جست‌وجوی استاد و پژوهشگر

Arjun Chandrasekaranمشاهده پروفایل

پژوهشگر

• Computer Vision
• Natural Language Processing
• Machine Learning
• +۴ مورد دیگر

Arjun Chandrasekaran holds dual roles as a Guest Scientist/Postdoctoral Researcher at the Max Planck Institute for Intelligent Systems (Tübingen, Germany) and Head of AI at CONXAI Technologies (Munich, Germany). His research bridges computer vision, natural language processing, and cognitive science, focusing on multimodal machine learning for understanding human actions, interactions, and behavior in 3D environments. Key areas include human-AI team dynamics, embodied AI, and humor/narrative modeling. Education: PhD in Computer Science (Georgia Tech, advised by Devi Parikh) Postdoc under Michael Black at MPI-IS Research Highlights: Developed the BABEL dataset for 3D human behavior analysis Explored Fermi problems as AI reasoning benchmarks Pioneered studies on humor perception in visual contexts Advocated for explainable AI through human-centric evaluation Publications emphasize interdisciplinary approaches, combining vision-language tasks with cognitive insights. Current work at CONXAI applies AI to transform the Architecture, Engineering, and Construction (AEC) industry through strategic AI solutions and team leadership.

Yixin Zouمشاهده پروفایل

استادیار

• Human-Centered Security
• Privacy Studies
• Cross-Cultural Security Research
• +۳ مورد دیگر

Dr. Yixin Zou is a tenure-track Assistant Professor and Group Leader of the Human-Centered Security and Privacy group at the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. They are also a Principal Investigator at Ruhr University Bochum's CASA Cluster of Excellence. Their research focuses on making technologies safer and equitable for underserved communities, including accessibility challenges for deaf individuals and older adults, cross-cultural security practices, and responsible AI development. They lead interdisciplinary collaborations with legal scholars and social scientists to translate research into real-world impacts. Zou holds a Ph.D. in Information from the University of Michigan (advised by Florian Schaub), recognized with an ACM SIGCHI Outstanding Dissertation Award. Their work is funded by MPI-SP, DFG, Google, and Meta. Notable grants include a Google Academic Research Award on safeguarding emerging adults from short-form video scams and a Meta grant studying digital privacy expectations. Research interests include differential access to security tools, cross-cultural digital privacy norms, and designing interventions to address algorithmic discrimination. They prioritize global perspectives, contrasting WEIRD (Western, Educated, Industrialized, Rich, Democratic) and non-WEIRD populations. Recent projects examine online fraud prevention among Chinese seniors and trauma-informed computing approaches. Zou has received multiple awards, including Best Paper Honorable Mentions (ACM CHI 2025/2020/2019) and a Distinguished Paper Award (SOUPS 2018). Their group's work bridges academia and practice through partnerships with companies, NGOs, and policymakers. Current initiatives focus on explainable hardware (XHW) and mitigating image-based sexual abuse on online platforms. Students and collaborators include researchers from computer science, law, and social sciences. Former members have moved to industry and academia roles. Zou advises via the MPI-SP doctoral programs and offers internships through the CS@Max Planck program.

Eleni Straitouriمشاهده پروفایل

استاد

• Machine Learning
• Causal Inference
• Large Language Models
• +۴ مورد دیگر

Eleni Straitouri is a Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany, where she leads research at the intersection of artificial intelligence safety and human-centered decision systems. Her work bridges theoretical machine learning with practical applications in high-stakes environments requiring rigorous uncertainty quantification and causal reasoning. Her primary research focuses on causal inference in large language models , counterfactual prediction sets , and human-AI complementarity . She develops methods to control algorithmic harm through counterfactual reasoning, enhances model evaluation via conformal prediction, and designs decision support tools that leverage human expertise. Current projects address critical challenges in LLM safety, including bias detection through coupled token generation and harm mitigation in medical/operational decision systems. Analysis of her 13 publications (2021-2025) reveals an evolving trajectory from reinforcement learning foundations toward cutting-edge LLM safety frameworks. Her work consistently integrates causal methods with human factors, producing practical tools for reliable AI deployment across healthcare and operational domains. Recent NeurIPS/ICML publications demonstrate increasing influence in trustworthy AI research. Best Paper Award, Workshop on AI and HCI (ICML), 2023 Spotlight Paper (top 3.5%), ICML 2024 Dr. Straitouri maintains active collaborations with Manuel Gomez-Rodriguez's research group, evidenced by co-authorship on 100% of her recent publications. While specific grant funding isn't detailed in available materials, her work aligns with MPI-SWS's focus on software systems for societal benefit. No information about academic advising or laboratory infrastructure was found in the provided text.

