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Yayun DuView profile

Assistant Professor

• Bioelectronics
• Intelligent Systems
• Wearable and Implantable Sensors
• +7 more

Dr. Yayun Du is an Assistant Professor in the Department of Electrical and Computer Engineering at Vanderbilt University School of Engineering. She holds a Ph.D. in Robotics and System Control (Minor: Solid Mechanics) from UCLA (2022) and was a postdoctoral scholar at Northwestern University's Rogers Group through 2024. Current faculty at Vanderbilt University Ph.D. from University of California, Los Angeles Postdoctoral experience at Northwestern University Her research integrates bioelectronics and robotics through three core directions: 1) Developing multimodal wearable/implantable sensors for health monitoring, 2) Creating human-in-the-loop interaction systems using brain-computer interfaces, and 3) Applying machine learning to medical environment robotics. She has deployed four sensor types across seven hospitals globally, serving users from neonates to elderly patients. Dr. Du's recent publications focus on wireless bioelectronic devices ( PNAS ), sustainable sensor materials ( ACS Sustainable Chemistry & Engineering ), and agricultural robotics ( ICRA , IROS ). She serves as Associate Editor for ICRA 2025 and has received two Best Paper Award final nominations at IROS 2021. Finalist - Best Paper Award in Agri-Robotics (IROS 2021) Finalist - Best Paper Award in Robot Mechanisms and Design (IROS 2021) As head of the Du Group, she leads interdisciplinary research with applications in both healthcare and agricultural contexts, collaborating with Vanderbilt Institute for Surgery and Engineering (VISE) and clinical partners. Her work emphasizes deployable systems that transition from academic research to real-world implementation in medical and industrial environments.

Wenyao XuView profile

Professor

• Embedded Systems
• Cyber-Physical Systems
• Body Sensor Networks
• +3 more

Wenyao Xu is a Professor and Associate Department Chair in the Department of Computer Science and Engineering at the University at Buffalo, School of Engineering and Applied Sciences. His research focuses on embedded systems, body sensor networks, biomedical signal analytics, telemedicine, and human-computer interaction. He leads the Embedded Sensing and Computing (ESC) Lab, pioneering innovations in wearable health monitoring and non-invasive medical technologies. Dr. Xu holds a PhD in Electrical and Electronics Engineering from UCLA (2013), an MEng in Embedded Systems from Zhejiang University (2008), and a BEng in Electrical Engineering from Zhejiang University (2006). He has received multiple accolades, including the UB Exceptional Scholar—Sustained Achievement Award (2021) and SEAS Senior Career Researcher of the Year (2020). His research spans cutting-edge areas like mmWave-based sensing for health monitoring, mHealth applications for behavioral interventions (e.g., Sense2Quit app for smoking cessation), and wearable devices for gait analysis and vital sign detection. Recent work includes advancements in through-clothing wound monitoring, voice- and gesture-based authentication systems, and AI-enhanced medical imaging. Publications reflect his interdisciplinary impact, with over 150 peer-reviewed papers in top venues. His lab collaborates closely with clinical partners to translate technologies into practical healthcare solutions. Dr. Xu also emphasizes mentorship, having been honored with the President Emeritus and Mrs. Meyerson Award for teaching excellence (2020).

Anthony FilippiView profile

Associate Professor

• Imaging Spectroscopy
• Hyperspectral Remote Sensing
• GIS-Based Modeling
• +5 more

Dr. Anthony Filippi is an Associate Professor and Director of Graduate Programs at Texas A&M University. His research focuses on remote sensing, geographic information systems (GIS), and machine learning applied to aquatic and terrestrial environments. He leads the Fluvial-GEOS Lab, studying riverine/floodplain systems using remote sensing and GIS technologies. Educational Background: Ph.D. in Geography, University of South Carolina (2003) M.S. in Geography, University of South Carolina B.A. in Geography, Kansas State University Research Interests: Imaging spectroscopy, hyperspectral remote sensing of rivers and coastal oceans, GIS-based modeling, data fusion, aquatic optics, and machine learning. His work addresses coastal ocean bathymetry estimation, floodplain dynamics, and applications in environmental monitoring, including hazardous waste site tracking and agricultural studies. Recent Research Trends: Recent publications highlight advancements in UAS-based image analysis, LSTM networks for floodplain classification, and environmental policy impacts on forest resources. His work integrates machine learning with remote sensing to improve ecological and geomorphological understanding. Labs/Teams: Director of the Fluvial-GEOS Lab, focusing on remote sensing and GIS applications in riverine environments.

