where his group has pioneered techniques like Neural Network Backflow (NNBF) that represent state-of-the-art accuracy for simulating fermions and frustrated magnetism; and Machine Learning for Experiment , where his group develops techniques to analyze experimental data like scanning transmission electron microscopy images. His publication record demonstrates consistent innovation in bridging theoretical quantum information science with practical applications. Recent work focuses on neural network approaches to quantum simulation, quantum error correction/mitigation, and novel qubit architectures like the Floquet Fluxonium Molecule. His research shows a clear trajectory from fundamental questions about the quantum-classical boundary to practical implementations in quantum hardware. Clark actively mentors graduate students, with recent thesis defenses by Faisal Alam, Matt Thibodeau, Chad Germany, James Allen, and Abid. His group has secured significant funding from the NSF and IBM's IIDAI institute to support research in quantum computing and machine learning applications for nano-photonics manufacturing and error mitigation. The Clark Research Group maintains strong connections with experimental teams, particularly in superconducting qubit development and materials characterization. They've developed computational tools like QOSY (Quantum Operators from SYmmetry) that are publicly available on GitHub and have gained recognition in the quantum information community.

Christopher FerrieView profile

Associate Professor

• Quantum Estimation and Control
• Quantum Machine Learning
• Quantum Information Science
• +5 more

Christopher Ferrie is an Associate Professor at the University of Technology Sydney (UTS), where he is affiliated with the Faculty of Engineering and Information Technology and the Centre for Quantum Software and Information (QSI). His academic career spans quantum information science, machine learning, and scientific education, with a strong emphasis on both theoretical research and public engagement through science communication. Full-time faculty member at UTS Active researcher in quantum information science Director of the Centre for Quantum Software and Information Author of numerous scientific publications and popular science books Dr. Ferrie earned his PhD in Applied Mathematics from the Institute for Quantum Computing and University of Waterloo in Canada in 2012. His doctoral work focused on quantum information and laid the foundation for his subsequent research career in quantum computing and related fields. Dr. Ferrie's research interests span several interconnected domains within quantum information science. His primary focus is on quantum estimation and control, with particular emphasis on applying machine learning techniques to solve statistical problems in quantum information science. He investigates how quantum systems can be characterized, controlled, and optimized for practical applications. His work bridges theoretical quantum physics with practical implementations, exploring how quantum phenomena can be harnessed for computational advantage. Recent research directions include quantum machine learning, quantum neural networks, and quantum optimization algorithms, with applications ranging from quantum state tomography to solving combinatorial optimization problems. Analysis of Dr. Ferrie's recent publications reveals a strong focus on practical quantum computing challenges. His work consistently addresses the intersection of quantum information theory and machine learning, with particular emphasis on making quantum algorithms more efficient, interpretable, and robust against noise. A significant portion of his recent research explores variational quantum algorithms and their optimization, reflecting the current priorities in near-term quantum computing. His publications also demonstrate growing interest in quantum machine learning applications and the development of techniques for quantum error mitigation and characterization. Dr. Ferrie has secured multiple research grants supporting his work in quantum computing and related fields. His funded projects span quantum control, quantum probability, quantum machine learning, and statistical decision theory, reflecting the breadth of his research program. While specific major awards aren't detailed in the available information, his sustained funding and publication record indicate significant recognition within the quantum information science community. Dr. Ferrie is actively involved in research supervision and teaching, with current funding supporting multiple PhD students and postdoctoral researchers. His teaching responsibilities include courses on quantum computing, where he introduces students to the fundamentals of quantum information processing. His research group at the Centre for Quantum Software and Information focuses on developing novel quantum algorithms and exploring the practical implementation challenges of quantum computing. The Centre for Quantum Software and Information at UTS serves as the primary research environment for Dr. Ferrie's work. This center brings together researchers working on various aspects of quantum computing, from hardware development to algorithm design and applications. Dr. Ferrie's team within the center focuses specifically on quantum software development, quantum algorithm design, and the application of machine learning techniques to quantum information problems. The collaborative environment enables interdisciplinary research that bridges theoretical quantum physics with practical computing applications.