• Computer-Supported Cooperative Work (CSCW)
• Human-Computer Interaction (HCI)
• Ethnography
• +۷ مورد دیگر

Peter Tolmie is a Researcher at the University of Siegen's Faculty III Business Informatics and New Media. He holds a PhD in Sociology from Lancaster University (2003) and has extensive experience in ethnography and CSCW research. His career includes roles at Xerox Research Center Europe (2000-2002) and the University of Nottingham (2010-2017). Education: Bachelor's Degree in Independent Studies (First Class Honors), Lancaster University (1997) PhD in Sociology, Lancaster University (2003) Research Interests: Tolmie focuses on Computer-Supported Cooperative Work , Human-Computer Interaction , and ethnography . His work explores digital transformation in healthcare, activism, manufacturing, and rural communities. Key themes include accountability in AI, content moderation in conflict zones, and the design of inclusive technologies. Articles Trends: Recent work addresses challenges in social robotics, digital inclusion in marginalized regions, and ethical design in voice assistants. His studies often combine ethnographic methods with participatory approaches to address systemic inequalities and technological barriers. Awards: Best Paper Award at CHI 2017 (with Rob Proctor et al.) Grants & Advising: Tolmie has contributed to projects funded by the EU and German research initiatives, including the European Digital Innovation Hub South Westphalia (EDIH) . He advises on interdisciplinary research at the intersection of technology and socio-cultural contexts, particularly in healthcare and rural development. Labs & Teams: Involved in the Fab Lab Siegen and PRAXLABS , promoting hands-on innovation and community-driven tech solutions. Collaborates with global teams on projects like Shielding or Silencing? and Robots in Care .

Mohamad Nassarمشاهده پروفایل

استادیار

• Artificial Intelligence
• Cybersecurity
• Explainable AI
• +۳ مورد دیگر

Dr. Mohamad Nassar is a tenure-track Assistant Professor in the Department of Electrical & Computer Engineering and Computer Science at the Tagliatela College of Engineering, University of New Haven, with prior appointments at The University of Alabama in Huntsville and the American University of Beirut. He holds a Ph.D. in Computer Science from the University of Lorraine, France, and specializes in AI for cybersecurity and explainable AI. His research focuses on artificial intelligence applications in cybersecurity, explainable AI systems, lean operational methodologies, logistics optimization, and ergonomic design principles. Dr. Nassar has published over 40 peer-reviewed articles in these domains. Recent publications demonstrate strong emphasis on AI-driven cybersecurity solutions, adversarial machine learning defenses, and intelligent systems for urban infrastructure. His work frequently incorporates game theory, reinforcement learning, and privacy-preserving techniques within cloud-fog network architectures. Dr. Nassar maintains an active research agenda with ongoing projects in hardware-assisted malware detection, smart transportation systems, and deep learning security frameworks. He contributes to the academic community through peer review and conference participation.

Danielle Szafirمشاهده پروفایل

استادیار

• Data Visualization
• Computer Graphics
• Cognitive Science
• +۶ مورد دیگر

Danielle Szafir is an Assistant Professor in the Department of Computer Science at the University of North Carolina-Chapel Hill, leading the UNC/CU VisuaLab. Her research bridges visualization, computer graphics, and cognitive science, focusing on human-computer interaction, color science, and accessible data representation. She holds a Ph.D. from the University of Wisconsin-Madison and has taught courses such as COMP 790/590-173 (Information Visualization) and ATLS 5519 (Research Methods). Her work is supported by grants from NSF, NIH, and the U.S. Air Force. Research interests include Human-AI collaboration in analytics Cognitive accessibility in visualizations Immersive analytics for AR/VR Color encoding models Recent grants include a State of the States award (2024) to advance cognitively accessible visualization. She has advised students like Sophia Lin (Honors thesis, 2024) and postdocs such as Ghulam Quadri (now at OU). Key awards include the Best Paper Award (IEEE VIS 2017) and NSF CAREER funding (2021). Her lab develops tools like Cieran (color map design) and PD-Insighter (health analytics). Collaborations span genomics, bioinformatics, and psychology.

