جست‌وجوی استاد و پژوهشگر

Thorsten Kochمشاهده پروفایل

استاد

• Mathematical Optimization
• High-Performance Computing
• Artificial Intelligence
• +۴ مورد دیگر

Thorsten Koch is a Professor for Software and Algorithms for Discrete Optimization at Technische Universität Berlin , with multiple leadership roles including Head of the Applied Algorithmic Intelligence Methods (A²IM) , Digital Data and Information for Society, Science, and Culture (D²IS²C) , Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV) , and Forschungs- und Kompetenzzentrum Digitalisierung Berlin (digiS) . Based at Zuse Institute Berlin and affiliated with TU Berlin's Institute for Mathematics, he focuses on integrating mathematical optimization with high-performance computing and artificial intelligence to solve complex real-world problems. Research Pillars : Mathematical optimization algorithms Quantum computing applications AI/ML integration in decision systems Energy systems optimization Scientific software development Leadership Roles : Head of Applied Algorithmic Intelligence Methods (A²IM) Head of Digital Data & Information for Society, Science, and Culture (D²IS²C) Head of Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV) Head of Forschungs- und Kompetenzzentrum Digitalisierung Berlin (digiS) Key Collaborations : Working with IBM Quantum on quantum optimization Collaborating across institutions for energy system modeling Developing open-source optimization tools like SCIP Contributing to digital library infrastructure Recent Research Trends : Quantum optimization benchmarking Machine learning-aided optimization Multi-objective decision frameworks Energy infrastructure optimization Adaptive algorithm design CO2 network modeling Impact : Advancing hybrid optimization methods Developing open-source tools for scientific computing Building digital infrastructures for libraries and research Exploring quantum-classical algorithm synergies

• Smart Electricity Grids
• Energy Storage Systems
• Electromagnetic Compatibility
• +۳ مورد دیگر

Daniel Månsson is a Professor at the Department of Electrical Engineering, Royal Institute of Technology (KTH), specializing in smart electricity grids and power system components. His work spans electromagnetic compatibility (EMC) of large distributed systems, energy storage optimization, and privacy protection in smart metering. PhD in Engineering Physics (with specialization in Electromagnetism) Docent (Swedish Academic Title) in Electrical Engineering His research focuses on: Optimization of self-sufficient microgrids with energy hubs Smart meter privacy protection using energy storage EMC analysis of photovoltaic systems and UWB transients Hybrid energy storage system performance in renewable grids Recent publications indicate expertise in: Electromagnetic interference from solar PV systems Cyber-physical security in smart meters Conducted emission analysis Greenhouse gas reduction through optimized storage

• Algorithmic Game Theory
• Market Equilibrium Computation
• Fair Division
• +۵ مورد دیگر

Ruta Mehta is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign . Since 2016 she has led a vibrant research program in algorithmic game theory, market design, and fair division, while actively shaping the community through service roles such as Program Co-Chair of WINE 2020 and Area Chair of EC 2021. Education Ph.D. in Computer Science & Engineering, Indian Institute of Technology Bombay, 2012. (Advisors: Prof. Milind Sohoni & Prof. Bharat Adsul; ACM India Doctoral Dissertation Award 2012) M.Tech. in Computer Science & Engineering, Indian Institute of Technology Bombay, 2005 B.E. in Computer Engineering, Maharaja Sayajirao University (MSU) Baroda, 2003 Research Interests Mehta’s work lies at the intersection of theoretical computer science , mathematical economics , and social choice theory . She investigates the computability and complexity of equilibria—both market and Nash—under a variety of utility models, and designs provably efficient algorithms that are practical for real-world resource-allocation tasks. Current themes include: Algorithmic Game Theory: equilibrium computation, smoothed analysis, learning in games Fair Division: envy-freeness up to any good (EFX), mixed manna, competitive equilibrium with chores Interdisciplinary Applications: genetic evolution, machine-learning markets, climate-aware allocation Publication Trends Her 15 most recent works (2017–2025) span Operations Research , Mathematics of Operations Research , STOC, SODA, EC, ITCS, AAMAS, and NeurIPS. The articles cluster around three thrusts: (i) rigorous hardness and approximation results for market equilibrium in Leontief and PLC exchange economies, (ii) algorithmic advances toward guaranteed EFX allocations and competitive equilibrium with mixed manna, and (iii) novel game-theoretic analyses of genetic diversity and strategic resource allocation under budget constraints. Scientific Awards & Honors NSF CAREER Award (2018) Outstanding Post-Doctoral Researcher Award, Georgia Tech (2014) Rising Stars in EECS (2013) ACM India Doctoral Dissertation Award (2012) Google India Anita Borg Memorial Scholarship (2012) IBM PhD Award (2010) IBM PhD Fellowship (2009–2010) Grants, Advising, and Community Leadership Mehta currently mentors a growing group of graduate students and post-docs. She is PI on an NSF CAREER grant and has served on federal panels reviewing NSF CISE proposals. Beyond research, she founded the EC (AGT) Mentoring Workshop , co-located with the ACM Economics & Computation conference, to broaden participation of women and under-represented minorities in algorithmic game theory. Labs & Teams Her research group operates within the Theory & Algorithms cluster at UIUC, leveraging ties with the Decision & Control group and the Social & Algorithmic Thinking initiative. She is an active member of ACM SIGecom and regularly organizes reading groups on algorithmic game theory and fair division.

