جست‌وجوی استاد و پژوهشگر

• Endocrinology
• Diabetes
• Metabolism
• +۳ مورد دیگر

Jelena Kravarusic, MD, PhD serves as Assistant Professor in the Department of Medicine at Northwestern University's Feinberg School of Medicine, specializing within the Division of Endocrinology, Metabolism and Molecular Medicine. She maintains active clinical practice at Northwestern Memorial Hospital with board certification in Endocrinology, Diabetes & Metabolism. Her educational foundation includes: MD from University of Novi Sad (1997) PhD from University of Illinois, Chicago (2004) Internal Medicine Residency and Chief Medical Residency at Mercy Hospital, Chicago (2008) Endocrinology Fellowship at Northwestern University, McGaw Medical Center (2010) Dr. Kravarusic's research pioneers diabetes technology integration into clinical practice, with emphasis on automated insulin delivery systems and continuous glucose monitoring . Her work bridges engineering innovations with patient-centered care models, particularly investigating real-world implementation challenges for type 1 and type 2 diabetes management. Current projects focus on optimizing human-technology interaction through user-centered design principles. Publication analysis reveals a clear trajectory toward technology-driven diabetes therapeutics , with recent high-impact clinical trials (including NEJM 2025 publication) demonstrating significant contributions to evidence-based guidelines. Her collaborative approach spans multi-center studies addressing critical gaps in automated insulin delivery efficacy and discontinuation protocols. Teaching excellence defines her academic contributions: 2024 Teaching Pin from Augusta Webster Office of Medical Education 2021 Division Faculty Teaching Award from Department of Medicine 2021 Teaching Pin from Augusta Webster Office of Medical Education As clinician-educator, she actively mentors medical trainees while maintaining robust industry partnerships with AbbVie, Bayer, MannKind, and Tandem Technologies related to diabetes technology innovation. Her clinical practice at 645 N Michigan Avenue integrates research findings into patient care. Professional engagement includes active membership in the Endocrine Society (2023-present) and leadership within Northwestern's diabetes research consortium focused on next-generation therapeutic delivery systems.

Xenophon Papademetrisمشاهده پروفایل

استاد

• Medical Image Analysis
• Machine Learning
• Biomedical Software Development
• +۷ مورد دیگر

Xenophon Papademetris is a Professor of Biomedical Informatics & Data Science and Radiology & Biomedical Imaging at Yale School of Medicine. He serves as Associate Director of Biomedical Imaging Data Sciences at Yale Biomedical Imaging Institute and directs the Medical Software and Medical Artificial Intelligence Certificate Program. PhD in Electrical and Information Sciences from Yale University (2000) BA from Cambridge University (1994) Postdoctoral Fellowship at Yale University (2002) His research focuses on medical image analysis, machine learning, and biomedical software development. He has developed tools like BioImage Suite Web and contributed to standards committees at the Association for the Advancement of Medical Instrumentation (AAMI). His work spans modalities including MRI, CT, PET, and optical imaging. Recent publications emphasize neuroimaging analysis, explainable AI in healthcare, and multimodal data integration across species. He leads NIH-funded research under the BRAIN Initiative (R24 MH114805) and has authored a textbook on Medical Software published by Cambridge University Press. IEEE Senior Member Yale Brown-Coxe Postdoctoral Fellowship Harding Bliss Prize for Excellence in Engineering He directs the BioImage Suite Project, creating web-based image analysis tools using JavaScript and WebAssembly. His teaching includes both academic courses and a Coursera program on Medical Software with over 14,000 enrollments.

