ForesIQ Talent Intelligence
We look deeper so you can see further. Know who to seek.
ForesIQ is a Research Talent Intelligence platform providing high-precision data on frontier researchers conventional search fails to surface. It gives talent teams and research leaders one clear workspace where each person is mapped, scored and placed in context, revealing why their work matters.
Behind every score
Depth you can see. Intelligence you can act on.
We analyse more than a decade of published research across 6 scoring dimensions. Every researcher is placed in the context of their field, giving your team a deeper foundation for deciding whose work deserves attention.
- 185,000+
- identity-checked researchers. Professional identities checked before listing.
- 257,123
- research-area standings. A separate standing in each mapped research area.
- 6
- scoring dimensions. Different views of the published work, brought into one score.
- 1,542,738
- scoring evaluations. The depth of analysis behind the current database.
- 16
- years of research, 2010–2025. The research history behind today’s standing.
- 5,952
- identities held for further review. Ambiguous records stay back until identities can be resolved.
Figures from release v77, current as of 16 October 2026.
Who’s working on the problem you need to solve?
Explore the people developing the models, methods and systems shaping your field. Start with a research area and see precise expertise, conference publications, employer, country and research signals together. Your search starts with the work that makes someone relevant.
Computer Vision
Phillip surname hidden in preview
Not on record
- ForesIQ Score
- 92 · Elite
- Momentum
- Steady
- Activity
- Prolific
- Self-Supervised Learning
- Representation Learning
- Image Generation
- Manipulation & Grasping
- Generative AI
- Reinforcement Learning
Zeynep surname hidden in preview
Not on record
- ForesIQ Score
- 89 · Top Tier
- Momentum
- Steady
- Activity
- Prolific
- Explainable AI
- Zero-Shot Learning
- Large Language Models
- Multimodal Learning
- Vision-Language Models
- Image Classification
Luping surname hidden in preview
University of Sydney
- ForesIQ Score
- 84 · Top Tier
- Momentum
- Steady
- Activity
- Prolific
- Semi-Supervised Learning
- Medical Imaging
- Multimodal Learning
- Medical Image Segmentation
- Image Classification
- Radiology AI
Machine Learning
Sergey surname hidden in preview
Not on record
- ForesIQ Score
- 99 · Elite
- Momentum
- Steady
- Activity
- Prolific
- Manipulation & Grasping
- Reinforcement Learning
- Imitation Learning
- Goal-Conditioned & Meta RL
- Robot Learning
- Large Language Models
Timothy surname hidden in preview
Not on record
- ForesIQ Score
- 96 · Elite
- Momentum
- Rising
- Activity
- Active
- Meta-Learning
- Reinforcement Learning
- Large Language Models
- Capsules, Memory & Attention Modules
- Manipulation & Grasping
- Generalization & Deep Learning Theory
Zeynep surname hidden in preview
Not on record
- ForesIQ Score
- 88 · Top Tier
- Momentum
- Steady
- Activity
- Prolific
- Explainable AI
- Zero-Shot Learning
- Large Language Models
- Multimodal Learning
- Vision-Language Models
- Image Classification
Natural Language Processing
Christopher surname hidden in preview
Stanford University
- ForesIQ Score
- 99 · Elite
- Momentum
- Steady
- Activity
- Prolific
- Information Extraction
- Large Language Models
- Syntax & Parsing
- Machine Translation
- Question Answering
- Word Embeddings & Lexical Semantics
Luke surname hidden in preview
Not on record
- ForesIQ Score
- 99 · Elite
- Momentum
- Steady
- Activity
- Prolific
- Semantic Parsing & Text-to-SQL
- Large Language Models
- Question Answering
- Transformers
- Cross-Lingual & Low-Resource NLP
- Zero-Shot Learning
Percy surname hidden in preview
Not on record
- ForesIQ Score
- 99 · Elite
- Momentum
- Steady
- Activity
- Prolific
- Question Answering
- Large Language Models
- Transfer Learning
- Foundation Models
- Evaluation & Benchmarking
- Semantic Parsing & Text-to-SQL
AI Systems & Infrastructure
Ion surname hidden in preview
Not on record
- ForesIQ Score
- 99 · Elite
- Momentum
- Steady
- Activity
- Prolific
- LLM Inference Serving Systems
- Large Language Models
- ML Systems & Inference Serving
- Reinforcement Learning
- Computer Networking
- Datacenter Systems
Gennady surname hidden in preview
Not on record
- ForesIQ Score
- 89 · Top Tier
- Momentum
- Steady
- Activity
- Prolific
- Memory Systems
- ML Systems & Inference Serving
- Large Language Models
- GPU Computing
- LLM Training Systems
- Transformers
Moritz surname hidden in preview
Not on record
- ForesIQ Score
- 68 · Notable
- Momentum
- Steady
- Activity
- Active
- Causal Inference
- Large Language Models
- Fairness & Bias
- Generalization & Deep Learning Theory
- Optimization
- Differential Privacy
Robotics
Chelsea surname hidden in preview
Not on record
- ForesIQ Score
- 99 · Elite
- Momentum
- Steady
- Activity
- Prolific
- Manipulation & Grasping
- Reinforcement Learning
- Robot Learning
- Imitation Learning
- Large Language Models
- Meta-Learning
Davide surname hidden in preview
University of Zurich
- ForesIQ Score
- 99 · Elite
- Momentum
- Steady
- Activity
- Prolific
- Drones & Aerial Vehicles
- Robot State Estimation & Localization
- SLAM
- Event Cameras
- Reinforcement Learning
- Motion Planning
Dorsa surname hidden in preview
Not on record
- ForesIQ Score
- 98 · Elite
- Momentum
- Rising
- Activity
- Prolific
- Manipulation & Grasping
- Imitation Learning
- Reinforcement Learning
- Large Language Models
- Human-Robot Interaction
- Robot Learning
Inside a researcher profile
What makes this researcher worth your attention?
