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Jens KoberView profile

Associate Professor

• Robotics
• Motor Skill Learning
• Reinforcement Learning
• +5 more

Jens Kober is an Associate Professor in the Cognitive Robotics department at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science, and a key member of the TU Delft Robotics Institute. His research bridges machine learning and robotics for real-world applications. His research focuses on Motor Skill Learning, Reinforcement Learning, and Interactive Learning for robotics, with applications spanning robot manipulation, locomotion, surgical assistance, and wind turbine control. Key areas include: Developing robust learning algorithms for compliant robots Creating interactive learning frameworks for human-robot collaboration Applying deep learning to robot control under uncertainty Transferring machine learning techniques to medical robotics His recent publications reveal strong trends in interactive imitation learning and reinforcement learning for dynamic tasks , particularly in robot manipulation (e.g., pick-and-place ambiguity resolution) and locomotion (explosive jumping with soft quadrupeds). The work increasingly integrates vision systems and focuses on real-world applicability in retail, dentistry, and renewable energy. Scientific recognition includes: Robotics: Science and Systems Early Career Award (2022) IEEE-RAS Early Academic Career Award (2018) Georges Giralt PhD Award for best European Robotics thesis (2013) Kober actively supervises PhD students and leads major collaborative projects including the NWO-funded DL-foRCe (Deep Learning for Robust Robot Control), Robot-Assisted Tooth Removal, and the industry-backed AI for Retail Lab with Ahold Delhaize. His work connects academic research with industrial applications through grants from NWO, TKI Wind op Zee, and industry partnerships. He directs the Cognitive Robotics group within the TU Delft Robotics Institute, collaborating closely with the Delft Center for Systems and Control and the ELLIS Unit Delft. His team develops foundational learning algorithms while deploying them in applied settings like the AIR Lab Delft test site for retail robotics.

Rémi MonassonView profile

Professor

• Statistical Physics
• Machine Learning
• Biological Data Analysis
• +5 more

Rémi Monasson is a Directeur de Recherche at CNRS and Professor at Ecole Polytechnique, based at the Laboratoire de Physique Théorique of École Normale Supérieure (ENS) in Paris. His work bridges statistical physics, machine learning, and biological data analysis, with particular emphasis on protein modeling and neural network theory. Research Interests: High-Dimensional Inference and Applications to Biological Data (Genomics, Neurobiology) Statistical Physics of Learning in Neural Networks Analysis of Algorithms and Combinatorial Optimization Problems Protein Sequence Analysis and Structure Prediction Statistical Mechanics of Biological Systems Monasson's research program integrates principles from statistical physics to develop novel computational approaches for understanding complex biological systems. His work on Restricted Boltzmann Machines for protein family modeling has opened new avenues for protein design and evolutionary analysis. In neural network theory, he explores how physical principles can explain learning and memory mechanisms in both artificial and biological systems. His approach combines rigorous theoretical frameworks with practical applications in molecular biology and neuroscience. Recent Publication Trends: Monasson's recent publications (2023-2024) demonstrate a deep integration of statistical physics with cutting-edge problems in biology and machine learning. Key themes include protein sequence analysis using probabilistic models, neural network dynamics and learning mechanisms, and applications to immunology and molecular recognition. His work frequently develops novel computational methods grounded in statistical physics principles, with applications ranging from RNA switch design to T-cell receptor specificity prediction. The interdisciplinary nature of his research is evident in collaborations spanning physics, biology, computer science, and medicine. Current Research Projects: ANR project RBMPro on theory and application of Restricted Boltzmann Machines to protein family modeling (2017-2021) HFSP on Analog Computation Underlying Language Mechanisms (2016-2020) PSL* project Data Science (2017-2019) Workshop on Tumors and Immune Systems: From Theory to Therapy (2019) ANR project Coevstat on coevolution in proteins (2013-2016) Teaching Activities: Model-Guided Data Science (2019): Lectures on "Unsupervised learning of features from data: a statistical physics approach" Ecole Polytechnique: Physics of Biological Systems (PHY 552B) since 2012 Netadis Summer School on Complex Systems (2013): lectures on random graphs and maps ICFP Master program: Collective phenomena: from phase transitions to statistical field theory (2011-2015)