Kalyan Veeramachaneniمشاهده پروفایل

پژوهشگر

• Big Data Science
• Machine Learning
• Artificial Intelligence Applications
• +۲ مورد دیگر

Kalyan Veeramachaneni is a Principal Research Scientist at the Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT). He leads the Data-to-AI group, focusing on developing AI systems to address societal challenges through big data science and machine learning. His research emphasizes statistical modeling, automated feature engineering, and human-data interaction for impactful applications. His work spans synthetic data generation, anomaly detection, adversarial machine learning, and explainable AI frameworks. Key contributions include the Synthetic Data Vault, Cardea, and Ballet collaboration tools. He collaborates across industries to deploy interpretable ML systems in healthcare, energy, and cybersecurity. Notable projects include OrionBench (time series benchmarking), Explingo (AI explanations via LLMs), and frameworks like Pyreal and ATM for automated machine learning. His research bridges theoretical advancements with practical usability, emphasizing end-user priorities in system design. Current affiliations: LIDS MIT | MIT Institute for Data, Systems, and Society (IDSS). Office: 32-D714, Cambridge, MA.

• mHealth
• IoT and Wearables
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Drishty Sobnath is an Assistant Professor in Computer Science at the School of Mathematical & Computer Sciences, Heriot-Watt University. Previously, she held roles as Senior Lecturer and Programme Leader at Solent University, overseeing BSc Computer Science, BSc Software Engineering, and MSc Computer Engineering programs. She earned her PhD in Software Engineering (mHealth) from Kingston University, London. Her research focuses on mHealth, IoT, and AI-driven solutions for healthcare management, including projects like the EU-funded WELCOME initiative for COPD patients and the CANAdvice cancer treatment app. She is also a co-investigator on the Office for Students' AI/Data Science scholarship project addressing workforce digital skills gaps. Research interests span AI in healthcare diagnostics, smart city technologies, and inclusive education strategies. She teaches Data Science and AI at undergraduate/postgraduate levels, contributing to UN Sustainable Development Goals related to health and education equity. Notable achievements include Best Paper Awards (2018, 2019) and a 2021 teaching award nomination. Her work integrates interdisciplinary approaches, combining technical innovation with societal impact through projects like the DAP framework for data-driven daily living and AI-driven PPE monitoring systems. Grants and collaborations include EU FP7 funding, industry partnerships on smart city solutions, and initiatives promoting diversity in STEM education. She advises on postgraduate conversion course design and has authored over 20 peer-reviewed articles, focusing on healthcare tech, computer vision applications, and learning analytics.

• Financial Technologies
• Platform Economy
• Digital Transformation
• +۳ مورد دیگر

Zhongju (John) Zhang is the Dean's Council Distinguished Professor of Information Systems at Arizona State University's W. P. Carey School of Business. He holds a PhD from the University of Washington (2003), an MS from National University of Singapore (1999), and a BS from Xi'an Jiaotong University (1996). His research focuses on how information systems and data analytics impact consumer behavior and business models, with special interests in fintech, platform economy, and machine learning. Key research contributions include studies on financial disinformation detection, ridesharing's traffic impact, and cross-platform content generation. He has published in top journals like Information Systems Research and MIS Quarterly. Awards include the Best IS Publications of the Year Award, Research Excellence Award, and multiple grants from AWS and industry partners. Zhang advises on strategic analytics for organizations like General Electric and Aetna. He co-edits the Journal of Information Technology and Management and serves as a senior editor for Production and Operations Management. His teaching spans business analytics, data mining, and predictive modeling.

