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David Blaauw is the Kensall D. Wise Collegiate Professor of Electrical Engineering and Computer Science (EECS) at the University of Michigan. His research focuses on ultra-low-power analog/mixed-signal circuits, mm-scale sensors, neural networks, and biomedical applications. He leads the Blaauw Lab, which has pioneered innovations like the Michigan Micro Mote (M^3) and neural recording probes. His work emphasizes real-world deployability, with applications in environmental monitoring (e.g., monarch butterflies), medical devices, and robotics. Education: B.S. in Physics and Computer Science, Duke University (1986) Ph.D. in Computer Science, University of Illinois Urbana-Champaign (1991) Research Interests: Blaauw’s lab explores ultra-low-power computing, mm-scale systems, RF communication, in-memory computing, and genomics acceleration. Key projects include: Millimeter-scale computers (e.g., 0.04mm³ temperature sensors) Wireless neural interfaces for brain-machine communication Energy-efficient accelerators for edge AI and genomics Micro-robotics with sensing/actuation/computation Awards: IEEE Fellow 2016 SIA-SRC Faculty Award Motorola Innovation Award Best Paper Awards at ISSCC, ISCA, and RFIC Advising & Impact: Over 600 publications, 65 patents, and 4 startup companies spun from his lab. Current research includes genome sequencing accelerators (GenAx) and neural recording dust for brain mapping. He directs the Michigan Integrated Circuits Lab and chairs major conferences like ISSCC and DAC. Labs/Teams: Blaauw Lab (University of Michigan) Michigan Integrated Circuits Lab (MICAL)

• Ultra-Low Power Circuits
• IoT and Sensor Networks
• RF Communication
• +7 more

Hasan Davulcu is a Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). He holds a B.S. in Mathematics from Middle East Technical University (Turkey) and M.S./Ph.D. in Computer Science from Stony Brook University (NY). His research focuses on sociocultural modeling, AI, machine learning, and behavioral analytics for fraud detection. He leads the CIPS-AI Lab, developing data mining tools for semantic information extraction from social media and web data. Affiliations: Senior Global Futures Scientist (Global Futures Scientists and Scholars Program), Co-founder & CIO of ARTIS MAGI (AI-driven behavioral analysis startup). Education: Ph.D. Computer Science (Stony Brook, 2002), M.S. Computer Science (Stony Brook, 1995), B.S. Mathematics (METU, 1993). Research interests include: Sociocultural modeling and persuasive AI. Web/social media mining, information extraction, and database systems. Behavioral analytics for fraud detection and countering extremist influence. Key achievements: 2011 HSCB Focus Exceptional Scientific Achievement Award for work on sociocultural modeling in the DOD Minerva project. Principal Investigator on NSF and DoD grants, including behavioral analytics for financial fraud and social influence analysis of extremist groups. Grants & Projects: NSF PFI:BIC Grant (2014-2019): Behavioral analytics for fraud detection via visual analytics infrastructure. DoD Minerva (2015-2019): Measuring social influence of extremist groups. ONR Projects (2018-2021): Modeling polarization, adversarial framing, and disinformation tracking. Labs/Teams: Cognitive Information Processing Systems (CIPS-AI) Lab, which pioneers data mining techniques for unstructured social media data and semantic representation systems.

• Sociocultural Modeling
• Artificial Intelligence
• Machine Learning
• +5 more

Aldo Gangemi is a Full Professor in the Department of Philosophy at the University of Bologna, specializing in Informatics (INFO-01/A). His research integrates Semantic Technologies, Natural Language Processing, Data Science, and Cognitive Science to address challenges in knowledge representation, ontology engineering, and cultural heritage informatics. He co-founded the STLab at ISTC-CNR and DHARC at the University of Bologna, and is on leave from Sorbonne Paris Nord University (Computer Science Lab - LIPN). Scientific roles include serving as area chair for Web Semantics , editorial board member for Semantic Web and Applied Ontology , and conference chairs for major events like WWW2015 and ESWC2018. He has led European projects such as GALEN, WonderWeb, NeOn, and MARIO, and developed software tools like FRED, Aemoo, and Framester. Research interests focus on semantic technologies, knowledge patterns, and applications in humanities, medicine, law, and fisheries. Over 250 peer-reviewed publications span these areas, with emphasis on ontology-based knowledge integration, multimodal reasoning, and ethical AI. His work bridges cognitive science and technological innovation, particularly in virtual reality's societal applications and cultural heritage preservation. Notable contributions include the PRIVAFRAME knowledge graph for sensitive data, the ImageSchemaNet ontology for embodied cognition, and the Sandra neuro-symbolic reasoner. His interdisciplinary approach addresses challenges in AI ethics, creative systems, and citizen-driven data curation.