Loren FrankView profile

Professor

• Neuroscience
• Cognitive Neuroscience
• Neural Networks
• +3 more

Loren Frank, PhD, is a Professor in the Department of Neurology at the University of California San Francisco (UCSF), where he has been conducting groundbreaking neuroscience research since 2003. His laboratory focuses on understanding how the hippocampus generates thoughts and simulations of future scenarios to guide decision-making, bridging memory and cognition through hippocampal neural ensembles and memory replay .

Qimin YanView profile

Associate Professor

• Condensed Matter Physics
• Quantum Materials
• Machine Learning for Materials
• +3 more

Dr. Qimin Yan is an Associate Professor of Physics at Northeastern University, specializing in computational and data-driven approaches to quantum materials and energy conversion. He joined Northeastern in 2022 after six years as an Assistant Professor at Temple University. Ph.D. in Materials (2012), University of California, Santa Barbara Postdoctoral Researcher (2013-2016), Lawrence Berkeley National Lab & UC Berkeley His research focuses on solid-state quantum materials , machine learning frameworks for materials design, and quantum defects for information technologies . Recent work includes pressure-controlled magnetism and photomultiferroicity in 2D systems. Scientific awards include: DOE Early Career Award (2019) NSF CAREER Award (2022)

• Natural Language Processing
• Data Management
• Entity Resolution
• +4 more

Lucian Popa is a Principal Research Scientist at IBM Research - Almaden, leading a team developing human-in-the-loop systems to scale NLP technologies for diverse users and applications. His work integrates human intelligence, data, and machine learning to advance transparent, data-driven AI systems. His research spans Natural Language Processing, Data Management, and Explainable AI, with seminal contributions to schema mapping, data exchange, and entity resolution. He pioneers active learning frameworks that enhance human-AI collaboration for entity linking and annotation efficiency, focusing on making AI systems accessible in low-resource settings. Recent publications reveal a cohesive trajectory toward human-centered AI, emphasizing explainability in text understanding, entity resolution, and misinformation detection. His work consistently addresses real-world challenges through hybrid intelligence frameworks that balance automation with human expertise across financial, scientific, and social domains. Scientific recognition includes: Alonzo Church Award for Outstanding Contributions to Logic and Computation (2020) Test-of-Time Awards at ICDT, PODS, and EDBT conferences ACM Distinguished Member designation While no formal students or major grants are documented in public sources, Popa directs IBM research initiatives including the HIL framework for entity resolution and AutoAI for text. His team actively develops products like Global Table Extraction (GTE) that transform document processing through visual-context-aware AI. At IBM Almaden, he collaborates with researchers including Ron Fagin and Marina Danilevsky to bridge theoretical computer science with practical NLP applications, maintaining leadership in knowledge-intensive AI system design.