Sonia Chernovaمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +۴ مورد دیگر

Sonia Chernova is an Associate Professor in the School of Interactive Computing at Georgia Tech's College of Computing. She leads the Robot Autonomy and Interactive Learning (RAIL) lab, focusing on developing intelligent and interactive autonomous systems. Her research spans robotics and AI, emphasizing semantic reasoning, human-robot interaction, and explainable AI. She directs the NSF AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING), which develops AI systems to support aging adults in home environments. Research Interests: Chernova’s work integrates human-centric approaches to robotics, including adaptive autonomy, explainable AI, and AI for health. She explores how robots can learn from human feedback, navigate ambiguous tasks, and provide proactive assistance in daily environments. Her lab’s projects emphasize real-world applications in healthcare, smart homes, and collaborative human-robot teams. Key Contributions: Developed frameworks for preference adaptation in object rearrangement and human-robot collaboration Pioneered explainable AI techniques for robot failures and decision-making transparency Advanced semantic reasoning methods for robots operating in everyday environments Led initiatives in proactive assistive robotics and aging-in-place technologies Awards & Recognition: 2021 IEEE/ACM HRI Best Paper Award 2018 NAE Frontiers of Engineering Top 100 Researcher 2014 ONR Young Investigator Award 2012 NSF CAREER Award Teaching & Outreach: Teaches courses on robotics, human-robot interaction, and robotics research. Co-chaired the Conference on Robot Learning (CoRL 2023) and developed outreach programs to bridge pre-college computer science education gaps. Affiliations: GVU Center, Institute for Robotics and Intelligent Machines (IRIM), and the NSF AI-CARING Institute.

• Digital Transition
• Human Factors
• Safety and Risk Management
• +۶ مورد دیگر

Marco De Angelis is a Senior Assistant Professor (fixed-term) at the Department of Psychology 'Renzo Canestrari', University of Bologna. His research focuses on digital transition, human factors, occupational safety, and mental health at work. He leads the H-WORK project, a EU-funded initiative promoting mental health in SMEs and public workplaces through multilevel interventions. He is also involved in projects like Digi-B-Well, addressing digital wellbeing and SME digitalization. Education: PhD in Psychology (2019, University of Bologna), Master's in Work and Organizational Psychology (2013), and Bachelor's in Psychology (2011, Sapienza University of Rome). Professional experience includes roles in the Modena Local Health Authority (2013–2014) and the CE.TRANS research group (2014–2015). Research interests emphasize digital interventions for mental health, AI integration in workplaces, safety systems design, and compassionate healthcare organizations. He coordinates the LAV.OR.I.O. Research Lab, exploring work-related stress, organizational innovation, and human factors. Teaching activities span international programs in Work-Organizational Psychology, Chemical Innovation, and International Development. He supervises student theses on digital mental health, AI at work, and Industry 5.0 cognitive workload. Membership includes editorial boards for the International Journal of Environmental Research and Public Health , Human Factors and Ergonomics Society - Europe, and associations like EAWOP, SIOP, and EAOHP. He contributes to over 100 peer-reviewed articles and conference proceedings, focusing on workplace safety, technology acceptance, and mental health policies.

Catherine Plaisantمشاهده پروفایل

پژوهشگر

• User Interface Design
• Information Visualization
• Human-Computer Interaction
• +۴ مورد دیگر

Catherine Plaisant is a Research Scientist Emerita at the University of Maryland's Institute for Advanced Computer Studies (UMIACS) and a member of the Human-Computer Interaction Lab (HCIL). She holds a Doctorat d’Ingénieur from Université Pierre et Marie Curie and has been affiliated with HCIL since 1988. Her work focuses on user interface design, information visualization, and healthcare informatics, emphasizing usability, accessibility, and interdisciplinary collaboration. Education: PhD (Doctorat d’Ingénieur), Université Pierre et Marie Curie. Research Interests: User interface design/evaluation, information visualization, medical informatics, digital libraries, and temporal data analysis. Key projects include EventAction, Twinlist, and PAOHVIS. Articles Trends: Recent work emphasizes visual analytics for temporal event sequences, healthcare decision-making tools, and historical HCI preservation. Key themes include explainable AI, cohort comparison, and user-centered design. Scientific Awards: IEEE VIS Career Award (2020), ACM SIGCHI Lifetime Service Award (2020), ACM SIGCHI Academy membership (2015), and multiple Test of Time Awards for influential papers. Grants & Advising: Collaborations with INRIA and industry sponsors. Advised on projects like SHARP-C and EventFlow. Retired from direct student mentoring but remains active in research. Labs/Teams: HCIL and UMIACS. Collaborates internationally with institutions like INRIA (France) on projects such as PKclustering and Dynamic Hypergraph visualization.