Giulia Giordanoمشاهده پروفایل

استاد

• Automatic control
• Complex networks
• Control systems
• +۱۲ مورد دیگر

Giulia Giordano is a Full Professor in the Department of Industrial Engineering at the University of Trento, Italy, where she leads the Dynamical Networks and Systems Biology research group. She also holds a dual appointment as Visiting Professor and Delft Technology Fellow at the Delft Center for Systems and Control, Delft University of Technology, The Netherlands. Her career includes previous positions as Assistant Professor at Delft University of Technology (2017-2019), Postdoctoral Research Fellow at Lund University, Sweden (2016-2017), and Research Fellow at the University of Udine, Italy (2016). Giulia earned her Ph.D. in Industrial and Information Engineering: Automation (Excellent) from the University of Udine with a thesis titled "Structural Analysis and Control of Dynamical Networks." She completed her M.Sc. and B.Sc. in Electrical Engineering (both Summa cum laude) at the same institution. She also undertook research visits at Caltech (2012) as a SURF Fellow and at the University of Stuttgart (2015) as a DAAD Research Scholar. Her primary research focuses on the analysis and control of dynamical networks with applications in systems biology, mathematical ecology, and mathematical epidemiology. She develops mathematical frameworks that bridge control theory, network theory, and dynamical systems to address complex problems in biological systems. Her recent work spans epidemic modeling, opinion dynamics, biochemical networks, and neurological disorders, with a particular emphasis on structural analysis of networked systems. She employs both theoretical and computational approaches to understand system behavior under uncertainty. Giulia's publications reveal a strong interdisciplinary focus, spanning from theoretical control systems to practical applications in epidemiology and biology. Her recent work shows increasing emphasis on epidemic modeling (particularly related to mpox and SARS-CoV-2), network synchronization, and the application of control theory to biological phenomena like fibromyalgia pathogenesis and opinion formation. Many of her papers appear in top-tier control journals including Automatica and IEEE Transactions on Automatic Control. 2024: Outstanding Service as Associate Editor of IEEE Control Systems Letters 2021: SIAM Activity Group on Control and Systems Theory Prize 2020: Outstanding Reviewer, Annals of Internal Medicine 2017: NAHS Best Paper Prize and EECI PhD Award 2016: Outstanding TAC Reviewer, IEEE Transactions on Automatic Control Giulia actively mentors students and postdoctoral researchers, currently supervising five postdoctoral researchers and two Ph.D. students at the University of Trento. She has advised numerous M.Sc. and B.Sc. students on topics ranging from bio-inspired modeling to optimal control of epidemic systems. Her research is supported by competitive grants including the ERC Starting Grant INSPIRE (Integrated Structural and Probabilistic Approaches for Biological and Epidemiological Systems). She serves as Associate Editor for IEEE Control Systems Letters and Automatica, and is a Senior Member of IEEE and the Control Systems Society. Giulia leads the Dynamical Networks and Systems Biology research group at the University of Trento, which maintains strong international collaborations across Europe and North America. The group's work combines theoretical advances in control theory with practical applications to pressing problems in public health and biological systems, demonstrating the power of mathematical approaches to understanding complex phenomena in the life sciences.

Mark Andrejevicمشاهده پروفایل

استاد

• Digital Media
• Surveillance
• Data Mining
• +۷ مورد دیگر

Mark Andrejevic is a Professor in the School of Media, Film, and Journalism at Monash University. He specializes in the socio-cultural implications of digital surveillance, data mining, and automated decision-making. His research spans Big Data ethics, privacy regulation, and the impact of technology on democracy. He leads major projects like the ARC Centre of Excellence for Automated Decision-Making and Society, focusing on AI’s societal implications. Andrejevic has been awarded Fellow of the Australian Academy of Humanities and the Nancy Baym Book Award. His work intersects with UN Sustainable Development Goals addressing justice and innovation. Research interests include automated surveillance systems, digital privacy policies, and the ethical governance of emerging technologies. He collaborates on projects such as facial recognition technology ethics and pandemic-era surveillance practices. His recent publications analyze platform accountability, algorithmic bias, and the societal effects of workplace monitoring technologies. He actively contributes to public discourse via media engagements and policy-focused talks on topics like facial recognition in education and pandemic-era data collection. Projects: ARC QEII Fellowship ($390k, 2010–2014), Australian Public Attitudes to AI, and facial recognition ethics studies. Grants: Over $390k from ARC for privacy research, plus ongoing funding through collaborative initiatives. Awards: Australian Academy of Humanities Fellowship (2020), Nancy Baym Book Award (2014). He supervises graduate students in Digital Media, Surveillance Studies, and Critical Theory. His interdisciplinary work bridges academia, policy, and industry to address technology’s societal challenges while advocating for democratic values.