Joaquín Ortega CastroView profile

Senior Lecturer

• Theoretical Chemistry
• Computational Chemistry
• Physical Chemistry
• +3 more

Joaquín Ortega Castro is a Senior Lecturer (Associate Professor level) in the Department of Chemistry at the University of the Balearic Islands (UIB) . He earned his PhD in Chemical Sciences from the University of Granada in 2007 and carried out post-doctoral research stays at the University of Erlangen-Nürnberg and University of Cambridge . Educational background PhD in Chemical Sciences, University of Granada (2007) Research stays: University of Erlangen-Nürnberg, University of Cambridge (×2) Research interests Dr Ortega Castro’s work lies at the intersection of theoretical chemistry, computational chemistry, and materials science . His research spans: Environmental chemistry: selective detection and removal of heavy metals, organic contaminants in membranes Solid-state chemistry: CO₂ capture on porous frameworks, halide perovskites for photovoltaics Photo-switchable materials: design of molecules for LEDs, luminescent probes, and optoelectronic devices Surface science: adsorption of organics on mineral surfaces Pharmaceutical chemistry: crystallization inhibitors and public–private collaborations Across these domains he applies quantum-chemical methods, molecular dynamics, multiscale modeling, and machine-learning techniques to predict structure–property relationships and guide experiments. Scientific output and recognition With 98 peer-reviewed articles and participation in 13 funded projects , his work is internationally recognized. He has been awarded two consecutive six-year research accreditation periods and four six-year teaching accreditation periods by the Spanish national evaluation agency (ANECA/CNEAI). Advising and coordination roles Directed 3 doctoral theses Supervised 4 master’s theses (TFM) Supervised 5 bachelor’s theses (TFG) Local coordinator of the Erasmus Mundus Master’s Degree in Theoretical Chemistry and Computational Modelling (TCCM) Laboratory and team affiliations He is an active member of the Flow Injection and Trace Analysis (FI-TRACE) and Nanocapture and Supramolecular Systems (SUPRANANO) consolidated research groups. Additionally, he manages the social media outreach for the UIB Department of Chemistry on Twitter (@DQuimicaUIB) and Instagram (rsociales_dqu), promoting scientific dissemination and community engagement.

Arindam SikdarView profile

Research Fellow

• Computer Vision
• Pattern Recognition
• Visual Surveillance
• +3 more

Dr. Arindam Sikdar is a Postdoctoral Research Fellow at Edge Hill University , working on the EPSRC-funded ATRACT project. Previously, he contributed to the SPHERE project at the University of Bristol . With 11 publications in international journals and conferences, his work spans computer vision, pattern recognition, and digital healthcare.

Naoshige UchidaView profile

Professor

• Neuroscience
• Neurobiology
• Computational Neuroscience
• +7 more

Naoshige Uchida is a Professor of Molecular and Cellular Biology at Harvard University within the Faculty of Arts and Sciences. He leads the Uchida Lab, which focuses on understanding the neural mechanisms underlying sensory processing, decision making, and reward-based learning. His research employs a multidisciplinary approach combining behavioral paradigms, electrophysiological recordings, molecular tools, and computational modeling to investigate how neural circuits process information and guide behavior. Dr. Uchida's research interests center on the neuronal processes by which sensory information and memory about previous experiences guide animal behavior. His lab has developed sophisticated odor-guided perceptual decision tasks in rats and mice, combined with multi-electrode recording techniques to monitor neural activity during behavior. A major focus of his work examines dopamine signaling, particularly how dopamine neurons encode reward prediction errors and contribute to learning. His research spans multiple levels of analysis from neural circuit dynamics to computational principles of decision making. Analysis of Dr. Uchida's recent publications reveals a strong emphasis on dopamine signaling in the striatum, particularly in the tail region, and its role in value-based decision making, threat processing, and social behavior. His work increasingly integrates computational approaches including reinforcement learning models, deep learning for neural data analysis, and distributional coding frameworks. There is a clear trajectory toward understanding how neural circuits represent uncertainty, perform probabilistic inference, and implement hierarchical decision processes that bridge sensory input to behavioral output. Dr. Uchida has made significant contributions to understanding the neural basis of decision making, with particular emphasis on dopamine signaling and striatal function. His work has implications for understanding both normal cognitive processes and disorders involving disrupted reward processing. Through his leadership of the Uchida Lab, he continues to advance our understanding of how neural circuits implement the computations necessary for adaptive behavior. His research program involves extensive collaboration across disciplines, utilizing advanced techniques including multi-electrode recordings, optogenetics, viral tracing, and computational modeling. The lab maintains strong connections with other neuroscience research groups at Harvard and beyond, contributing to a vibrant research environment focused on understanding the neural basis of cognition and behavior.