Lifeng ZhouView profile

Assistant Professor

• Robotics
• Algorithms
• Combinatorial Optimization
• +4 more

Lifeng Zhou is an Assistant Professor in the Department of Electrical and Computer Engineering at Drexel University, where he leads the Zhou Lab focused on advancing robustness and reliability in multi-robot systems through integration of foundation models. His research addresses real-world challenges in environmental monitoring, disaster response, and urban mobility. Education PhD, Electrical and Computer Engineering, Virginia Tech, 2020 MS, Control Science and Engineering, Shanghai Jiao Tong University, 2016 BS, Automation, Huazhong University of Science and Technology, 2013 Research Focus Dr. Zhou's work integrates robotics, algorithms, game theory and machine learning to develop secure and scalable autonomous systems. Primary research thrusts include: Resilient multi-robot coordination in adversarial environments Large language model integration for robotic decision-making Game-theoretic resource allocation strategies Risk-aware planning for autonomous vehicles Publication Trends Recent work (2024-2025) demonstrates strong focus on large language model applications in multi-robot systems, with 12/15 articles exploring LLM integration for flocking, scene segmentation, and decision-making. Additional emphasis includes adversarial robustness in target tracking (5 articles) and autonomous driving applications (4 articles). Awards and Recognition Best Paper Award, WACV 2025 LLVM-AD Workshop Professional Service Associate Editor, ICRA Conference Editorial Board Laboratory Focus The Zhou Lab develops foundational algorithms for secure and scalable multi-robot systems, with current projects spanning environmental monitoring drones, disaster response coordination, and autonomous vehicle perception systems.

Nazanin TajikView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Infrastructure Resilience
• +4 more

Nazanin Tajik is an Assistant Professor in the Department of Industrial and Systems Engineering at Mississippi State University (MSU). She holds a Ph.D. from the University of Oklahoma, an M.S. from the University of Tehran, and a B.S. from Sharif University of Technology. Her research focuses on integrating artificial intelligence, machine learning, and social science concepts to develop cross-disciplinary frameworks for infrastructure resilience, smart transportation systems, and disaster management. Her academic journey includes a Ph.D. at the University of Oklahoma where she contributed to the Risk-Based Systems Analytics Laboratory. At MSU, she established a research center bridging AI/ML tools with socio-technical systems. Key research domains include cyber-physical-social infrastructure resilience, search-and-rescue planning, and game-theoretic robotic designs. Tajik's work emphasizes optimization algorithms for network vulnerability assessment, resource allocation in disaster scenarios, and adaptive recovery strategies. She is actively involved with professional organizations such as INFORMS, ISE, and POMS, reflecting her commitment to advancing operations research and systems engineering methodologies.

• Smart Sustainable and Resilient Cities
• Energy Efficient Communities & Cities
• Crowdsensing-based Infrastructure Monitoring (CbIM)
• +4 more

Mustafa Gül is a Professor in the Department of Civil and Environmental Engineering at the University of Alberta’s Faculty of Engineering. He also serves as Director of Internationalization at the Faculty of Engineering’s Deans Office. His research focuses on smart, sustainable, and resilient cities, with an emphasis on infrastructure monitoring and energy-efficient systems. Education: PhD in Civil Engineering (University of Central Florida, 2009), MSc in Electrical Engineering (University of Central Florida, 2011), MSc in Civil Engineering (Boğaziçi University, 2004), BSc in Civil Engineering (Boğaziçi University, 2002). Dr. Gül’s research spans two primary domains: Crowdsensing-based Monitoring of Built and Natural Environments (CoMBiNE) using AI, signal processing, and data analytics for infrastructure health; and Energy-Efficient Smart Cities through solar PV integration, IoT applications, and net-zero energy homes. His work bridges structural engineering, machine learning, and sustainable urban development. Recent publications highlight advancements in smartphone-based damage detection, UAV-assisted disaster assessment, and AI-driven energy systems. His team’s work on crowdsensing bridges, solar PV optimization, and smartphone analytics has been widely recognized in journals like Structural Control and Health Monitoring and Energy and AI . Notably, his 2017 paper earned the Best Paper Award at ISARC. Students supervised include Azim, R. , Keskin, M. , and Do, N. T. , among others. Scientific Awards: Best Paper Award, 34th International Symposium on Automation and Robotics in Construction (ISARC 2017) Dr. Gül’s projects often involve interdisciplinary collaboration, leveraging sensor networks, computer vision, and optimization algorithms to address urban resilience and energy sustainability. His leadership extends to course CIV E 779 and ongoing research initiatives in Alberta, Canada.