• Semantic Technologies
• Knowledge Representation
• Natural Language Processing
• +6 more

Ketika Garg is a Postdoctoral Scholar Research Associate in the Division of the Humanities and Social Sciences at the California Institute of Technology (Caltech), with an office in the Broad Center for Biological Sciences. Her research focuses on the interplay between individual and social decisions, using experimental and computational methods to explore how social environments influence decision-making and collective behavior. She investigates contexts ranging from traditional foraging paradigms to modern social media landscapes, developing innovative experimental frameworks to study these dynamics. Her research interests span computational neuroscience, social media analysis, and collective behavior, with a particular emphasis on understanding exploration-exploitation trade-offs in both natural and digital environments. She has contributed to studies on hunter-gatherer foraging networks, online toxicity dynamics, and the evolution of search strategies in collective foraging systems. Her work bridges disciplines such as psychology, ecology, and computer science to address fundamental questions in decision-making and social interaction. Dr. Garg’s publications reflect her interdisciplinary approach, covering topics like synergy in collective problem-solving, the roots of online toxicity, and the application of Lévy walks in virtual foraging experiments. While no formal awards or grants are explicitly listed in the provided materials, her research trajectory demonstrates a commitment to advancing methodologies in computational social science. Contact: kgarg@caltech.edu Office: Broad Center for Biological Sciences (96) Phone: 626-395-1755

• Computational Neuroscience
• Social Media Analysis
• Collective Behavior
• +3 more

Vivek Srikumar is an Associate Professor in the Kahlert School of Computing at the University of Utah, co-leading the Utah NLP group and affiliated with the Utah Center for Data Science. His research focuses on Machine Learning and Natural Language Processing, particularly in structured prediction, bias mitigation, and healthcare NLP applications. He teaches Machine Learning (CS 6350/DS 4350) and has been supported by NSF, NIH, and corporate grants from Intel, Google, and others. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2013) Postdoctoral Researcher at Stanford University's NLP Group (2013-2014) Visiting Researcher at Allen Institute for Artificial Intelligence (2022 sabbatical) Research Interests: Srikumar explores text understanding, structured learning, and robust AI systems. His work addresses challenges in table-based reasoning, adversarial robustness, and ethical AI. He develops methods to ensure models use appropriate evidence and mitigate biases in representations. Grants & Collaborations: Supported by NSF, NIH, BSF, and industry partnerships with Intel, Google, Verisk, Bloomberg, and Nvidia. Notable projects include table QA systems (TempTabQA), bias mitigation (OSCaR/VERB), and crisis counseling NLP tools (ClientBot). Advising: Supervised over 30 students, including 15+ Ph.D./M.S. alumni now in academia and industry (e.g., Google, Amazon, Microsoft). Current advisees focus on multimodal reasoning, healthcare NLP, and AI ethics. Labs/Teams: Utah NLP Group and Utah Center for Data Science. Active in reproducibility efforts (LogFlux) and open-source tools (CogCompNLP/Pylon frameworks).

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +5 more

Rong Pan is a Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Industrial Engineering from Pennsylvania State University (2002), an M.S. from Florida A&M University (1999), and a B.S. in Materials Science from Shanghai Jiao Tong University (1995). His research focuses on quality and reliability engineering, design of experiments, time series analysis, and statistical learning theory. Key projects involve NSF-funded research on reliability prediction, accelerated life testing, and degradation modeling. He serves as an Associate Editor for the Journal of Quality Technology and has authored over 80 publications. Courses taught include Reliability Engineering, Design of Experiments, and Statistics for Data Analysts. His academic service includes roles as a referee for IEEE Transactions and IIE journals. Research interests emphasize statistical methods for reliability improvement, with recent work on Bayesian inference models, optimal experimental design, and machine learning applications in industrial systems. Grants include collaborations with the NSF, Arizona Department of Transportation, and Science Foundation Arizona. His work bridges theoretical advancements and practical applications in manufacturing, energy systems, and semiconductor reliability. Education: Ph.D. (2002), M.S. (1999), B.S. (1995) Key Research Areas: Reliability Engineering, Bayesian Methods, Time Series, DOE Active Grants: NSF CMMI, SUNY IT Visiting Scholar Program Teaching: IEE 573 Reliability Engineering, DSE 501 Statistics Service: Journal of Quality Technology (Associate Editor), IEEE Transactions (Referee)