Kshitij JerathView profile

Associate Professor

• Self-organized dynamics
• Multi-agent systems
• Robotic swarms
• +5 more

Kshitij Jerath serves as Associate Professor in the Department of Mechanical and Industrial Engineering, Robotics at the Francis College of Engineering, University of Massachusetts Lowell. His research focuses on self-organized dynamics in complex systems, multi-agent control, and robotic swarms, with significant contributions to traffic flow theory and sensor characterization. He directs the Emergent Dynamics, Control and Analytics Labs (EXALABS), advancing bottom-up control algorithms for minimal-intervention system guidance. Dr. Jerath's academic background includes: Ph.D. in Mechanical Engineering from Pennsylvania State University (2014), dissertation: 'Influential subspaces in self-organizing multi-agent systems' M.S. in Electrical Engineering from Pennsylvania State University (2011), thesis: 'Sensor noise modeling, characterization and simulation: An Allan variance tutorial' M.S. in Mechanical Engineering from Pennsylvania State University (2010), thesis: 'Impact of adaptive cruise control on the formation of self-organized traffic jams on highways' Bachelor's equivalent in Mechanical and Automation Engineering from Amity School of Engineering and Technology, India His research spans self-organized dynamics , multi-agent systems , and robotic swarm control , applying statistical mechanics principles to model emergent behavior in transportation networks and complex systems. Current work focuses on influencing macro-scale dynamics through minimal intervention by small agent subsets, with extensions to social ensembles and neural systems. His methodologies integrate control theory, network science, and machine learning for real-world applications in autonomous vehicles and system reliability. Recent publications (2023-2025) reveal strong trends in relational network applications for multi-agent learning, adaptive data granulation techniques, and human-swarm interaction frameworks. Key developments include database-inspired algorithms for sensor characterization, renormalization group approaches to traffic modeling, and fault-tolerant recovery mechanisms for robotic teams. These works demonstrate increasing convergence of control theory, database systems, and reinforcement learning in addressing complex system challenges. Dr. Jerath has received notable recognition including: Two Best Presentation awards at American Control Conference (2014, 2012) Kulakowski Travel Award from Penn State (2014) National Merit-cum-Means Scholarship from Indian Government (2013) 2nd place in ITS America Student Essay Competition (2012) His research is supported by grants including the CPS: Medium project 'Automated Discovery of Data Validity for Safety-Critical Feedback Control in Connected Vehicles' (2019) and a Graduate Teaching Fellowship from Penn State (2013). EXALABS maintains active collaborations with transportation agencies and robotics researchers to translate theoretical advances into practical applications. The Emergent Dynamics, Control and Analytics Labs (EXALABS) develops frameworks for modeling, quantifying, and influencing collective behavior across scales. Current projects include human-guided swarm control in virtual reality, traffic flow optimization using connected vehicle networks, and adaptive granulation techniques for large-scale sensor data. The lab employs interdisciplinary approaches combining control theory, statistical mechanics, and machine learning to solve problems in robotics, transportation, and system reliability.

• Bioinformatics
• Machine Learning in Biology
• Computational Pathology
• +7 more

Satwik Rajaram is an Assistant Professor in the Lyda Hill Department of Bioinformatics at UT Southwestern Medical Center, with secondary appointments in the Department of Pathology and the Center for Alzheimer's and Neurodegenerative Diseases. He leads a multidisciplinary research group focused on applying machine learning to understand tissue organization in cancer and neurodegeneration. His research interests lie at the intersection of computational biology, machine learning, and pathology. He develops biology-guided deep learning methods to decode tissue morphology, aiming to transform histopathological images into predictive biomarkers. His lab investigates kidney cancer heterogeneity, neurodegenerative tauopathies, and treatment response in rectal cancer using neoadjuvant radiotherapy. The group emphasizes interpretable AI, experimental design, and scalable computational pipelines. The recent publications reflect a strong trend in applying deep learning and statistical modeling to high-throughput microscopy and genomic data, with a focus on cellular heterogeneity, tumor evolution, and disease stratification. His work bridges physics-inspired methods with biological discovery, particularly in oncology and neuroscience. Dr. Rajaram has mentored several graduate students and postdoctoral researchers, including Aleksandra Nielsen, Sriya Veerapaneni, and Paul Acosta. His lab collaborates across disciplines and has developed open-source software tools such as PhenoRipper and SimuCell. The Rajaram Lab operates in a dynamic, startup-like environment within a top-tier medical center, leveraging world-class computational resources like BioHPC and the Bioinformatics Core Facility. The team includes postdoctoral researchers, graduate students, data scientists, and computational biologists working on cutting-edge problems in computational pathology.