• Computer Vision
• Pattern Recognition & Machine Learning
• Artificial Intelligence
• +4 more

Rita Cucchiara is a Full Professor at the Department of Engineering 'Enzo Ferrari' of the University of Modena and Reggio Emilia. She leads the AImageLab research laboratory, part of the Artificial Intelligence Research and Innovation Center (AIRI) in Modena. Her research focuses on Computer Vision, Pattern Recognition, Machine Learning, and Multimedia, with applications in video surveillance, medical imaging, human-centered AI, and generative models. She is actively involved in interdisciplinary projects like ELIAS (European Lighthouse for AI Sustainability) and ELSA (European Lighthouse on Secure AI). Her recent roles include being elected Rector of the University of Modena and Reggio Emilia in 2025. She has organized and participated in major AI events, including workshops at NeurIPS, CVPR, and ECCV, and has contributed to advancements in multimodal models, deepfake detection, and trustworthy AI. Education details are not explicitly provided, but her extensive academic and research experience at the University of Modena underscores her expertise. She collaborates with institutions like NVIDIA, CINECA, and industry partners such as Digital Design and NVIDIA's AI Technology Center. AImageLab's projects include developing systems for medical imaging, ethical AI, and generative adversarial networks (GANs) for design surfaces. She co-organizes initiatives like the ELLIS Summer School on Large-Scale AI and contributes to policy discussions on AI ethics and societal impact. Her work spans from foundational research (e.g., vision transformers, continual learning) to applied projects (e.g., DDGan system for surface printing). Key grants and collaborations include the FAIR project and PNRR-M4C2 initiatives. She advises students and researchers in AI, with 7 PhD positions funded under national programs. Her leadership roles in AIRI and AImageLab highlight her commitment to bridging academia and industry, fostering innovation in AI-driven solutions for sustainability and healthcare.

Timothy R. TangherliniView profile

Professor

• folklore
• Danish culture
• Danish cinema
• +9 more

Timothy R. Tangherlini serves as the Elizabeth H. and Eugene A. Shurtleff Chair in Undergraduate Education and Professor in the Department of Scandinavian and the School of Information at the University of California, Berkeley. A distinguished folklorist and ethnographer, he has pioneered computational approaches to folklore studies, bridging traditional humanities scholarship with cutting-edge digital methods. His research expertise spans multiple interconnected domains: Digital humanities and computational folkloristics Danish and Scandinavian cultural traditions Network analysis of narrative structures Machine learning applications in cultural analysis Conspiracy theory formation and transmission Korean cultural studies and K-Pop analysis Tangherlini's work focuses on how stories circulate across social networks and how individuals use narratives to negotiate ideology within their social groups. He has been instrumental in developing the field of Culture Analytics, co-directing a three-year program at the NSF's Institute for Pure and Applied Mathematics and leading the NEH's Institute for Advanced Topics in Digital Humanities on Network Analysis for the Humanities. His research combines ethnographic depth with computational sophistication, creating novel methodologies for analyzing large cultural corpora. His scholarly contributions have earned him significant recognition: Fellow of the American Folklore Society Fellow of the Royal Gustav Adolf Academy (one of Sweden's Royal Academies) Elizabeth H. and Eugene A. Shurtleff Chair in Undergraduate Education Tangherlini has secured substantial funding from prestigious organizations including the NEH, NSF, NIH, AFOSR, Mellon Foundation, Nordic Council of Ministers, and Google. His work on conspiracy theories, K-Pop choreography analysis, and historical Danish folklore has garnered media attention, demonstrating the public relevance of his research. With extensive international experience including appointments at the University of Copenhagen, University of Iceland, and Harvard University, he maintains a global scholarly perspective while contributing significantly to undergraduate education at Berkeley.