John Reynoldsمشاهده پروفایل

استاد

• Polymer Chemistry
• Materials Science
• Electroactive Polymers
• +۶ مورد دیگر

Dr. John Reynolds is a Professor of Chemistry and Biochemistry at the Georgia Institute of Technology with a 40-year legacy in polymer chemistry. He serves as founding Director of the Georgia Tech Polymer Network (GTPN) and a member of the Center for Organic Photonics and Electronics (COPE). Research spans conjugated polymers, electrochromism, organic LEDs, photovoltaics, and bioelectronics Expert in optoelectronic and redox properties of electroactive materials Co-editor of the Handbook of Conducting Polymers His group has published over 450 peer-reviewed papers and holds ~45 issued patents. Recent research focuses on: Advanced electrochromic materials for visible and infrared applications Next-generation organic solar cells with green processing techniques Supercapacitor and electrochemical transistor materials Space exploration polymer applications Scientific recognition includes: ACS Cope Scholar Award (2020) ACS Florida Award (2019) ACS Applied Polymer Science Award (2012) Fellowships from Royal Society of Chemistry, Materials Research Society, and PMSE (2013) His editorial contributions include serving on boards for multiple prestigious journals including ACS Central Science and Chemistry of Materials . The Reynolds Group actively trains PhD and postdoctoral researchers, with recent members advancing to positions at University of Michigan, ExxonMobil, Northwestern, and Intel.

• Human Geography
• Science and Technology Studies
• Climate Change Adaptation
• +۴ مورد دیگر

Dr. Sophie Adams serves as an ARC DECRA Fellow in the Department of Sociology at La Trobe University, where she conducts interdisciplinary research at the intersection of human geography and Science and Technology Studies. Her work critically examines the social dimensions of climate change mitigation and adaptation, with particular emphasis on renewable energy transitions and the societal implications of technological solutions to environmental crises. Adams' research program centers on the politics of environmental knowledge , investigating how climate science informs policy formation and the negotiation of social objectives in decarbonisation processes. She employs qualitative and participatory methodologies to analyze climate change adaptation in energy systems , social license for technological interventions , and the redistribution of expertise in public health and environmental governance. Her scholarship consistently addresses power dynamics in knowledge production and the democratic challenges of sustainability transitions. Analysis of her publication trajectory (2019-2025) reveals increasing focus on energy-social interfaces, with significant contributions to understanding virtual power plants, demand-side management, and the role of modeling in climate policy. Her work demonstrates methodological innovation through participatory approaches while maintaining critical engagement with resilience thinking and adaptation frameworks. Scientific recognition includes: Australian Research Council Discovery Early Career Researcher Award (ARC DECRA) Supported by her DECRA fellowship, Adams leads independent research projects examining the societal dimensions of energy transitions. Her collaborative work spans geography, public health, and energy engineering, as evidenced by interdisciplinary co-authorship patterns. Current projects investigate Australian householders' conditions for participating in virtual power plants and the social implications of climate modeling in adaptation planning, reflecting her commitment to socially relevant environmental research.

Chanchal K. Royمشاهده پروفایل

استاد

• Code Clone Detection and Analysis
• Software Evolution and Maintenance
• Software Analytics Research
• +۳ مورد دیگر

Chanchal K. Roy is Professor of Software Engineering/Computer Science at the University of Saskatchewan and Co-Director of the Software Research Lab. He leads an NSERC CREATE graduate program on Software Analytics Research and co-leads the Data Management group for an NSERC CFREF project on Food Security, with over 170 publications cited 6,000+ times. His research centers on software clone detection using the widely adopted NICAD system, software evolution, empirical studies, and AI-driven software analytics. Recent work integrates large language models for code generation, clone detection in the AI era, and developer interactions with tools like ChatGPT, emphasizing practical applications in maintenance and analytics. Analysis of his 15 most recent publications reveals a strong trend toward AI/ML integration in software engineering: 12 of 15 articles (2025) explore LLMs, quantum computing, or deep learning for tasks like bug localization, code snippet generation, and feature-toggle analysis. Key themes include empirical validation of AI tools, Stack Overflow data mining, and cross-domain frameworks for Society 5.0. His scientific awards include: Most Influential Paper Awards (SANER 2018, ICPC 2018) Outstanding Young Computer Science Researcher Award (CS-Can/Info-Can, 2018) New Researcher Award (University of Saskatchewan, 2019) New Scientist Research Award (College of Arts and Science, 2019) As lead of the NSERC CREATE program and CFREF data group, he mentors graduate students in software analytics while securing major grants. He actively serves on program committees for ASE, ICSE, and FSE, reviewing journals and organizing workshops on clone detection and empirical methods. His lab focuses on real-world applications in food security data management and software evolution. The Software Research Lab, co-directed by Roy, drives projects like NICAD and the NSERC CREATE initiative, emphasizing open-source contributions and industry collaboration. Current efforts include quantum-SE integration and AI-augmented maintenance tools under the CFREF food security mandate.