See the expertise behind the name. Their research, specialisms and available career context come together in one profile, giving your team a clearer view of what they work on and why it could matter to you.
How we build confidence in the data
Researcher profile preview · Computer Vision
Luping surname hidden in preview
University of Sydney
Research bio
Plain-language summary
First published in 2007. Over the period 2020-2024, their publications expand from medical imaging tasks, such as brain tumor segmentation and MRI synthesis, to also cover semi-supervised learning, image retrieval, and radiology report generation. Their tools include deep learning, GANs, and transformers, applied to PET reconstruction as well. Most-cited subject: Multimodal Learning.
- Semi-Supervised Learning
- Medical Imaging
- Multimodal Learning
- Medical Image Segmentation
- Image Classification
- Radiology AI
Researcher profile preview · Machine Learning
Sergey surname hidden in preview
Not on record
Research bio
Plain-language summary
First published in 2009. From 2022 to 2023, the researcher has focused on reinforcement learning for robotics, producing works on offline RL (e.g., Q-Transformer), imitation learning, and large language models for robot control (e.g., RT-2). They have also contributed to bipedal locomotion, social navigation, and scalable robot learning at University of California, Berkeley. Their most-cited subject is reinforcement learning.
- Manipulation & Grasping
- Reinforcement Learning
- Imitation Learning
- Goal-Conditioned & Meta RL
- Robot Learning
- Large Language Models
Researcher profile preview · Natural Language Processing
Luke surname hidden in preview
Not on record
Research bio
Plain-language summary
Research in Natural Language Processing on Semantic Parsing & Text-to-SQL, Large Language Models and Question Answering, published from 1998 to 2026. Papers at EMNLP (51), ACL (43) and ICLR (26).
- Semantic Parsing & Text-to-SQL
- Large Language Models
- Question Answering
- Transformers
- Cross-Lingual & Low-Resource NLP
- Zero-Shot Learning
Researcher profile preview · AI Systems & Infrastructure
Gennady surname hidden in preview
Not on record
Research bio
Plain-language summary
Research in AI Systems & Infrastructure on Memory Systems, ML Systems & Inference Serving and Large Language Models, published from 2008 to 2026. Papers at MLSys (12), ISCA (9) and ASPLOS (7).
- Memory Systems
- ML Systems & Inference Serving
- Large Language Models
- GPU Computing
- LLM Training Systems
- Transformers
Researcher profile preview · Robotics
Davide surname hidden in preview
University of Zurich
Research bio
Plain-language summary
Over a publication span from 2004 to 2026, their research has addressed SLAM, event cameras, and optical flow. Their recent papers from 2022 to 2024 focus on motion planning, object detection, and robot control. Their most-cited subject is SLAM.
- Drones & Aerial Vehicles
- Robot State Estimation & Localization
- SLAM
- Event Cameras
- Reinforcement Learning
- Motion Planning
Proprietary Scoring System
Who’s established? Who’s gathering momentum?
Some searches call for established research standing. Others call for a closer look at rising talent. ForesIQ’s artifact-based scoring brings standing, momentum and activity together, grounded in published work and read within each research area. See what makes a record stand out, then explore the work behind it.
Different signals. Different reasons to look closer.
Established
- ForesIQ Score
- Elite
- Momentum
- Steady
- Activity
- Active
Strong standing with a steady trajectory.
Rising
- ForesIQ Score
- Strong
- Momentum
- Explosive
- Activity
- Prolific
Established work with rapid upward momentum.
Illustrative combinations, both read within the same research area.
The signals at a glance
A sharper read, in a few signals.
Get your bearings quickly. Explore the detail behind the people who catch your attention.
ForesIQ Score
Their research standing within the field.
- Elite
- Top Tier
- Strong
- Notable
- Emerging
- Low Signal
Momentum
How their research trajectory is developing.
- Explosive
- Rising
- Steady
- Plateauing
- Declining
Activity
The pace of their recent research output.
- Prolific
- Active
- Moderate
- Light
- Inactive
Filters
How specific does your search need to be?
A particular research method. A rising specialist. Expertise in the right country. Bring those priorities together with filters built around research talent. Focus the view on people worth investigating, then save the search so the next conversation starts further ahead.
- Research area
- Robotics
- Areas of expertise
- Diffusion Policies, Sim-to-Real Transfer
- Standing and momentum
- Top Tier, Rising
A more focused view of your field.
Your team’s workspace
How does discovery become shared team knowledge?
Keep the thinking with the people you discover. Add notes and tags, organise saved lists and return to searches your team can build on. Export the view you need and carry that research context into your existing workflow.
Notes and tags
Keep the reason for your interest beside the record.
Saved lists and searches
Build on the research your team has already done.
Export
Carry the view you need into your next step.
Where would a clearer view of research talent take your team?
We’ll send you a personalised demo of ForesIQ Talent Intelligence. See how the database, researcher profiles and proprietary signals could fit the way your team discovers and evaluates frontier research talent.