• Sanitation Planning
• Wastewater Management
• Sustainable Urban Development
• +۵ مورد دیگر

Jennifer R Mc Conville is a researcher at Chalmers University of Technology's Department of Urban Design and Planning. Her work focuses on sustainable sanitation systems, particularly in developing countries, with extensive research on wastewater management, community-based approaches, and resource recovery. Key affiliations: Formas-funded projects (2012–2020) Geographic focus: Bolivia, West Africa, Sweden Methodologies: Gamification, participatory planning, regime analysis Research interests include: Sanitation planning frameworks Decentralized wastewater systems Equity in urban-rural transitions Policy implementation barriers Recent publications analyze: Circular economy in sanitation Decision-support tools Community-managed infrastructure Role-playing in engineering education

• Machine Learning
• Human-Computer Interaction
• 3D Graphics
• +۸ مورد دیگر

Chiara Innocente is a PhD student in Management, Production, and Design at the Department of Management and Production Engineering (DIGEP) of Politecnico di Torino. She holds the academic rank of Consultant Lecturer and contributes to teaching in courses related to 3D graphics in biomedical engineering applications. Her research spans computational modeling, human-computer interaction, and machine learning, with a focus on rehabilitation engineering, cultural heritage preservation, and medical imaging. Education: PhD in Management, Production, and Design (2022-2025) Research interests include: Machine learning applications in healthcare and rehabilitation engineering Human-computer interaction for augmented/virtual reality 3D graphics and computational modeling for biometric and cultural heritage Convolutional neural networks in medical image analysis Her recent publications highlight interdisciplinary work bridging computer science with clinical applications and cultural preservation. She collaborates on national patents for athlete performance evaluation systems and participates in the Augmented Humans research framework.

• Cryptography
• Computer Security
• Algorithms
• +۶ مورد دیگر

Rafail Ostrovsky is a Distinguished Professor of Computer Science and Mathematics (by courtesy) at UCLA's Henry Samueli School of Engineering and Applied Science. He holds the Norman E. Friedman Chair in Knowledge Sciences and serves as Director of the Center for Information and Computation Security. With over 350 refereed publications and 15 issued USPTO patents, he is one of the most influential researchers in theoretical computer science and cryptography. Professor Ostrovsky's research spans cryptography, network algorithms, and search and classification of large-scale, high-dimensional data. His work focuses on foundational aspects of secure computation including zero-knowledge proofs, secure multi-party computation, private information retrieval, and privacy-preserving data analysis. His contributions to streaming algorithms, metric embedding, and clustering for high-dimensional data have established important theoretical frameworks with practical applications. He has pioneered techniques for secure computation that maintain privacy while enabling collaborative data analysis. His recent publications demonstrate continued leadership in advancing secure computation protocols, with emphasis on efficiency improvements, practical implementations, and novel applications in blockchain technology and distributed systems. His work consistently addresses fundamental theoretical challenges while maintaining relevance to real-world security problems, bridging the gap between theoretical cryptography and practical security solutions. Selected Honors: 1993 Henry Taub Prize 2017 IEEE Computer Society Edward J. McCluskey Technical Achievement Award 2018 RSA Award for Excellence in Mathematics 2022 W. Wallace McDowell Award (the highest award given by the IEEE Computer Society) Professor Ostrovsky has served in significant leadership roles including chair of the IEEE Technical Committee on Mathematical Foundations of Computing (2015-2018) and chair of the IEEE FOCS 2011 Program Committee. He has served on over 40 international conference program committees and currently serves on the editorial boards of the Journal of ACM and Algorithmica Journal. As a Fellow of the National Academy of Inventors, AAAS, ACM, IEEE, and IACR, and as a foreign member of Academia Europaea, his contributions have been widely recognized across multiple disciplines. He actively teaches advanced courses including Introduction to Cryptography (CS183), Foundations of Cryptography (CS282A/M209A), and Cryptographic Protocols (CS282B/M209B), mentoring the next generation of security researchers while continuing to push the boundaries of secure computation through his research.