Xia Ben Huمشاهده پروفایل

استاد

• Machine Learning
• Automated Machine Learning
• Deep Learning
• +۷ مورد دیگر

Xia Ben Hu is a Professor in the Department of Computer Science at Rice University's Brown School of Engineering. He leads research in automated and interpretable machine learning algorithms with applications across social informatics, health informatics, and information security. His work has resulted in widely adopted systems including AutoKeras, TODS, and RLCard. Education: PhD from Arizona State University (supervised by Dr. Huan Liu) Master and Bachelor degrees from Beihang University Prof. Hu's research focuses on developing automated and interpretable machine learning algorithms for large-scale, networked, dynamic and sparse data. His work spans automated machine learning (AutoML), deep learning, fairness in AI, time series analysis, and interpretable AI. He has made significant contributions to neural architecture search, collaborative filtering, anomaly detection, and reinforcement learning in imperfect information games. His publication record shows a clear progression from foundational work in network embedding and collaborative filtering (2017) to more recent work on LLM optimization, quantization, and extending context windows (2024). A consistent thread throughout his research is the focus on making complex machine learning systems more accessible, efficient, and interpretable. Selected Awards: ACM SIGKDD Rising Star Award (2021) NSF CAREER Award (2018) Teaching + Research Excellence Award, Rice University (2023) Multiple Best Paper Awards at top venues including ICML, CIKM, and AMIA Prof. Hu has successfully mentored numerous graduate students, with recent graduates securing tenure-track positions at major universities. His research is generously supported by federal agencies including DARPA (XAI, D3M, NGS2), NSF (CAREER, III, SaTC), NIH, and industrial sponsors such as Adobe, Apple, Google, LinkedIn, and JP Morgan. He has served as General Co-Chair for WSDM 2020 and ICHI 2023, and Program Chair for AIHC 2024. He leads the DATA Lab at Rice University, which develops open-source systems for automated machine learning and reinforcement learning. The lab's AutoKeras system has over 8,000 GitHub stars and 1,000 forks, and their work has been integrated into TensorFlow, Apple production systems, and Bing production systems.

Sri Kollaمشاهده پروفایل

استاد

• Electrical Engineering
• Renewable Energy and Energy Sustainability
• Control Systems
• +۵ مورد دیگر

Sri Kolla, Ph.D. is a tenured Professor in the Department of Electronics and Computer Engineering Technology at Bowling Green State University (BGSU) , where he has served since August 2002. He also served as a Visiting Professor at the Indian Institute of Science (2017) and as a Fulbright Research Scholar (2008-2009). His academic career spans faculty roles at Penn State University, University of Toledo, and consortium graduate faculty at Indiana State University. Education: Ph.D. in Electrical Engineering and Computer Science (University of Toledo, 1989) M.S. in Electrical and Computer Engineering (University of Saskatchewan, 1986) M.E. in Electrical Engineering (Indian Institute of Science, 1983) B.E. in Electrical Engineering (Andhra University, 1981) Research Interests: Dr. Kolla specializes in Electrical Power and Energy Systems with Smart Grid applications, Control Systems for networked environments, and Machine Learning techniques for power system diagnostics. His work focuses on fault detection in microgrids using LSTM networks, stability robustness of discrete-time systems, and multi-agent protection schemes for power infrastructure. Scientific Contributions: Developed robust control frameworks for microgrid systems under parameter variations (2023-2025) Pioneered AI-based fault identification in induction motors and transformers (1995-2000) Advanced networked control system designs addressing time delays (2002-2012) Published 82+ technical articles in IEEE, ISA Transactions, and conference proceedings Honors and Recognition: Recipient of the Fulbright-Nehru Academic and Professional Excellence Award and Whiteford Scholarship . Senior member of IEEE and ISA , with listings in Marquis Who’s Who and fellowships in The Institute of Engineers (India) .