• Quality and Reliability Engineering
• Design of Experiments
• Time Series Analysis
• +6 more

Özer Özkahraman is a postdoctoral researcher at the Division of Robotics, Perception and Learning (RPL) at KTH Royal Institute of Technology. He works under Ivan Stenius and John Folkesson, focusing on underwater mission planning, simulation, and integration of autonomous systems. His email is ozero@kth.se . He completed his PhD at KTH under Petter Ögren, researching large-scale multi-agent coverage planning for autonomous underwater vehicles (AUVs). Current projects include the SMaRCSim multi-domain simulation platform and development of underwater vehicles like LoLo, SAM, and Evolo. Research interests span autonomous underwater systems, multi-agent coordination, control systems, and simulation infrastructure. He emphasizes modular, accessible frameworks for vehicle testing and real-world deployment. His work bridges theoretical methods (e.g., control barrier functions) with practical applications in marine robotics. Publications focus on AUV navigation, environmental sensing, and adaptive control. Projects like Real2Sim aim to align simulation with real-world vehicle dynamics using motion capture data. He collaborates internationally on topics like data-driven damage detection and model compression for resource-constrained robots. No academic awards are explicitly mentioned. He actively seeks collaborators for projects in sonar simulation, flow field modeling, and cyber-physical system integration.

• Autonomous Underwater Vehicles (AUVs)
• Multi-Agent Systems
• Robotics
• +6 more

Eetu Mäkelä is a Professor of Digital Humanities at the University of Helsinki, leading the research group at the Helsinki Centre for Digital Humanities. He focuses on computational methods in humanities and social sciences, including datafication and interdisciplinary collaboration. He serves as Technical Director of DARIAH-FI and a Research Programme Director at the Helsinki Institute for Social Sciences and Humanities. Currently, he heads the preparatory group for the Helsinki Liberal Arts and Sciences Bachelor’s Programme. His research emphasizes technological and theoretical foundations of computational research, with notable contributions to linked open data, sociolinguistic analysis, and historical text mining. He has developed widely used tools like Recon, Palladio, and Octavo, which are employed in academic and public sectors. His work has garnered over 20 awards, including best paper and open science recognitions. Key areas of expertise include digital humanities methodologies, data integration, and open science practices. He actively contributes to teaching, designing courses such as 'Methods for Digital Humanities' and demonstrating innovative pedagogical approaches. His recent projects involve analyzing 18th-century philosophical texts and sociolinguistic change using computational methods. Awards: Multiple best paper awards, open data awards, and open science awards. Grants/Advising: Leads research initiatives funded by the Academy of Finland and other bodies. Mentors interdisciplinary research teams but no specific student names listed. He maintains active roles in academic infrastructure, including the DARIAH-FI initiative and the Helsinki Institute’s datafication program. His work bridges technical innovation with humanities scholarship, emphasizing practical, enduring systems for academic and public use.

• Digital Humanities
• Human Sciences–Computing Interaction
• Computational Humanities
• +3 more

Svetlana Lazebnik is a Full Professor and Willett Faculty Scholar in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), part of the Grainger College of Engineering. She holds a Ph.D. from UIUC (2006) and previously served as an Assistant Professor at the University of North Carolina at Chapel Hill (2007–2011). Her research focuses on computer vision, including generative models for virtual try-on, image stylization, scene understanding, and joint modeling of images and language. She has advised numerous Ph.D. students and postdocs, many of whom now hold prominent academic and industry roles. Education: Ph.D. in Computer Science, UIUC (2006); supervised by Jean Ponce. Research Interests: Her work spans generative adversarial networks (GANs), diffusion models, virtual try-on systems (e.g., Dressing-in-Order, Street Try-On), exemplar-based stylization, and large-scale photo analysis. She has pioneered spatial pyramid matching and contributed to binary code learning for image retrieval. Key Awards: NSF CAREER Award (2008), Microsoft Research Faculty Fellow (2009), Sloan Research Fellow (2013), IEEE Fellow (2021), and the Longuet-Higgins Prize (2016) for her CVPR 2006 paper. Teaching: Recent courses include CS 444 (Deep Learning for Computer Vision), CS 543 (Computer Vision), and a Ph.D. Job Search Seminar. She has also taught at UNC Chapel Hill. Grants & Funding: Supported by NSF, Amazon, AWS, Microsoft, Sloan Foundation, Google, ARO, and Adobe. Notable grants include CCF 2348624 and IIS 1718221. Labs/Groups: Leader in the Illinois CS Vision Group, contributing to collaborative projects on embodied AI, multi-agent systems, and visual-semantic reasoning.

