Saran
Karthick
b.Tech Student • Machine Learning Engineer
I engineer the underlying logic of the new internet. Focused on Machine Learning, Intelligent Automations, and crafting seamless human-computer experiences.
The Projects
def __init__(self, in_features, out_features, bias=True):
super(Linear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.Tensor(out_features, in_features))
if bias:
self.bias = Parameter(torch.Tensor(out_features))
else:
self.register_parameter('bias', None)
def forward(self, input):
return F.linear(input, self.weight, self.bias)Project 01
Metro-Mind AI
A decision-support dashboard and real-time train scheduling for the Kochi Metro.
Real-time Scheduling Efficiency
99.98%
ML Predictive Accuracy
96.4%
System Load Optimization
-420ms
import cv2
import numpy as np
def process_frame(frame, threshold=0.8):
tensor = transform(frame).unsqueeze(0)
with torch.no_grad():
outputs = model(tensor)
return filter_boxes(outputs, threshold)Project 02
Memora
A Retrieval-Augmented Generation (RAG) for personal knowledge management capabilities.
Object Detection Latency
12ms
False Positive Reduction
45.2%
Concurrent Streams
1,200+
function verifyTransaction(signature: string, payload: any): boolean {
const hash = crypto.createHash('sha256').update(JSON.stringify(payload)).digest('hex');
return crypto.verify('sha256', Buffer.from(payload), pubKey, Buffer.from(signature, 'hex'));
}Project 03
AgriQCert
A end-to-end platform for a digital product passport system for agricultural imports and exports.
Cryptographic Verification
<1ms
Throughput (TPS)
85,000
Consensus Finality
400ms
func (c *Cache) Get(key string) (interface{}, bool) {
c.mu.RLock()
defer c.mu.RUnlock()
item, found := c.items[key]
if !found || item.Expiration > 0 && time.Now().UnixNano() > item.Expiration {
return nil, false
}
return item.Object, true
}Project 04
Dark-Cloud
A social platform for connecting like-minds around the world.
Cache Hit Ratio
99.1%
Memory Overhead
1.2MB
Response Time (p99)
0.8ms
SELECT user_id, COUNT(*) as events, APPROX_QUANTILES(duration_ms, 100)[OFFSET(95)] as p95_duration FROM event_logs WHERE timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 HOUR) GROUP BY user_id;
Project 05
Arise
An interactive productivity tracker web app.
Events Processed/Sec
4.5M
Query Acceleration
14x
Storage Compression
82%
app.use(async (ctx, next) => {
const rateLimit = await checkRateLimit(ctx.ip);
if (!rateLimit.allowed) {
ctx.status = 429;
ctx.body = 'Too Many Requests';
return;
}
await next();
});Project 06
Drift
A hybrid learning platform combining AI and interactive content.
Request Routing
3μs
Active Connections
2.5M
DDoS Mitigation Rate
100%
The Skills
Certificates
CERT-ID: AWS-ASA-73921 VALIDATION: OK ISSUER: Amazon Web Services SCORE: 920/1000
Certificate 01
Supervised Machine Learning
An online non-credit course authorized by DeepLearning.AI and Stanford University and offered through Coursera.
Designation
Machine Learning
Validation Details
Completed
import tensorflow as tf model = tf.keras.models.Sequential([ tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(10) ])
Certificate 02
Google Gemini Student Certificate
Completed the Google Gemini Student Certificate and skill lab badges.
Focus
Google AI Labs
No of skill badges
20+
const UI = () => (
<div className="grid">
<PremiumAesthetic />
</div>
);
export default UI;Certificate 03
Machine Learning with Python
Advanced certification in React, UI/UX principles, and responsive web design.
Focus
Machine Learning
Projects completed
5
SELECT user_id, COUNT(*) FROM analytics.events GROUP BY user_id;
Certificate 04
Oracle Cloud Infrastructure 2025 .
Certified AI Foundations Associate
Focus
Cloud Infrastructure
time
25+ hours