جست‌وجوی استاد و پژوهشگر

Sujoy Sikdarمشاهده پروفایل

استادیار

• Artificial Intelligence
• Computational Social Choice
• Mechanism Design
• +۴ مورد دیگر

Sujoy Sikdar is an Assistant Professor in the Department of Computer Science at Binghamton University, affiliated with the Thomas J. Watson College of Engineering and Applied Science. He joined the university in 2020 after completing his PhD and MS in Computer Science at Rensselaer Polytechnic Institute (RPI), advised by Professors Lirong Xia and Sibel Adalı. His research spans artificial intelligence, computational social choice, mechanism design, and algorithmic decision-making, particularly in fair resource allocation and voting systems. Education: Bachelor of Engineering in Information Technology, Manipal Institute of Technology (2009) Master of Science in Computer Science, Rensselaer Polytechnic Institute (2015) Doctor of Philosophy in Computer Science, Rensselaer Polytechnic Institute (2018) Research Interests: Sikdar focuses on interdisciplinary topics at the intersection of computer science and microeconomics, including fair division algorithms, computational social choice, and AI for social good. His work emphasizes algorithmic fairness, strategyproof mechanisms, and applications in education and cybersecurity. Publications: His recent work includes studies on envy-free allocations, anti-collusion testing methods, and equitable resource distribution. These contributions highlight his expertise in bridging theoretical computer science with practical societal challenges. Awards: He received the Best Paper Award at SocialCom2013 for foundational work in social computing and information credibility. Teaching: He teaches courses such as Introduction to Machine Learning, Data Mining, and Design and Analysis of Algorithms, reflecting his commitment to both research and education. Labs/Teams: While specific lab affiliations are not detailed, his research group likely focuses on computational social choice and fair algorithms, as evidenced by his publication record.

• Machine Learning
• Precision Healthcare
• Reinforcement Learning
• +۵ مورد دیگر

Yonatan Mintz is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on applying machine learning and automated decision-making to healthcare and sociotechnical systems, emphasizing fairness, precision interventions, and optimization. Prior to UW-Madison, he was a postdoctoral research fellow at Georgia Tech, and earned his BS from Georgia Tech (2012) and PhD from UC Berkeley (2018). He has industry experience at Caterpillar and Google. Education: PhD 2018, University of California, Berkeley in Industrial Engineering and Operations Research B.S. 2012, Georgia Institute of Technology in Industrial and Systems Engineering Research interests include reinforcement learning, precision healthcare (e.g., personalized drug dosing, neurodegenerative monitoring), fairness in AI, and optimization methodologies. He explores applications in healthcare analytics, behavioral interventions, and ethical AI frameworks. Key research trends in his articles include: Integration of machine learning with healthcare decision-making Development of adaptive control systems for personalized treatments Analysis of non-stationary environments in bandit algorithms Ethical considerations in human-AI collaboration Awards: 2019 NeurIPS Best Poster Award for AI for Social Good 2017 Grassi Fellowship (Doctoral) 2012 ISyE Senior Design Winner (Undergraduate) Teaching focuses on dynamic programming, reinforcement learning, and optimization, with courses like COMP SCI 723 - Dynamic Programming and I SY E 604 - Special Topics in Manufacturing .

Francisco Iacobelliمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Health Informatics
• Human-Computer Interaction
• +۳ مورد دیگر

Francisco Iacobelli is an Associate Professor in the Department of Health Informatics and Data Science and the Department of Surgery at Loyola University Chicago. He is also affiliated with the Center for Advancing Safety and Machine Intelligence (CASMI) at Northwestern University. His research focuses on equitable AI applications to address health disparities, particularly for marginalized and low-literacy communities. He holds a B.S. in Systems Engineering from Universidad Diego Portales (Chile), an M.Sc. in Computer Science from DePaul University, and a Ph.D. in Computer Science from Northwestern University. Education: B.S. Systems Engineering, Universidad Diego Portales, Santiago, Chile M.Sc. Computer Science, DePaul University, Chicago, U.S. Ph.D. Computer Science, Northwestern University, Evanston, U.S. Research Interests: Designing AI systems for health literacy in underserved populations Intelligent tutoring systems for low-literacy groups Natural Language Processing for healthcare communication Applications of machine learning to social determinants of health Publications reflect a focus on equitable AI solutions for healthcare challenges, including interventions for breast cancer survivors, cultural adaptation of technologies, and foundational work in human-computer interaction. Labs/Teams: Active contributor to Loyola's Health Informatics initiatives and collaborator with CASMI on safety-critical AI systems.