Donna BeersView profile

Professor

• Algebra
• Graph Theory
• Network Theory
• +4 more

Donna Beers is a Professor of Mathematics in the Department of Computer, Data, and Mathematical Sciences at Simmons University. She teaches courses spanning calculus, discrete mathematics, linear algebra, modern algebra, graph theory, and machine learning mathematics. Dr. Beers actively promotes undergraduate research, guiding projects in areas like probiotics efficacy, cryptography, healthcare utilization, and social network analysis. Her research integrates algebra, graph theory, and network theory with applications in machine learning and social systems. She focuses on dismantling barriers to STEM inclusion and leads funded initiatives to enhance mentorship and equity in introductory STEM courses. Awards & Honors: Northeastern Section MAA Award for Distinguished Teaching MAA Certificate for Meritorious Service Inaugural Toby Sloane Award for Student Centeredness She mentors students in interdisciplinary research and collaborates on grants to foster inclusive STEM education. Professional service includes leadership in the Mathematical Association of America and editorial roles for journals like the American Mathematical Monthly .

Timothy BuschmanView profile

Professor

• Cognitive Control
• Executive Control
• Working Memory
• +6 more

Timothy Buschman is a Professor at the Princeton Neuroscience Institute, Princeton University, where he leads the Buschman Lab. His research focuses on understanding the neural mechanisms of executive control - our ability to internally guide actions toward goals. His work centers on three key brain regions: prefrontal cortex, parietal cortex, and basal ganglia. Dr. Buschman received his Ph.D. from the Massachusetts Institute of Technology. His laboratory employs a multidisciplinary approach using cutting-edge techniques in non-human primate and rodent models, including large-scale multiple-electrode electrophysiology and optogenetic control of neural circuits. His primary research interests include Cognitive Control, Working Memory, Attention, Cognitive Flexibility, Neural Oscillations, and Brain Stimulation. His lab investigates how cognition arises from neural activity patterns and seeks to understand disruptions of these processes in neuropsychiatric disorders like autism, schizophrenia, and Parkinson's disease. Analysis of his recent publications reveals a strong focus on low-dimensional neural dynamics, working memory mechanisms, and the shared neural substrates underlying cognitive control processes. His work increasingly integrates computational approaches with experimental neuroscience to develop unified theories of cognitive function. Dr. Buschman was awarded the 2023 Troland Research Award, recognizing his significant contributions to basic research in psychology. His research program includes multiple interconnected projects: Working Memory mechanisms, Cognitive Flexibility (how behavior changes based on context), Attention filtering, Synchronous Oscillations in neural processing, Brain Stimulation technologies, and Categorization processes. The Buschman Lab combines experimental and theoretical approaches to understand how cognition emerges from neural activity, with implications for both artificial intelligence and treatment of neural disorders.