Kamyar Sarsharمشاهده پروفایل

استاد

• Business Process Management
• E-Business
• IT in Healthcare

Kamyar Sarshar is a Professor of Business Informatics at the Hamburg School of Business Administration (HSBA) since January 2014, serving as Academic Director of the Bachelor's program in Business Informatics within the Department of Technology & Markets. His educational background includes: Computer Science degree from the University of Hamburg MBA from Cardiff University, United Kingdom PhD from the Institute of Business Informatics in Saarbrücken Prior to HSBA, he worked as a Managing Consultant at Logica (now CGI), leading cross-industry process management projects. Research interests focus on: Business Process Management E-Business IT in Healthcare His work emphasizes empirical evaluation of modeling techniques like Event-driven Process Chains and Petri nets, particularly in healthcare applications and end-user usability. Publications from 2004-2008 reveal consistent contributions to business process modeling methodology, with strong empirical validation across healthcare, energy, and construction sectors, highlighting practical applicability and user-centered design principles. No scientific awards are documented in the provided materials. Information regarding student supervision, research grants, laboratories, or collaborative teams is not specified in the source text.

Seyed M.R. Iravaniمشاهده پروفایل

استاد

• Stochastic processes
• Queuing theory
• Game theory
• +۷ مورد دیگر

Seyed M.R. Iravani is Professor of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering, with courtesy appointment at the Kellogg School of Management. His research focuses on applications of stochastic processes, queuing theory, game theory, and social networks to design and control of manufacturing, service operations, healthcare systems, supply chains, and nonprofit systems. His research interests emphasize improving flexibility, coordination, and responsiveness in complex operations systems. Professor Iravani develops mathematical optimization models to analyze optimal system behavior, provide managerial insights, and create practical implementation strategies for real-world operations challenges. His work spans manufacturing systems, healthcare operations, supply chain management, service operations, and nonprofit operations. The analysis of Professor Iravani's publications reveals a consistent focus on operations management challenges across multiple domains. His research employs analytical methods including stochastic processes, game theory, queueing theory, and social networks to address problems in capacity allocation, resource management, supply chain coordination, and service system design. A notable trend is his increasing focus on healthcare operations and nonprofit systems in recent years, while maintaining strong contributions to fundamental operations management theory. Professor Iravani has served on editorial boards of leading journals including Operations Research, Management Science, Service Science, IIE Transactions, and Naval Research Logistics. He has collaborated with organizations such as GM, Ford, Motorola, GE, Northwestern Memorial Hospital, Chicago Food Depository, Facebook, and Roti Mediterranean Restaurants to improve operational performance. He teaches courses including Production Planning and Scheduling, Service Operations Management, Operations, and Operations Management for MBA students. He has also authored the textbook 'Operations Engineering and Management: Concepts, Analytics and Principles for Improvement' published by McGraw Hill in 2020, which has become a standard resource in operations engineering education.