• Climate Change Mitigation
• Urban Studies
• Transportation Policy
• +۳ مورد دیگر

Felix Creutzig is a Professor of Innovation and Policy Acceleration at the University of Sussex Business School, affiliated with the Science Policy Research Unit (SPRU). He also leads the Cities working group at the Potsdam Institute for Climate Impact Research and holds the Bennett Institute Chair. His roles include membership in various climate advisory boards at municipal, national, and enterprise levels. Creutzig has extensive international experience, including visiting fellowships at Princeton University, UC Berkeley, and the China Energy Foundation in Beijing. Education: Studied physics and medical sciences, earned a PhD in computational neuroscience/machine learning. Research focuses on urban climate change mitigation, transport systems, and lifestyle impacts, with pioneering work on global urban energy consumption typologies. He integrates artificial intelligence and digitalization into climate governance, co-leading initiatives to bridge AI and climate communities (e.g., NeurIPS 2019 keynote). His work on IPCC assessments includes coordinating Chapter 5 of the 6th Assessment Report on demand and social aspects of climate mitigation. Impact: Influenced Berlin’s bicycle policies and shaped frameworks for managing mobility data as a public good. His research is cited widely in media such as the Guardian, Spiegel, and New York Times. Teaching & Supervision: Over 15 years teaching urban environment and sustainable economics, supervised 10 PhD students, 9 postdoctoral researchers, and 14 Master’s students. Led summer schools on sustainable transport and urban systems. Labs/Teams: Leads crosscutting efforts on digitalization/AI in climate mitigation at Potsdam Institute and SPRU.

Peter A.M. Ruijten-Dodoiuمشاهده پروفایل

استادیار

• Education Technologies
• Student Growth and Development
• Process-Based Assessment
• +۳ مورد دیگر

Peter A.M. Ruijten-Dodoiu is an Assistant Professor in the Department of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology (TU/e). He holds dual roles as EAISI Foundational Assistant Professor and EAISI Mobility Assistant Professor, focusing on education technologies, student growth, and process-based assessment. His research emphasizes how innovative technologies can enhance education and human-technology interaction, particularly in social psychology and persuasive systems. Academically, Ruijten-Dodoiu earned a BEng in Electrical Engineering from Avans University of Applied Sciences (2005), an MSc in Human-Technology Interaction from TU/e (2011), and a PhD in Social Psychology from TU/e (2015) on 'Responses to Human-Like Artificial Agents'. He transitioned from lecturer to assistant professor in 2017. His research explores education technologies, student development, and process-based learning methodologies. Recent work includes serious games for sustainable behavior change, robot-human interaction in healthcare, and ethical design of autonomous systems. His projects, such as the GUARDIAN initiative, address societal challenges through interdisciplinary collaboration. Ruijten-Dodoiu has advised over 78 students and contributes to courses like 'Automotive Societal Factors' and 'Behavioral Research Methods'. He actively participates in conferences and media discussions on robotics ethics, persuasive technologies, and future societal impacts of AI. His research spans 42 publications, with a focus on educational innovation, human-robot interaction, and sustainable behavior. He leads teams at TU/e's EAISI (Eindhoven Artificial Intelligence Systems Institute) and collaborates across engineering, psychology, and design disciplines to advance socially responsible technology.

• Macroeconomics
• Applied Economics
• Network Theory
• +۴ مورد دیگر

Giovanni Bonaccorsi serves as a Junior Assistant Professor (RTD-A) in the Department of Management, Economics, and Industrial Engineering at Politecnico di Milano since 2022, following a research fellowship in the same department from 2020-2022. His academic credentials include a PhD in Economics and Data Science from IMT School for Advanced Studies Lucca, an MSc in Economics from the University of Pisa, and a BSc in Economics and Management from the University of Pisa. His research integrates network theory, econometrics, and data science to analyze macroeconomic phenomena, with specialized focus on pandemic policy impacts through social network mobility data and household consumption patterns via high-frequency transaction datasets. This work addresses critical intersections between public health interventions and economic stability. His publication portfolio reveals dominant research trajectories in pandemic economics, emphasizing territorial resilience modeling, mobility restriction effects, and health-economic tradeoffs using advanced network analytics and large-scale empirical datasets. He delivers instruction in Strategy and Innovation in Digital Environments and Business Data Analytics at Politecnico di Milano's School of Management, applying his research expertise to contemporary business analytics education.