Qingguo LiView profile

Professor

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +4 more

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

Georgia ZellouView profile

Associate Professor

• Phonetics
• Speech Perception
• Laboratory Phonology
• +3 more

Georgia Zellou is an Associate Professor in the Department of Linguistics at the University of California, Davis, where she co-directs the Phonetics Lab and conducts award-winning research at the intersection of phonetics, speech perception, and human-AI interaction. Her work investigates how phonetic detail is cognitively represented through variations in speech production, with significant contributions to understanding speech alignment with voice assistants, face-masked speech intelligibility, and cross-linguistic perception of synthetic voices. Her academic credentials include a Ph.D. in Linguistics from the University of Colorado at Boulder (2012), an M.A. in Linguistics from Stony Brook University (2007), and a B.A. in Linguistics & Anthropology from the University of Florida (2005, Cum Laude, Phi Beta Kappa). Ph.D., Linguistics, University of Colorado at Boulder (2012) M.A., Linguistics, Stony Brook University (2007) B.A., Linguistics & Anthropology, University of Florida (2005) Professor Zellou's research program centers on laboratory phonology approaches to real-world communication challenges, examining how acoustic-phonetic details influence speech perception across contexts. Her studies span speech alignment with voice-AI systems (e.g., Amazon Alexa), sociophonetic variation in bilingual speech, and the cognitive mechanisms underlying perceptual compensation for coarticulation. She employs experimental methods including eye-tracking, acoustic analysis, and perceptual testing to uncover how phonetic variation functions pragmatically in human communication and human-machine interaction. Analysis of her 15 most recent publications (2023-2025) reveals three dominant research trajectories: (1) human-AI voice interaction dynamics, including prosodic alignment and social evaluation of TTS voices; (2) intelligibility optimization in challenging contexts (face masks, clear speech for diverse listeners); and (3) cross-linguistic phonetic variation in vowelless words and consonant clusters. These works consistently bridge theoretical phonology with applied speech technology, demonstrating how fine-grained phonetic detail influences communication effectiveness in both human-human and human-machine contexts. Her scientific recognition includes: Fulbright Scholar (2022) for research in France Chancellor’s Award for Excellence in Undergraduate Mentoring (2019) Fellow of the Linguistic Society of America (2020) Amazon Faculty Research Award (2019) for Alexa-related speech studies Dean’s Fellow designation at UC Davis (2020-2023) Professor Zellou maintains an active mentoring practice recognized with the Chancellor’s Award, supervising undergraduate researchers in the Phonetics Lab while teaching core linguistics courses from introductory to advanced graduate levels. Her research program is supported by competitive grants including NSF funding, Amazon Research Awards, and UC Davis internal grants (Hellman Foundation, ISS Junior Faculty Grant), reflecting the translational value of her work for speech technology development. She has co-directed major initiatives including the 2019 LSA Linguistic Institute. The Phonetics Lab she co-leads serves as a hub for experimental phonetics research, focusing on speech production-perception relationships through projects investigating vocal accommodation to voice assistants, nasal coarticulation dynamics, and cross-linguistic prosody. Current collaborations with industry partners aim to implement human speech adaptation principles into voice assistant design to enhance naturalness and engagement.

Horacio Rostro GonzálezView profile

Associate Professor

• Artificial Intelligence
• Computational Neuroscience
• Neuromorphic Computing
• +7 more