• Public Health
• Healthcare Policy
• Clinical Sciences
• +۷ مورد دیگر

Dominic King is an Honorary Clinical Lecturer at the Department of Surgery & Cancer, Faculty of Medicine, Imperial College London. He transitioned from a full-time NIHR Clinical Lecturer role in 2016 to focus on executive leadership in AI healthcare sectors, including roles at Microsoft AI (Health), DeepMind, Google Health, and UnitedHealth Group. His expertise spans behavioral economics, digital health innovation, and clinical informatics. Education & Research: King holds a PhD in Behavioral Economics, co-authoring the influential Mindspace Report on public policy design. His research focuses on Public Health, Clinical Sciences, and Applied Economics, with contributions to the HELIX Centre (a hospital design initiative) and co-founding Hark, a clinical task management platform acquired by Google. Professional Contributions: He pioneered early digital health programs, leading to numerous products and collaborations with Imperial College. His work bridges clinical practice with AI-driven solutions, emphasizing patient safety and healthcare optimization. Notable projects include AI systems for breast cancer screening and frameworks for esports athlete wellness. Key Themes in Publications: Recent articles explore barriers in healthcare access, biologic therapy efficacy in IBD, AI diagnostic tools, and musculoskeletal interventions. His work consistently integrates behavioral insights with technological innovation to address clinical challenges. Advising & Grants: As a startup founder and academic collaborator, King has secured funding for digital health ventures and co-authored studies on health informatics. His affiliations include the Centre for Health Policy and Patient Safety Translational Research Centre , reflecting his commitment to systemic healthcare improvement. Labs & Teams: Key affiliations include the HELIX Centre (Imperial/Royal College of Art collaboration) and the Behavioural Insights Team , leveraging multidisciplinary approaches to healthcare innovation.

Aliyu Abubakarمشاهده پروفایل

مدرس

• Deep Learning
• Image Analysis
• Medical Imaging
• +۵ مورد دیگر

Dr. Aliyu Abubakar is a Lecturer in Computer Science at Teesside University's Department of Computing & Games. He holds a PhD in Computer Science from the University of Bradford (2023), an M.Sc. in Cyber Security (2015), and a B.Sc. in Computer Science from Gombe State University (2012). Prior to his current role, he was a Postdoctoral Research Associate at the University of Liverpool (2022–2024). His research focuses on deep learning applications in medical imaging, cybersecurity, and pattern recognition. He is a member of the Nigeria Computer Society (NCS) and the Cyber Security Experts Association of Nigeria (CSEAN). Education: B.Sc. Computer Science, Gombe State University (2008–2012) M.Sc. Cyber Security, University of Bradford (2014–2015) Ph.D. Computer Science, University of Bradford (2018–2022) Research Interests: Abubakar’s work spans medical image analysis (e.g., burns assessment, kidney transplantation viability, malaria detection), cybersecurity (deepfake detection, P2P network security), and electrical engineering (partial discharge analysis). His deep learning models address challenges in healthcare diagnostics and industrial safety. Recent studies include human deception detection via facial image comparison and automated liver viability assessment using pre-trained neural networks. Key Trends in Publications: His 2020–2024 articles emphasize cross-disciplinary applications of deep learning, particularly in healthcare and electrical systems. Collaborations span institutions like the University of Liverpool and NHS clinical partners, reflecting his commitment to translational research. Awards & Grants: While no specific awards are listed, his high citation counts (e.g., 32 citations for malaria detection work) highlight impactful contributions. Grants and funding details are not disclosed in the provided texts. Labs & Teams: Active collaborations with multidisciplinary teams in medical imaging and cybersecurity, as evidenced by co-authored papers with clinicians and electrical engineers.

Samuel Clowes Hunekeمشاهده پروفایل

استادیار

• Modern European History
• History of Sexuality
• Legal History
• +۴ مورد دیگر

Samuel Clowes Huneke is Assistant Professor of History at George Mason University, specializing in modern European history with particular focus on Germany. His research intersects queer theory, legal history, and Cold War studies, examining how state structures interact with marginalized communities. PhD from Stanford University (2019) MSc in Applicable Mathematics (LSE, 2012) BA in German and Mathematics (Amherst College, 2011) Major publications include States of Liberation (2022), analyzing gay experiences in Cold War Germany, and A Queer Theory of the State (2023), exploring queer theory's engagement with state structures. His co-edited volume Reimagining Citizenship (2025) addresses postwar European identity politics. Recent work examines both GDR and Weimar-era queer histories, with a forthcoming monograph I Will Not Abandon You (2026) on Nazi-era queer women. He directs the East German Poster Database project and contributes to public discourse through media outlets like CNN, Washington Post, and Boston Review. David Barclay Book Prize (2023) Charles E. Smith Award (2022) Waterloo Centre Finalist Active in digital humanities initiatives and Cold War oral history projects, Huneke bridges mathematical precision with historical analysis. His teaching includes courses on Nazi Germany, Cold War Europe, and global sexuality history.