Marcel BreeuwerView profile

Professor

• Medical Image Analysis
• Visualization
• Computational Modeling
• +6 more

Marcel Breeuwer is a part-time Full Professor at Eindhoven University of Technology (TU/e) in two departments: Biomedical Engineering and Electrical Engineering . His research bridges Medical Image Analysis and Signal Processing Systems to develop computational models for diagnosing and managing cardiovascular disease , neurological disorders , and cancer . He also contributes to data compression , image-guided surgery , and machine learning in clinical contexts. Education: MSc in Electrical Engineering (Information Theory), Delft University of Technology (1982) PhD in auditory-augmented lipreading, VU University Amsterdam (1985) Dr. Breeuwer’s work focuses on deep learning applications in medical imaging , generative AI for synthetic medical data, and semi-supervised surgical phase recognition . His recent publications highlight advancements in myelin quantification for multiple sclerosis and AI-assisted motion analysis in spinal diagnostics. He holds fellowships with the European Alliance for Medical and Biological Engineering Sciences and serves on the board of the Dutch Society of Pattern Recognition and Image Processing . Collaborative projects include NeuroPlatform and EAISI Health initiatives.

• Aerospace Engineering
• Control Systems
• Fuzzy Logic
• +7 more

Dr. Sreenatha Anavatti is a Senior Lecturer at the School of Engineering and Information Technology at the University of New South Wales (UNSW), Canberra campus (Australian Defence Force Academy). With over three decades of academic experience, he has held positions at prestigious institutions including the Indian Institute of Technology, Bombay, where he served as Assistant Professor (1991-1997) and Associate Professor (1997-1998), before joining UNSW ADFA as Lecturer (1998-2001) and subsequently Senior Lecturer (2001-present). Dr. Anavatti's research spans the interdisciplinary fields of aerospace engineering, control systems, and artificial intelligence. His work primarily focuses on the application of fuzzy logic and neural networks to aerospace applications, including wing rock attitude control for flexible spacecraft, control of flexible robotic arms, and design and analysis of robust autopilots for aircraft and missiles. His research interests also encompass flight dynamics, flexible spacecraft dynamics and control, active vibration control, and practical applications of fuzzy and neural network systems. His expertise bridges theoretical control systems with practical aerospace applications, making significant contributions to both academic knowledge and real-world engineering solutions. His extensive publication record demonstrates significant contributions to the fields of UAV control systems, swarm robotics, and neuro-fuzzy applications. His recent work shows a strong trend toward integrating machine learning techniques with traditional control systems, particularly in the areas of autonomous vehicle navigation, source localization, and robust control under uncertainty. His research has evolved from foundational work in classical control systems to cutting-edge applications of deep learning and reinforcement learning in robotics and aerospace systems, with publications spanning from 1990 to the present, including numerous book chapters, journal articles, and conference papers. Dr. Anavatti has taught a wide range of courses including Flight Mechanics, Flight Control Systems, Missile Guidance, Spacecraft Dynamics, Orbital Mechanics, and Classical and Modern Control Theory. His academic journey reflects a consistent focus on advancing control system methodologies while adapting to emerging technologies in artificial intelligence and robotics.

Milan G. ĐorđevićView profile

Assistant Professor

• Theoretical Computing
• Artificial Intelligence
• Machine Learning
• +3 more

Milan G. Đorđević serves as an Assistant Professor at the Faculty of Mathematics and Computer Sciences, Alfa BK University, where he has taught core computer science courses for over a decade including Programming 1, Object-Oriented Programming, Data Structures and Algorithms, Operating Systems, and Computer Architecture. He completed his doctoral studies in 2012 at the University of Primorska, Faculty of Mathematics and Information Technology, Department of Computing and Informatics, supported by a scholarship from Slovenia awarded for his exceptional academic performance during higher education in Belgrade. His research focuses on interdisciplinary applications of computing theory: Artificial Intelligence and Machine Learning systems development Combinatorial Optimization with Nature-Inspired Algorithms Computational modeling of Misinformation Spread in Social Networks Dr. Đorđević has authored over 30 international publications and received recognition through: Microsoft Imagine Cup award for mentoring award-winning student projects Slovenian Doctoral Scholarship for academic excellence As an academic advisor, he has guided numerous bachelor's and master's theses to international competition success. He maintains active professional engagement through long-standing memberships in IEEE, IET, and ACM, contributing to global computer science discourse while addressing contemporary societal challenges through computational research.