Yves-Alexandre de Montjoyeمشاهده پروفایل

دانشیار

• Computational Privacy
• AI Safety
• Machine Learning
• +۳ مورد دیگر

Yves-Alexandre de Montjoye is an Associate Professor of Applied Mathematics and Computer Science at Imperial College London, where he leads the Computational Privacy Group. He holds a joint affiliation between the Department of Computing and the Data Science Institute. His roles include serving as a Special Adviser on AI and Data Protection to the EC Justice Commissioner Didier Reynders, a Parliament-appointed Commissioner for the Belgian Data Protection Agency, and a Special Adviser to EC Competition Commissioner Margrethe Vestager, co-authoring the 'Competition Policy for the Digital Era' report. He earned his PhD from MIT in 2015 under Alex 'Sandy' Pentland. His master's degrees include an M.Sc. in Applied Mathematics from UCLouvain, an M.Sc. (Centralien) from École Centrale Paris, and an M.Sc. in Mathematical Engineering from KU Leuven. He also holds a B.Sc. in Engineering from UCLouvain. His research interests focus on computational privacy, anonymization techniques, AI safety, and machine learning attacks. He develops methods to 'red team' AI systems and create privacy-preserving mechanisms. His work addresses vulnerabilities such as membership inference, attribute inference, and re-identification risks in datasets, with applications to location tracking, synthetic data, and LLMs. His articles analyze adversarial attacks against privacy systems, emphasizing robustness and practical guarantees. He advocates for privacy-by-design approaches in big data analytics and has explored ethical AI, competition policy in digital markets, and humanitarian uses of mobile data. While no scientific awards are explicitly listed, his contributions have been widely covered in media. He is currently recruiting motivated PhD students for his group at Imperial College. His advising and grants narrative includes work on privacy-preserving technologies and policy implications of AI, with collaborations across academia and public institutions. He is affiliated with the Computational Privacy Group and contributes to platforms like OPAL for privacy analytics. His office is in the ACE Extension building (ACEX 259), accessible via Exhibition Road.

Emily Prud'hommeauxمشاهده پروفایل

دانشیار

• Natural Language Processing
• Speech Signal Processing
• Computational Linguistics
• +۳ مورد دیگر

Emily Prud'hommeaux is an Associate Professor in the Computer Science Department at Boston College. She holds a B.A. from Harvard University, an M.A. from the University of California, Los Angeles, and a Ph.D. from Oregon Health & Science University. Her research focuses on natural language processing and speech signal processing, particularly in under-resourced and endangered language contexts, with applications to health and accessibility. Her work emphasizes community-centered approaches to language technology, including developing tools for endangered language documentation and supporting neurodiverse communication needs. She co-founded the Computational Linguistics and Speech Processing lab at RIT and leads a lab at Boston College, supported by an NSF DLI grant for corpus development in endangered languages. Recent achievements include student recognitions such as Gavin Zhang’s McCarthy Prize and Neuhauser Award, and Robbie Jimerson’s successful defense of a dissertation on Seneca language ASR. Collaborators include Dr. Eric Le Ferrand (postdoc, 2023) and former student Dr. Zoey Liu, now a faculty member at the University of Florida.