Jaejin Jangمشاهده پروفایل

دانشیار

• Production System Optimization
• Supply Chain Management
• Biofuel Supply Chains
• +۱ مورد دیگر

Jaejin Jang is Associate Professor and Department Chair of Industrial & Manufacturing Engineering at UW-Milwaukee. His research optimizes production systems and supply chains, with recent focus on biofuel subsidies and water-energy nexus management. Publications appear in International Journal of Energy Research and other leading journals. He developed algorithms for semiconductor fabrication throughput improvement and scheduling optimization under uncertainty. His doctoral work at Purdue focused on industrial system efficiency.

Prashanth Thattai Ravikumarمشاهده پروفایل

مدرس

• Human-AI Co-creativity
• Generative Audio Technologies
• Real-Time Music Creation
• +۵ مورد دیگر

Prashanth Thattai Ravikumar is a Lecturer in the Computing Department at Goldsmiths, University of London. His research focuses on enhancing human-AI co-creativity through generative audio and music systems, with applications in VR, gaming, and EdTech. He leads courses in MOOCs, Digital Signal Processing (DSP), and computer graphics. Education: PhD in Human-AI Co-creativity (National University of Singapore, 2020); MA in Communications and New Media (NUS, 2014); BTech in Computer Science and Engineering (National Institute of Technology, Trichy, 2012). Research interests include developing efficient software for generative audio, real-time music creation, and deep neural networks. His work emphasizes human-AI collaboration to produce novel creative experiences. Key projects include Harmonaise (algorithmic music generation from gestures) and Syntex (audio texture datasets for neural networks). Recent work trends focus on parametric audio systems, AI-driven music interfaces, and scalable audio datasets. His 2022 Arts X Tech Grant supported a pioneering music technology project in Singapore. Awards: Arts X Tech Grant (2022) Grants: $30k National Arts Council award for music tech innovation Lab/Team affiliations include projects like COVID-Life (cellular automaton game) and Rhythm Partner Study , exploring interactive AI-driven music systems.

Ram Mohapatraمشاهده پروفایل

استاد

• Mathematical Analysis
• Operator Theory
• Variational Inequalities
• +۳ مورد دیگر

Ram Mohapatra is a Professor in the Department of Mathematics at the University of Central Florida (UCF), part of the College of Sciences. His research interests span mathematical analysis, operator theory, variational inequalities, approximation theory, cybersecurity, and fluid dynamics. He has published extensively on topics including inverse scattering problems, generalized inverses, and optimal control theory. His work often intersects with applications in engineering and data science. Recent research focuses on operator theory applications, tensor decompositions, and mathematical modeling of physical systems. He has contributed to advancements in frame theory, numerical radius studies, and cybersecurity methodologies. His academic activities include teaching undergraduate and graduate courses in mathematics, such as MAC 1105C and MAC 2311C, and maintaining active collaborations in interdisciplinary research areas. Professional contributions include over 150 peer-reviewed articles and editorial roles in mathematics journals. While no explicit awards are listed in the provided texts, his prolific publication record underscores his scholarly impact in applied and theoretical mathematics.

Qi Zhangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Reinforcement Learning
• Trustworthy AI Systems
• +۴ مورد دیگر

Qi Zhang is an Assistant Professor in the Department of Computer Science and Engineering, AI Institute at the Molinaroli College of Engineering and Computing, University of South Carolina. His research focuses on developing safe, reliable, and trustworthy AI systems through advancements in reinforcement learning and decision-making algorithms for uncertain environments. He holds a Ph.D. from the University of Michigan (2020) and a B.E. from Shanghai Jiao Tong University (2015). Research interests include artificial intelligence, reinforcement learning, multi-agent systems, and decision-making under uncertainty. Key themes involve leveraging domain knowledge for robust AI solutions and ensuring ethical, transparent, and risk-aware system designs. His work spans applications in autonomous systems, healthcare, robotics, and materials science. Current projects emphasize improving algorithmic trustworthiness, safety, and adaptability across diverse contexts. He is affiliated with the AI Institute at USC and maintains an active research lab focused on these areas.