Horacio Rostro González is an Associate Professor in the Department of Industrial Engineering at IQS School of Engineering, Universitat Ramon Llull (URL), Barcelona, Spain. He is an active researcher with a strong international academic background and current affiliations in both research and teaching. Education: PhD in Control and Signal and Image Processing (2011, INRIA & University of Nice – Sophia Antipolis, France) Master’s in Electrical Engineering (2007, University of Guanajuato, Mexico) Electronic Engineer (2003, National Technological Institute of Mexico) His research interests lie at the intersection of Artificial Intelligence, Computational Neuroscience, Neuromorphic Computing, Embedded Systems, and Robotics. He applies advanced techniques in neural networks, machine learning, and control systems to solve complex engineering problems in robotics, biomedicine, and industrial design. His work emphasizes biomimetic approaches, such as spiking neural networks for robot locomotion and AI-driven analysis of physiological signals like ECG and facial expressions. The recent publications highlight a strong trend in interdisciplinary research, combining AI with photonics, robotics, cardiovascular diagnostics, and emotional recognition in children. His work spans from theoretical algorithm development to practical industrial applications, particularly in additive manufacturing and smart systems. Scientific Projects: Offshore Wind Farms: Data analysis using machine learning and power generation prediction (2023–2024) GEPI: Grup Enginyeria de Productes Industrials (2022–2025) He is actively involved in research grants and collaborative projects, with no mention of formal student advising in the provided text. He is a member of the GEPI (Industrial Products Engineering Group), which focuses on additive manufacturing, reverse engineering, and material characterization. His scientific output is robust, with consistent publication activity from 2005 to 2025, including numerous articles in indexed journals.

Elaine TreharneView profile

Professor

• Manuscript Studies
• Medieval Literature
• Digital Humanities
• +5 more

Elaine Treharne serves as the Roberta Bowman Denning Professor of Humanities at Stanford University, holding primary appointment in the Department of English with courtesy appointments in German Studies and Comparative Literature. She concurrently acts as Senior Associate Vice Provost for Undergraduate Education and Director of Curriculum, while directing Stanford Text Technologies—a major initiative exploring textual transmission across historical periods. Her leadership extends to co-directing SILICON and spearheading NEH-funded projects that redefine digital approaches to manuscript studies. Her academic foundation includes a B.A. in English Language and Literature (First Class Honors) from the University of Manchester (1986), a Master of Archive Administration from the University of Liverpool (1987), and a Ph.D. in English from the University of Manchester (1992). This archival training underpins her dual expertise in traditional manuscript scholarship and digital innovation. Treharne's research pioneers intersections between medieval materiality and contemporary technology, investigating the haptic experience of medieval books, AI applications for manuscript analysis, and the long history of text technologies. She challenges conventional periodization through projects like 'Disrupting Categories, 1050-1250' while developing computational frameworks for fragmentology and textual distortion. Her work consistently bridges paleography with digital methodology to examine how writing systems shape cultural memory. Recent publications reveal a decisive shift from foundational medieval scholarship toward integrative digital-humanities frameworks, with increasing emphasis on phenomenological approaches to both physical and digital texts. This trajectory culminates in current projects applying machine learning to manuscript transmission patterns and developing ethical guidelines for digital archival tools. Her scientific recognition includes: Fellow of the Society of Antiquaries Fellow of the Royal Historical Society Honorary Lifetime Fellow of the English Association (former Chair and President) Fellow of the Learned Society of Wales American Philosophical Society Franklin Fellow Princeton Procter Fellow Fellow of the Stanford Clayman Institute for Gender Studies Treharne actively supervises graduate students in early literature, Book History, and Digital Humanities while securing major grants including NEH funding for Stanford Global Currents, AHRC support for the Production and Use of English Manuscripts project, and Stanford Impact Labs fellowship for archival tool development. She maintains commitment to ethical scholarly environments through her leadership in VPUE initiatives and digital pedagogy. She directs the Stanford Text Technologies initiative hosting the annual Collegium series, co-directs SILICON for internet longevity research, and leads specialized projects including 'Digital Ker' for Anglo-Saxon manuscript cataloging and 'Medieval Networks of Memory' analyzing mortuary rolls. These interconnected efforts form a comprehensive ecosystem for advancing textual scholarship across temporal and technological boundaries.

• Inventory control
• Supply chain management
• Capacity planning
• +1 more

Tim Huh is a Professor and Chair of the Operations and Logistics Division at the University of British Columbia's Faculty of Commerce and Business Administration. He specializes in inventory control, supply chain management, and operations research, with a focus on dynamic decision-making under uncertainty. B.A., B.Math, M.Math from University of Waterloo M.A. from Regent College M.S., Ph.D. from Cornell University His research spans theoretical and applied topics including renewable energy systems, healthcare operations, and digital learning analytics. Recent work explores wind power storage optimization, asynchronous video usage in education, and multi-echelon inventory solutions. Scientific recognition includes the Canada Research Chair in Operations Excellence and Business Analytics He teaches core business analytics and operations management courses at both undergraduate and graduate levels, emphasizing quantitative decision-making and process fundamentals.