• Computer Vision
• Generative Models
• Image-Text Joint Modeling
• +5 more

Sewon Min is an Assistant Professor at UC Berkeley's Electrical Engineering and Computer Sciences (EECS) department and a research scientist at the Allen Institute for AI. Her research focuses on Natural Language Processing (NLP) and Machine Learning, particularly Large Language Models (LLMs), emphasizing data-centric approaches and ethical AI practices. She holds a Ph.D. from the University of Washington (2024) and a B.S. from Seoul National University (2018). Her work includes advancements in retrieval-based models, mixture-of-experts architectures, and data privacy in LLMs. Notable projects include FlexOlmo (flexible data use in LLMs) and OLMoE (open mixture-of-experts models). She has been recognized with the ACM Doctoral Dissertation Award Honorable Mention (2025) and WAGS/ProQuest Innovation in Technology Award. Recent articles highlight her contributions to reasoning models, data tracing (OLMoTrace), and scalable retrieval systems (MassiveDS). She leads the Berkeley NLP Group and collaborates with BAIR, exploring topics like model transparency and ethical data usage.

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +2 more

Anant Sahai is the Qualcomm Chair Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. He holds affiliations with the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Laboratory for Information and System Sciences (BLISS), and the Berkeley Wireless Research Center (BWRC). His academic journey includes a BS from UC Berkeley (1994), and MS (1996) and PhD (2001) degrees from MIT. He previously worked at Enuvis, Inc., focusing on adaptive software radio techniques for low-SNR GPS environments. Research interests span machine learning, wireless communication, information theory, signal processing, and decentralized control, with a focus on intersections between these fields. Key areas include spectrum sharing, ultra-reliable low-latency wireless protocols, and the foundations of overparameterized machine learning. Recent work explores in-context learning in modern AI models. He has received awards such as the IEEE ComSoc Leonard G. Abraham Prize (2012) and teaching/mentorship accolades at Berkeley. He advises UC Berkeley’s Eta Kappa Nu chapter and coordinates machine learning efforts for NSF’s SpectrumX. Current teaching includes CS 182/282A on deep neural networks. His lab focuses on theoretical and applied challenges in communication systems, AI, and control theory. Awards: IEEE ComSoc Leonard G. Abraham Prize (2012), Teaching Excellence Awards (2015–2017) Grants: NSF Center for Spectrum Innovation (SpectrumX), multiple collaborative projects in wireless and AI Labs/Teams: BLISS, BAIR, BWRC

• Machine Learning
• Wireless Communication
• Information Theory
• +4 more

Qian Lou serves as an Assistant Professor in the Department of Computer Science at the University of Central Florida and is an active member of the university's Cybersecurity and Privacy Cluster. His research program targets critical challenges in deep learning systems, specifically enhancing efficiency, privacy, and security for applications in computer vision and natural language processing. His academic foundation includes: Ph.D. in Computer Engineering from Indiana University, Bloomington M.S. in Computer Engineering from Indiana University Bloomington B.S. in Computer Science from Shandong University Lou's research spans deep learning methodologies with specialized focus on computer vision and natural language processing systems. His work integrates cybersecurity principles into machine learning architectures, particularly through privacy-preserving techniques and secure computational frameworks. This interdisciplinary approach bridges computer systems architecture with practical AI deployment challenges, emphasizing efficiency optimizations for resource-constrained environments. His scholarly recognition includes: 2018 AAMAS Best Paper Finalist 2016 AAMAS Best Demonstration Paper Finalist University of Southern California Merit Fellowship Professionally, Lou contributes extensively to the academic ecosystem through program committee service and peer review activities for leading conferences and journals in artificial intelligence and computer science. His industry experience at Samsung Research AI Center informs his applied research perspective and technology transfer approach. Within UCF's Cybersecurity and Privacy Cluster, Lou collaborates on cross-disciplinary initiatives addressing real-world security vulnerabilities in AI systems, working alongside researchers from engineering, data science, and policy domains to develop robust computational frameworks.