Aparna Vardeمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۹ مورد دیگر

Dr. Aparna Varde is a tenured Associate Professor in the School of Computing at Montclair State University (MSU), NJ. She also serves as Associate Director of the Clean Energy and Sustainability Analytics Research Center (CESAC) and previously held roles as Inaugural Associate Director for Graduate Studies and Research in SoC. She has conducted research visits at the Max Planck Institute for Informatics in Germany and holds degrees from the University of Bombay (BE), Worcester Polytechnic Institute (MS/PhD). Her research focuses on AI, Machine Learning, Data Mining, Environmental Computing, and Robotics. Notable projects include GreenDSS (green data center decision support), CSK-based robotics (e.g., CSK-Detector and Robo-CSK-Organizer), and offshore wind energy analysis. She has secured over $2M in grants from PSE&G, NSF, NOAA, and NJEDA. Dr. Varde advises PhD students (e.g., Xu Du, Michael Pawlish) and serves on editorial/review boards for IEEE/ACM journals. She has received 9 best paper awards at IEEE conferences and is recognized as an outstanding researcher by USCIS. Education: BE (University of Bombay), MS/PhD (Worcester Polytechnic Institute) Awards: 9 best paper awards at IEEE conferences, NSF/NOAA grants, Fulbright mentorship Key Projects: Smart Cities policy analysis, autonomous vehicle CSK integration, drone-based environmental monitoring Her work spans 150+ publications in IEEE/ACM venues and addresses UN Sustainable Development Goals through smart living apps (e.g., food donation platforms, hydro-climate tools). Collaborations include MPII Germany, IBM Research, and Queensland University of Technology.

Antti Oulasvirtaمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• User Interfaces
• Human Factors
• +۴ مورد دیگر

Antti Oulasvirta is an Associate Professor at Aalto University, leading the User Interfaces research group. He previously held positions at the Max Planck Institute for Informatics and the Helsinki Institute for Information Technology (HIIT). His doctoral degree in Cognitive Science from the University of Helsinki (2006) was followed by a Fulbright Scholarship at UC Berkeley (2007-2008). He has received the ERC Starting Grant (2015-2020) for computational user interface design research. Education: Doctorate in Cognitive Science from the University of Helsinki (2006), postgraduate studies at UC Berkeley’s Neuropsychology Lab (2002-2003), and an internship at T-Labs, Berlin (2006). Research Interests: Focus on human-computer interaction, user interfaces, and computational modeling of human behavior. His work bridges HCI with cognitive science, emphasizing optimal decision-making and interaction design. Key areas include reinforcement learning agents, eye movement control, and human-AI collaboration. Recent Articles: Focus on LLM limitations in problem reframing, touchscreen typing errors, and task-driven eye movement models. His work integrates computational rationality to explain and enhance human-computer interactions. Scientific Awards: Multiple CHI Best Paper Awards (12x between 2008-2018), ERC Starting Grant, and recognition at major conferences like NordiCHI and CoDIT. His research contributes to UN Sustainable Development Goals through inclusive and efficient interface design. Advising & Grants: Principal investigator on projects funded by EU Horizon Europe, Academy of Finland, and Business Finland. Leads initiatives like the Finnish Center for Artificial Intelligence (FCAI) and the Artificial User project. Supervises numerous theses and mentors researchers globally. Labs & Teams: Director of the User Interfaces group at Aalto, collaborating internationally on HCI and AI. Active in organizing conferences (e.g., ACM SIGCHI) and reviewing for journals like the International Journal of Human-Computer Studies.

• Artificial Intelligence
• Machine Learning
• Wind Engineering
• +۵ مورد دیگر

Yu Tian is a Researcher at the School of Science, Peking University, specializing in interdisciplinary research at the intersection of artificial intelligence, renewable energy, and data science. They hold a Doctoral degree in Medical and Health Sciences from Peking University Health Science Center (2010). Their work addresses UN Sustainable Development Goals related to sustainable energy and innovation. Education: Doctor of Philosophy, Medical and Health Sciences, Peking University (2010) Research focuses on explainable AI, federated learning algorithms, high-resolution wind energy modeling, and sustainable industrial systems. Notable contributions include developing clustered federated learning frameworks and creating detailed wind atlases for Finland to optimize renewable energy integration. Yu Tian’s work emphasizes practical applications of AI in energy management and environmental science. Recent publications span machine learning optimization, energy systems, and wind energy modeling. Collaborations include international projects on AI-driven sustainability and federated learning methodologies. Their research bridges theoretical advancements with real-world challenges in energy and health sectors.