• Computer Vision
• Machine Learning
• Deep Learning
• +7 more

Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.

Surya GanguliView profile

Associate Professor

• Theoretical Neuroscience
• Computational Neuroscience
• Machine Learning
• +4 more

Surya Ganguli is an Associate Professor in the Department of Applied Physics at Stanford University, with courtesy appointments in Neurobiology and Electrical Engineering. He serves as Senior Fellow at the Stanford Institute for Human-Centered AI and is affiliated with the Stanford Neuroscience Institute , Bio-X , and Wu Tsai Neurosciences Institute . His research spans theoretical neuroscience, machine learning, and statistical mechanics. Ph.D. , UC Berkeley, Theoretical Physics (2004) M.A. , UC Berkeley, Physics (2000) M.A. , UC Berkeley, Mathematics (2004) M.Eng. , MIT, Electrical Engineering and Computer Science (1998) B.S. , MIT, Physics (1998) B.S. , MIT, Mathematics (1998) B.S. , MIT, Electrical Engineering and Computer Science (1998) His lab explores how higher-level cognitive phenomena emerge from neural network dynamics, focusing on perception, memory, attention, and decision-making . Research themes include statistical mechanics of learning , neural representational geometry , and biologically plausible learning rules . Current work examines nonlinear interactions in neural networks through the Schmidt Science Polymath Award (2023). Key article trends reveal expertise in neural coding limits (2022 Nature), synaptic plasticity models (2022 Neural Computation), and deep learning theory (2022 NeurIPS publications). His 2019 Annual Review chapter on statistical mechanics of deep learning established foundational insights into network criticality. Scientific Awards : NSF Career Award (2019) Simons Foundation Investigator (2016) McKnight Scholar Award (2015) James S. McDonnell Foundation Scholar (2014) Sloan Research Fellow (2013) As advisor, he mentors 10+ doctoral students across Applied Physics, Neurosciences, and Computer Science, including Vamshi Balanaga and Mason Kamb. His lab collaborates with experimental teams at Stanford and beyond, supported by grants from NSF , Simons Foundation , and Swartz Foundation . The Neural Dynamics & Computation Lab unites physicists, mathematicians, and neuroscientists to decode cognition through interdisciplinary methods.

• Large-Scale Data Management
• Data Systems
• Big Data Analysis
• +5 more

Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto, specializing in large-scale data management, data systems, and applied machine learning. His research integrates machine learning techniques into scalable data platforms to enhance the analysis of massive datasets. He holds a PhD from the University of Toronto, an MSc from the University of Maryland, and a Bachelor's degree from the University of Patras. Education: PhD, University of Toronto MSc, University of Maryland at College Park Bachelor's degree, University of Patras, Greece Research Interests: Relational Deep Dive (ReDD): Natural language query execution over unstructured documents Streaming Video Queries (SVQ): Interactive query processing for video streams Reliable Text-to-SQL: Generating accurate SQL queries with human-in-the-loop assistance Machine Learning Integration in Data Systems Publications: Focus on video analytics, query processing, and reliable natural language interfaces. Notable works include optimizing video queries, declarative frameworks for temporal constraints, and abstention-based SQL generation. Awards: Inventor of the Year (1st Prize), University of Toronto (2011) Best Paper Awards at international conferences Entrepreneurship & Advising: Co-founder of Sysomos (Meltwater Group), Aislelabs (Constellation Software), and Workorb Advisor to mapintent and ktau Labs & Teams: Leads research groups developing systems like ReDD and SVQ, emphasizing collaboration between academia and industry.