Lynn WuView profile

Associate Professor

• Artificial Intelligence
• Enterprise Social Media
• Innovation
• +6 more

Lynn Wu is an Associate Professor at the Wharton School of the University of Pennsylvania, focusing on the intersection of artificial intelligence, analytics, and innovation. She teaches MBA, undergraduate, and doctoral courses on emerging technologies' transformative impact on business and society. Education: B.S. in Finance and Computer Science, MIT M.S. in Computer Science, MIT Ph.D. in Management Science, MIT Sloan Her research explores how AI and digital platforms reshape productivity, workforce dynamics, and innovation strategies, with applications in antitrust policy and startup ecosystems. Key trends in her publications include AI's role in post-IPO innovation, robotics' impact on managerial roles, and social media's ability to mitigate funding disparities. Scientific Awards: Kauffman Best Paper Award (2019) Sandy Slaughter Early Career Award (2019) AIS Early Career Award (2018) ISR Best Published Paper (2014) Best Paper Awards at ICIS (2009), HICSS (2013), etc. She has collaborated with IBM, Google, Meta, and advised the U.S. Department of Justice and World Bank, with her work cited by The New York Times , The Economist , and Harvard Business Review .

David Alan GoldbergView profile

Associate Professor

• Applied Probability
• Operations Research
• Stochastic Processes
• +7 more

David Alan Goldberg is an Associate Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University, part of Cornell Engineering. He joined Cornell in 2017 and previously held the A. Russel Chandler III Associate Professorship at Georgia Tech’s Industrial and Systems Engineering department. Goldberg earned his Ph.D. in Operations Research from MIT (2011) and a B.S. in Computer Science from Columbia University (2006). Education: B.S. in Computer Science, Columbia University (2006) Ph.D. in Operations Research, MIT (2011) Research Interests: Goldberg’s work focuses on applied probability and stochastic processes, including optimal stopping, inventory and queueing models, combinatorial optimization, and robust optimization. He develops algorithms and insights for complex systems, addressing challenges like the curse of dimensionality. His research spans applications in data science, operations research, and stochastic modeling. Notable contributions include distributionally robust inventory control and high-dimensional decision-making frameworks. Awards and Honors: 2025 Community-Engaged Practice and Innovation Award (David M. Einhorn Center) 2023 Sunny Yau ’72 Teaching Award (Cornell) 2019 INFORMS Applied Probability Society Best Publication Award 2015 NSF CAREER Award Multiple INFORMS Nicholson Student Paper Competitions (First Place, 2019 & 2015) Teaching and Service: Goldberg leads Cornell ORIE’s undergraduate research program, connecting students to real-world applications of OR and data science. He teaches courses in probability modeling, stochastic models, and academic skills for PhD students. He chairs the INFORMS Applied Probability Society and serves on editorial boards for Operations Research and Stochastic Systems . At Cornell, he advises the Undergraduate ORIE Society and directs undergraduate studies in ORIE. Labs & Collaborations: Goldberg’s research integrates theoretical rigor with practical applications, often involving collaborations across disciplines. His work bridges operations research, statistics, and computer science to address modern challenges in inventory systems, queueing networks, and decision-making under uncertainty.