Vidya K Muthukumarمشاهده پروفایل

استادیار

• Machine Learning Theory
• Statistical Signal Processing
• Game Theory
• +۳ مورد دیگر

Dr. Vidya K Muthukumar is the Harold R. and Mary Anne Nash Early Career Professor and Assistant Professor jointly appointed in the School of Electrical and Computer Engineering and H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. She holds a B.Tech (with honors) from the Indian Institute of Technology Madras and a Ph.D. in Electrical Engineering from the University of California, Berkeley. Her research includes internships at IBM Research and Simons Institute for the Theory of Computing. Her research focuses on theoretical aspects of machine learning, including game theory, online learning, statistical learning, multi-agent decision-making, and foundations of overparameterized models. Key interests include designing learning algorithms for strategic environments and analyzing high-dimensional model behaviors. Her publications predominantly explore machine learning theory, optimization, and statistical inference. Recent works emphasize robustness, generalization in overparameterized regimes, multi-agent systems, and efficient algorithms. Trends show strong integration of game theory with statistical learning and novel analyses of modern ML phenomena. Simons-Berkeley Research Fellowship IBM Science for Social Good Fellowship UC Berkeley EECS Outstanding Course Development and Teaching Award ECE Roger Webb Outstanding Junior Faculty Award She advises multiple PhD students including Tyler LaBonte, Kuo-Wei Lai, Chiraag Kaushik, Guanghui Wang, and Andrew McRae. Student projects involve task adaptation, robustness analysis, spectral methods, and optimization theory. She leads research initiatives at the Coda S1139 lab, focusing on theoretical machine learning and collaborative projects.

Ravi Sharmaمشاهده پروفایل

پژوهشگر

• Industrial Internet of Things (IIoT)
• Industry 4.0
• Wireless Sensor Networks
• +۱۱ مورد دیگر

Ravi Sharma is a postdoctoral researcher at the Department of Built Environment at Eindhoven University of Technology, specializing in Industrial Internet of Things (IIoT), Industry 4.0, and data-driven sustainable systems. He earned his PhD from Budapest University of Technology and Economics and a Master's from Indian Institute of Technology, Patna. Role: Researcher focusing on IIoT, ERP integration, and secure data transfer. Projects: Active in the WILSON project for federated digital twinning in building management. Sharma’s research spans IIoT, digital twins, 5G-enabled systems, and blockchain. His work addresses reducing human intervention in industrial data flows, enhancing security, and aligning technology with UN Sustainable Development Goals (SDGs). Recent publications highlight UAV-assisted code dissemination, secure pipeline monitoring, and AI-driven societal advancement. His publications (2025-2020) emphasize IIoT, Industry 4.0, and sustainable computing. Trends include blockchain for data integrity, 5G for real-time positioning, and semantic frameworks for interoperability in construction. Sharma's expertise contributes to SDGs like sustainable cities and responsible consumption. He collaborates across disciplines, including with the WILSON project team for lifecycle building management.

Dirk Peltمشاهده پروفایل

استادیار

• Happiness
• Wellbeing
• Gene-Environment Interaction
• +۷ مورد دیگر

Dirk Pelt is an Assistant Professor at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Behavioural and Movement Sciences and the departments of Biological Psychology, Methodology (APH), and Personalized Medicine (APH). His primary research focuses on wellbeing, happiness, and mental health, employing advanced methodologies like machine learning and genetic analysis. He leads the AI & Machine Learning Grant (2022-2023) and contributes to the WELL BEING research project (2018-2024). Key research interests include gene-environment interactions, psychometric modeling, and the exposome's role in wellbeing. He has supervised three PhD theses and actively presents at academic conferences, discussing topics like machine learning prediction models for wellbeing and faking detection in personality assessments. His work spans over 32 publications, with recent studies examining metabolite concentrations, social media's psychological impacts, and pandemic-related mental health dynamics. Dr. Pelt's contributions address UN Sustainable Development Goals related to good health and well-being. He teaches 'Measurement theory and assessment II' (2024-2025) and collaborates internationally on projects analyzing wellbeing spectra using network approaches and genetic-environmental disentanglement.

• Biometrics
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Marco Micheletto is an Assistant Professor at the University of Cagliari, affiliated with the Pattern Recognition and Applications Laboratory (PRA Lab). His research focuses on biometric security, including fingerprint liveness detection, presentation attack detection, and EEG signal processing for biometric applications. He also investigates deepfake detection and cybersecurity challenges in AI-driven systems. Key projects include the BullyBuster initiative for anti-bullying detection technologies and participation in the LivDet fingerprint liveness detection competitions. His work emphasizes improving system robustness against novel attacks and enhancing generalization in biometric and media forensic applications. Recent research trends highlight advancements in deepfake detection via texture/artifact analysis, fusion strategies for biometric systems, and leveraging AI for social issues like bullying prevention. His contributions span technical improvements in authentication systems, vulnerability assessments in healthcare AI, and forensic methodologies for media integrity. Collaborations involve interdisciplinary efforts in cybersecurity, healthcare, and human behavior analysis. Future work aims to address evolving adversarial techniques and promote ethical AI applications in societal challenges.