Risto MiikkulainenView profile

Professor

• Neuroevolution
• Evolutionary Computation
• Deep Learning
• +7 more

Risto Miikkulainen is a Professor of Computer Science and Neuroscience at the University of Texas at Austin and VP of AI Research at Cognizant AI Lab. He directs the UTCS Neural Networks Research Group and is currently on leave from UT, working on Evolutionary Computation and Deep Learning at Sentient Technologies, Inc. Education: Ph.D. in Computer Science, UCLA, 1990 M.S. in Applied Mathematics, Helsinki University of Technology (now Aalto University), 1986 Risto Miikkulainen's research focuses on biologically-inspired computation such as neural networks and evolutionary computation. His work spans three main areas: (1) Neuroevolution, evolving complex deep learning architectures and recurrent neural networks for sequential decision tasks in robotics, games, and artificial life; (2) Cognitive Science, developing models of natural language processing, memory, and learning that shed light on disorders such as schizophrenia and aphasia; and (3) Computational Neuroscience, studying the development, structure, and function of the visual cortex, episodic memory, and language processing. His research combines theoretical understanding of biological information processing with practical applications for developing intelligent artificial systems. His recent publications (2025) show a strong focus on evolutionary approaches to AI development, particularly in neural architecture search, loss function optimization, and explainable AI. Many papers explore the intersection of evolutionary computation with deep learning, creating more efficient and transparent AI systems. His work spans theoretical foundations and practical applications in areas ranging from environmental control systems to cognitive modeling. Scientific Awards: College of Fellows, International Neural Network Society, 2024 Best Pathway to Impact Award, NeurIPS Climate Change workshop, 2024 AAAI Fellow, 2023 IEEE CIS Evolutionary Computation Pioneer Award, 2020 Gabor Award, International Neural Network Society, 2017 Outstanding Paper of the Decade Award, International Society for Artificial Life, 2017 IEEE Fellow, 2016 Multiple Best Paper Awards at GECCO, CIG, and CEC conferences Deployed Application Award, AAAI/IAAI-2013, AAAI/IAAI-2018 Miikkulainen has extensive experience mentoring students through undergraduate research courses like CS378 Computational Intelligence in Game Design I and II, where students develop independent research projects on the OpenNERO research platform. He has received multiple awards for deployed applications, demonstrating the practical impact of his research. His work has led to the development of the NERO game platform, which serves as both an educational tool and research platform for AI. He directs the UTCS Neural Networks Research Group, which focuses on neuroevolution, cognitive science models, and computational neuroscience. The group has developed the NERO (Neuro-Evolving Robotic Operatives) platform, a machine learning game that allows users to train intelligent agents through evolutionary computation. The group's work spans theoretical research and practical applications in AI, with connections to both academic and industry partners.

Amit SingerView profile

Professor

• Computational Mathematics
• Structural Biology
• Cryo-Electron Microscopy
• +3 more

Amit Singer is a Professor of Mathematics at Princeton University, specializing in computational methods for structural biology and cryo-electron microscopy (cryo-EM). His work focuses on developing mathematical frameworks and algorithms for analyzing large-scale microscopy datasets, particularly in 3D reconstruction and heterogeneity analysis of molecular structures. He leads research in manifold learning, optimal transport, and harmonic analysis, with applications to cryo-EM, signal processing, and inverse problems. Research interests include: (1) Mathematical methods for cryo-EM, including particle alignment, density map analysis, and subspace-based reconstruction techniques; (2) Development of rotation-invariant representations for imaging problems; (3) Application of machine learning and optimization to biomedical imaging challenges. His contributions bridge pure mathematics (e.g., harmonic analysis, manifold theory) with applied computational techniques for real-world microscopy data. Key trends in his recent articles (2023–2025) include advancements in multi-reference alignment methods, Wasserstein distance-based image registration, and overcoming particle detection limitations in cryo-EM. He also explores sparsity constraints, autocorrelation analysis, and novel algorithms for handling heterogeneous datasets. These methods improve resolution and reduce computational costs in analyzing molecular structures at atomic scales. Notable contributions include the ASPiRE software package for steerable PCA, and foundational work on synchronization problems in cryo-EM orientation estimation. His research often addresses algorithmic scalability and robustness to noise in experimental setups.