• Decision and Control
• Game Theory
• Multi-Agent Systems
• +۷ مورد دیگر

Jeff Shamma is the Department Head and Professor of Industrial and Enterprise Systems Engineering (ISE) at the University of Illinois at Urbana-Champaign, holding the Jerry S. Dobrovolny Chair. He is also courtesy Professor in Aerospace Engineering and Mechanical Science and Engineering. Formerly, he held the Julian T. Hightower Chair at Georgia Institute of Technology and faculty positions at KAUST. Dr. Shamma earned his PhD in Systems Science and Engineering from MIT (1988) and a BS in Mechanical Engineering from Georgia Tech (1983). He is a Fellow of IEEE and IFAC, recipient of the IFAC High Impact Paper Award, AACC Donald P. Eckman Award, and NSF Young Investigator Award. His research spans Decision and Control , Game Theory , and Multi-Agent Systems , focusing on human-machine networks, distributed autonomy, and adaptive robotic systems. Recent work examines crowd dynamics, risk-sensitive control, and feedback linearization for constrained optimization. Jeff has served as Editor-in-Chief of IEEE Transactions on Control of Network Systems (2020–2024) and held editorial roles in journals like Annual Reviews in Control and IEEE Transactions on Robotics . His 15 most recent publications (2024–2025) analyze learning dynamics, multi-agent optimization, and UAV-crawler systems, reflecting trends in autonomous systems, game-theoretic modeling, and industrial inspection technologies. Scientific distinctions include: Fellow of IEEE and IFAC IFAC High Impact Paper Award (2020) AACC Donald P. Eckman Award (1996) NSF Young Investigator Award (1992) Mohammed Dahleh Distinguished Lecture Award (2013) Dr. Shamma advises current PhD students Hassan Abdelraouf, Aya Hamed, and Nawaf Otaibi, with former advisees including Sarah Toonsi (2025) and Fat-hy Rajab (2025). His lab integrates theoretical research with applied projects like FalconScan, a UAV-crawler system for industrial inspection, and develops magnetic legs for curved surface UAV landing.

Ruben Verborghمشاهده پروفایل

استاد

• Decentralization
• Linked Data
• Querying
• +۷ مورد دیگر

Ruben Verborgh is a Professor of Decentralized Web Technology at the Ghent University – imec and a Visiting Fellow at the Oxford Martin School (University of Oxford). He leads the Internet Technology and Data Science Lab (IDLab) and co-founded the Solid platform with Tim Berners-Lee to re-decentralize the Web. His research focuses on Linked Data Fragments , a paradigm for Web-scale query execution, and explores decentralized data governance , user-controlled data ownership , and rule-based Web agents for policy enforcement. He has co-authored two books on Linked Data and contributed to over 250 publications. Recent articles highlight trends in decentralized data ecosystems , including ODRL policy interoperability , event notification systems , and personal data vaults . His work bridges Linked Data , hypermedia APIs , and privacy-preserving technologies . Verborgh collaborates with institutions like MIT, Oxford, and the European Commission, and advises companies through Inrupt . His labs ( IDLab , Solid Ecosystem ) focus on sustainable data-driven societies.

• Integrated Circuits
• Mixed-Signal Computing
• Neural Recording Systems
• +۱ مورد دیگر

Boris Murmann is Professor at Stanford University, specializing in integrated circuit design, mixed-signal computing, and energy-efficient AI hardware. His research advances neural interface technologies, analog design automation, and tinyML systems. Recent work develops ultra-low-power neural recording ICs for brain-computer interfaces, RRAM-based memory systems, and open-source semiconductor design frameworks. Publications demonstrate innovations in compressive sensing for neural data, hardware-algorithm co-design, and reinforcement learning for analog circuit synthesis. Significant contributions include Medusa (TinyML processor), EMBER (RRAM macro), and methodologies for coarsely-quantized computer vision and analog design automation.

Farshad Khorramiمشاهده پروفایل

استاد

• Nonlinear Control
• Large Scale Systems
• Robotics and Automation
• +۵ مورد دیگر

Farshad Khorrami is a Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering and a faculty member of the Center for Advanced Technology in Telecommunications (CATT). His research spans nonlinear control, robotics, cyber-physical systems security, and machine learning applications. PhD in Electrical Engineering and MS in Mathematics from The Ohio State University BS in both Electrical Engineering and Mathematics from The Ohio State University He directs the Control/Robotics Research Laboratory (CRRL) and collaborates with the NYU Center for Cybersecurity. Recent projects include: EnIGMA: AI agent for cybersecurity applications (presented at ICML 2025) TRAPS: Real-time anomaly detection in power grid systems (DOE-funded) Detection of Hardware Trojans via short-term aging (ONR-funded) Khorrami contributes to hardware security research with novel testbeds for Trojan detection and holds 13 issued/2 pending US patents.