Arash Barfarمشاهده پروفایل

دانشیار

• Machine Learning
• Data Science
• Decision Support Systems
• +۳ مورد دیگر

Arash Barfar is an Associate Professor of Information Systems at the University of Nevada, Reno (UNR), within the College of Business. His research focuses on designing data-driven algorithms for social good, with publications in journals such as Decision Support Systems, Journal of the American Medical Informatics Association, and Expert Systems with Applications. He teaches undergraduate and graduate courses in machine learning and data science. Education: Ph.D. in Information Systems from the University of South Florida (USF). Research Interests: Machine Learning Applications for Social Impact Data-Driven Decision Support Systems Health Informatics Big Data Analytics Notable Awards: Graduate Faculty of the Year (Information Systems), 2023, 2020, and 2017 College of Business Faculty of the Year, 2017 Dean’s Award for Excellence in Teaching, 2017 Students’ List of Instructors with Greatest Impact, USF, 2015 Teaching & Advising: No specific advisees listed, but he has created and taught courses in machine learning and data science at both undergraduate and graduate levels. Labs/Teams: No dedicated labs or teams explicitly mentioned, though his research collaborations may involve interdisciplinary teams.

Simon A. Levinمشاهده پروفایل

استاد

• Ecology
• Evolutionary Biology
• Theoretical Biology
• +۴ مورد دیگر

Simon A. Levin is the James S. McDonnell Distinguished University Professor in Ecology and Evolutionary Biology at Princeton University and Director of the Center for BioComplexity within the High Meadows Environmental Institute. His research integrates theoretical biology, ecology, and evolutionary processes with socio-economic systems, focusing on biodiversity, disease dynamics, and collective behavior. He holds appointments in Princeton’s Department of Ecology and Evolutionary Biology and leads interdisciplinary initiatives in environmental complexity. Research Interests Structure and functioning of ecosystems Ecological and socioeconomic system interactions Evolutionary dynamics and cooperation Theoretical foundations of spatial ecology Climate change impacts and policy Selected Awards and Recognition Heineken Prize for Environmental Sciences Kyoto Prize in Basic Sciences National Medal of Science BBVA Frontiers of Knowledge Award Lab and Collaborations Levin’s lab explores collective behavior in biological systems and socio-ecological challenges. Recent work includes modeling epidemic dynamics, critical transitions in ecosystems, and the role of prosocial behavior in heterogeneous societies. Collaborations span ecology, mathematics, and social science, emphasizing interdisciplinary solutions to global environmental issues.

Rebecca Eynonمشاهده پروفایل

استاد

• Digital Education
• Educational Technology
• Digital Inequality
• +۵ مورد دیگر

Rebecca Eynon is Professor of Education, the Internet, and Society at the University of Oxford, holding a joint appointment between the Oxford Internet Institute (OII) and the Department of Education. At Oxford, she leads the Critical Digital Education Research group, focusing on the intersections between new technologies, learning and education across the lifecourse, with particular attention to questions of equity and social justice. Her academic journey at Oxford spans nearly two decades, progressing from Research Fellow (2005-2013) to Senior Research Fellow (2014-2020), Associate Professor (2014-2020), and finally to her current Professorship (2020-present). Professor Eynon's research program critically examines the relationship between digital technologies and educational equity, investigating how digital and social exclusion manifest in educational contexts. Her work spans multiple dimensions including the political economy of EdTech, the sociology of digital education, everyday learning with the Internet, and the role of computational social science in education research. She employs mixed-methods approaches to explore how marginalized communities engage with digital technologies for learning, and how these engagements relate to broader patterns of social inequality. Her research consistently challenges assumptions about technology as an inherent equalizer in education, revealing complex dynamics where digital tools can both enable and constrain educational opportunities. Her scholarly contributions show a clear trajectory toward increasingly critical examinations of AI in education, digital datafication, and algorithmic governance in learning environments. Recent publications demonstrate growing concern with ethical implications of digital trace data in education, structural inequalities in EdTech implementation, and the political economy of educational technology markets. This evolution reflects her commitment to not just documenting digital educational practices but interrogating the power structures that shape them. Professor Eynon has secured research funding from prestigious organizations including the British Academy, the European Commission, Google, and the Economic and Social Research Council (ESRC). She currently directs the ESRC-funded project 'Towards Equity Focused Approaches to EdTech' and serves as an international investigator for the Australian Research Council's Centre for the Digital Child. Her editorial leadership as co-editor of Learning, Media and Technology (2011-2021) has shaped scholarly discourse in the field, and she continues to serve on the editorial boards of this journal and Information and Learning Sciences . As an educator, Professor Eynon teaches in both the OII's MSc in Social Science of the Internet and the Department of Education's MSc in Education (Digital and Social Change). She supervises doctoral students working on topics including AI, digital divides, education inequality, ethics, learning, skills, and youth. Her supervision portfolio includes current students like Liam Bekirsky, Amanda Curtis, AM Ploin, Sarah Stewart, and Anju Gaston, as well as former students such as Ulrike Deetjen and Isis Hjorth. Her commitment to critical perspectives on digital education extends to policy engagement, where she has advised organizations including the Good Things Foundation and the Robertson Foundation, and contributed to discussions through the RSA, Digital Access for All Taskforce, and Centre for Data Ethics and Innovation.