• Deep Learning
• Computer Vision
• Natural Language Processing
• +3 more

Graham Neubig is an Associate Professor at the Language Technologies Institute (LTI) within Carnegie Mellon University (CMU). His research focuses on advancing artificial intelligence, particularly in natural language processing (NLP), multimodal reasoning, and large language models (LLMs). He explores topics such as AI safety, generative AI, and human-AI interaction, with an emphasis on practical applications like machine translation and web-agent systems. His work often involves developing frameworks for evaluating AI systems, such as OpenAgentSafety and BehaviorBox, which assess real-world agent performance and model behavior. Neubig's research also delves into improving LLM capabilities through reasoning analysis, hallucination detection (e.g., ZINA), and culturally aware systems (e.g., CAIRe). He has contributed to open-source projects like Pangea (a multilingual LLM) and frameworks such as Cmulab for model deployment. His recent work addresses challenges in agentic tasks, self-improving agents (Skillweaver), and benchmarking across domains like visual reasoning (VisualPuzzles) and software engineering. Notable achievements include advancing evaluation methodologies for LLMs, developing tools for ethical AI, and creating benchmark suites that test systems under realistic conditions. His lab collaborates on projects like the BrowserGym ecosystem and OpenHands platform, which aim to standardize web-agent research and AI-driven software development. Neubig's contributions span theoretical advancements and practical implementations, bridging the gap between cutting-edge research and real-world applications. He advises students such as Apurva Gandhi and actively publishes in top venues, addressing topics from instruction-following improvements to the societal impacts of AI. His work frequently emphasizes the importance of transparency, controllability, and cultural awareness in AI systems.

• Artificial Intelligence
• Natural Language Processing
• Multimodal Reasoning
• +4 more

Dr. Aniket Bera is an Associate Professor in Computer Science at Purdue University and holds an Adjunct Associate Professor role at the University of Maryland at College Park (UMIACS). He directs the IDEAS Lab at Purdue and previously served as a Research Assistant Professor at UNC Chapel Hill. His research focuses on Affective Computing, Computer Graphics (AR/VR), AI & Robotics, Social Robotics, and medical AI applications for mental health diagnostics. Affiliations: Purdue University (Primary), University of Maryland (Adjunct), UMIACS Career: Joined Purdue in 2017, extensive industry collaborations with Disney Research, Intel, and C-DAC Research Interests: Affective Computing: Emotion perception via gait analysis, speech, and facial/body expressions AR/VR: Redirected walking, virtual environments, and human motion modeling Medical AI: AI-driven mental health detection systems (e.g., VidSole dataset) in collaboration with medical schools Key Contributions: Developed Project Dost (mental health initiative) Received 2020 Brain & Behavior Seed Grant ($X) for emotion-gait research Authored 65+ papers (1,800+ citations) with awards at IEEE VR 2021 Funding & Leadership: Serves as Senior Editor for IEEE RA-L (Planning/Simulation) Conference Chair for ACM SIGGRAPH MIG 2022 Labs/Teams: IDEAS Lab (Purdue), UMD GAMMA Group

• Affective Computing
• Computer Graphics
• Augmented Reality/VR
• +8 more

Ming-Hsuan Yang is a Professor in the Department of Computer Science & Engineering at the University of California, Merced , where he also serves as the Graduate Chair for the Electrical Engineering and Computer Science (EECS) graduate group. His research spans computer vision , machine learning , and pattern recognition , with a focus on image and video restoration, object tracking, and 3D scene understanding. Ph.D., University of Illinois at Urbana-Champaign (2000) M.S., University of Texas at Austin (1994) M.S., University of Southern California (1992) B.S., National Tsing-Hua University, Taiwan (1991) His research interests include computer vision (object tracking, image deblurring, saliency detection), machine learning (transfer learning, sparse representation), and 3D reconstruction (Gaussian splatting, scene generation). He has pioneered methods in diffusion models , transformer architectures , and multi-modal vision-language systems . Recent publication trends show leadership in 3D mesh generation (ICCV 2025), video diffusion (CVPR 2025), and image restoration (PAMI 2025), with interdisciplinary applications in medical imaging (TMI 2024) and human motion analysis (WACV 2025). Scientific awards include Nvidia Fellowships and EECS Rising Stars recognitions for advisees, with Meta , Google DeepMind , and Adobe alumni placements. He has advised 18 PhD students and 13 MS students since 2009, with notable fellowships including Chancellor's Graduate Fellowship and GSOP Fellowship . His Visual Tracking and Learning Lab produces high-impact work in object tracking , image enhancement , and semantic segmentation , supported by NSF grants and industry collaborations . Lab alumni now lead R&D at top tech companies like Stability AI and Meta .

• Computer Vision
• Pattern Recognition
• Machine Learning
• +3 more