Xiaojun Zengمشاهده پروفایل

استاد

• Machine Learning
• Computational Intelligence
• Decision Support Systems
• +۵ مورد دیگر

Prof. Xiaojun Zeng is a Professor of Machine Learning and Optimisation at the Department of Computer Science, University of Manchester. He holds a B.Sc. in Mathematics from Xiamen University and a Ph.D. in Computation from the University of Manchester. His research focuses on computational intelligence, machine learning, decision support systems, computational finance, health informatics, and data mining. He has published over 200 papers and contributed to projects like the EU-funded IoTRAIN initiative, aiming to modernize IoT education in Iran and Iraq. He has received prestigious awards, including the Microsoft European Retail Application Developer Award (2003) and the European IST Award (1999). His work spans academic leadership roles, including serving as an Associate Editor for IEEE Transactions on Fuzzy Systems and involvement in international conferences. Prof. Zeng’s research also addresses energy demand management and sustainable development goals, reflecting a commitment to interdisciplinary innovation. Education: B.Sc. in Mathematics, Xiamen University M.Sc. in Control Theory and Operations Research, Xiamen University Ph.D. in Computation, University of Manchester Research Interests: Computational intelligence, machine learning applications in healthcare and finance, decision support systems, and IoT integration in engineering education. His work bridges theoretical advancements with practical solutions in retail pricing, energy systems, and smart grids. Key Projects: IoTRAIN (2020–2023): EU project enhancing IoT education in Iran/Iraq. EPSRC and EU Framework projects on pricing, QoS, and cloud computing. Awards: Microsoft European Retail Application Developer Award (2003) European IST Award (1999) Grants & Advising: Supervised 40+ students and contributed to funded projects like the UK EPSRC pricing decision support initiative and EU H2020 Big Data in Finance. His research also impacts industrial collaborations in petroleum pricing and retail revenue management. Labs & Affiliations: Member of the Data Science Institute, Machine Learning and Optimisation group, and Christabel Pankhurst Institute at the University of Manchester.

• Human-Computer Interaction
• Mobile Technology
• Digital Learning
• +۴ مورد دیگر

Steve Love is a Senior Researcher at the Digital Design Studio of The Glasgow School of Art, focusing on the impact of mobile technology and digital applications on human behavior, learning, and usability. His work spans interdisciplinary research in areas such as medtech for children, autonomous driving UX, cybersecurity, and mobile governance. He has led projects funded by the European Space Agency, ESA Digital Good Network, and industry partners like BT and Sharp Laboratories Europe. Notably, he received an Industrial Secondment Award from the Royal Academy of Engineering. Love's research explores topics including multimodal play in healthcare, ethical implications of digital tools, and user experience design for emerging technologies. He is the author of Understanding Mobile Human-Computer Interaction (2005) and editor of Handbook of Mobile Technology Research Methods (2009). His contributions include over 60 publications, emphasizing empirical studies on phishing avoidance, mobile commerce drivers, and cultural factors in technology adoption. His recent work highlights co-design methodologies in medtech (e.g., MRI preparation for children via mixed realities) and the psychological dimensions of autonomous vehicle interaction. Collaborations with institutions like the European Social Fund and Orange underscore his industry-academia partnerships. Key Projects: ESRC Digital Good Network (2025), Mixed Realities Play Kit (2023), Autonomous Driving Feedback (2021) Awards: Royal Academy of Engineering Industrial Secondment Award Labs/Teams: Digital Design Studio at GSA